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Orina F, Amukoye E, Bowyer C, Chakaya J, Das D, Devereux G, Dobson R, Dragosits U, Gray C, Kiplimo R, Lesosky M, Loh M, Meme H, Mortimer K, Ndombi A, Pearson C, Price H, Twigg M, West S, Semple S. Household carbon monoxide (CO) concentrations in a large African city: An unquantified public health burden? Environ Pollut 2024; 351:124054. [PMID: 38677455 DOI: 10.1016/j.envpol.2024.124054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 04/10/2024] [Accepted: 04/24/2024] [Indexed: 04/29/2024]
Abstract
Carbon monoxide (CO) is a poisonous gas produced by incomplete combustion of carbon-based fuels that is linked to mortality and morbidity. Household air pollution from burning fuels on poorly ventilated stoves can lead to high concentrations of CO in homes. There are few datasets available on household concentrations of CO in urban areas of sub-Saharan African countries. CO was measured every minute over 24 h in a sample of homes in Nairobi, Kenya. Data on household characteristics were gathered by questionnaire. Metrics of exposure were summarised and analysis of temporal changes in concentration was performed. Continuous 24-h data were available from 138 homes. The mean (SD), median (IQR) and maximum 24-h CO concentration was 4.9 (6.4), 2.8 (1.0-6.3) and 44 ppm, respectively. 50% of homes had detectable CO concentrations for 847 min (14h07m) or longer during the 24-h period, and 9% of homes would have activated a CO-alarm operating to European specifications. An association between a metric of total CO exposure and self-reported exposure to vapours >15 h per week was identified, however this were not statistically significant after adjustment for the multiple comparisons performed. Mean concentrations were broadly similar in homes from a more affluent area and an informal settlement. A model of typical exposure suggests that cooking is likely to be responsible for approximately 60% of the CO exposure of Nairobi schoolchildren. Household CO concentrations are substantial in Nairobi, Kenya, despite most homes using gas or liquid fuels. Concentrations tend to be highest during the evening, probably associated with periods of cooking. Household air pollution from cooking is the main source of CO exposure of Nairobi schoolchildren. The public health impacts of long-term CO exposure in cities in sub-Saharan Africa may be considerable and should be studied further.
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Affiliation(s)
- F Orina
- Centre for Respiratory Diseases Research, Kenya Medical Research Institute, Nairobi, Kenya
| | - E Amukoye
- Research and Development, Kenya Medical Research Institute, Nairobi, Kenya
| | - C Bowyer
- Faculty of Creative and Cultural Industries, University of Portsmouth, Portsmouth, UK
| | - J Chakaya
- Centre for Respiratory Diseases Research, Kenya Medical Research Institute, Nairobi, Kenya
| | - D Das
- Institute of Occupational Medicine, Research Avenue North Riccarton, Edinburgh EH14 4AP, UK; Department of Environment and Geography, University of York, YO10 5NG, UK
| | - G Devereux
- Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, L3 5QA, UK
| | - R Dobson
- Institute for Social Marketing and Health, University of Stirling, Stirling, FK9 4LA, UK
| | - U Dragosits
- UK Centre for Ecology & Hydrology, Bush Estate, Penicuik, Midlothian EH26 0QB, UK
| | - C Gray
- School of Social and Political Sciences, University of Glasgow, Glasgow, UK
| | - R Kiplimo
- Centre for Respiratory Diseases Research, Kenya Medical Research Institute, Nairobi, Kenya
| | - M Lesosky
- National Heart and Lung Institute, Imperial College London, London, SW3 6LR, UK
| | - M Loh
- Institute of Occupational Medicine, Research Avenue North Riccarton, Edinburgh EH14 4AP, UK
| | - H Meme
- Centre for Respiratory Diseases Research, Kenya Medical Research Institute, Nairobi, Kenya
| | - K Mortimer
- Cambridge Africa, Department of Pathology, University of Cambridge, Cambridge, UK; Department of Paediatrics and Child Health, School of Clinical Medicine, College of Health Sciences, University of KwaZulu Natal, Durban, South Africa
| | - A Ndombi
- Centre for Respiratory Diseases Research, Kenya Medical Research Institute, Nairobi, Kenya
| | - C Pearson
- UK Centre for Ecology & Hydrology, Bush Estate, Penicuik, Midlothian EH26 0QB, UK
| | - H Price
- Biological and Environmental Sciences, University of Stirling, Stirling, FK9 4LA, UK
| | - M Twigg
- UK Centre for Ecology & Hydrology, Bush Estate, Penicuik, Midlothian EH26 0QB, UK
| | - S West
- Stockholm Environment Institute, University of York, YO10 5NG, UK
| | - S Semple
- Institute for Social Marketing and Health, University of Stirling, Stirling, FK9 4LA, UK.
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Borgini A, Veronese C, De Marco C, Boffi R, Tittarelli A, Bertoldi M, Fern Ndez E, Tigova O, Gallus S, Lugo A, Gorini G, Carreras G, L Pez MJ, Continente X, Semple S, Dobson R, Clancy L, Keogan S, Tzortzi A, Vardavas C, Nicol S LP, Starchenko P, Soriano JB, Ruprecht AA. Particulate matter in aerosols produced by two last generation electronic cigarettes: a comparison in a real-world environment. Pulmonology 2024; 30:137-144. [PMID: 33879426 DOI: 10.1016/j.pulmoe.2021.03.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 03/08/2021] [Accepted: 03/11/2021] [Indexed: 11/25/2022] Open
Abstract
The design of e-cigarettes (e-cigs) is constantly evolving and the latest models can aerosolize using high-power sub-ohm resistance and hence may produce specific particle concentrations. The aim of this study was to evaluate the aerosol characteristics generated by two different types of electronic cigarette in real-world conditions, such as a sitting room or a small office, in number of particles (particles/cm3). We compared the real time and time-integrated measurements of the aerosol generated by the e-cigarette types Just Fog and JUUL. Real time (10s average) number of particles (particles/cm3) in 8 different aerodynamic sizes was measured using an optical particle counter (OPC) model Profiler 212-2. Tests were conducted with and without a Heating, Ventilating Air Conditioning System (HVACS) in operation, in order to evaluate the efficiency of air filtration. During the vaping sessions the OPC recorded quite significant increases in number of particles/cm3. The JUUL e-cig produced significantly lower emissions than Just Fog with and without the HVACS in operation. The study demonstrates the rapid volatility or change from liquid or semi-liquid to gaseous status of the e-cig aerosols, with half-life in the order of a few seconds (min. 4.6, max 23.9), even without the HVACS in operation. The e-cig aerosol generated by the JUUL proved significantly lower than that generated by the Just Fog, but this reduction may not be sufficient to eliminate or consistently reduce the health risk for vulnerable non e-cig users exposed to it.
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Affiliation(s)
- A Borgini
- Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - C Veronese
- Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
| | - C De Marco
- Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - R Boffi
- Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - A Tittarelli
- Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - M Bertoldi
- Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - E Fern Ndez
- Tobacco Control Unit, Bellvitge Biomedical Research Institute (IDIBELL), L...Hospitalet de Llobregat, Barcelona, Spain; Tobacco Control Unit, Department of Cancer Epidemiology and Prevention, Catalan Institute of Oncology (ICO), L...Hospitalet de Llobregat, Barcelona, Spain; Department of Clinical Sciences, School of Medicine and Health Sciences, Campus of Bellvitge, University of Barcelona, Spain; Consortium for Biomedical Research in Respirarory Diseases (CIBER en Enfermedades Respiratorias, CIBERES), Spain
| | - O Tigova
- Tobacco Control Unit, Bellvitge Biomedical Research Institute (IDIBELL), L...Hospitalet de Llobregat, Barcelona, Spain; Tobacco Control Unit, Department of Cancer Epidemiology and Prevention, Catalan Institute of Oncology (ICO), L...Hospitalet de Llobregat, Barcelona, Spain; Department of Clinical Sciences, School of Medicine and Health Sciences, Campus of Bellvitge, University of Barcelona, Spain; Consortium for Biomedical Research in Respirarory Diseases (CIBER en Enfermedades Respiratorias, CIBERES), Spain
| | - S Gallus
- Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - A Lugo
- Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - G Gorini
- Oncologic network, prevention and research institute (ISPRO), Florence, Italy
| | - G Carreras
- Oncologic network, prevention and research institute (ISPRO), Florence, Italy
| | - M J L Pez
- Public Health Agency of Barcelona (ASPB), Barcelona, Spain; Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain; Sant Pau Institute of Biomedical Research (IIB Sant Pau), Barcelona, Spain
| | - X Continente
- Public Health Agency of Barcelona (ASPB), Barcelona, Spain; Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain; Sant Pau Institute of Biomedical Research (IIB Sant Pau), Barcelona, Spain
| | - S Semple
- Faculty of Health Sciences and Sport, University of Stirling, Stirling, Scotland, United Kingdom
| | - R Dobson
- Faculty of Health Sciences and Sport, University of Stirling, Stirling, Scotland, United Kingdom
| | - L Clancy
- Tobacco Free Research Institute Ireland (TFRI), Ireland
| | - S Keogan
- Tobacco Free Research Institute Ireland (TFRI), Ireland
| | - A Tzortzi
- Hellenic Cancer Society ... George D. Behrakis Research Lab (HCS), Greece
| | - C Vardavas
- Hellenic Cancer Society ... George D. Behrakis Research Lab (HCS), Greece
| | | | - P Starchenko
- European Network on Smoking and Tobacco Prevention (ENSP), Belgium
| | - J B Soriano
- Fundaci..n para la Investigaci..n Biom..dica del Hospital Universitario La Princesa (IISP), Spain
| | - A A Ruprecht
- Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
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3
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Wu J, Biswas D, Ryan M, Bernstein BS, Rizvi M, Fairhurst N, Kaye G, Baral R, Searle T, Melikian N, Sado D, Lüscher TF, Grocott-Mason R, Carr-White G, Teo J, Dobson R, Bromage DI, McDonagh TA, Shah AM, O'Gallagher K. Artificial intelligence methods for improved detection of undiagnosed heart failure with preserved ejection fraction. Eur J Heart Fail 2024; 26:302-310. [PMID: 38152863 DOI: 10.1002/ejhf.3115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 10/20/2023] [Accepted: 12/07/2023] [Indexed: 12/29/2023] Open
Abstract
AIM Heart failure with preserved ejection fraction (HFpEF) remains under-diagnosed in clinical practice despite accounting for nearly half of all heart failure (HF) cases. Accurate and timely diagnosis of HFpEF is crucial for proper patient management and treatment. In this study, we explored the potential of natural language processing (NLP) to improve the detection and diagnosis of HFpEF according to the European Society of Cardiology (ESC) diagnostic criteria. METHODS AND RESULTS In a retrospective cohort study, we used an NLP pipeline applied to the electronic health record (EHR) to identify patients with a clinical diagnosis of HF between 2010 and 2022. We collected demographic, clinical, echocardiographic and outcome data from the EHR. Patients were categorized according to the left ventricular ejection fraction (LVEF). Those with LVEF ≥50% were further categorized based on whether they had a clinician-assigned diagnosis of HFpEF and if not, whether they met the ESC diagnostic criteria. Results were validated in a second, independent centre. We identified 8606 patients with HF. Of 3727 consecutive patients with HF and LVEF ≥50% on echocardiogram, only 8.3% had a clinician-assigned diagnosis of HFpEF, while 75.4% met ESC criteria but did not have a formal diagnosis of HFpEF. Patients with confirmed HFpEF were hospitalized more frequently; however the ESC criteria group had a higher 5-year mortality, despite being less comorbid and experiencing fewer acute cardiovascular events. CONCLUSIONS This study demonstrates that patients with undiagnosed HFpEF are an at-risk group with high mortality. It is possible to use NLP methods to identify likely HFpEF patients from EHR data who would likely then benefit from expert clinical review and complement the use of diagnostic algorithms.
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Affiliation(s)
- Jack Wu
- School of Cardiovascular and Metabolic Medicine & Sciences, British Heart Foundation Centre of Research Excellence, King's College London, London, UK
| | - Dhruva Biswas
- School of Cardiovascular and Metabolic Medicine & Sciences, British Heart Foundation Centre of Research Excellence, King's College London, London, UK
- King's College Hospital NHS Foundation Trust, London, UK
| | - Matthew Ryan
- School of Cardiovascular and Metabolic Medicine & Sciences, British Heart Foundation Centre of Research Excellence, King's College London, London, UK
- King's College Hospital NHS Foundation Trust, London, UK
| | - Brett S Bernstein
- School of Cardiovascular and Metabolic Medicine & Sciences, British Heart Foundation Centre of Research Excellence, King's College London, London, UK
- King's College Hospital NHS Foundation Trust, London, UK
| | - Maleeha Rizvi
- School of Cardiovascular and Metabolic Medicine & Sciences, British Heart Foundation Centre of Research Excellence, King's College London, London, UK
- Guy's and St Thomas' Hospital, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | | | - George Kaye
- King's College Hospital NHS Foundation Trust, London, UK
| | - Ranu Baral
- King's College Hospital NHS Foundation Trust, London, UK
| | - Tom Searle
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Narbeh Melikian
- School of Cardiovascular and Metabolic Medicine & Sciences, British Heart Foundation Centre of Research Excellence, King's College London, London, UK
- King's College Hospital NHS Foundation Trust, London, UK
| | - Daniel Sado
- School of Cardiovascular and Metabolic Medicine & Sciences, British Heart Foundation Centre of Research Excellence, King's College London, London, UK
- King's College Hospital NHS Foundation Trust, London, UK
| | - Thomas F Lüscher
- Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Richard Grocott-Mason
- Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Gerald Carr-White
- Guy's and St Thomas' Hospital, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - James Teo
- King's College Hospital NHS Foundation Trust, London, UK
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Richard Dobson
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Daniel I Bromage
- School of Cardiovascular and Metabolic Medicine & Sciences, British Heart Foundation Centre of Research Excellence, King's College London, London, UK
- King's College Hospital NHS Foundation Trust, London, UK
| | - Theresa A McDonagh
- School of Cardiovascular and Metabolic Medicine & Sciences, British Heart Foundation Centre of Research Excellence, King's College London, London, UK
- King's College Hospital NHS Foundation Trust, London, UK
| | - Ajay M Shah
- School of Cardiovascular and Metabolic Medicine & Sciences, British Heart Foundation Centre of Research Excellence, King's College London, London, UK
- King's College Hospital NHS Foundation Trust, London, UK
| | - Kevin O'Gallagher
- School of Cardiovascular and Metabolic Medicine & Sciences, British Heart Foundation Centre of Research Excellence, King's College London, London, UK
- King's College Hospital NHS Foundation Trust, London, UK
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4
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Dalla Costa G, Nos C, Zabalza A, Buron M, Magyari M, Sellebjerg F, Guerrero AI, Roselli L, La Porta ML, Martinis M, Bailon R, Kontaxis S, Laporta E, Garcia E, Pokorny FB, Schuller BW, Folarin A, Stewart C, Leocani L, Vairavan S, Cummins N, Dobson R, Hotopf M, Narayan V, Montalban X, Sorensen PS, Comi G. A wearable device perspective on the standard definitions of disability progression in multiple sclerosis. Mult Scler 2024; 30:103-112. [PMID: 38084497 DOI: 10.1177/13524585231214362] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
INTRODUCTION Multiple sclerosis (MS) is a leading cause of disability among young adults, but standard clinical scales may not accurately detect subtle changes in disability occurring between visits. This study aims to explore whether wearable device data provides more granular and objective measures of disability progression in MS. METHODS Remote Assessment of Disease and Relapse in Central Nervous System Disorders (RADAR-CNS) is a longitudinal multicenter observational study in which 400 MS patients have been recruited since June 2018 and prospectively followed up for 24 months. Monitoring of patients included standard clinical visits with assessment of disability through use of the Expanded Disability Status Scale (EDSS), 6-minute walking test (6MWT) and timed 25-foot walk (T25FW), as well as remote monitoring through the use of a Fitbit. RESULTS Among the 306 patients who completed the study (mean age, 45.6 years; females 67%), confirmed disability progression defined by the EDSS was observed in 74 patients, who had approximately 1392 fewer daily steps than patients without disability progression. However, the decrease in the number of steps experienced over time by patients with EDSS progression and stable patients was not significantly different. Similar results were obtained with disability progression defined by the 6MWT and the T25FW. CONCLUSION The use of continuous activity monitoring holds great promise as a sensitive and ecologically valid measure of disability progression in MS.
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Affiliation(s)
| | - Carlos Nos
- Servei de Neurologia-Neuroimmunologia, Centre d'Esclerosi Múltiple de Catalunya (Cemcat), Vall d'Hebron Institut de Recerca, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Ana Zabalza
- Servei de Neurologia-Neuroimmunologia, Centre d'Esclerosi Múltiple de Catalunya (Cemcat), Vall d'Hebron Institut de Recerca, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Mathias Buron
- Department of Neurology, Danish Multiple Sclerosis Center, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Melinda Magyari
- Department of Neurology, Danish Multiple Sclerosis Center, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Finn Sellebjerg
- Department of Neurology, Danish Multiple Sclerosis Center, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Ana Isabel Guerrero
- Servei de Neurologia-Neuroimmunologia, Centre d'Esclerosi Múltiple de Catalunya (Cemcat), Vall d'Hebron Institut de Recerca, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | | | | | | | - Raquel Bailon
- Biomedical Signal Interpretation & Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, Zaragoza, Spain
- Centro de Investigacion Biomedica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
| | - Spyridon Kontaxis
- Biomedical Signal Interpretation & Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, Zaragoza, Spain
- Centro de Investigacion Biomedica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
| | - Estela Laporta
- Biomedical Signal Interpretation & Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, Zaragoza, Spain
- Centro de Investigacion Biomedica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
| | - Esther Garcia
- Centro de Investigacion Biomedica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
- Department of Microelectronics and Electronic Systems, Autonomous University of Barcelona, Barcelona, Spain
| | - Florian B Pokorny
- Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
- Division of Phoniatrics, Medical University of Graz, Graz, Austria
| | - Björn W Schuller
- Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
- Group on Language, Audio & Music, Imperial College London, London, UK
| | - Amos Folarin
- Department of Biostatistics & Health informatics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Institute of Health Informatics, University College London, London, UK
| | - Callum Stewart
- Department of Biostatistics & Health informatics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Institute of Health Informatics, University College London, London, UK
| | | | - Srinivasan Vairavan
- Janssen Research and Development LLC, Janssen Global Services, LLC, Titusville, NJ, USA
| | - Nicholas Cummins
- Department of Biostatistics & Health informatics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Richard Dobson
- Department of Biostatistics & Health informatics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Matthew Hotopf
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Vaibhav Narayan
- Janssen Research and Development LLC, Janssen Global Services, LLC, Titusville, NJ, USA
| | - Xavier Montalban
- Servei de Neurologia-Neuroimmunologia, Centre d'Esclerosi Múltiple de Catalunya (Cemcat), Vall d'Hebron Institut de Recerca, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Per Soelberg Sorensen
- Department of Neurology, Danish Multiple Sclerosis Center, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Giancarlo Comi
- Vita-Salute San Raffaele University, Milan, Italy/Multiple Sclerosis Center, Casa di Cura Igea, Milan, Italy
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Msosa YJ, Grauslys A, Zhou Y, Wang T, Buchan I, Langan P, Foster S, Walker M, Pearson M, Folarin A, Roberts A, Maskell S, Dobson R, Kullu C, Kehoe D. Trustworthy Data and AI Environments for Clinical Prediction: Application to Crisis-Risk in People With Depression. IEEE J Biomed Health Inform 2023; 27:5588-5598. [PMID: 37669205 DOI: 10.1109/jbhi.2023.3312011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/07/2023]
Abstract
Depression is a common mental health condition that often occurs in association with other chronic illnesses, and varies considerably in severity. Electronic Health Records (EHRs) contain rich information about a patient's medical history and can be used to train, test and maintain predictive models to support and improve patient care. This work evaluated the feasibility of implementing an environment for predicting mental health crisis among people living with depression based on both structured and unstructured EHRs. A large EHR from a mental health provider, Mersey Care, was pseudonymised and ingested into the Natural Language Processing (NLP) platform CogStack, allowing text content in binary clinical notes to be extracted. All unstructured clinical notes and summaries were semantically annotated by MedCAT and BioYODIE NLP services. Cases of crisis in patients with depression were then identified. Random forest models, gradient boosting trees, and Long Short-Term Memory (LSTM) networks, with varying feature arrangement, were trained to predict the occurrence of crisis. The results showed that all the prediction models can use a combination of structured and unstructured EHR information to predict crisis in patients with depression with good and useful accuracy. The LSTM network that was trained on a modified dataset with only 1000 most-important features from the random forest model with temporality showed the best performance with a mean AUC of 0.901 and a standard deviation of 0.006 using a training dataset and a mean AUC of 0.810 and 0.01 using a hold-out test dataset. Comparing the results from the technical evaluation with the views of psychiatrists shows that there are now opportunities to refine and integrate such prediction models into pragmatic point-of-care clinical decision support tools for supporting mental healthcare delivery.
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6
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Wang T, Codling D, Bhugra D, Msosa Y, Broadbent M, Patel R, Roberts A, McGuire P, Stewart R, Dobson R, Harland R. Unraveling ethnic disparities in antipsychotic prescribing among patients with psychosis: A retrospective cohort study based on electronic clinical records. Schizophr Res 2023; 260:168-179. [PMID: 37669576 PMCID: PMC10881407 DOI: 10.1016/j.schres.2023.08.024] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 07/11/2023] [Accepted: 08/27/2023] [Indexed: 09/07/2023]
Abstract
BACKGROUND Previous studies have shown mixed evidence on ethnic disparities in antipsychotic prescribing among patients with psychosis in the UK, partly due to small sample sizes. This study aimed to examine the current state of antipsychotic prescription with respect to patient ethnicity among the entire population known to a large UK mental health trust with non-affective psychosis, adjusting for multiple potential risk factors. METHODS This retrospective cohort study included all patients (N = 19,291) who were aged 18 years or over at their first diagnoses of non-affective psychosis (identified with the ICD-10 codes of F20-F29) recorded in electronic health records (EHRs) at the South London and Maudsley NHS Trust until March 2021. The most recently recorded antipsychotic treatments and patient attributes were extracted from EHRs, including both structured fields and free-text fields processed using natural language processing applications. Multivariable logistic regression models were used to calculate the odds ratios (OR) for antipsychotic prescription according to patient ethnicity, adjusted for multiple potential contributing factors, including demographic (age and gender), clinical (diagnoses, duration of illness, service use and history of cannabis use), socioeconomic factors (level of deprivation and own-group ethnic density in the area of residence) and temporal changes in clinical guidelines (date of prescription). RESULTS The cohort consisted of 43.10 % White, 8.31 % Asian, 40.80 % Black, 2.64 % Mixed, and 5.14 % of patients from Other ethnicity. Among them, 92.62 % had recorded antipsychotic receipt, where 24.05 % for depot antipsychotics and 81.72 % for second-generation antipsychotic (SGA) medications. Most ethnic minority groups were not significantly different from White patients in receiving any antipsychotic. Among those receiving antipsychotic prescribing, Black patients were more likely to be prescribed depot (adjusted OR 1.29, 95 % confidence interval (CI) 1.14-1.47), but less likely to receive SGA (adjusted OR 0.85, 95 % CI 0.74-0.97), olanzapine (OR 0.82, 95 % CI 0.73-0.92) and clozapine (adjusted OR 0.71, 95 % CI 0.6-0.85) than White patients. All the ethnic minority groups were less likely to be prescribed olanzapine than the White group. CONCLUSIONS Black patients with psychosis had a distinct pattern in antipsychotic prescription, with less use of SGA, including olanzapine and clozapine, but more use of depot antipsychotics, even when adjusting for the effects of multiple demographic, clinical and socioeconomic factors. Further research is required to understand the sources of these ethnic disparities and eliminate care inequalities.
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Affiliation(s)
- Tao Wang
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, London SE5 8AF, United Kingdom.
| | - David Codling
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, London SE5 8AF, United Kingdom; South London and Maudsley National Health Service (NHS) Foundation Trust, Denmark Hill, London SE5 8AZ, United Kingdom
| | - Dinesh Bhugra
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, London SE5 8AF, United Kingdom
| | - Yamiko Msosa
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, London SE5 8AF, United Kingdom
| | - Matthew Broadbent
- South London and Maudsley National Health Service (NHS) Foundation Trust, Denmark Hill, London SE5 8AZ, United Kingdom
| | - Rashmi Patel
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, London SE5 8AF, United Kingdom; South London and Maudsley National Health Service (NHS) Foundation Trust, Denmark Hill, London SE5 8AZ, United Kingdom
| | - Angus Roberts
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, London SE5 8AF, United Kingdom; South London and Maudsley National Health Service (NHS) Foundation Trust, Denmark Hill, London SE5 8AZ, United Kingdom
| | - Philip McGuire
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom; Oxford Health, Oxford Health NHS Foundation Trust, Oxford, United Kingdom
| | - Robert Stewart
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, London SE5 8AF, United Kingdom; South London and Maudsley National Health Service (NHS) Foundation Trust, Denmark Hill, London SE5 8AZ, United Kingdom
| | - Richard Dobson
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Denmark Hill, London SE5 8AF, United Kingdom; South London and Maudsley National Health Service (NHS) Foundation Trust, Denmark Hill, London SE5 8AZ, United Kingdom; Institute of Health Informatics, University College London, Euston Road, London NW1 2DA, United Kingdom; Health Data Research UK London, University College London, Euston Road, London NW1 2DA, United Kingdom
| | - Robert Harland
- South London and Maudsley National Health Service (NHS) Foundation Trust, Denmark Hill, London SE5 8AZ, United Kingdom
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7
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Olah J, Diederen K, Gibbs-Dean T, Kempton MJ, Dobson R, Spencer T, Cummins N. Online speech assessment of the psychotic spectrum: Exploring the relationship between overlapping acoustic markers of schizotypy, depression and anxiety. Schizophr Res 2023; 259:11-19. [PMID: 37080802 DOI: 10.1016/j.schres.2023.03.044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 03/22/2023] [Accepted: 03/23/2023] [Indexed: 04/22/2023]
Abstract
BACKGROUND Remote assessment of acoustic alterations in speech holds promise to increase scalability and validity in research across the psychosis spectrum. A feasible first step in establishing a procedure for online assessments is to assess acoustic alterations in psychometric schizotypy. However, to date, the complex relationship between alterations in speech related to schizotypy and those related to comorbid conditions such as symptoms of depression and anxiety has not been investigated. This study tested whether (1) depression, generalized anxiety and high psychometric schizotypy have similar voice characteristics, (2) which acoustic markers of online collected speech are the strongest predictors of psychometric schizotypy, (3) whether including generalized anxiety and depression symptoms in the model can improve the prediction of schizotypy. METHODS We collected cross-sectional, online-recorded speech data from 441 participants, assessing demographics, symptoms of depression, generalized anxiety and psychometric schizotypy. RESULTS Speech samples collected online could predict psychometric schizotypy, depression, and anxiety symptoms with weak to moderate predictive power, and with moderate and good predictive power when basic demographic variables were added to the models. Most influential features of these models largely overlapped. The predictive power of speech marker-based models of schizotypy significantly improved after including symptom scores of depression and generalized anxiety in the models (from R2 = 0.296 to R2 = 0. 436). CONCLUSIONS Acoustic features of online collected speech are predictive of psychometric schizotypy as well as generalized anxiety and depression symptoms. The acoustic characteristics of schizotypy, depression and anxiety symptoms significantly overlap. Speech models that are designed to predict schizotypy or symptoms of the schizophrenia spectrum might therefore benefit from controlling for symptoms of depression and anxiety.
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Affiliation(s)
- Julianna Olah
- Institute of Psychiatry, Psychology and Neuroscience, Department of Psychosis Studies, King's College London, London SE5 8AF, UK.
| | - Kelly Diederen
- Institute of Psychiatry, Psychology and Neuroscience, Department of Psychosis Studies, King's College London, London SE5 8AF, UK
| | - Toni Gibbs-Dean
- Institute of Psychiatry, Psychology and Neuroscience, Department of Psychosis Studies, King's College London, London SE5 8AF, UK
| | - Matthew J Kempton
- Institute of Psychiatry, Psychology and Neuroscience, Department of Psychosis Studies, King's College London, London SE5 8AF, UK
| | - Richard Dobson
- Institute of Psychiatry, Psychology and Neuroscience, Department of Biostatistics & Health Informatics, King's College London, London SE5 8AF, UK
| | - Thomas Spencer
- Institute of Psychiatry, Psychology and Neuroscience, Department of Psychosis Studies, King's College London, London SE5 8AF, UK
| | - Nicholas Cummins
- Institute of Psychiatry, Psychology and Neuroscience, Department of Biostatistics & Health Informatics, King's College London, London SE5 8AF, UK
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8
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Whittaker R, Dobson R, Jin CK, Style R, Jayathissa P, Hiini K, Ross K, Kawamura K, Muir P. An example of governance for AI in health services from Aotearoa New Zealand. NPJ Digit Med 2023; 6:164. [PMID: 37658119 PMCID: PMC10474148 DOI: 10.1038/s41746-023-00882-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 07/21/2023] [Indexed: 09/03/2023] Open
Abstract
Artificial Intelligence (AI) is undergoing rapid development, meaning that potential risks in application are not able to be fully understood. Multiple international principles and guidance documents have been published to guide the implementation of AI tools in various industries, including healthcare practice. In Aotearoa New Zealand (NZ) we recognised that the challenge went beyond simply adapting existing risk frameworks and governance guidance to our specific health service context and population. We also deemed prioritising the voice of Māori (the indigenous people of Aotearoa NZ) a necessary aspect of honouring Te Tiriti (the Treaty of Waitangi), as well as prioritising the needs of healthcare service users and their families. Here we report on the development and establishment of comprehensive and effective governance over the development and implementation of AI tools within a health service in Aotearoa NZ. The implementation of the framework in practice includes testing with real-world proposals and ongoing iteration and refinement of our processes.
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Affiliation(s)
- R Whittaker
- Te Whatu Ora Waitematā, Auckland, New Zealand.
- National Institute for Health Innovation, University of Auckland, Auckland, New Zealand.
| | - R Dobson
- Te Whatu Ora Waitematā, Auckland, New Zealand
- National Institute for Health Innovation, University of Auckland, Auckland, New Zealand
| | - C K Jin
- Te Whatu Ora Waitematā, Auckland, New Zealand
| | - R Style
- Te Whatu Ora Waitematā, Auckland, New Zealand
| | | | - K Hiini
- Te Whatu Ora Waitematā, Auckland, New Zealand
| | - K Ross
- Precision Driven Health, Auckland, New Zealand
| | - K Kawamura
- Te Whatu Ora Waitematā, Auckland, New Zealand
| | - P Muir
- Te Whatu Ora Waitematā, Auckland, New Zealand
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9
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Ewing J, Oommen T, Thomas J, Kasaragod A, Dobson R, Brooks C, Jayakumar P, Cole M, Ersal T. Terrain Characterization via Machine vs. Deep Learning Using Remote Sensing. Sensors (Basel) 2023; 23:5505. [PMID: 37420672 DOI: 10.3390/s23125505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 05/26/2023] [Accepted: 06/08/2023] [Indexed: 07/09/2023]
Abstract
Terrain traversability is critical for developing Go/No-Go maps for ground vehicles, which significantly impact a mission's success. To predict the mobility of terrain, one must understand the soil characteristics. In-situ measurements performed in the field are the current method of collecting this information, which is time-consuming, costly, and can be lethal for military operations. This paper investigates an alternative approach using thermal, multispectral, and hyperspectral remote sensing from an unmanned aerial vehicle (UAV) platform. Remotely sensed data combined with machine learning (linear, ridge, lasso, partial least squares (PLS), support vector machines (SVM), and k nearest neighbors (KNN)) and deep learning (multi-layer perceptron (MLP) and convolutional neural network (CNN)) are used to perform a comparative study to estimate the soil properties, such as the soil moisture and terrain strength, used to generate prediction maps of these terrain characteristics. This study found that deep learning outperformed machine learning. Specifically, a multi-layer perceptron performed the best for predicting the percent moisture content (R2/RMSE = 0.97/1.55) and the soil strength (in PSI), as measured by a cone penetrometer for the averaged 0-6" (CP06) (R2/RMSE = 0.95/67) and 0-12" depth (CP12) (R2/RMSE = 0.92/94). A Polaris MRZR vehicle was used to test the application of these prediction maps for mobility purposes, and correlations were observed between the CP06 and the rear wheel slip and the CP12 and the vehicle speed. Thus, this study demonstrates the potential of a more rapid, cost-efficient, and safer approach to predict terrain properties for mobility mapping using remote sensing data with machine and deep learning algorithms.
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Affiliation(s)
- Jordan Ewing
- Department of Geological Engineering, Michigan Technological University, Houghton, MI 49931, USA
| | - Thomas Oommen
- Department of Geological Engineering, Michigan Technological University, Houghton, MI 49931, USA
| | - Jobin Thomas
- Department of Geological Engineering, Michigan Technological University, Houghton, MI 49931, USA
| | - Anush Kasaragod
- Department of Geological Engineering, Michigan Technological University, Houghton, MI 49931, USA
| | | | | | | | - Michael Cole
- U.S. Army DEVCOM Ground Vehicle Systems Center, Warren, MI 48092, USA
| | - Tulga Ersal
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
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10
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Siddi S, Bailon R, Giné-Vázquez I, Matcham F, Lamers F, Kontaxis S, Laporta E, Garcia E, Lombardini F, Annas P, Hotopf M, Penninx BWJH, Ivan A, White KM, Difrancesco S, Locatelli P, Aguiló J, Peñarrubia-Maria MT, Narayan VA, Folarin A, Leightley D, Cummins N, Vairavan S, Ranjan Y, Rintala A, de Girolamo G, Simblett SK, Wykes T, Myin-Germeys I, Dobson R, Haro JM. The usability of daytime and night-time heart rate dynamics as digital biomarkers of depression severity. Psychol Med 2023; 53:3249-3260. [PMID: 37184076 DOI: 10.1017/s0033291723001034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
BACKGROUND Alterations in heart rate (HR) may provide new information about physiological signatures of depression severity. This 2-year study in individuals with a history of recurrent major depressive disorder (MDD) explored the intra-individual variations in HR parameters and their relationship with depression severity. METHODS Data from 510 participants (Number of observations of the HR parameters = 6666) were collected from three centres in the Netherlands, Spain, and the UK, as a part of the remote assessment of disease and relapse-MDD study. We analysed the relationship between depression severity, assessed every 2 weeks with the Patient Health Questionnaire-8, with HR parameters in the week before the assessment, such as HR features during all day, resting periods during the day and at night, and activity periods during the day evaluated with a wrist-worn Fitbit device. Linear mixed models were used with random intercepts for participants and countries. Covariates included in the models were age, sex, BMI, smoking and alcohol consumption, antidepressant use and co-morbidities with other medical health conditions. RESULTS Decreases in HR variation during resting periods during the day were related with an increased severity of depression both in univariate and multivariate analyses. Mean HR during resting at night was higher in participants with more severe depressive symptoms. CONCLUSIONS Our findings demonstrate that alterations in resting HR during all day and night are associated with depression severity. These findings may provide an early warning of worsening depression symptoms which could allow clinicians to take responsive treatment measures promptly.
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Affiliation(s)
- S Siddi
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - R Bailon
- Aragón Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain
- Centros de investigación biomédica en red en el área de bioingeniería, biomateriales y nanomedicina (CIBER-BBN), Madrid, Spain
| | - I Giné-Vázquez
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - F Matcham
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
- School of Psychology, University of Sussex, Falmer, UK
| | - F Lamers
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - S Kontaxis
- Aragón Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain
- Centros de investigación biomédica en red en el área de bioingeniería, biomateriales y nanomedicina (CIBER-BBN), Madrid, Spain
| | - E Laporta
- Centros de investigación biomédica en red en el área de bioingeniería, biomateriales y nanomedicina (CIBER-BBN), Madrid, Spain
| | - E Garcia
- Centros de investigación biomédica en red en el área de bioingeniería, biomateriales y nanomedicina (CIBER-BBN), Madrid, Spain
- Microelectrónica y Sistemas Electrónicos, Universidad Autónoma de Barcelona, CIBERBBN, Barcelona, Spain
| | - F Lombardini
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - P Annas
- H. Lundbeck A/S, Valby, Denmark
| | - M Hotopf
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
| | - B W J H Penninx
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - A Ivan
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - K M White
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - S Difrancesco
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - P Locatelli
- Department of Engineering and Applied Science, University of Bergamo, Bergamo, Italy
| | - J Aguiló
- Centros de investigación biomédica en red en el área de bioingeniería, biomateriales y nanomedicina (CIBER-BBN), Madrid, Spain
- Microelectrónica y Sistemas Electrónicos, Universidad Autónoma de Barcelona, CIBERBBN, Barcelona, Spain
| | - M T Peñarrubia-Maria
- Catalan Institute of Health, Primary Care Research Institute (IDIAP Jordi Gol), CIBERESP, Barcelona, Spain
| | - V A Narayan
- Research and Development Information Technology, Janssen Research & Development, LLC, Titusville, NJ, USA
| | - A Folarin
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - D Leightley
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - N Cummins
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - S Vairavan
- Research and Development Information Technology, Janssen Research & Development, LLC, Titusville, NJ, USA
| | - Y Ranjan
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - A Rintala
- Department for Neurosciences, Center for Contextual Psychiatry, Katholieke Universiteit Leuven, Leuven, Belgium
- Faculty of Social Services and Health Care, LAB University of Applied Sciences, Lahti, Finland
| | - G de Girolamo
- IRCCS Instituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - S K Simblett
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - T Wykes
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
| | - I Myin-Germeys
- Department for Neurosciences, Center for Contextual Psychiatry, Katholieke Universiteit Leuven, Leuven, Belgium
| | - R Dobson
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - J M Haro
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
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11
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Green RE, Lord J, Scelsi MA, Xu J, Wong A, Naomi-James S, Handy A, Gilchrist L, Williams DM, Parker TD, Lane CA, Malone IB, Cash DM, Sudre CH, Coath W, Thomas DL, Keuss S, Dobson R, Legido-Quigley C, Fox NC, Schott JM, Richards M, Proitsi P. Investigating associations between blood metabolites, later life brain imaging measures, and genetic risk for Alzheimer's disease. Alzheimers Res Ther 2023; 15:38. [PMID: 36814324 PMCID: PMC9945600 DOI: 10.1186/s13195-023-01184-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 02/08/2023] [Indexed: 02/24/2023]
Abstract
BACKGROUND Identifying blood-based signatures of brain health and preclinical pathology may offer insights into early disease mechanisms and highlight avenues for intervention. Here, we systematically profiled associations between blood metabolites and whole-brain volume, hippocampal volume, and amyloid-β status among participants of Insight 46-the neuroscience sub-study of the National Survey of Health and Development (NSHD). We additionally explored whether key metabolites were associated with polygenic risk for Alzheimer's disease (AD). METHODS Following quality control, levels of 1019 metabolites-detected with liquid chromatography-mass spectrometry-were available for 1740 participants at age 60-64. Metabolite data were subsequently clustered into modules of co-expressed metabolites using weighted coexpression network analysis. Accompanying MRI and amyloid-PET imaging data were present for 437 participants (age 69-71). Regression analyses tested relationships between metabolite measures-modules and hub metabolites-and imaging outcomes. Hub metabolites were defined as metabolites that were highly connected within significant (pFDR < 0.05) modules or were identified as a hub in a previous analysis on cognitive function in the same cohort. Regression models included adjustments for age, sex, APOE genotype, lipid medication use, childhood cognitive ability, and social factors. Finally, associations were tested between AD polygenic risk scores (PRS), including and excluding the APOE region, and metabolites and modules that significantly associated (pFDR < 0.05) with an imaging outcome (N = 1638). RESULTS In the fully adjusted model, three lipid modules were associated with a brain volume measure (pFDR < 0.05): one enriched in sphingolipids (hippocampal volume: ß = 0.14, 95% CI = [0.055,0.23]), one in several fatty acid pathways (whole-brain volume: ß = - 0.072, 95%CI = [- 0.12, - 0.026]), and another in diacylglycerols and phosphatidylethanolamines (whole-brain volume: ß = - 0.066, 95% CI = [- 0.11, - 0.020]). Twenty-two hub metabolites were associated (pFDR < 0.05) with an imaging outcome (whole-brain volume: 22; hippocampal volume: 4). Some nominal associations were reported for amyloid-β, and with an AD PRS in our genetic analysis, but none survived multiple testing correction. CONCLUSIONS Our findings highlight key metabolites, with functions in membrane integrity and cell signalling, that associated with structural brain measures in later life. Future research should focus on replicating this work and interrogating causality.
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Affiliation(s)
- Rebecca E Green
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK.,UK National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre, South London and Maudsley Trust, London, UK
| | - Jodie Lord
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK
| | - Marzia A Scelsi
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London (UCL), London, UK
| | - Jin Xu
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK.,Institute of Pharmaceutical Science, King's College London, London, UK
| | - Andrew Wong
- MRC Unit for Lifelong Health & Ageing at UCL, University College London, Floor 5, MRC LHA at UCL, 1 - 19 Torrington Place, London, WC1E 7HB, UK
| | - Sarah Naomi-James
- MRC Unit for Lifelong Health & Ageing at UCL, University College London, Floor 5, MRC LHA at UCL, 1 - 19 Torrington Place, London, WC1E 7HB, UK.,Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - Alex Handy
- University College London, Institute of Health Informatics, London, UK
| | - Lachlan Gilchrist
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK
| | - Dylan M Williams
- MRC Unit for Lifelong Health & Ageing at UCL, University College London, Floor 5, MRC LHA at UCL, 1 - 19 Torrington Place, London, WC1E 7HB, UK.,Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Thomas D Parker
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.,Department of Brain Sciences, Imperial College London, London, W12 0NN, UK.,UK DRI Centre for Care Research and Technology, Imperial College London, London, W12 0BZ, UK
| | - Christopher A Lane
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - Ian B Malone
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - David M Cash
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.,UK Dementia Research Institute at University College London, London, UK
| | - Carole H Sudre
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London (UCL), London, UK.,MRC Unit for Lifelong Health & Ageing at UCL, University College London, Floor 5, MRC LHA at UCL, 1 - 19 Torrington Place, London, WC1E 7HB, UK.,Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.,School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - William Coath
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - David L Thomas
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.,Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sarah Keuss
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - Richard Dobson
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK.,UK National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre, South London and Maudsley Trust, London, UK.,University College London, Institute of Health Informatics, London, UK.,Health Data Research UK London, University College London, London, UK.,NIHR Biomedical Research Centre at University College London Hospitals NHS Foundation Trust, London, UK
| | - Cristina Legido-Quigley
- Institute of Pharmaceutical Science, King's College London, London, UK.,Steno Diabetes Center Copenhagen, Gentofte, Denmark
| | - Nick C Fox
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.,UK Dementia Research Institute at University College London, London, UK
| | - Jonathan M Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.
| | - Marcus Richards
- MRC Unit for Lifelong Health & Ageing at UCL, University College London, Floor 5, MRC LHA at UCL, 1 - 19 Torrington Place, London, WC1E 7HB, UK.
| | - Petroula Proitsi
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK.
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12
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El-Medany A, Sunderland N, Dobson R, Stuart G, Nisbet A. Catheter ablation for supraventricular arrhythmias in adults with congenital heart disease: Recurrence rates and predictors of acute procedural success. International Journal of Cardiology Congenital Heart Disease 2023. [DOI: 10.1016/j.ijcchd.2023.100445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023] Open
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13
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de Angel V, Adeleye F, Zhang Y, Cummins N, Munir S, Lewis S, Laporta Puyal E, Matcham F, Sun S, Folarin AA, Ranjan Y, Conde P, Rashid Z, Dobson R, Hotopf M. The Feasibility of Implementing Remote Measurement Technologies in Psychological Treatment for Depression: Mixed Methods Study on Engagement. JMIR Ment Health 2023; 10:e42866. [PMID: 36692937 PMCID: PMC9906314 DOI: 10.2196/42866] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 11/10/2022] [Accepted: 11/26/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Remote measurement technologies (RMTs) such as smartphones and wearables can help improve treatment for depression by providing objective, continuous, and ecologically valid insights into mood and behavior. Engagement with RMTs is varied and highly context dependent; however, few studies have investigated their feasibility in the context of treatment. OBJECTIVE A mixed methods design was used to evaluate engagement with active and passive data collection via RMT in people with depression undergoing psychotherapy. We evaluated the effects of treatment on 2 different types of engagement: study attrition (engagement with study protocol) and patterns of missing data (engagement with digital devices), which we termed data availability. Qualitative interviews were conducted to help interpret the differences in engagement. METHODS A total of 66 people undergoing psychological therapy for depression were followed up for 7 months. Active data were gathered from weekly questionnaires and speech and cognitive tasks, and passive data were gathered from smartphone sensors and a Fitbit (Fitbit Inc) wearable device. RESULTS The overall retention rate was 60%. Higher-intensity treatment (χ21=4.6; P=.03) and higher baseline anxiety (t56.28=-2.80, 2-tailed; P=.007) were associated with attrition, but depression severity was not (t50.4=-0.18; P=.86). A trend toward significance was found for the association between longer treatments and increased attrition (U=339.5; P=.05). Data availability was higher for active data than for passive data initially but declined at a sharper rate (90%-30% drop in 7 months). As for passive data, wearable data availability fell from a maximum of 80% to 45% at 7 months but showed higher overall data availability than smartphone-based data, which remained stable at the range of 20%-40% throughout. Missing data were more prevalent among GPS location data, followed by among Bluetooth data, then among accelerometry data. As for active data, speech and cognitive tasks had lower completion rates than clinical questionnaires. The participants in treatment provided less Fitbit data but more active data than those on the waiting list. CONCLUSIONS Different data streams showed varied patterns of missing data, despite being gathered from the same device. Longer and more complex treatments and clinical characteristics such as higher baseline anxiety may reduce long-term engagement with RMTs, and different devices may show opposite patterns of missingness during treatment. This has implications for the scalability and uptake of RMTs in health care settings, the generalizability and accuracy of the data collected by these methods, feature construction, and the appropriateness of RMT use in the long term.
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Affiliation(s)
- Valeria de Angel
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Fadekemi Adeleye
- Department of Psychology, King's College London, London, United Kingdom
| | - Yuezhou Zhang
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Nicholas Cummins
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Sara Munir
- Lewisham Talking Therapies, South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Serena Lewis
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Department of Psychology, University of Bath, Bath, United Kingdom
| | - Estela Laporta Puyal
- Biomedical Signal Interpretation and Computational Simulation Group, Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
- Centro de Investigación Biomédica en Red of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Faith Matcham
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- School of Psychology, University of Sussex, Brighton, United Kingdom
| | - Shaoxiong Sun
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Amos A Folarin
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
- Health Data Research UK London, University College London, London, United Kingdom
- NIHR Biomedical Research Centre at University College London Hospitals, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Yatharth Ranjan
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Pauline Conde
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Zulqarnain Rashid
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Richard Dobson
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Matthew Hotopf
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
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14
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Fitzpatrick NK, Dobson R, Roberts A, Jones K, Shah AD, Nenadic G, Ford E. Understanding stakeholder views around the creation of a consented donated databank of clinical free text to develop and train natural language processing models for research: an exploratory study (Preprint). JMIR Med Inform 2023; 11:e45534. [PMID: 37133927 DOI: 10.2196/45534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 02/24/2023] [Accepted: 03/19/2023] [Indexed: 03/21/2023] Open
Abstract
BACKGROUND Information stored within electronic health records is often recorded as unstructured text. Special computerized natural language processing (NLP) tools are needed to process this text; however, complex governance arrangements make such data in the National Health Service hard to access, and therefore, it is difficult to use for research in improving NLP methods. The creation of a donated databank of clinical free text could provide an important opportunity for researchers to develop NLP methods and tools and may circumvent delays in accessing the data needed to train the models. However, to date, there has been little or no engagement with stakeholders on the acceptability and design considerations of establishing a free-text databank for this purpose. OBJECTIVE This study aimed to ascertain stakeholder views around the creation of a consented, donated databank of clinical free text to help create, train, and evaluate NLP for clinical research and to inform the potential next steps for adopting a partner-led approach to establish a national, funded databank of free text for use by the research community. METHODS Web-based in-depth focus group interviews were conducted with 4 stakeholder groups (patients and members of the public, clinicians, information governance leads and research ethics members, and NLP researchers). RESULTS All stakeholder groups were strongly in favor of the databank and saw great value in creating an environment where NLP tools can be tested and trained to improve their accuracy. Participants highlighted a range of complex issues for consideration as the databank is developed, including communicating the intended purpose, the approach to access and safeguarding the data, who should have access, and how to fund the databank. Participants recommended that a small-scale, gradual approach be adopted to start to gather donations and encouraged further engagement with stakeholders to develop a road map and set of standards for the databank. CONCLUSIONS These findings provide a clear mandate to begin developing the databank and a framework for stakeholder expectations, which we would aim to meet with the databank delivery.
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Affiliation(s)
| | - Richard Dobson
- Department of Biostatistics and Health Informatics, King's College London, London, United Kingdom
| | - Angus Roberts
- Department of Biostatistics and Health Informatics, King's College London, London, United Kingdom
| | - Kerina Jones
- Department of Population Data Science, Swansea University Medical School, Swansea, United Kingdom
| | - Anoop D Shah
- Institute of Health Informatics, University College London, London, United Kingdom
- University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Goran Nenadic
- Department of Computer Science, University of Manchester, Manchester, United Kingdom
| | - Elizabeth Ford
- Brighton and Sussex Medical School, Brighton, United Kingdom
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15
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Farran D, Bean D, Wang T, Msosa Y, Casetta C, Dobson R, Teo JT, Scott P, Gaughran F. Corrigendum "Anticoagulation for atrial fibrillation in people with serious mental illness in the general hospital setting" [J. Psychiatr. Res. 153 (2022) 167-173]. J Psychiatr Res 2022; 162:228. [PMID: 36581539 DOI: 10.1016/j.jpsychires.2022.12.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Dina Farran
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Daniel Bean
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Health Data Research UK London, Institute of Health Informatics, University College London, London, UK
| | - Tao Wang
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Yamiko Msosa
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Cecilia Casetta
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Department of Health Sciences, ASST Santi Paolo Carlo, Milano, Italy
| | - Richard Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Health Data Research UK London, Institute of Health Informatics, University College London, London, UK; NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, UK
| | - James T Teo
- Department of Neurosciences, King's College Hospital NHS Foundation Trust, Denmark Hill, London, UK
| | - Paul Scott
- Department of Cardiology, King's College Hospital, Denmark Hill, London, UK
| | - Fiona Gaughran
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, UK; South London and Maudsley NHS Foundation Trust, London, UK
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16
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Abdimalik M, Dawoodji A, Olsson-Brown A, Dobson R. Evaluating risk factors, biomarkers and management of myocarditis following treatment with checkpoint inhibitors. A retrospective analysis. Eur Heart J 2022. [DOI: 10.1093/eurheartj/ehac544.1684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Immunotherapy is an exciting treatment of many malignancies. They upregulate the immune system to attack cancerous cells. Immune checkpoint inhibitor (ICIs) are among armamentarium of immunotherapy agents that target immune system negative regulation and are known to induce myocarditis [1]. This is a rare but increasingly recognised complication as immunotherapy regimens become more widely used as first line therapy for a range of tumour types. Due to its infrequent presentation, there remains uncertainty regarding its diagnosis and management [2]. We conducted a single centre retrospective analysis of patients undergoing immunotherapy at a cancer centre in United Kingdom. The cases were referred to the regional cardio-oncology service for suspected myocarditis.
In this single center retrospective analysis, data of 60 patients with suspected myocarditis (based on symptoms, cardiac biomarkers or ECG) presenting over a 2 year period were evaluated. Of those 25 (42%) developed MRI-proven myocarditis with mean period to toxicity post treatment of 99.5 days. In the same time period 1027 patients experienced all-type immunotherapy related adverse events (irAEs) thus myocarditis accounted for 2.4% of all ICI-induced toxicity. 35% had significant pre-existing cardiac history including ischaemic heart disease, valvular disease and ventricular impairment. Patients with myocarditis had average peak troponin of 276 and average peak pro-BNP of 2,265. Less than 10% of patients with myocarditis had pre-existing autoimmune diagnosis (rheumatoid arthritis, type 1 diabetes and inflammatory bowel disease). Average maximum grade toxicity was 3.
The majority of patients were managed with steroids with 48% receiving intravenous methylprednisolone followed by oral prednisolone and 28% patients receiving only oral steroids. 44% of patients also required steroid sparing agents such as tacrolimus or mycophenalate mofetil. Steroid tapering was adjusted based on the patient's symptoms with 32% of patients needing dose escalation for a flare. No patients restarted immunotherapy once myocarditis had been identified and overall survival remained comparable to those without myocarditis.
In conclusion, myocarditis is a late complication of ICIs with higher incidence among patients with significant cardiac history. Associated biomarkers are significantly elevated and correlate well with the presence of myocarditis. Steroids remain the mainstay of treatment for those who develop myocarditis secondary to immunotherapy but there is also an important role for other agents such as tacrolimus or mycophenalate mofetil.
Funding Acknowledgement
Type of funding sources: None.
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Affiliation(s)
- M Abdimalik
- Liverpool Heart and Chest Hospital , Liverpool , United Kingdom
| | - A Dawoodji
- Liverpool Heart and Chest Hospital , Liverpool , United Kingdom
| | - A Olsson-Brown
- Liverpool Heart and Chest Hospital , Liverpool , United Kingdom
| | - R Dobson
- Liverpool Heart and Chest Hospital , Liverpool , United Kingdom
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17
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Deng Y, Wen Y, Qian L, Anton EP, Xu H, Pushparajah K, Ibrahim Z, Dobson R, Young A. Multi-modal Latent-Space Self-alignment for Super-Resolution Cardiac MR Segmentation. Stat Atlases Comput Models Heart 2022; 13593:26-35. [PMID: 37133264 PMCID: PMC10148962 DOI: 10.1007/978-3-031-23443-9_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
2D cardiac MR cine images provide data with a high signal-to-noise ratio for the segmentation and reconstruction of the heart. These images are frequently used in clinical practice and research. However, the segments have low resolution in the through-plane direction, and standard interpolation methods are unable to improve resolution and precision. We proposed an end-to-end pipeline for producing high-resolution segments from 2D MR images. This pipeline utilised a bilateral optical flow warping method to recover images in the through-plane direction, while a SegResNet automatically generated segments of the left and right ventricles. A multi-modal latent-space self-alignment network was implemented to guarantee that the segments maintain an anatomical prior derived from unpaired 3D high-resolution CT scans. On 3D MR angiograms, the trained pipeline produced high-resolution segments that preserve an anatomical prior derived from patients with various cardiovascular diseases.
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Affiliation(s)
- Yu Deng
- School of Biomedical Engineering and Imaging Science, King's College London, London, UK
| | - Yang Wen
- Animal Imaging and Technology Core, Center for Biomedical Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Laboratory for Functional and Metabolic Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Linglong Qian
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Esther Puyol Anton
- School of Biomedical Engineering and Imaging Science, King's College London, London, UK
| | - Hao Xu
- School of Biomedical Engineering and Imaging Science, King's College London, London, UK
| | - Kuberan Pushparajah
- School of Biomedical Engineering and Imaging Science, King's College London, London, UK
| | - Zina Ibrahim
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Richard Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Alistair Young
- School of Biomedical Engineering and Imaging Science, King's College London, London, UK
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18
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Angel B, Ajnakina O, Albala C, Lera L, Márquez C, Leipold L, Bilovich A, Dobson R, Bendayan R. Grip Strength Trajectories and Cognition in English and Chilean Older Adults: A Cross-Cohort Study. J Pers Med 2022; 12:jpm12081230. [PMID: 36013179 PMCID: PMC9410389 DOI: 10.3390/jpm12081230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/25/2022] [Accepted: 07/25/2022] [Indexed: 11/27/2022] Open
Abstract
Growing evidence about the link between cognitive and physical decline suggests the early changes in physical functioning as a potential biomarker for cognitive impairment. Thus, we compared grip-strength trajectories over 12-16 years in three groups classified according to their cognitive status (two stable patterns, normal and impaired cognitive performance, and a declining pattern) in two representative UK and Chilean older adult samples. The samples consisted of 7069 UK (ELSA) and 1363 Chilean participants (ALEXANDROS). Linear Mixed models were performed. Adjustments included socio-demographics and health variables. The Declined and Impaired group had significantly lower grip-strength at baseline when compared to the Non-Impaired. In ELSA, the Declined and Impaired showed a faster decline in their grip strength compared to the Non-Impaired group but differences disappeared in the fully adjusted models. In ALEXANDROS, the differences were only found between the Declined and Non-Impaired and they were partially attenuated by covariates. Our study provides robust evidence of the association between grip strength and cognitive performance and how socio-economic factors might be key to understanding this association and their variability across countries. This has implications for future epidemiological research, as hand-grip strength measurements have the potential to be used as an indicator of cognitive performance.
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Affiliation(s)
- Bárbara Angel
- Public Health Nutrition Unit, Institute of Nutrition and Food Technology, University of Chile, Santiago 7830490, Chile; (B.A.); (L.L.); (C.M.)
| | - Olesya Ajnakina
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London WC2R 2LS, UK; (O.A.); (L.L.); (R.D.); (R.B.)
- Department of Behavioural Science and Health, Institute of Epidemiology and Health Care, University College London, London WC1E 6BT, UK
| | - Cecilia Albala
- Public Health Nutrition Unit, Institute of Nutrition and Food Technology, University of Chile, Santiago 7830490, Chile; (B.A.); (L.L.); (C.M.)
- Correspondence: ; Tel.: +56-2-2978-1455
| | - Lydia Lera
- Public Health Nutrition Unit, Institute of Nutrition and Food Technology, University of Chile, Santiago 7830490, Chile; (B.A.); (L.L.); (C.M.)
- Latin Division, Keiser University eCampus, Fort Lauderdale, FL 33409, USA
| | - Carlos Márquez
- Public Health Nutrition Unit, Institute of Nutrition and Food Technology, University of Chile, Santiago 7830490, Chile; (B.A.); (L.L.); (C.M.)
| | - Leona Leipold
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London WC2R 2LS, UK; (O.A.); (L.L.); (R.D.); (R.B.)
- NIHR Biomedical Research Centre at South London and Maudsley, NHS Foundation Trust and King’s College London, London SE5 8AF, UK
| | - Avri Bilovich
- Centre for the Study of Decision-Making Uncertainty, University College London, London WC1E 6BT, UK;
| | - Richard Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London WC2R 2LS, UK; (O.A.); (L.L.); (R.D.); (R.B.)
- NIHR Biomedical Research Centre at South London and Maudsley, NHS Foundation Trust and King’s College London, London SE5 8AF, UK
- Health Data Research UK London, University College London, London WC1E 6BT, UK
- Institute of Health Informatics, University College London, London WC1E 6BT, UK
- NIHR Biomedical Research Centre, University College London Hospitals NHS Foundation Trust, London NW1 2PG, UK
| | - Rebecca Bendayan
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London WC2R 2LS, UK; (O.A.); (L.L.); (R.D.); (R.B.)
- NIHR Biomedical Research Centre at South London and Maudsley, NHS Foundation Trust and King’s College London, London SE5 8AF, UK
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19
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Allen-Philbey K, Stennett A, Begum T, Johnson AC, MacDougall A, Green S, Dobson R, Giovannoni G, Gnanapavan S, Marta M, Smets I, Turner BP, Baker D, Mathews J, Schmierer K. Did it hurt? COVID-19 vaccination experience in people with multiple sclerosis. Mult Scler Relat Disord 2022; 65:104022. [PMID: 35816953 PMCID: PMC9250705 DOI: 10.1016/j.msard.2022.104022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 06/29/2022] [Accepted: 07/01/2022] [Indexed: 11/19/2022]
Abstract
Background Current guidelines recommend vaccination against SARS-CoV2 for people with multiple sclerosis (pwMS). The long-term review of the safety and effectiveness of COVID-19 vaccines in pwMS is limited. Methods Service re-evaluation. PwMS using the MS service at Barts Health National Health Service Trust were sent questionnaires via email to report symptoms following first and second COVID-19 vaccinations (n = 570). A retrospective review of electronic health records was conducted for clinical and safety data post-vaccination(s); cut-off was end of September 2021. Separate logistic regressions were carried out for symptoms experienced at each vaccination. Two sets of regressions were fitted with covariates: (i) Disease-modifying therapy type and (ii) patient characteristics for symptoms experienced. Results 193/570 pwMS responded. 184 pwMS had both vaccinations. 144 received the AZD1222 and 49 the BNT162b2 vaccine. 87% and 75% of pwMS experienced any symptoms at first and second vaccinations, respectively. The majority of symptoms resolved within a short timeframe. No severe adverse effects were reported. Two pwMS subsequently died; one due to COVID-19 and one due to aspiration pneumonia. Males were at a reduced risk of reporting symptoms at first vaccination. There was evidence that pwMS in certain treatment groups were at reduced risk of reporting symptoms at second vaccination only. Conclusions Findings are consistent with our preliminary data. Symptoms post-vaccination were similar to the non-MS population and were mostly temporary. It is important to inform the MS community of vaccine safety data.
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Affiliation(s)
- K Allen-Philbey
- The Blizard Institute, Centre for Neuroscience, Surgery & Trauma, Queen Mary University of London, Barts and The London School of Medicine & Dentistry, 4 Newark Street, London E1 2AT, UK; Clinical Board Medicine (Neuroscience), The Royal London Hospital, Barts Health NHS Trust, London, UK
| | - A Stennett
- Clinical Board Medicine (Neuroscience), The Royal London Hospital, Barts Health NHS Trust, London, UK; Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, Barts and The London School of Medicine & Dentistry, London, UK
| | - T Begum
- Clinical Board Medicine (Neuroscience), The Royal London Hospital, Barts Health NHS Trust, London, UK
| | - A C Johnson
- Clinical Board Medicine (Neuroscience), The Royal London Hospital, Barts Health NHS Trust, London, UK
| | - A MacDougall
- Department of Medical Statistics, London School of Hygiene & Tropical Medicine, UK
| | - S Green
- Clinical Board Medicine (Neuroscience), The Royal London Hospital, Barts Health NHS Trust, London, UK
| | - R Dobson
- Clinical Board Medicine (Neuroscience), The Royal London Hospital, Barts Health NHS Trust, London, UK; Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, Barts and The London School of Medicine & Dentistry, London, UK
| | - G Giovannoni
- The Blizard Institute, Centre for Neuroscience, Surgery & Trauma, Queen Mary University of London, Barts and The London School of Medicine & Dentistry, 4 Newark Street, London E1 2AT, UK; Clinical Board Medicine (Neuroscience), The Royal London Hospital, Barts Health NHS Trust, London, UK; Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, Barts and The London School of Medicine & Dentistry, London, UK
| | - S Gnanapavan
- The Blizard Institute, Centre for Neuroscience, Surgery & Trauma, Queen Mary University of London, Barts and The London School of Medicine & Dentistry, 4 Newark Street, London E1 2AT, UK; Clinical Board Medicine (Neuroscience), The Royal London Hospital, Barts Health NHS Trust, London, UK
| | - M Marta
- The Blizard Institute, Centre for Neuroscience, Surgery & Trauma, Queen Mary University of London, Barts and The London School of Medicine & Dentistry, 4 Newark Street, London E1 2AT, UK; Clinical Board Medicine (Neuroscience), The Royal London Hospital, Barts Health NHS Trust, London, UK
| | - I Smets
- The Blizard Institute, Centre for Neuroscience, Surgery & Trauma, Queen Mary University of London, Barts and The London School of Medicine & Dentistry, 4 Newark Street, London E1 2AT, UK; Clinical Board Medicine (Neuroscience), The Royal London Hospital, Barts Health NHS Trust, London, UK
| | - B P Turner
- The Blizard Institute, Centre for Neuroscience, Surgery & Trauma, Queen Mary University of London, Barts and The London School of Medicine & Dentistry, 4 Newark Street, London E1 2AT, UK; Clinical Board Medicine (Neuroscience), The Royal London Hospital, Barts Health NHS Trust, London, UK
| | - D Baker
- The Blizard Institute, Centre for Neuroscience, Surgery & Trauma, Queen Mary University of London, Barts and The London School of Medicine & Dentistry, 4 Newark Street, London E1 2AT, UK
| | - J Mathews
- Clinical Board Medicine (Neuroscience), The Royal London Hospital, Barts Health NHS Trust, London, UK
| | - K Schmierer
- The Blizard Institute, Centre for Neuroscience, Surgery & Trauma, Queen Mary University of London, Barts and The London School of Medicine & Dentistry, 4 Newark Street, London E1 2AT, UK; Clinical Board Medicine (Neuroscience), The Royal London Hospital, Barts Health NHS Trust, London, UK.
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20
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Wightman DP, Jansen IE, Savage JE, Shadrin AA, Bahrami S, Holland D, Rongve A, Børte S, Winsvold BS, Drange OK, Martinsen AE, Skogholt AH, Willer C, Bråthen G, Bosnes I, Nielsen JB, Fritsche LG, Thomas LF, Pedersen LM, Gabrielsen ME, Johnsen MB, Meisingset TW, Zhou W, Proitsi P, Hodges A, Dobson R, Velayudhan L, Heilbron K, Auton A, Sealock JM, Davis LK, Pedersen NL, Reynolds CA, Karlsson IK, Magnusson S, Stefansson H, Thordardottir S, Jonsson PV, Snaedal J, Zettergren A, Skoog I, Kern S, Waern M, Zetterberg H, Blennow K, Stordal E, Hveem K, Zwart JA, Athanasiu L, Selnes P, Saltvedt I, Sando SB, Ulstein I, Djurovic S, Fladby T, Aarsland D, Selbæk G, Ripke S, Stefansson K, Andreassen OA, Posthuma D. Author Correction: A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer's disease. Nat Genet 2022; 54:1062. [PMID: 35726068 DOI: 10.1038/s41588-022-01126-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Douglas P Wightman
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU Amsterdam, Amsterdam, the Netherlands
| | - Iris E Jansen
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU Amsterdam, Amsterdam, the Netherlands
| | - Jeanne E Savage
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU Amsterdam, Amsterdam, the Netherlands
| | - Alexey A Shadrin
- NORMENT Centre, University of Oslo, Oslo, Norway.,Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Shahram Bahrami
- NORMENT Centre, University of Oslo, Oslo, Norway.,Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Dominic Holland
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | - Arvid Rongve
- Department of Research and Innovation, Helse Fonna, Haugesund Hospital, Haugesund, Norway.,The University of Bergen, Institute of Clinical Medicine (K1), Bergen, Norway
| | - Sigrid Børte
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Research and Communication Unit for Musculoskeletal Health (FORMI), Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway.,K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Bendik S Winsvold
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway.,Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Ole Kristian Drange
- Department of Mental Health, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.,Division of Mental Health Care, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Amy E Martinsen
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
| | - Anne Heidi Skogholt
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Cristen Willer
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Geir Bråthen
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Neurology and Clinical Neurophysiology, University Hospital of Trondheim, Trondheim, Norway.,Department of Geriatrics, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Ingunn Bosnes
- Department of Mental Health, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Psychiatry, Hospital Namsos, Nord-Trøndelag Health Trust, Namsos, Norway
| | - Jonas Bille Nielsen
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, USA.,Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Lars G Fritsche
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Laurent F Thomas
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Linda M Pedersen
- Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
| | - Maiken E Gabrielsen
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Marianne Bakke Johnsen
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Research and Communication Unit for Musculoskeletal Health (FORMI), Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway.,K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Tore Wergeland Meisingset
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Neurology and Clinical Neurophysiology, University Hospital of Trondheim, Trondheim, Norway
| | - Wei Zhou
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.,Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Petroula Proitsi
- Institute of Psychiatry Psychology and Neurosciences, King's College London, London, UK
| | - Angela Hodges
- Institute of Psychiatry Psychology and Neurosciences, King's College London, London, UK
| | - Richard Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, London, UK.,NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, UK.,Health Data Research UK London, University College London, London, UK.,Institute of Health Informatics, University College London, London, UK.,NIHR Biomedical Research Centre at University College London Hospitals NHS Foundation Trust, London, UK
| | - Latha Velayudhan
- Institute of Psychiatry Psychology and Neurosciences, King's College London, London, UK
| | | | | | | | - Julia M Sealock
- Division of Genetic Medicine, Department of Medicine Vanderbilt University Medical Center Nashville, Nashville, TN, USA.,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lea K Davis
- Division of Genetic Medicine, Department of Medicine Vanderbilt University Medical Center Nashville, Nashville, TN, USA.,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nancy L Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Chandra A Reynolds
- Department of Psychology, University of California-Riverside, Riverside, CA, USA
| | - Ida K Karlsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,Institute of Gerontology and Aging Research Network - Jönköping (ARN-J), School of Health and Welfare, Jönköping University, Jönköping, Sweden
| | | | | | | | - Palmi V Jonsson
- Department of Geriatric Medicine, Landspitali University Hospital, Reykjavik, Iceland.,Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Jon Snaedal
- Department of Geriatric Medicine, Landspitali University Hospital, Reykjavik, Iceland
| | - Anna Zettergren
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden
| | - Ingmar Skoog
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden.,Region Västra Götaland, Sahlgrenska University Hospital, Psychiatry, Cognition and Old Age Psychiatry Clinic, Gothenburg, Sweden
| | - Silke Kern
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden.,Region Västra Götaland, Sahlgrenska University Hospital, Psychiatry, Cognition and Old Age Psychiatry Clinic, Gothenburg, Sweden
| | - Margda Waern
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden.,Region Västra Götaland, Sahlgrenska University Hospital, Psychosis Clinic, Gothenburg, Sweden
| | - Henrik Zetterberg
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK.,UK Dementia Research Institute at UCL, London, UK.,Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Eystein Stordal
- Department of Mental Health, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Psychiatry, Hospital Namsos, Nord-Trøndelag Health Trust, Namsos, Norway
| | - Kristian Hveem
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.,HUNT Research Center, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - John-Anker Zwart
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
| | - Lavinia Athanasiu
- NORMENT Centre, University of Oslo, Oslo, Norway.,Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Per Selnes
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
| | - Ingvild Saltvedt
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Geriatrics, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Sigrid B Sando
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Neurology and Clinical Neurophysiology, University Hospital of Trondheim, Trondheim, Norway
| | - Ingun Ulstein
- Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway.,NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Tormod Fladby
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Neurology, Akershus University Hospital, Lørenskog, Norway
| | - Dag Aarsland
- Institute of Psychiatry Psychology and Neurosciences, King's College London, London, UK.,Centre of Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway
| | - Geir Selbæk
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway.,Norwegian National Advisory Unit on Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway
| | - Stephan Ripke
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin, Berlin, Germany
| | | | - Ole A Andreassen
- NORMENT Centre, University of Oslo, Oslo, Norway.,Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU Amsterdam, Amsterdam, the Netherlands. .,Department of Child and Adolescent Psychiatry and Pediatric Psychology, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam University Medical Center, Amsterdam, the Netherlands.
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21
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Matcham F, Leightley D, Siddi S, Lamers F, White K, Annas P, De Girolamo G, Difrancesco S, Haro J, Horsfall M, Ivan A, Lavelle G, Li Q, Lombardini F, Mohr D, Narayan V, Oetzmann C, Penninx B, Simblett S, Bruce S, Nica R, Wykes T, Brasen J, Myin-Germeys I, Rintala A, Conde P, Dobson R, Folarin A, Stewart C, Ranjan Y, Rashid Z, Cummins N, Manyakov N, Vairavan S, Hotopf M. Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD): Recruitment, retention, and data availability in a longitudinal remote measurement study. Eur Psychiatry 2022. [PMCID: PMC9564033 DOI: 10.1192/j.eurpsy.2022.315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Introduction
Major Depressive Disorder (MDD) is prevalent, often chronic, and requires ongoing monitoring of symptoms to track response to treatment and identify early indicators of relapse. Remote Measurement Technologies (RMT) provide an exciting opportunity to transform the measurement and management of MDD, via data collected from inbuilt smartphone sensors and wearable devices alongside app-based questionnaires and tasks.
Objectives
To describe the amount of data collected during a multimodal longitudinal RMT study, in an MDD population.
Methods
RADAR-MDD is a multi-centre, prospective observational cohort study. People with a history of MDD were provided with a wrist-worn wearable, and several apps designed to: a) collect data from smartphone sensors; and b) deliver questionnaires, speech tasks and cognitive assessments and followed-up for a maximum of 2 years.
Results
A total of 623 individuals with a history of MDD were enrolled in the study with 80% completion rates for primary outcome assessments across all timepoints. 79.8% of people participated for the maximum amount of time available and 20.2% withdrew prematurely. Data availability across all RMT data types varied depending on the source of data and the participant-burden for each data type. We found no evidence of an association between the severity of depression symptoms at baseline and the availability of data. 110 participants had > 50% data available across all data types, and thus able to contribute to multiparametric analyses.
Conclusions
RADAR-MDD is the largest multimodal RMT study in the field of mental health. Here, we have shown that collecting RMT data from a clinical population is feasible.
Disclosure
No significant relationships.
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22
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Shek A, Theochari E, Searle T, Kraljevic Z, Viana P, Bruno E, Teo J, Dobson R, Richardson MP. 152 Automating the assessment of first seizure care pathways and clinical outcomes using electronic patient records. J Neurol Neurosurg Psychiatry 2022. [DOI: 10.1136/jnnp-2022-abn.181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
PurposeElectronic Patient Records (EPR) are ideal sources of information to provide evidence for the optimisation of health management. However, beyond direct patient care, their use for secondary purposes such as research or service improvement has been limited.AimsWe aimed to explore the feasibility of using novel patient data analytics tools (CogStack & MedCAT) at King’s College Hospital trust (KCH) to identify suspected first seizure patients and compare their man- agement against NICE guidelines.MethodWe utilised CogStack to search 1.4 billion EPR documents at KCH to identify suspected first seizure patients. We then retrieved their subsequent records, and used a combination of manual extraction and a natural language processing tool (MedCAT), to extract information about their symptomatic presenta- tion, final diagnosis, timing of investigations, and specialist appointments.Results226 patients attended the emergency department with suspected first seizures. MedCAT could be feasibly trained with expert knowledge to increase the accuracy of automatically extracting relevant textual information from EPR. Our analysis of patient records identified steps in the clinical pathway that frequently fell short of the NICE guidelines.ConclusionEPR are feasible to be mined at scale for the rapid analysis of service demand and monitoring patient health trajectories. The insights of this study have been used to improve first seizure management in KCH, demonstrating the value of NLP application in healthcare.anthony.shek@kcl.ac.uk107
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23
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Handy A, Banerjee A, Wood AM, Dale C, Sudlow CLM, Tomlinson C, Bean D, Thygesen JH, Mizani MA, Katsoulis M, Takhar R, Hollings S, Denaxas S, Walker V, Dobson R, Sofat R. Evaluation of antithrombotic use and COVID-19 outcomes in a nationwide atrial fibrillation cohort. Heart 2022; 108:923-931. [PMID: 35273122 PMCID: PMC8931797 DOI: 10.1136/heartjnl-2021-320325] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 01/24/2022] [Indexed: 01/06/2023] Open
Abstract
OBJECTIVE To evaluate antithrombotic (AT) use in individuals with atrial fibrillation (AF) and at high risk of stroke (CHA2DS2-VASc score ≥2) and investigate whether pre-existing AT use may improve COVID-19 outcomes. METHODS Individuals with AF and CHA2DS2-VASc score ≥2 on 1 January 2020 were identified using electronic health records for 56 million people in England and were followed up until 1 May 2021. Factors associated with pre-existing AT use were analysed using logistic regression. Differences in COVID-19-related hospitalisation and death were analysed using logistic and Cox regression in individuals with pre-existing AT use versus no AT use, anticoagulants (AC) versus antiplatelets (AP), and direct oral anticoagulants (DOACs) versus warfarin. RESULTS From 972 971 individuals with AF (age 79 (±9.3), female 46.2%) and CHA2DS2-VASc score ≥2, 88.0% (n=856 336) had pre-existing AT use, 3.8% (n=37 418) had a COVID-19 hospitalisation and 2.2% (n=21 116) died, followed up to 1 May 2021. Factors associated with no AT use included comorbidities that may contraindicate AT use (liver disease and history of falls) and demographics (socioeconomic status and ethnicity). Pre-existing AT use was associated with lower odds of death (OR=0.92, 95% CI 0.87 to 0.96), but higher odds of hospitalisation (OR=1.20, 95% CI 1.15 to 1.26). AC versus AP was associated with lower odds of death (OR=0.93, 95% CI 0.87 to 0.98) and higher hospitalisation (OR=1.17, 95% CI 1.11 to 1.24). For DOACs versus warfarin, lower odds were observed for hospitalisation (OR=0.86, 95% CI 0.82 to 0.89) but not for death (OR=1.00, 95% CI 0.95 to 1.05). CONCLUSIONS Pre-existing AT use may be associated with lower odds of COVID-19 death and, while not evidence of causality, provides further incentive to improve AT coverage for eligible individuals with AF.
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Affiliation(s)
- Alex Handy
- Institute of Health Informatics, University College London, London, UK
| | - Amitava Banerjee
- Institute of Health Informatics, University College London, London, UK
- University College London Hospitals National Health Service Trust, London, UK
- Barts Health National Health Service Trust, The Royal London Hospital, London, UK
| | - Angela M Wood
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
| | - Caroline Dale
- Institute of Health Informatics, University College London, London, UK
| | - Cathie L M Sudlow
- British Heart Foundation Data Science Centre, Health Data Research UK, London, UK
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Usher Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, UK
| | - Christopher Tomlinson
- Institute of Health Informatics, University College London, London, UK
- University College London Hospitals National Health Service Trust, London, UK
- UKRI Centre for Doctoral Training in AI-enabled Healthcare Systems, University College London, London, UK
- National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK
| | - Daniel Bean
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley National Health Service Trust, London, UK
- Health Data Research UK London, University College London, London, UK
| | - Johan H Thygesen
- Institute of Health Informatics, University College London, London, UK
| | - Mehrdad A Mizani
- Institute of Health Informatics, University College London, London, UK
| | - Michail Katsoulis
- Institute of Health Informatics, University College London, London, UK
- MRC Unit for Lifelong Health and Ageing and Centre for Longitudinal Studies, University College London, London, UK
| | - Rohan Takhar
- Institute of Health Informatics, University College London, London, UK
| | | | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
- British Heart Foundation Data Science Centre, Health Data Research UK, London, UK
- National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK
- British Heart Foundation Research Accelerator, University College London, London, UK
| | - Venexia Walker
- Bristol Medical School: Population Health Sciences, University of Bristol, Bristol, UK
| | - Richard Dobson
- Institute of Health Informatics, University College London, London, UK
- National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley National Health Service Trust, London, UK
- Health Data Research UK London, University College London, London, UK
| | - Reecha Sofat
- Institute of Health Informatics, University College London, London, UK
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
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24
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de Angel V, Lewis S, Munir S, Matcham F, Dobson R, Hotopf M. Using digital health tools for the Remote Assessment of Treatment Prognosis in Depression (RAPID): a study protocol for a feasibility study. BMJ Open 2022; 12:e059258. [PMID: 35523486 PMCID: PMC9083394 DOI: 10.1136/bmjopen-2021-059258] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
INTRODUCTION Digital health tools such as smartphones and wearable devices could improve psychological treatment outcomes in depression through more accurate and comprehensive measures of patient behaviour. However, in this emerging field, most studies are small and based on student populations outside of a clinical setting. The current study aims to determine the feasibility and acceptability of using smartphones and wearable devices to collect behavioural and clinical data in people undergoing therapy for depressive disorders and establish the extent to which they can be potentially useful biomarkers of depression and recovery after treatment. METHODS AND ANALYSIS This is an observational, prospective cohort study of 65 people attending psychological therapy for depression in multiple London-based sites. It will collect continuous passive data from smartphone sensors and a Fitbit fitness tracker, and deliver questionnaires, speech tasks and cognitive assessments through smartphone-based apps. Objective data on sleep, physical activity, location, Bluetooth contact, smartphone use and heart rate will be gathered for 7 months, and compared with clinical and contextual data. A mixed methods design, including a qualitative interview of patient experiences, will be used to evaluate key feasibility indicators, digital phenotypes of depression and therapy prognosis. Patient and public involvement was sought for participant-facing documents and the study design of the current research proposal. ETHICS AND DISSEMINATION Ethical approval has been obtained from the London Westminster Research Ethics Committee, and the Health Research Authority, Integrated Research Application System (project ID: 270918). Privacy and confidentiality will be guaranteed and the procedures for handling, processing, storage and destruction of the data will comply with the General Data Protection Regulation. Findings from this study will form part of a doctoral thesis, will be presented at national and international meetings or academic conferences and will generate manuscripts to be submitted to peer-reviewed journals. TRIAL REGISTRATION NUMBER https://doi.org/10.17605/OSF.IO/PMYTA.
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Affiliation(s)
- Valeria de Angel
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UK
| | - Serena Lewis
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Department of Psychology, University of Bath, Bath, UK
| | - Sara Munir
- Lewisham Talking Therapies, South London and Maudsley NHS Foundation Trust, London, UK
| | - Faith Matcham
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Richard Dobson
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UK
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Matthew Hotopf
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UK
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25
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Mitratza M, Goodale BM, Shagadatova A, Kovacevic V, van de Wijgert J, Brakenhoff TB, Dobson R, Franks B, Veen D, Folarin AA, Stolk P, Grobbee DE, Cronin M, Downward GS. The performance of wearable sensors in the detection of SARS-CoV-2 infection: a systematic review. Lancet Digit Health 2022; 4:e370-e383. [PMID: 35461692 PMCID: PMC9020803 DOI: 10.1016/s2589-7500(22)00019-x] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 12/08/2021] [Accepted: 01/20/2022] [Indexed: 01/09/2023]
Abstract
Containing the COVID-19 pandemic requires rapidly identifying infected individuals. Subtle changes in physiological parameters (such as heart rate, respiratory rate, and skin temperature), discernible by wearable devices, could act as early digital biomarkers of infections. Our primary objective was to assess the performance of statistical and algorithmic models using data from wearable devices to detect deviations compatible with a SARS-CoV-2 infection. We searched MEDLINE, Embase, Web of Science, the Cochrane Central Register of Controlled Trials (known as CENTRAL), International Clinical Trials Registry Platform, and ClinicalTrials.gov on July 27, 2021 for publications, preprints, and study protocols describing the use of wearable devices to identify a SARS-CoV-2 infection. Of 3196 records identified and screened, 12 articles and 12 study protocols were analysed. Most included articles had a moderate risk of bias, as per the National Institute of Health Quality Assessment Tool for Observational and Cross-Sectional Studies. The accuracy of algorithmic models to detect SARS-CoV-2 infection varied greatly (area under the curve 0·52-0·92). An algorithm's ability to detect presymptomatic infection varied greatly (from 20% to 88% of cases), from 14 days to 1 day before symptom onset. Increased heart rate was most frequently associated with SARS-CoV-2 infection, along with increased skin temperature and respiratory rate. All 12 protocols described prospective studies that had yet to be completed or to publish their results, including two randomised controlled trials. The evidence surrounding wearable devices in the early detection of SARS-CoV-2 infection is still in an early stage, with a limited overall number of studies identified. However, these studies show promise for the early detection of SARS-CoV-2 infection. Large prospective, and preferably controlled, studies recruiting and retaining larger and more diverse populations are needed to provide further evidence.
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Affiliation(s)
- Marianna Mitratza
- Julius Global Health, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.
| | | | - Aizhan Shagadatova
- Julius Global Health, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | | | - Janneke van de Wijgert
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | | | - Richard Dobson
- Institute of Health Informatics, University College London, London, UK
| | | | - Duco Veen
- Julius Global Health, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands; Julius Clinical Research BV, Zeist, Netherlands; Optentia Research Program, North-West University, Potchefstroom, South Africa
| | - Amos A Folarin
- Institute of Health Informatics, University College London, London, UK; National Institute for Health Research Maudsley Biomedical Research Centre, King's College London, London, UK; Department of Biostatistics and Health Informatics, South London and Maudsley NHS Foundation Trust, London, UK
| | - Pieter Stolk
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Diederick E Grobbee
- Julius Global Health, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands; Julius Clinical Research BV, Zeist, Netherlands
| | | | - George S Downward
- Julius Global Health, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
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26
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Lord J, Green R, Choi SW, Hübel C, Aarsland D, Velayudhan L, Sham P, Legido-Quigley C, Richards M, Dobson R, Proitsi P. Disentangling Independent and Mediated Causal Relationships Between Blood Metabolites, Cognitive Factors, and Alzheimer's Disease. Biol Psychiatry Glob Open Sci 2022; 2:167-179. [PMID: 36325159 PMCID: PMC9616368 DOI: 10.1016/j.bpsgos.2021.07.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 06/18/2021] [Accepted: 07/07/2021] [Indexed: 01/12/2023] Open
Abstract
Background Education and cognition demonstrate consistent inverse associations with Alzheimer's disease (AD). The biological underpinnings, however, remain unclear. Blood metabolites reflect the end point of biological processes and are accessible and malleable. Identifying metabolites with etiological relevance to AD and disentangling how these relate to cognitive factors along the AD causal pathway could, therefore, offer unique insights into underlying causal mechanisms. Methods Using data from the largest metabolomics genome-wide association study (N ≈ 24,925) and three independent AD cohorts (N = 4725), cross-trait polygenic scores were generated and meta-analyzed. Metabolites genetically associated with AD were taken forward for causal analyses. Bidirectional two-sample Mendelian randomization interrogated univariable causal relationships between 1) metabolites and AD; 2) education and cognition; 3) metabolites, education, and cognition; and 4) education, cognition, and AD. Mediating relationships were computed using multivariable Mendelian randomization. Results Thirty-four metabolites were genetically associated with AD at p < .05. Of these, glutamine and free cholesterol in extra-large high-density lipoproteins demonstrated a protective causal effect (glutamine: 95% confidence interval [CI], 0.70 to 0.92; free cholesterol in extra-large high-density lipoproteins: 95% CI, 0.75 to 0.92). An AD-protective effect was also observed for education (95% CI, 0.61 to 0.85) and cognition (95% CI, 0.60 to 0.89), with bidirectional mediation evident. Cognition as a mediator of the education-AD relationship was stronger than vice versa, however. No evidence of mediation via any metabolite was found. Conclusions Glutamine and free cholesterol in extra-large high-density lipoproteins show protective causal effects on AD. Education and cognition also demonstrate protection, though education's effect is almost entirely mediated by cognition. These insights provide key pieces of the AD causal puzzle, important for informing future multimodal work and progressing toward effective intervention strategies.
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Affiliation(s)
- Jodie Lord
- Institute of Psychology, Psychiatry and Neuroscience, King's College London, London, United Kingdom
| | - Rebecca Green
- Institute of Psychology, Psychiatry and Neuroscience, King's College London, London, United Kingdom
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Shing Wan Choi
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Christopher Hübel
- Institute of Psychology, Psychiatry and Neuroscience, King's College London, London, United Kingdom
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
- National Centre for Register-based Research, Aarhus University, Aarhus, Denmark
| | - Dag Aarsland
- Institute of Psychology, Psychiatry and Neuroscience, King's College London, London, United Kingdom
- Center for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway
| | - Latha Velayudhan
- Institute of Psychology, Psychiatry and Neuroscience, King's College London, London, United Kingdom
| | - Pak Sham
- Department of Psychiatry, University of Hong Kong, Hong Kong, China
| | - Cristina Legido-Quigley
- Institute of Pharmaceutical Science, King's College London, London, United Kingdom
- Steno Diabetes Center, Copenhagen, Aarhus University, Aarhus, Denmark
| | - Marcus Richards
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, United Kingdom
| | - Richard Dobson
- Institute of Psychology, Psychiatry and Neuroscience, King's College London, London, United Kingdom
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
- Farr Institute of Health Informatics Research, UCL Institute of Health Informatics, London, United Kingdom
| | - Petroula Proitsi
- Institute of Psychology, Psychiatry and Neuroscience, King's College London, London, United Kingdom
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Wang T, Bendayan R, Msosa Y, Pritchard M, Roberts A, Stewart R, Dobson R. Patient-centric characterization of multimorbidity trajectories in patients with severe mental illnesses: A temporal bipartite network modeling approach. J Biomed Inform 2022; 127:104010. [PMID: 35151869 PMCID: PMC8894882 DOI: 10.1016/j.jbi.2022.104010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 11/30/2021] [Accepted: 01/30/2022] [Indexed: 11/25/2022]
Abstract
Multimorbidity is a major factor contributing to increased mortality among people with severe mental illnesses (SMI). Previous studies either focus on estimating prevalence of a disease in a population without considering relationships between diseases or ignore heterogeneity of individual patients in examining disease progression by looking merely at aggregates across a whole cohort. Here, we present a temporal bipartite network model to jointly represent detailed information on both individual patients and diseases, which allows us to systematically characterize disease trajectories from both patient and disease centric perspectives. We apply this approach to a large set of longitudinal diagnostic records for patients with SMI collected through a data linkage between electronic health records from a large UK mental health hospital and English national hospital administrative database. We find that the resulting diagnosis networks show disassortative mixing by degree, suggesting that patients affected by a small number of diseases tend to suffer from prevalent diseases. Factors that determine the network structures include an individual's age, gender and ethnicity. Our analysis on network evolution further shows that patients and diseases become more interconnected over the illness duration of SMI, which is largely driven by the process that patients with similar attributes tend to suffer from the same conditions. Our analytic approach provides a guide for future patient-centric research on multimorbidity trajectories and contributes to achieving precision medicine.
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Affiliation(s)
- Tao Wang
- Department of Biostatistics and Health Informatics, King's College London, Denmark Hill, London SE5 8AF, United Kingdom.
| | - Rebecca Bendayan
- Department of Biostatistics and Health Informatics, King's College London, Denmark Hill, London SE5 8AF, United Kingdom; National Institute for Health Research, Maudsley Biomedical Research Centre, South London and Maudsley National Health Service (NHS) Foundation Trust, Denmark Hill, London SE5 8AZ, United Kingdom
| | - Yamiko Msosa
- Department of Biostatistics and Health Informatics, King's College London, Denmark Hill, London SE5 8AF, United Kingdom
| | - Megan Pritchard
- National Institute for Health Research, Maudsley Biomedical Research Centre, South London and Maudsley National Health Service (NHS) Foundation Trust, Denmark Hill, London SE5 8AZ, United Kingdom
| | - Angus Roberts
- Department of Biostatistics and Health Informatics, King's College London, Denmark Hill, London SE5 8AF, United Kingdom; National Institute for Health Research, Maudsley Biomedical Research Centre, South London and Maudsley National Health Service (NHS) Foundation Trust, Denmark Hill, London SE5 8AZ, United Kingdom
| | - Robert Stewart
- National Institute for Health Research, Maudsley Biomedical Research Centre, South London and Maudsley National Health Service (NHS) Foundation Trust, Denmark Hill, London SE5 8AZ, United Kingdom; Department of Psychological Medicine, King's College London, Denmark Hill, London SE5 8AF, United Kingdom
| | - Richard Dobson
- Department of Biostatistics and Health Informatics, King's College London, Denmark Hill, London SE5 8AF, United Kingdom; National Institute for Health Research, Maudsley Biomedical Research Centre, South London and Maudsley National Health Service (NHS) Foundation Trust, Denmark Hill, London SE5 8AZ, United Kingdom; Institute of Health Informatics, University College London, Euston Road, London NW1 2DA, United Kingdom; Health Data Research UK London, University College London, Euston Road, London NW1 2DA, United Kingdom
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28
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Coats T, Bean D, Basset A, Sirkis T, Brammeld J, Johnson S, Thomas I, Gilkes A, Raj K, Dennis M, Knapper S, Mehta P, Khwaja A, Hunter H, Tauro S, Bowen D, Jones G, Dobson R, Russell N, Dillon R. A novel algorithmic approach to generate consensus treatment guidelines in adult acute myeloid leukaemia. Br J Haematol 2022; 196:1337-1343. [PMID: 34957541 DOI: 10.1111/bjh.18013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 12/08/2021] [Indexed: 11/29/2022]
Abstract
Induction therapy for acute myeloid leukaemia (AML) has changed with the approval of a number of new agents. Clinical guidelines can struggle to keep pace with an evolving treatment and evidence landscape and therefore identifying the most appropriate front-line treatment is challenging for clinicians. Here, we combined drug eligibility criteria and genetic risk stratification into a digital format, allowing the full range of possible treatment eligibility scenarios to be defined. Using exemplar cases representing each of the 22 identified scenarios, we sought to generate consensus on treatment choice from a panel of nine aUK AML experts. We then analysed >2500 real-world cases using the same algorithm, confirming the existence of 21/22 of these scenarios and demonstrating that our novel approach could generate a consensus AML induction treatment in 98% of cases. Our approach, driven by the use of decision trees, is an efficient way to develop consensus guidance rapidly and could be applied to other disease areas. It has the potential to be updated frequently to capture changes in eligibility criteria, novel therapies and emerging trial data. An interactive digital version of the consensus guideline is available.
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Affiliation(s)
- Thomas Coats
- Haematology Department, Royal Devon & Exeter NHS Foundation Trust, Exeter, UK
- Biostatistics and Health Informatics, King's College London, UK
| | - Daniel Bean
- Biostatistics and Health Informatics, King's College London, UK
- Health Data Research UK London, University College London, UK
| | - Aymeric Basset
- Biostatistics and Health Informatics, King's College London, UK
| | | | | | - Sean Johnson
- Centre for Trials Research, Cardiff University, Cardiff, UK
| | - Ian Thomas
- Centre for Trials Research, Cardiff University, Cardiff, UK
| | - Amanda Gilkes
- Haematology, Cardiff University School of Medicine, Cardiff, UK
| | - Kavita Raj
- Guys' and St Thomas' NHS Foundation Trust, London, UK
| | - Mike Dennis
- Haematology, The Christie NHS Foundation Trust, Manchester, UK
| | - Steve Knapper
- Haematology, Cardiff University School of Medicine, Cardiff, UK
| | - Priyanka Mehta
- Haematology, University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK
| | - Asim Khwaja
- University College London Hospitals NHS Foundation Trust, London, UK
| | - Hannah Hunter
- University Hospitals Plymouth NHS Trust, Plymouth, UK
| | - Sudhir Tauro
- Haematology, Ninewells Hospital & School of Medicine, University of Dundee, Dundee, UK
| | - David Bowen
- Haematology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Gail Jones
- The Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK
| | - Richard Dobson
- Biostatistics and Health Informatics, King's College London, UK
- Health Data Research UK London, University College London, UK
| | - Nigel Russell
- Guys' and St Thomas' NHS Foundation Trust, London, UK
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29
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Al Khleifat A, Iacoangeli A, van Vugt JJFA, Bowles H, Moisse M, Zwamborn RAJ, van der Spek RAA, Shatunov A, Cooper-Knock J, Topp S, Byrne R, Gellera C, López V, Jones AR, Opie-Martin S, Vural A, Campos Y, van Rheenen W, Kenna B, Van Eijk KR, Kenna K, Weber M, Smith B, Fogh I, Silani V, Morrison KE, Dobson R, van Es MA, McLaughlin RL, Vourc'h P, Chio A, Corcia P, de Carvalho M, Gotkine M, Panades MP, Mora JS, Shaw PJ, Landers JE, Glass JD, Shaw CE, Basak N, Hardiman O, Robberecht W, Van Damme P, van den Berg LH, Veldink JH, Al-Chalabi A. Structural variation analysis of 6,500 whole genome sequences in amyotrophic lateral sclerosis. NPJ Genom Med 2022; 7:8. [PMID: 35091648 PMCID: PMC8799638 DOI: 10.1038/s41525-021-00267-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 10/21/2021] [Indexed: 02/01/2023] Open
Abstract
There is a strong genetic contribution to Amyotrophic lateral sclerosis (ALS) risk, with heritability estimates of up to 60%. Both Mendelian and small effect variants have been identified, but in common with other conditions, such variants only explain a little of the heritability. Genomic structural variation might account for some of this otherwise unexplained heritability. We therefore investigated association between structural variation in a set of 25 ALS genes, and ALS risk and phenotype. As expected, the repeat expansion in the C9orf72 gene was identified as associated with ALS. Two other ALS-associated structural variants were identified: inversion in the VCP gene and insertion in the ERBB4 gene. All three variants were associated both with increased risk of ALS and specific phenotypic patterns of disease expression. More than 70% of people with respiratory onset ALS harboured ERBB4 insertion compared with 25% of the general population, suggesting respiratory onset ALS may be a distinct genetic subtype.
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Affiliation(s)
- Ahmad Al Khleifat
- King's College London, Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience, De Crespigny Park, London, UK
| | - Alfredo Iacoangeli
- King's College London, Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience, De Crespigny Park, London, UK
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Joke J F A van Vugt
- Department of Neurology, UMC Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Harry Bowles
- King's College London, Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience, De Crespigny Park, London, UK
| | - Matthieu Moisse
- KU Leuven - University of Leuven, Department of Neurosciences, Experimental Neurology; VIB Center for Brain & Disease Research, Laboratory of Neurobiology, Leuven, Belgium
| | - Ramona A J Zwamborn
- Department of Neurology, UMC Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Rick A A van der Spek
- Department of Neurology, UMC Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Aleksey Shatunov
- King's College London, Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience, De Crespigny Park, London, UK
| | - Johnathan Cooper-Knock
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Simon Topp
- King's College London, Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience, De Crespigny Park, London, UK
| | - Ross Byrne
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, Dublin, Ireland
| | - Cinzia Gellera
- Department of Neurology and Laboratory of Neuroscience, IRCCS Istituto Auxologico Italiano and Department of Pathophysiology and Transplantation, "Dino Ferrari" Center, Università degli Studi di Milano, Milano, Italy
| | - Victoria López
- Department of Neurology and Laboratory of Neuroscience, IRCCS Istituto Auxologico Italiano and Department of Pathophysiology and Transplantation, "Dino Ferrari" Center, Università degli Studi di Milano, Milano, Italy
| | - Ashley R Jones
- King's College London, Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience, De Crespigny Park, London, UK
| | - Sarah Opie-Martin
- King's College London, Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience, De Crespigny Park, London, UK
| | - Atay Vural
- Koc University, School of Medicine, Translational Medicine Research Center- NDAL, Istanbul, Turkey
| | - Yolanda Campos
- Mitochondrial pathology Unit, Instituto de Salud Carlos III, Madrid, Spain
| | - Wouter van Rheenen
- Department of Neurology, UMC Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Brendan Kenna
- Department of Neurology, UMC Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Kristel R Van Eijk
- Department of Neurology, UMC Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Kevin Kenna
- Department of Neurology, UMC Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Markus Weber
- Neuromuscular Diseases Unit/ALS Clinic, Kantonsspital St. Gallen, St. Gallen, Switzerland
| | - Bradley Smith
- King's College London, Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience, De Crespigny Park, London, UK
| | - Isabella Fogh
- King's College London, Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience, De Crespigny Park, London, UK
| | - Vincenzo Silani
- Department of Neurology and Laboratory of Neuroscience, IRCCS Istituto Auxologico Italiano and Department of Pathophysiology and Transplantation, "Dino Ferrari" Center, Università degli Studi di Milano, Milano, Italy
| | - Karen E Morrison
- Faculty of Medicine, Health and Life Sciences, Queen's University Belfast, Belfast, Northern Ireland, UK
| | - Richard Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Institute of Health Informatics, University College London, London, UK
| | - Michael A van Es
- Department of Neurology, UMC Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Russell L McLaughlin
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, Dublin, Ireland
| | | | - Adriano Chio
- Rita Levi Montalcini, Department of Neuroscience, ALS Centre, University of Torino, Turin, Italy
- Azienda Ospedaliera Citta della Salute e della Scienza, Torino, Italy
| | - Philippe Corcia
- Centre SLA, CHRU de Tours, Tours, France
- Federation des Centres SLA Tours and Limoges, LITORALS, Tours, France
| | - Mamede de Carvalho
- Physiology Institute, Faculty of Medicine, Instituto de Medicina Molecular, University of Lisbon, Lisbon, Portugal
| | | | - Monica P Panades
- Neurology Department, Hospital Universitari de Bellvitge, Barcelona, Spain
| | | | - Pamela J Shaw
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - John E Landers
- Department of Neurology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Jonathan D Glass
- Department of Neurology, Center for Neurodegenerative Diseases, Emory University, Atlanta, GA, USA
| | - Christopher E Shaw
- King's College London, Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience, De Crespigny Park, London, UK
- King's College Hospital, Denmark Hill, London, UK
| | - Nazli Basak
- Koc University, School of Medicine, Translational Medicine Research Center- NDAL, Istanbul, Turkey
| | - Orla Hardiman
- Academic Unit of Neurology, Trinity College Dublin, Trinity Biomedical Sciences Institute, Dublin, Republic of Ireland
- Department of Neurology, Beaumont Hospital, Dublin, Republic of Ireland
| | - Wim Robberecht
- KU Leuven - University of Leuven, Department of Neurosciences, Experimental Neurology; VIB Center for Brain & Disease Research, Laboratory of Neurobiology, Leuven, Belgium
- Neurology Department, University Hospitals Leuven, Leuven, Belgium
| | - Philip Van Damme
- KU Leuven - University of Leuven, Department of Neurosciences, Experimental Neurology; VIB Center for Brain & Disease Research, Laboratory of Neurobiology, Leuven, Belgium
- Neurology Department, University Hospitals Leuven, Leuven, Belgium
| | - Leonard H van den Berg
- Department of Neurology, UMC Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Jan H Veldink
- Department of Neurology, UMC Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Ammar Al-Chalabi
- King's College London, Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience, De Crespigny Park, London, UK.
- King's College Hospital, Denmark Hill, London, UK.
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30
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Bendayan R, Kraljevic Z, Shaari S, Das-Munshi J, Leipold L, Chaturvedi J, Mirza L, Aldelemi S, Searle T, Chance N, Mascio A, Skiada N, Wang T, Roberts A, Stewart R, Bean D, Dobson R. Mapping multimorbidity in individuals with schizophrenia and bipolar disorders: evidence from the South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLAM BRC) case register. BMJ Open 2022; 12:e054414. [PMID: 35074819 PMCID: PMC8788233 DOI: 10.1136/bmjopen-2021-054414] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 11/29/2021] [Indexed: 11/04/2022] Open
Abstract
OBJECTIVES The first aim of this study was to design and develop a valid and replicable strategy to extract physical health conditions from clinical notes which are common in mental health services. Then, we examined the prevalence of these conditions in individuals with severe mental illness (SMI) and compared their individual and combined prevalence in individuals with bipolar (BD) and schizophrenia spectrum disorders (SSD). DESIGN Observational study. SETTING Secondary mental healthcare services from South London PARTICIPANTS: Our maximal sample comprised 17 500 individuals aged 15 years or older who had received a primary or secondary SMI diagnosis (International Classification of Diseases, 10th edition, F20-31) between 2007 and 2018. MEASURES We designed and implemented a data extraction strategy for 21 common physical comorbidities using a natural language processing pipeline, MedCAT. Associations were investigated with sex, age at SMI diagnosis, ethnicity and social deprivation for the whole cohort and the BD and SSD subgroups. Linear regression models were used to examine associations with disability measured by the Health of Nations Outcome Scale. RESULTS Physical health data were extracted, achieving precision rates (F1) above 0.90 for all conditions. The 10 most prevalent conditions were diabetes, hypertension, asthma, arthritis, epilepsy, cerebrovascular accident, eczema, migraine, ischaemic heart disease and chronic obstructive pulmonary disease. The most prevalent combination in this population included diabetes, hypertension and asthma, regardless of their SMI diagnoses. CONCLUSIONS Our data extraction strategy was found to be adequate to extract physical health data from clinical notes, which is essential for future multimorbidity research using text records. We found that around 40% of our cohort had multimorbidity from which 20% had complex multimorbidity (two or more physical conditions besides SMI). Sex, age, ethnicity and social deprivation were found to be key to understand their heterogeneity and their differential contribution to disability levels in this population. These outputs have direct implications for researchers and clinicians.
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Affiliation(s)
- Rebecca Bendayan
- Department of Biostatistics and Health Informatics, Institute of Pyschiatry, Psychology and Neurosciences, King's College London, London, UK
- NIHR Biomedical Research Centre and Maudsley NHS Foundation Trust, King's College London, London, UK
| | - Zeljko Kraljevic
- Department of Biostatistics and Health Informatics, Institute of Pyschiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Shaweena Shaari
- NIHR Biomedical Research Centre and Maudsley NHS Foundation Trust, King's College London, London, UK
| | - Jayati Das-Munshi
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Leona Leipold
- NIHR Biomedical Research Centre and Maudsley NHS Foundation Trust, King's College London, London, UK
| | - Jaya Chaturvedi
- Department of Biostatistics and Health Informatics, Institute of Pyschiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Luwaiza Mirza
- NIHR Biomedical Research Centre and Maudsley NHS Foundation Trust, King's College London, London, UK
| | - Sarah Aldelemi
- NIHR Biomedical Research Centre and Maudsley NHS Foundation Trust, King's College London, London, UK
| | - Thomas Searle
- Department of Biostatistics and Health Informatics, Institute of Pyschiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Natalia Chance
- NIHR Biomedical Research Centre and Maudsley NHS Foundation Trust, King's College London, London, UK
| | - Aurelie Mascio
- Department of Biostatistics and Health Informatics, Institute of Pyschiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Naoko Skiada
- Department of Biostatistics and Health Informatics, Institute of Pyschiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Tao Wang
- Department of Biostatistics and Health Informatics, Institute of Pyschiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Angus Roberts
- Department of Biostatistics and Health Informatics, Institute of Pyschiatry, Psychology and Neurosciences, King's College London, London, UK
- NIHR Biomedical Research Centre and Maudsley NHS Foundation Trust, King's College London, London, UK
| | - Robert Stewart
- NIHR Biomedical Research Centre and Maudsley NHS Foundation Trust, King's College London, London, UK
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Daniel Bean
- Department of Biostatistics and Health Informatics, Institute of Pyschiatry, Psychology and Neurosciences, King's College London, London, UK
- Health Data Research UK London, University College London, London, UK
| | - Richard Dobson
- Department of Biostatistics and Health Informatics, Institute of Pyschiatry, Psychology and Neurosciences, King's College London, London, UK
- NIHR Biomedical Research Centre and Maudsley NHS Foundation Trust, King's College London, London, UK
- Institute of Health Informatics, University College London, London, UK
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31
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De Angel V, Lewis S, White K, Oetzmann C, Leightley D, Oprea E, Lavelle G, Matcham F, Pace A, Mohr DC, Dobson R, Hotopf M. Digital health tools for the passive monitoring of depression: a systematic review of methods. NPJ Digit Med 2022; 5:3. [PMID: 35017634 PMCID: PMC8752685 DOI: 10.1038/s41746-021-00548-8] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 11/28/2021] [Indexed: 12/27/2022] Open
Abstract
The use of digital tools to measure physiological and behavioural variables of potential relevance to mental health is a growing field sitting at the intersection between computer science, engineering, and clinical science. We summarised the literature on remote measuring technologies, mapping methodological challenges and threats to reproducibility, and identified leading digital signals for depression. Medical and computer science databases were searched between January 2007 and November 2019. Published studies linking depression and objective behavioural data obtained from smartphone and wearable device sensors in adults with unipolar depression and healthy subjects were included. A descriptive approach was taken to synthesise study methodologies. We included 51 studies and found threats to reproducibility and transparency arising from failure to provide comprehensive descriptions of recruitment strategies, sample information, feature construction and the determination and handling of missing data. The literature is characterised by small sample sizes, short follow-up duration and great variability in the quality of reporting, limiting the interpretability of pooled results. Bivariate analyses show consistency in statistically significant associations between depression and digital features from sleep, physical activity, location, and phone use data. Machine learning models found the predictive value of aggregated features. Given the pitfalls in the combined literature, these results should be taken purely as a starting point for hypothesis generation. Since this research is ultimately aimed at informing clinical practice, we recommend improvements in reporting standards including consideration of generalisability and reproducibility, such as wider diversity of samples, thorough reporting methodology and the reporting of potential bias in studies with numerous features.
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Affiliation(s)
- Valeria De Angel
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UK.
| | - Serena Lewis
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Department of Psychology, University of Bath, Bath, UK
| | - Katie White
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Carolin Oetzmann
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Daniel Leightley
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Emanuela Oprea
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Grace Lavelle
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Faith Matcham
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Alice Pace
- Chelsea And Westminster Hospital NHS Foundation Trust, London, UK
| | - David C Mohr
- Center for Behavioral Intervention Technologies, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA
- Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA
| | - Richard Dobson
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UK
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, 16 De Crespigny Park, London, SE5 8AF, UK
| | - Matthew Hotopf
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UK
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32
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Al Khleifat A, Iacoangeli A, Jones AR, van Vugt JJFA, Moisse M, Shatunov A, Zwamborn RAJ, van der Spek RAA, Cooper-Knock J, Topp S, van Rheenen W, Kenna B, Van Eijk KR, Kenna K, Byrne R, López V, Opie-Martin S, Vural A, Campos Y, Weber M, Smith B, Fogh I, Silani V, Morrison KE, Dobson R, van Es MA, McLaughlin RL, Vourc’h P, Chio A, Corcia P, de Carvalho M, Gotkine M, Panades MP, Mora JS, Shaw PJ, Landers JE, Glass JD, Shaw CE, Basak N, Hardiman O, Robberecht W, Van Damme P, van den Berg LH, Veldink JH, Al-Chalabi A. Telomere length analysis in amyotrophic lateral sclerosis using large-scale whole genome sequence data. Front Cell Neurosci 2022; 16:1050596. [PMID: 36589292 PMCID: PMC9799999 DOI: 10.3389/fncel.2022.1050596] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 11/15/2022] [Indexed: 12/23/2022] Open
Abstract
Background Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by the loss of upper and lower motor neurons, leading to progressive weakness of voluntary muscles, with death following from neuromuscular respiratory failure, typically within 3 to 5 years. There is a strong genetic contribution to ALS risk. In 10% or more, a family history of ALS or frontotemporal dementia is obtained, and the Mendelian genes responsible for ALS in such families have now been identified in about 50% of cases. Only about 14% of apparently sporadic ALS is explained by known genetic variation, suggesting that other forms of genetic variation are important. Telomeres maintain DNA integrity during cellular replication, differ between sexes, and shorten naturally with age. Sex and age are risk factors for ALS and we therefore investigated telomere length in ALS. Methods Samples were from Project MinE, an international ALS whole genome sequencing consortium that includes phenotype data. For validation we used donated brain samples from motor cortex from people with ALS and controls. Ancestry and relatedness were evaluated by principal components analysis and relationship matrices of DNA microarray data. Whole genome sequence data were from Illumina HiSeq platforms and aligned using the Isaac pipeline. TelSeq was used to quantify telomere length using whole genome sequence data. We tested the association of telomere length with ALS and ALS survival using Cox regression. Results There were 6,580 whole genome sequences, reducing to 6,195 samples (4,315 from people with ALS and 1,880 controls) after quality control, and 159 brain samples (106 ALS, 53 controls). Accounting for age and sex, there was a 20% (95% CI 14%, 25%) increase of telomere length in people with ALS compared to controls (p = 1.1 × 10-12), validated in the brain samples (p = 0.03). Those with shorter telomeres had a 10% increase in median survival (p = 5.0×10-7). Although there was no difference in telomere length between sporadic ALS and familial ALS (p=0.64), telomere length in 334 people with ALS due to expanded C9orf72 repeats was shorter than in those without expanded C9orf72 repeats (p = 5.0×10-4). Discussion Although telomeres shorten with age, longer telomeres are a risk factor for ALS and worsen prognosis. Longer telomeres are associated with ALS.
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Affiliation(s)
- Ahmad Al Khleifat
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King’s College London, London, United Kingdom
- Ahmad Al Khleifat,
| | - Alfredo Iacoangeli
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King’s College London, London, United Kingdom
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Ashley R. Jones
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King’s College London, London, United Kingdom
| | - Joke J. F. A. van Vugt
- Department of Neurology, University Medical Center (UMC) Utrecht Brain Center, Utrecht University, Utrecht, Netherlands
| | - Matthieu Moisse
- Department of Neurosciences, Experimental Neurology, KU Leuven—University of Leuven, Leuven, Belgium
- VIB Center for Brain & Disease Research, Laboratory of Neurobiology, Leuven, Belgium
| | - Aleksey Shatunov
- Institute of Medicine, North-Eastern Federal University, Yakutsk, Russia
- Department of Molecular and Clinical Pharmacology, University of Liverpool, Liverpool, United Kingdom
| | - Ramona A. J. Zwamborn
- Department of Neurology, University Medical Center (UMC) Utrecht Brain Center, Utrecht University, Utrecht, Netherlands
| | - Rick A. A. van der Spek
- Department of Neurology, University Medical Center (UMC) Utrecht Brain Center, Utrecht University, Utrecht, Netherlands
| | - Johnathan Cooper-Knock
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, United Kingdom
| | - Simon Topp
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King’s College London, London, United Kingdom
| | - Wouter van Rheenen
- Department of Neurology, University Medical Center (UMC) Utrecht Brain Center, Utrecht University, Utrecht, Netherlands
| | - Brendan Kenna
- Department of Neurology, University Medical Center (UMC) Utrecht Brain Center, Utrecht University, Utrecht, Netherlands
| | - Kristel R. Van Eijk
- Department of Neurology, University Medical Center (UMC) Utrecht Brain Center, Utrecht University, Utrecht, Netherlands
| | - Kevin Kenna
- Department of Neurology, University Medical Center (UMC) Utrecht Brain Center, Utrecht University, Utrecht, Netherlands
| | - Ross Byrne
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, Dublin, Ireland
| | - Victoria López
- Computational Biology Unit, Instituto de Salud Carlos III, Madrid, Spain
| | - Sarah Opie-Martin
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King’s College London, London, United Kingdom
| | - Atay Vural
- School of Medicine, Translational Medicine Research Center-NDAL, Koc University, Istanbul, Turkey
| | - Yolanda Campos
- Computational Biology Unit, Instituto de Salud Carlos III, Madrid, Spain
| | - Markus Weber
- School of Medicine, Translational Medicine Research Center-NDAL, Koc University, Istanbul, Turkey
- Neuromuscular Diseases Unit/ALS Clinic, Kantonsspital St. Gallen, St. Gallen, Switzerland
| | - Bradley Smith
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King’s College London, London, United Kingdom
| | - Isabella Fogh
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King’s College London, London, United Kingdom
| | - Vincenzo Silani
- Department of Neurology and Laboratory of Neuroscience, IRCCS Istituto Auxologico Italiano, Milan, Italy
- Department of Pathophysiology and Transplantation, “Dino Ferrari” Center, Università degli Studi di Milano, Milan, Italy
| | - Karen E. Morrison
- Faculty of Medicine, Health and Life Sciences, Queen’s University Belfast, Belfast, United Kingdom
| | - Richard Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
| | - Michael A. van Es
- Department of Neurology, University Medical Center (UMC) Utrecht Brain Center, Utrecht University, Utrecht, Netherlands
| | - Russell L. McLaughlin
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, Dublin, Ireland
| | | | - Adriano Chio
- Department of Neuroscience, ALS Centre, University of Torino, Turin, Italy
- Azienda Ospedaliera Citta della Salute e della Scienza, Turin, Italy
| | - Philippe Corcia
- Centre SLA, CHRU de Tours, Tours, France
- Federation des Centres SLA Tours and Limoges, LITORALS, Tours, France
| | - Mamede de Carvalho
- Physiology Institute, Faculty of Medicine, Instituto de Medicina Molecular, University of Lisbon, Lisbon, Portugal
| | - Marc Gotkine
- Department of Neurology, Hadassah Medical Organization and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | | | | | - Pamela J. Shaw
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, United Kingdom
| | - John E. Landers
- Department of Neurology, University of Massachusetts Medical School, Worcester, MA, United States
| | - Jonathan D. Glass
- Department of Neurology, Center for Neurodegenerative Diseases, Emory University, Atlanta, GA, United States
| | - Christopher E. Shaw
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King’s College London, London, United Kingdom
- King’s College Hospital, London, United Kingdom
| | - Nazli Basak
- School of Medicine, Translational Medicine Research Center-NDAL, Koc University, Istanbul, Turkey
| | - Orla Hardiman
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
- Department of Neurology, Beaumont Hospital, Dublin, Ireland
| | - Wim Robberecht
- Department of Neurosciences, Experimental Neurology, KU Leuven—University of Leuven, Leuven, Belgium
- Department of Neurology, University Hospitals Leuven, Leuven, Belgium
| | - Philip Van Damme
- Department of Neurosciences, Experimental Neurology, KU Leuven—University of Leuven, Leuven, Belgium
- Department of Neurology, University Hospitals Leuven, Leuven, Belgium
| | - Leonard H. van den Berg
- Department of Neurology, University Medical Center (UMC) Utrecht Brain Center, Utrecht University, Utrecht, Netherlands
| | - Jan H. Veldink
- Department of Neurology, University Medical Center (UMC) Utrecht Brain Center, Utrecht University, Utrecht, Netherlands
| | - Ammar Al-Chalabi
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King’s College London, London, United Kingdom
- King’s College Hospital, London, United Kingdom
- *Correspondence: Ammar Al-Chalabi,
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33
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Green R, Lord J, Xu J, Maddock J, Kim M, Dobson R, Legido-Quigley C, Wong A, Richards M, Proitsi P. Metabolic correlates of late midlife cognitive outcomes: findings from the 1946 British Birth Cohort. Brain Commun 2021; 4:fcab291. [PMID: 35187482 PMCID: PMC8853724 DOI: 10.1093/braincomms/fcab291] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 09/17/2021] [Accepted: 12/10/2021] [Indexed: 11/14/2022] Open
Abstract
Investigating associations between metabolites and late midlife cognitive function could reveal potential markers and mechanisms relevant to early dementia. Here, we systematically explored the metabolic correlates of cognitive outcomes measured across the seventh decade of life, while untangling influencing life course factors. Using levels of 1019 metabolites profiled by liquid chromatography-mass spectrometry (age 60-64), we evaluated relationships between metabolites and cognitive outcomes in the British 1946 Birth Cohort (N = 1740). We additionally conducted pathway and network analyses to allow for greater insight into potential mechanisms, and sequentially adjusted for life course factors across four models, including sex and blood collection (Model 1), Model 1 + body mass index and lipid medication (Model 2), Model 2 + social factors and childhood cognition (Model 3) and Model 3 + lifestyle influences (Model 4). After adjusting for multiple tests, 155 metabolites, 10 pathways and 5 network modules were associated with cognitive outcomes. Of the 155, 35 metabolites were highly connected in their network module (termed 'hub' metabolites), presenting as promising marker candidates. Notably, we report relationships between a module comprised of acylcarnitines and processing speed which remained robust to life course adjustment, revealing palmitoylcarnitine (C16) as a hub (Model 4: β = -0.10, 95% confidence interval = -0.15 to -0.052, P = 5.99 × 10-5). Most associations were sensitive to adjustment for social factors and childhood cognition; in the final model, four metabolites remained after multiple testing correction, and 80 at P < 0.05. Two modules demonstrated associations that were partly or largely attenuated by life course factors: one enriched in modified nucleosides and amino acids (overall attenuation = 39.2-55.5%), and another in vitamin A and C metabolites (overall attenuation = 68.6-92.6%). Our other findings, including a module enriched in sphingolipid pathways, were entirely explained by life course factors, particularly childhood cognition and education. Using a large birth cohort study with information across the life course, we highlighted potential metabolic mechanisms associated with cognitive function in late midlife, suggesting marker candidates and life course relationships for further study.
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Affiliation(s)
- Rebecca Green
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Jodie Lord
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Jin Xu
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Jane Maddock
- MRC Unit for Lifelong Health & Ageing at UCL, University College London, London, UK
| | - Min Kim
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
| | - Richard Dobson
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | | | - Andrew Wong
- MRC Unit for Lifelong Health & Ageing at UCL, University College London, London, UK
| | - Marcus Richards
- MRC Unit for Lifelong Health & Ageing at UCL, University College London, London, UK
| | - Petroula Proitsi
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
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34
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Mirza L, Das-Munshi J, Chaturvedi J, Wu H, Kraljevic Z, Searle T, Shaari S, Mascio A, Skiada N, Roberts A, Bean D, Stewart R, Dobson R, Bendayan R. Investigating the association between physical health comorbidities and disability in individuals with severe mental illness. Eur Psychiatry 2021; 64:e77. [PMID: 34842128 PMCID: PMC8727716 DOI: 10.1192/j.eurpsy.2021.2255] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 11/12/2021] [Accepted: 11/13/2021] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Research suggests that an increased risk of physical comorbidities might have a key role in the association between severe mental illness (SMI) and disability. We examined the association between physical multimorbidity and disability in individuals with SMI. METHODS Data were extracted from the clinical record interactive search system at South London and Maudsley Biomedical Research Centre. Our sample (n = 13,933) consisted of individuals who had received a primary or secondary SMI diagnosis between 2007 and 2018 and had available data for Health of Nations Outcome Scale (HoNOS) as disability measure. Physical comorbidities were defined using Chapters II-XIV of the International Classification of Diagnoses (ICD-10). RESULTS More than 60 % of the sample had complex multimorbidity. The most common organ system affected were neurological (34.7%), dermatological (15.4%), and circulatory (14.8%). All specific comorbidities (ICD-10 Chapters) were associated with higher levels of disability, HoNOS total scores. Individuals with musculoskeletal, skin/dermatological, respiratory, endocrine, neurological, hematological, or circulatory disorders were found to be associated with significant difficulties associated with more than five HoNOS domains while others had a lower number of domains affected. CONCLUSIONS Individuals with SMI and musculoskeletal, skin/dermatological, respiratory, endocrine, neurological, hematological, or circulatory disorders are at higher risk of disability compared to those who do not have those comorbidities. Individuals with SMI and physical comorbidities are at greater risk of reporting difficulties associated with activities of daily living, hallucinations, and cognitive functioning. Therefore, these should be targeted for prevention and intervention programs.
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Affiliation(s)
- Luwaiza Mirza
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, London, United Kingdom
| | - Jayati Das-Munshi
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, London, United Kingdom
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Jaya Chaturvedi
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Honghan Wu
- Health Data Research UK London, University College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
| | - Zeljko Kraljevic
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Thomas Searle
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Shaweena Shaari
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, London, United Kingdom
| | - Aurelie Mascio
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Naoko Skiada
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Angus Roberts
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, London, United Kingdom
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
- Health Data Research UK London, University College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
| | - Daniel Bean
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
- Health Data Research UK London, University College London, London, United Kingdom
| | - Robert Stewart
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, London, United Kingdom
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Richard Dobson
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, London, United Kingdom
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
- Health Data Research UK London, University College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
| | - Rebecca Bendayan
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, London, United Kingdom
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
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35
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Studer R, Sartini C, Suzart-Woischnik K, Agrawal R, Natani H, Gill SK, Wirta SB, Asselbergs FW, Dobson R, Denaxas S, Kotecha D. Identification and Mapping Real-World Data Sources for Heart Failure, Acute Coronary Syndrome, and Atrial Fibrillation. Cardiology 2021; 147:98-106. [PMID: 34781301 PMCID: PMC8985014 DOI: 10.1159/000520674] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 09/27/2021] [Indexed: 11/19/2022]
Abstract
BACKGROUND Transparent and robust real-world evidence sources are increasingly important for global health, including cardiovascular (CV) diseases. We aimed to identify global real-world data (RWD) sources for heart failure (HF), acute coronary syndrome (ACS), and atrial fibrillation (AF). METHODS We conducted a systematic review of publications with RWD pertaining to HF, ACS, and AF (2010-2018), generating a list of unique data sources. Metadata were extracted based on the source type (e.g., electronic health records, genomics, and clinical data), study design, population size, clinical characteristics, follow-up duration, outcomes, and assessment of data availability for future studies and linkage. RESULTS Overall, 11,889 publications were retrieved for HF, 10,729 for ACS, and 6,262 for AF. From these, 322 (HF), 287 (ACS), and 220 (AF) data sources were selected for detailed review. The majority of data sources had near complete data on demographic variables (HF: 94%, ACS: 99%, and AF: 100%) and considerable data on comorbidities (HF: 77%, ACS: 93%, and AF: 97%). The least reported data categories were drug codes (HF, ACS, and AF: 10%) and caregiver involvement (HF: 6%, ACS: 1%, and AF: 1%). Only a minority of data sources provided information on access to data for other researchers (11%) or whether data could be linked to other data sources to maximize clinical impact (20%). The list and metadata for the RWD sources are publicly available at www.escardio.org/bigdata. CONCLUSIONS This review has created a comprehensive resource of CV data sources, providing new avenues to improve future real-world research and to achieve better patient outcomes.
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Affiliation(s)
- Rachel Studer
- Novartis Pharma AG, Novartis Campus, Basel, Switzerland
| | | | | | | | | | - Simrat K Gill
- Institute of Cardiovascular Sciences, University of Birmingham, Medical School, Vincent Drive, Birmingham, United Kingdom
| | | | - Folkert W Asselbergs
- Institute of Health Informatics, Institute of Cardiovascular Science & Health Data Research UK, University College London, London, United Kingdom
- Department of Cardiology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Richard Dobson
- Institute of Health Informatics, Institute of Cardiovascular Science & Health Data Research UK, University College London, London, United Kingdom
| | - Spiros Denaxas
- Institute of Health Informatics, Institute of Cardiovascular Science & Health Data Research UK, University College London, London, United Kingdom
| | - Dipak Kotecha
- Institute of Cardiovascular Sciences, University of Birmingham, Medical School, Vincent Drive, Birmingham, United Kingdom
- Department of Cardiology, University Medical Centre Utrecht, Utrecht, The Netherlands
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom
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36
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Wightman DP, Jansen IE, Savage JE, Shadrin AA, Bahrami S, Holland D, Rongve A, Børte S, Winsvold BS, Drange OK, Martinsen AE, Skogholt AH, Willer C, Bråthen G, Bosnes I, Nielsen JB, Fritsche LG, Thomas LF, Pedersen LM, Gabrielsen ME, Johnsen MB, Meisingset TW, Zhou W, Proitsi P, Hodges A, Dobson R, Velayudhan L, Heilbron K, Auton A, Sealock JM, Davis LK, Pedersen NL, Reynolds CA, Karlsson IK, Magnusson S, Stefansson H, Thordardottir S, Jonsson PV, Snaedal J, Zettergren A, Skoog I, Kern S, Waern M, Zetterberg H, Blennow K, Stordal E, Hveem K, Zwart JA, Athanasiu L, Selnes P, Saltvedt I, Sando SB, Ulstein I, Djurovic S, Fladby T, Aarsland D, Selbæk G, Ripke S, Stefansson K, Andreassen OA, Posthuma D. Author Correction: A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer's disease. Nat Genet 2021; 53:1722. [PMID: 34773122 DOI: 10.1038/s41588-021-00977-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Douglas P Wightman
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU Amsterdam, Amsterdam, the Netherlands
| | - Iris E Jansen
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU Amsterdam, Amsterdam, the Netherlands
| | - Jeanne E Savage
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU Amsterdam, Amsterdam, the Netherlands
| | - Alexey A Shadrin
- NORMENT Centre, University of Oslo, Oslo, Norway.,Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Shahram Bahrami
- NORMENT Centre, University of Oslo, Oslo, Norway.,Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Dominic Holland
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | - Arvid Rongve
- Department of Research and Innovation, Helse Fonna, Haugesund Hospital, Haugesund, Norway.,The University of Bergen, Institute of Clinical Medicine (K1), Bergen, Norway
| | - Sigrid Børte
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Research and Communication Unit for Musculoskeletal Health (FORMI), Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway.,K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Bendik S Winsvold
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway.,Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Ole Kristian Drange
- Department of Mental Health, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.,Division of Mental Health Care, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Amy E Martinsen
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
| | - Anne Heidi Skogholt
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Cristen Willer
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Geir Bråthen
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Neurology and Clinical Neurophysiology, University Hospital of Trondheim, Trondheim, Norway.,Department of Geriatrics, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Ingunn Bosnes
- Department of Mental Health, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Psychiatry, Hospital Namsos, Nord-Trøndelag Health Trust, Namsos, Norway
| | - Jonas Bille Nielsen
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, USA.,Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Lars G Fritsche
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Laurent F Thomas
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Linda M Pedersen
- Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
| | - Maiken E Gabrielsen
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Marianne Bakke Johnsen
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Research and Communication Unit for Musculoskeletal Health (FORMI), Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway.,K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Tore Wergeland Meisingset
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Neurology and Clinical Neurophysiology, University Hospital of Trondheim, Trondheim, Norway
| | - Wei Zhou
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.,Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Petroula Proitsi
- Institute of Psychiatry Psychology and Neurosciences, King's College London, London, UK
| | - Angela Hodges
- Institute of Psychiatry Psychology and Neurosciences, King's College London, London, UK
| | - Richard Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, London, UK.,NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, UK.,Health Data Research UK London, University College London, London, UK.,Institute of Health Informatics, University College London, London, UK.,NIHR Biomedical Research Centre at University College London Hospitals NHS Foundation Trust, London, UK
| | - Latha Velayudhan
- Institute of Psychiatry Psychology and Neurosciences, King's College London, London, UK
| | | | | | | | - Julia M Sealock
- Division of Genetic Medicine, Department of Medicine Vanderbilt University Medical Center Nashville, Nashville, TN, USA.,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lea K Davis
- Division of Genetic Medicine, Department of Medicine Vanderbilt University Medical Center Nashville, Nashville, TN, USA.,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nancy L Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Chandra A Reynolds
- Department of Psychology, University of California-Riverside, Riverside, CA, USA
| | - Ida K Karlsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,Institute of Gerontology and Aging Research Network - Jönköping (ARN-J), School of Health and Welfare, Jönköping University, Jönköping, Sweden
| | | | | | | | - Palmi V Jonsson
- Department of Geriatric Medicine, Landspitali University Hospital, Reykjavik, Iceland.,Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Jon Snaedal
- Department of Geriatric Medicine, Landspitali University Hospital, Reykjavik, Iceland
| | - Anna Zettergren
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden
| | - Ingmar Skoog
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden.,Region Västra Götaland, Sahlgrenska University Hospital, Psychiatry, Cognition and Old Age Psychiatry Clinic, Gothenburg, Sweden
| | - Silke Kern
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden.,Region Västra Götaland, Sahlgrenska University Hospital, Psychiatry, Cognition and Old Age Psychiatry Clinic, Gothenburg, Sweden
| | - Margda Waern
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden.,Region Västra Götaland, Sahlgrenska University Hospital, Psychosis Clinic, Gothenburg, Sweden
| | - Henrik Zetterberg
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK.,UK Dementia Research Institute at UCL, London, UK.,Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Eystein Stordal
- Department of Mental Health, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Psychiatry, Hospital Namsos, Nord-Trøndelag Health Trust, Namsos, Norway
| | - Kristian Hveem
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.,HUNT Research Center, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - John-Anker Zwart
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
| | - Lavinia Athanasiu
- NORMENT Centre, University of Oslo, Oslo, Norway.,Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Per Selnes
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
| | - Ingvild Saltvedt
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Geriatrics, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Sigrid B Sando
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Neurology and Clinical Neurophysiology, University Hospital of Trondheim, Trondheim, Norway
| | - Ingun Ulstein
- Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway.,NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Tormod Fladby
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Neurology, Akershus University Hospital, Lørenskog, Norway
| | - Dag Aarsland
- Institute of Psychiatry Psychology and Neurosciences, King's College London, London, UK.,Centre of Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway
| | - Geir Selbæk
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway.,Norwegian National Advisory Unit on Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway
| | - Stephan Ripke
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin, Berlin, Germany
| | | | - Ole A Andreassen
- NORMENT Centre, University of Oslo, Oslo, Norway.,Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU Amsterdam, Amsterdam, the Netherlands. .,Department of Child and Adolescent Psychiatry and Pediatric Psychology, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam University Medical Center, Amsterdam, the Netherlands.
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Searle T, Ibrahim Z, Teo J, Dobson R. Estimating redundancy in clinical text. J Biomed Inform 2021; 124:103938. [PMID: 34695581 DOI: 10.1016/j.jbi.2021.103938] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 08/19/2021] [Accepted: 10/17/2021] [Indexed: 12/15/2022]
Abstract
The current mode of use of Electronic Health Records (EHR) elicits text redundancy. Clinicians often populate new documents by duplicating existing notes, then updating accordingly. Data duplication can lead to propagation of errors, inconsistencies and misreporting of care. Therefore, measures to quantify information redundancy play an essential role in evaluating innovations that operate on clinical narratives. This work is a quantitative examination of information redundancy in EHR notes. We present and evaluate two methods to measure redundancy: an information-theoretic approach and a lexicosyntactic and semantic model. Our first measure trains large Transformer-based language models using clinical text from a large openly available US-based ICU dataset and a large multi-site UK based Hospital. By comparing the information-theoretic efficient encoding of clinical text against open-domain corpora, we find that clinical text is ∼1.5× to ∼3× less efficient than open-domain corpora at conveying information. Our second measure, evaluates automated summarisation metrics Rouge and BERTScore to evaluate successive note pairs demonstrating lexicosyntactic and semantic redundancy, with averages from ∼43 to ∼65%.
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Affiliation(s)
- Thomas Searle
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Zina Ibrahim
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - James Teo
- King's College Hospital NHS Foundation Trust, London, UK.
| | - Richard Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Institute of Health Informatics, University College London, London, UK.
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Cimpeanu O, Sim K, Lau Y, Dobson R, Marshall G, Padfield G, Wright G, Connelly D. Negative impact of socioeconomic deprivation on clinical outcomes after cryoablation for atrial fibrillation: 18-month study. Eur Heart J 2021. [DOI: 10.1093/eurheartj/ehab724.0433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Background
Lower socioeconomic status has also been shown to associate with higher incidence of atrial fibrillation (AF), increased mortality and morbidity. However, the impact of socioeconomic deprivation on clinical outcomes post AF cryoablation has yet to be investigated.
Aim
To assess the impact of socioeconomic deprivation (as categorised by Scottish Index of Multiple Deprivation, SIMD) on the medical management and clinical outcomes of patients with AF post cryoablation.
Methods
A retrospective study of paroxysmal or persistent AF patients after cryoablation. Parameters included basic demographics, weight, past medical history (hypertension, heart failure, diabetes, stroke, myocardial infarction, sleep apnoea) and alcohol misuse. Medical treatment post ablation (Beta blocker, calcium channel blocker, flecainide, amiodarone, dronaderone, sotolol, anticoagulant use) were also recorded.
Socioeconomic deprivation index, as per SIMD was recorded (1 – most deprived and 10 – least deprived), and accordingly placed into quintile (SIMD 1–2,3–4,5–6,7–8, 9–10). Follow-up for 18 months.
Clinical outcome assessed was rate of readmission for symptomatic AF, rate of heart failure admission, stroke, bleeding diathesis and all-cause mortality.
Results
383 patients were identified: 78 from the lowest quintile (SIMD 1–2), 68 (SIMD 3–4), 64 (SIMD 5–6), 62 (SIMD 7–8), and 111 from the highest quintile (SIMD 9–10). No statistical difference exists between age, gender or weight. Lowest socioeconomic quintile has higher incidence of heart failure (p=0.006) and hypertension (p=0.005) but other past medical history was no different. No difference in incidence of alcohol misuse.
Medicine prescription was not different. Echo features: left ventricular function, atrial size and valvular dysfunction were not different between all groups.
18 months follow-up demonstrated that both readmission for symptomatic documented AF and recurrence of symptoms at 18 months were higher among patients of lowest socioeconomic quintile (Keplan Meier plot, p=0.014 and p=0.006 respectively). Stepwise multiple regression analysis also confirmed multiple socioeconomic deprivation as an independent predictor for more adverse clinical outcome (p=0.02).
Risk of symptom recurrence at 18 months in patients from the least deprived background is less than one third as compared to the ones from the most deprived background (Odd-ratio 0.32 (0.17 - 0.59))
Risk of readmission for AF in patients from the wealthiest socioeconomic group is also less than a third as compared to those of most deprived social group (Odd-ratio 0.31 (95% CI 0.15–0.61)).
Other clinical outcomes including risk of admissions for heart failure, stroke, bleeding diathesis and all-cause mortality was not statistically different across all groups.
Summary
After cryoablation, patients from the lowest socioeconomic group are more likely to experience symptoms recurrence and readmission for symptomatic AF
Funding Acknowledgement
Type of funding sources: None.
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Affiliation(s)
- O Cimpeanu
- Glasgow Royal Infirmary, Glasgow, United Kingdom
| | - K.Y.T Sim
- Queen Elizabeth University Hospital, Glasgow, United Kingdom
| | - Y Lau
- Golden Jubilee National Hospital, Glasgow, United Kingdom
| | - R Dobson
- Golden Jubilee National Hospital, Glasgow, United Kingdom
| | - G Marshall
- Glasgow Royal Infirmary, Glasgow, United Kingdom
| | - G Padfield
- Forth Valley Royal Hospital, Larbert, United Kingdom
| | - G Wright
- Golden Jubilee National Hospital, Glasgow, United Kingdom
| | - D.T Connelly
- Golden Jubilee National Hospital, Glasgow, United Kingdom
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Brakenhoff TB, Franks B, Goodale BM, van de Wijgert J, Montes S, Veen D, Fredslund EK, Rispens T, Risch L, Dowling AV, Folarin AA, Bruijning P, Dobson R, Heikamp T, Klaver P, Cronin M, Grobbee DE. A prospective, randomized, single-blinded, crossover trial to investigate the effect of a wearable device in addition to a daily symptom diary for the Remote Early Detection of SARS-CoV-2 infections (COVID-RED): a structured summary of a study protocol for a randomized controlled trial. Trials 2021; 22:694. [PMID: 34635140 PMCID: PMC8503725 DOI: 10.1186/s13063-021-05643-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 09/17/2021] [Indexed: 11/10/2022] Open
Abstract
OBJECTIVES It is currently thought that most-but not all-individuals infected with SARS-CoV-2 develop symptoms, but the infectious period starts on average 2 days before the first overt symptoms appear. It is estimated that pre- and asymptomatic individuals are responsible for more than half of all transmissions. By detecting infected individuals before they have overt symptoms, wearable devices could potentially and significantly reduce the proportion of transmissions by pre-symptomatic individuals. Using laboratory-confirmed SARS-CoV-2 infections (detected via serology tests [to determine if there are antibodies against the SARS-CoV-2 in the blood] or SARS-CoV-2 infection tests such as polymerase chain reaction [PCR] or antigen tests) as the gold standard, we will determine the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for the following two algorithms to detect first time SARS-CoV-2 infection including early or asymptomatic infection: • The algorithm using Ava bracelet data when coupled with self-reported Daily Symptom Diary data (Wearable + Symptom Data Algo; experimental condition) • The algorithm using self-reported Daily Symptom Diary data alone (Symptom Only Algo; control condition) In addition, we will determine which of the two algorithms has superior performance characteristics for detecting SARS-CoV-2 infection including early or asymptomatic infection as confirmed by SARS-CoV-2 virus testing. TRIAL DESIGN The trial is a randomized, single-blinded, two-period, two-sequence crossover trial. The study will start with an initial learning phase (maximum of 3 months), followed by period 1 (3 months) and period 2 (3 months). Subjects entering the study at the end of the recruitment period may directly start with period 1 and will not be part of the learning phase. Each subject will undergo the experimental condition (the Wearable + Symptom Data Algo) in either period 1 or period 2 and the control condition (Symptom Only Algo) in the other period. The order will be randomly assigned, resulting in subjects being allocated 1:1 to either sequence 1 (experimental condition first) or sequence 2 (control condition first). Based on demographics, medical history and/or profession, each subject will be stratified at baseline into a high-risk and normal-risk group within each sequence. PARTICIPANTS The trial will be conducted in the Netherlands. A target of 20,000 subjects will be enrolled. Based on demographics, medical history and/or profession, each subject will be stratified at baseline into a high-risk and normal-risk group within each sequence. This results in approximately 6500 normal-risk individuals and 3500 high-risk individuals per sequence. Subjects will be recruited from previously studied cohorts as well as via public campaigns and social media. All data for this study will be collected remotely through the Ava COVID-RED app, the Ava bracelet, surveys in the COVID-RED web portal and self-sampling serology and PCR kits. More information on the study can be found in www.covid-red.eu . During recruitment, subjects will be invited to visit the COVID-RED web portal. After successfully completing the enrolment questionnaire, meeting eligibility criteria and indicating interest in joining the study, subjects will receive the subject information sheet and informed consent form. Subjects can enrol in COVID-RED if they comply with the following inclusion and exclusion criteria: Inclusion criteria: • Resident of the Netherlands • At least 18 years old • Informed consent provided (electronic) • Willing to adhere to the study procedures described in the protocol • Must have a smartphone that runs at least Android 8.0 or iOS 13.0 operating systems and is active for the duration of the study (in the case of a change of mobile number, the study team should be notified) • Be able to read, understand and write Dutch Exclusion criteria: • Previous positive SARS-CoV-2 test result (confirmed either through PCR/antigen or antibody tests; self-reported) • Current suspected (e.g. waiting for test result) COVID-19 infection or symptoms of a COVID-19 infection (self-reported) • Participating in any other COVID-19 clinical drug, vaccine or medical device trial (self-reported) • Electronic implanted device (such as a pacemaker; self-reported) • Pregnant at the time of informed consent (self-reported) • Suffering from cholinergic urticaria (per the Ava bracelet's user manual; self-reported) • Staff involved in the management or conduct of this study INTERVENTION AND COMPARATOR: All subjects will be instructed to complete the Daily Symptom Diary in the Ava COVID-RED app daily, wear their Ava bracelet each night and synchronize it with the app each day for the entire period of study participation. Provided with wearable sensor and/or self-reported symptom data within the last 24 h, the Ava COVID-RED app's underlying algorithms will provide subjects with a real-time indicator of their overall health and well-being. Subjects will see one of three messages, notifying them that no seeming deviations in symptoms and/or physiological parameters have been detected; some changes in symptoms and/or physiological parameters have been detected and they should self-isolate; or alerting them that deviations in their symptoms and/or physiological parameters could be suggestive of a potential COVID-19 infection and to seek additional testing. We will assess the intraperson performance of the algorithms in the experimental condition (Wearable + Symptom Data Algo) and control conditions (Symptom Only Algo). Note that both algorithms will also instruct to seek testing when any SARS-CoV-2 symptoms are reported in line with those defined by the Dutch national institute for public health and the environment 'Rijksinstituut voor Volksgezondheid en Milieu' (RIVM) guidelines. MAIN OUTCOMES The trial will evaluate the use and performance of the Ava COVID-RED app and Ava bracelet, which uses sensors to measure breathing rate, pulse rate, skin temperature and heart rate variability for the purpose of early and asymptomatic detection and monitoring of SARS-CoV-2 in general and high-risk populations. Using laboratory-confirmed SARS-CoV-2 infections (detected via serology tests, PCR tests and/or antigen tests) as the gold standard, we will determine the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for each of the following two algorithms to detect first-time SARS-CoV-2 infection including early or asymptomatic infection: the algorithm using Ava bracelet data when coupled with the self-reported Daily Symptom Diary data and the algorithm using self-reported Daily Symptom Diary data alone. In addition, we will determine which of the two algorithms has superior performance characteristics for detecting SARS-CoV-2 infection including early or asymptomatic infection as confirmed by SARS-CoV-2 virus testing. The protocol contains an additional twenty secondary and exploratory objectives which address, among others, infection incidence rates, health resource utilization, symptoms reported by SARS-CoV-2-infected participants and the rate of breakthrough and asymptomatic SARS-CoV-2 infections among individuals vaccinated against COVID-19. PCR or antigen testing will occur when the subject receives a notification from the algorithm to seek additional testing. Subjects will be advised to get tested via the national testing programme and report the testing result in the Ava COVID-RED app and a survey. If they cannot obtain a test via the national testing programme, they will receive a nasal swab self-sampling kit at home, and the sample will be tested by PCR in a trial-affiliated laboratory. In addition, all subjects will be asked to take a capillary blood sample at home at baseline (between month 0 and 3.5 months after the start of subject recruitment), at the end of the learning phase (month 3; note that this sampling moment is skipped if a subject entered the study at the end of the recruitment period), period 1 (month 6) and period 2 (month 9). These samples will be used for SARS-CoV-2-specific antibody testing in a trial-affiliated laboratory, differentiating between antibodies resulting from a natural infection and antibodies resulting from COVID-19 vaccination (as vaccination will gradually be rolled out during the trial period). Baseline samples will only be analysed if the sample collected at the end of the learning phase is positive, or if the subject entered the study at the end of the recruitment period, and samples collected at the end of period 1 will only be analysed if the sample collected at the end of period 2 is positive. When subjects obtain a positive PCR/antigen or serology test result during the study, they will continue to be in the study but will be moved into a so-called COVID-positive mode in the Ava COVID-RED app. This means that they will no longer receive recommendations from the algorithms but can still contribute and track symptom and bracelet data. The primary analysis of the main objective will be executed using the data collected in period 2 (months 6 through 9). Within this period, serology tests (before and after period 2) and PCR/antigen tests (taken based on recommendations by the algorithms) will be used to determine if a subject was infected with SARS-CoV-2 or not. Within this same time period, it will be determined if the algorithms gave any recommendations for testing. The agreement between these quantities will be used to evaluate the performance of the algorithms and how these compare between the study conditions. RANDOMIZATION All eligible subjects will be randomized using a stratified block randomization approach with an allocation ratio of 1:1 to one of two sequences (experimental condition followed by control condition or control condition followed by experimental condition). Based on demographics, medical history and/or profession, each subject will be stratified at baseline into a high-risk and normal-risk group within each sequence, resulting in approximately equal numbers of high-risk and normal-risk individuals between the sequences. BLINDING (MASKING) In this study, subjects will be blinded to the study condition and randomization sequence. Relevant study staff and the device manufacturer will be aware of the assigned sequence. The subject will wear the Ava bracelet and complete the Daily Symptom Diary in the Ava COVID-RED app for the full duration of the study, and they will not know if the feedback they receive about their potential infection status will only be based on the data they entered in the Daily Symptom Diary within the Ava COVID-RED app or based on both the data from the Daily Symptom Diary and the Ava bracelet. NUMBERS TO BE RANDOMIZED (SAMPLE SIZE) A total of 20,000 subjects will be recruited and randomized 1:1 to either sequence 1 (experimental condition followed by control condition) or sequence 2 (control condition followed by experimental condition), taking into account their risk level. This results in approximately 6500 normal-risk and 3500 high-risk individuals per sequence. TRIAL STATUS Protocol version: 3.0, dated May 3, 2021. Start of recruitment: February 19, 2021. End of recruitment: June 3, 2021. End of follow-up (estimated): November 2021 TRIAL REGISTRATION: The Netherlands Trial Register on the 18th of February, 2021 with number NL9320 ( https://www.trialregister.nl/trial/9320 ) FULL PROTOCOL: The full protocol is attached as an additional file, accessible from the Trials website (Additional file 1). In the interest in expediting dissemination of this material, the familiar formatting has been eliminated; this letter serves as a summary of the key elements of the full protocol.
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Affiliation(s)
| | | | | | - Janneke van de Wijgert
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
| | | | - Duco Veen
- Julius Clinical, Zeist, the Netherlands.,Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands.,Optentia Research Program, North-West University, Potchefstroom, South Africa
| | | | | | - Lorenz Risch
- Labormedizinisches zentrum Dr. Risch, Vaduz, Liechtenstein.,Faculty of Medical Sciences, Private Universität im Fürstentum Liechtenstein, Triesen, Liechtenstein.,Center of Laboratory Medicine, University Institute of Clinical Chemistry, University of Bern, Bern, Switzerland
| | - Ariel V Dowling
- Data Sciences Institute, Takeda Pharmaceuticals U.S.A. Inc., Cambridge, MA, USA
| | - Amos A Folarin
- National Institute for Health Research Maudsley Biomedical Research Centre, King's College London, London, UK.,Institute of Health Informatics, University College London, London, UK.,South London and Maudsley NHS Foundation Trust, London, UK
| | - Patricia Bruijning
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Richard Dobson
- Institute of Health Informatics, University College London, London, UK
| | | | | | | | - Diederick E Grobbee
- Julius Clinical, Zeist, the Netherlands.,Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
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Katsoulis M, Lai AG, Diaz-Ordaz K, Gomes M, Pasea L, Banerjee A, Denaxas S, Tsilidis K, Lagiou P, Misirli G, Bhaskaran K, Wannamethee G, Dobson R, Batterham RL, Kipourou DK, Lumbers RT, Wen L, Wareham N, Langenberg C, Hemingway H. Identifying adults at high-risk for change in weight and BMI in England: a longitudinal, large-scale, population-based cohort study using electronic health records. Lancet Diabetes Endocrinol 2021; 9:681-694. [PMID: 34481555 PMCID: PMC8440227 DOI: 10.1016/s2213-8587(21)00207-2] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 06/17/2021] [Accepted: 07/20/2021] [Indexed: 01/12/2023]
Abstract
BACKGROUND Targeted obesity prevention policies would benefit from the identification of population groups with the highest risk of weight gain. The relative importance of adult age, sex, ethnicity, geographical region, and degree of social deprivation on weight gain is not known. We aimed to identify high-risk groups for changes in weight and BMI using electronic health records (EHR). METHODS In this longitudinal, population-based cohort study we used linked EHR data from 400 primary care practices (via the Clinical Practice Research Datalink) in England, accessed via the CALIBER programme. Eligible participants were aged 18-74 years, were registered at a general practice clinic, and had BMI and weight measurements recorded between Jan 1, 1998, and June 30, 2016, during the period when they had eligible linked data with at least 1 year of follow-up time. We calculated longitudinal changes in BMI over 1, 5, and 10 years, and investigated the absolute risk and odds ratios (ORs) of transitioning between BMI categories (underweight, normal weight, overweight, obesity class 1 and 2, and severe obesity [class 3]), as defined by WHO. The associations of demographic factors with BMI transitions were estimated by use of logistic regression analysis, adjusting for baseline BMI, family history of cardiovascular disease, use of diuretics, and prevalent chronic conditions. FINDINGS We included 2 092 260 eligible individuals with more than 9 million BMI measurements in our study. Young adult age was the strongest risk factor for weight gain at 1, 5, and 10 years of follow-up. Compared with the oldest age group (65-74 years), adults in the youngest age group (18-24 years) had the highest OR (4·22 [95% CI 3·86-4·62]) and greatest absolute risk (37% vs 24%) of transitioning from normal weight to overweight or obesity at 10 years. Likewise, adults in the youngest age group with overweight or obesity at baseline were also at highest risk to transition to a higher BMI category; OR 4·60 (4·06-5·22) and absolute risk (42% vs 18%) of transitioning from overweight to class 1 and 2 obesity, and OR 5·87 (5·23-6·59) and absolute risk (22% vs 5%) of transitioning from class 1 and 2 obesity to class 3 obesity. Other demographic factors were consistently less strongly associated with these transitions; for example, the OR of transitioning from normal weight to overweight or obesity in people living in the most socially deprived versus least deprived areas was 1·23 (1·18-1·27), for men versus women was 1·12 (1·08-1·16), and for Black individuals versus White individuals was 1·13 (1·04-1·24). We provide an open access online risk calculator, and present high-resolution obesity risk charts over a 1-year, 5-year, and 10-year follow-up period. INTERPRETATION A radical shift in policy is required to focus on individuals at the highest risk of weight gain (ie, young adults aged 18-24 years) for individual-level and population-level prevention of obesity and its long-term consequences for health and health care. FUNDING The British Hearth Foundation, Health Data Research UK, the UK Medical Research Council, and the National Institute for Health Research.
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Affiliation(s)
- Michail Katsoulis
- Institute of Health Informatics, University College London, London, UK; Health Data Research UK, University College London, London, UK.
| | - Alvina G Lai
- Institute of Health Informatics, University College London, London, UK; Health Data Research UK, University College London, London, UK
| | - Karla Diaz-Ordaz
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - Manuel Gomes
- Department of Applied Health Research, University College London, London, UK
| | - Laura Pasea
- Institute of Health Informatics, University College London, London, UK
| | - Amitava Banerjee
- Institute of Health Informatics, University College London, London, UK; University College London Hospitals NHS Trust, London, UK; Barts Health NHS Trust, The Royal London Hospital, London, UK
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK; Health Data Research UK, University College London, London, UK; Alan Turing Institute, London, UK; National Institute of Health Research, University College London Hospitals Biomedical Research Centre, London, UK
| | - Kostas Tsilidis
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Pagona Lagiou
- Department of Hygiene, Epidemiology and Medical Statistics, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece; Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA
| | | | - Krishnan Bhaskaran
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Goya Wannamethee
- Department of Primary Care and Population Health, University College London, London, UK
| | - Richard Dobson
- Health Data Research UK, University College London, London, UK; Institute of Health Informatics, University College London, London, UK; Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Rachel L Batterham
- Centre for Obesity Research, University College London, London, UK; National Institute of Health Research, University College London Hospitals Biomedical Research Centre, London, UK; University College London Hospitals Bariatric Centre for Weight Management and Metabolic Surgery, London, UK
| | - Dimitra-Kleio Kipourou
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - R Thomas Lumbers
- Institute of Health Informatics, University College London, London, UK; Health Data Research UK, University College London, London, UK
| | - Lan Wen
- Department of Hygiene, Epidemiology and Medical Statistics, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Nick Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK; Computational Medicine, Berlin Institute of Health, Charité-University Medicine Berlin, Berlin, Germany
| | - Harry Hemingway
- Institute of Health Informatics, University College London, London, UK; Health Data Research UK, University College London, London, UK; National Institute of Health Research, University College London Hospitals Biomedical Research Centre, London, UK
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41
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Wightman DP, Jansen IE, Savage JE, Shadrin AA, Bahrami S, Holland D, Rongve A, Børte S, Winsvold BS, Drange OK, Martinsen AE, Skogholt AH, Willer C, Bråthen G, Bosnes I, Nielsen JB, Fritsche LG, Thomas LF, Pedersen LM, Gabrielsen ME, Johnsen MB, Meisingset TW, Zhou W, Proitsi P, Hodges A, Dobson R, Velayudhan L, Heilbron K, Auton A, Sealock JM, Davis LK, Pedersen NL, Reynolds CA, Karlsson IK, Magnusson S, Stefansson H, Thordardottir S, Jonsson PV, Snaedal J, Zettergren A, Skoog I, Kern S, Waern M, Zetterberg H, Blennow K, Stordal E, Hveem K, Zwart JA, Athanasiu L, Selnes P, Saltvedt I, Sando SB, Ulstein I, Djurovic S, Fladby T, Aarsland D, Selbæk G, Ripke S, Stefansson K, Andreassen OA, Posthuma D. A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer's disease. Nat Genet 2021; 53:1276-1282. [PMID: 34493870 PMCID: PMC10243600 DOI: 10.1038/s41588-021-00921-z] [Citation(s) in RCA: 348] [Impact Index Per Article: 116.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 07/16/2021] [Indexed: 12/12/2022]
Abstract
Late-onset Alzheimer's disease is a prevalent age-related polygenic disease that accounts for 50-70% of dementia cases. Currently, only a fraction of the genetic variants underlying Alzheimer's disease have been identified. Here we show that increased sample sizes allowed identification of seven previously unidentified genetic loci contributing to Alzheimer's disease. This study highlights microglia, immune cells and protein catabolism as relevant to late-onset Alzheimer's disease, while identifying and prioritizing previously unidentified genes of potential interest. We anticipate that these results can be included in larger meta-analyses of Alzheimer's disease to identify further genetic variants that contribute to Alzheimer's pathology.
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Affiliation(s)
- Douglas P Wightman
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU Amsterdam, Amsterdam, the Netherlands
| | - Iris E Jansen
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU Amsterdam, Amsterdam, the Netherlands
| | - Jeanne E Savage
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU Amsterdam, Amsterdam, the Netherlands
| | - Alexey A Shadrin
- NORMENT Centre, University of Oslo, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Shahram Bahrami
- NORMENT Centre, University of Oslo, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Dominic Holland
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | - Arvid Rongve
- Department of Research and Innovation, Helse Fonna, Haugesund Hospital, Haugesund, Norway
- The University of Bergen, Institute of Clinical Medicine (K1), Bergen, Norway
| | - Sigrid Børte
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Research and Communication Unit for Musculoskeletal Health (FORMI), Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Bendik S Winsvold
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Ole Kristian Drange
- Department of Mental Health, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Division of Mental Health Care, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Amy E Martinsen
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
| | - Anne Heidi Skogholt
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Cristen Willer
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Geir Bråthen
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Neurology and Clinical Neurophysiology, University Hospital of Trondheim, Trondheim, Norway
- Department of Geriatrics, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Ingunn Bosnes
- Department of Mental Health, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Psychiatry, Hospital Namsos, Nord-Trøndelag Health Trust, Namsos, Norway
| | - Jonas Bille Nielsen
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, USA
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Lars G Fritsche
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Laurent F Thomas
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Linda M Pedersen
- Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
| | - Maiken E Gabrielsen
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Marianne Bakke Johnsen
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Research and Communication Unit for Musculoskeletal Health (FORMI), Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Tore Wergeland Meisingset
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Neurology and Clinical Neurophysiology, University Hospital of Trondheim, Trondheim, Norway
| | - Wei Zhou
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Petroula Proitsi
- Institute of Psychiatry Psychology and Neurosciences, King's College London, London, UK
| | - Angela Hodges
- Institute of Psychiatry Psychology and Neurosciences, King's College London, London, UK
| | - Richard Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, London, UK
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, UK
- Health Data Research UK London, University College London, London, UK
- Institute of Health Informatics, University College London, London, UK
- NIHR Biomedical Research Centre at University College London Hospitals NHS Foundation Trust, London, UK
| | - Latha Velayudhan
- Institute of Psychiatry Psychology and Neurosciences, King's College London, London, UK
| | | | | | - Julia M Sealock
- Division of Genetic Medicine, Department of Medicine Vanderbilt University Medical Center Nashville, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lea K Davis
- Division of Genetic Medicine, Department of Medicine Vanderbilt University Medical Center Nashville, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nancy L Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Chandra A Reynolds
- Department of Psychology, University of California-Riverside, Riverside, CA, USA
| | - Ida K Karlsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Institute of Gerontology and Aging Research Network - Jönköping (ARN-J), School of Health and Welfare, Jönköping University, Jönköping, Sweden
| | | | | | | | - Palmi V Jonsson
- Department of Geriatric Medicine, Landspitali University Hospital, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Jon Snaedal
- Department of Geriatric Medicine, Landspitali University Hospital, Reykjavik, Iceland
| | - Anna Zettergren
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden
| | - Ingmar Skoog
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden
- Region Västra Götaland, Sahlgrenska University Hospital, Psychiatry, Cognition and Old Age Psychiatry Clinic, Gothenburg, Sweden
| | - Silke Kern
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden
- Region Västra Götaland, Sahlgrenska University Hospital, Psychiatry, Cognition and Old Age Psychiatry Clinic, Gothenburg, Sweden
| | - Margda Waern
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden
- Region Västra Götaland, Sahlgrenska University Hospital, Psychosis Clinic, Gothenburg, Sweden
| | - Henrik Zetterberg
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Eystein Stordal
- Department of Mental Health, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Psychiatry, Hospital Namsos, Nord-Trøndelag Health Trust, Namsos, Norway
| | - Kristian Hveem
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- HUNT Research Center, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - John-Anker Zwart
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
| | - Lavinia Athanasiu
- NORMENT Centre, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Per Selnes
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
| | - Ingvild Saltvedt
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Geriatrics, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Sigrid B Sando
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Neurology and Clinical Neurophysiology, University Hospital of Trondheim, Trondheim, Norway
| | - Ingun Ulstein
- Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Tormod Fladby
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
| | - Dag Aarsland
- Institute of Psychiatry Psychology and Neurosciences, King's College London, London, UK
- Centre of Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway
| | - Geir Selbæk
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
- Norwegian National Advisory Unit on Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway
| | - Stephan Ripke
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin, Berlin, Germany
| | | | - Ole A Andreassen
- NORMENT Centre, University of Oslo, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU Amsterdam, Amsterdam, the Netherlands.
- Department of Child and Adolescent Psychiatry and Pediatric Psychology, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam University Medical Center, Amsterdam, the Netherlands.
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Wickstrøm KE, Vitelli V, Carr E, Holten AR, Bendayan R, Reiner AH, Bean D, Searle T, Shek A, Kraljevic Z, Teo J, Dobson R, Tonby K, Köhn-Luque A, Amundsen EK. Regional performance variation in external validation of four prediction models for severity of COVID-19 at hospital admission: An observational multi-centre cohort study. PLoS One 2021; 16:e0255748. [PMID: 34432797 PMCID: PMC8386866 DOI: 10.1371/journal.pone.0255748] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 07/22/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Prediction models should be externally validated to assess their performance before implementation. Several prediction models for coronavirus disease-19 (COVID-19) have been published. This observational cohort study aimed to validate published models of severity for hospitalized patients with COVID-19 using clinical and laboratory predictors. METHODS Prediction models fitting relevant inclusion criteria were chosen for validation. The outcome was either mortality or a composite outcome of mortality and ICU admission (severe disease). 1295 patients admitted with symptoms of COVID-19 at Kings Cross Hospital (KCH) in London, United Kingdom, and 307 patients at Oslo University Hospital (OUH) in Oslo, Norway were included. The performance of the models was assessed in terms of discrimination and calibration. RESULTS We identified two models for prediction of mortality (referred to as Xie and Zhang1) and two models for prediction of severe disease (Allenbach and Zhang2). The performance of the models was variable. For prediction of mortality Xie had good discrimination at OUH with an area under the receiver-operating characteristic (AUROC) 0.87 [95% confidence interval (CI) 0.79-0.95] and acceptable discrimination at KCH, AUROC 0.79 [0.76-0.82]. In prediction of severe disease, Allenbach had acceptable discrimination (OUH AUROC 0.81 [0.74-0.88] and KCH AUROC 0.72 [0.68-0.75]). The Zhang models had moderate to poor discrimination. Initial calibration was poor for all models but improved with recalibration. CONCLUSIONS The performance of the four prediction models was variable. The Xie model had the best discrimination for mortality, while the Allenbach model had acceptable results for prediction of severe disease.
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Affiliation(s)
- Kristin E. Wickstrøm
- Department of Medical Biochemistry, Blood Cell Research Group, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Valeria Vitelli
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Ewan Carr
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Aleksander R. Holten
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Acute Medicine, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Rebecca Bendayan
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, London, United Kingdom
| | - Andrew H. Reiner
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
| | - Daniel Bean
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
- Health Data Research UK London, University College London, London, United Kingdom
| | - Tom Searle
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, London, United Kingdom
| | - Anthony Shek
- Department of Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Zeljko Kraljevic
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - James Teo
- Department of Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
- King’s College Hospital NHS Foundation Trust, London, United Kingdom
| | - Richard Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, London, United Kingdom
- Health Data Research UK London, University College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
- NIHR Biomedical Research Centre at University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Kristian Tonby
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Infectious Diseases, Oslo University Hospital, Oslo, Norway
| | | | - Erik K. Amundsen
- Department of Medical Biochemistry, Blood Cell Research Group, Oslo University Hospital, Oslo, Norway
- Department of Life Sciences and Health, Oslo Metropolitan University, Oslo, Norway
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Mascio A, Stewart R, Botelle R, Williams M, Mirza L, Patel R, Pollak T, Dobson R, Roberts A. Cognitive Impairments in Schizophrenia: A Study in a Large Clinical Sample Using Natural Language Processing. Front Digit Health 2021; 3:711941. [PMID: 34713182 PMCID: PMC8521945 DOI: 10.3389/fdgth.2021.711941] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 06/25/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Cognitive impairments are a neglected aspect of schizophrenia despite being a major factor of poor functional outcome. They are usually measured using various rating scales, however, these necessitate trained practitioners and are rarely routinely applied in clinical settings. Recent advances in natural language processing techniques allow us to extract such information from unstructured portions of text at a large scale and in a cost effective manner. We aimed to identify cognitive problems in the clinical records of a large sample of patients with schizophrenia, and assess their association with clinical outcomes. Methods: We developed a natural language processing based application identifying cognitive dysfunctions from the free text of medical records, and assessed its performance against a rating scale widely used in the United Kingdom, the cognitive component of the Health of the Nation Outcome Scales (HoNOS). Furthermore, we analyzed cognitive trajectories over the course of patient treatment, and evaluated their relationship with various socio-demographic factors and clinical outcomes. Results: We found a high prevalence of cognitive impairments in patients with schizophrenia, and a strong correlation with several socio-demographic factors (gender, education, ethnicity, marital status, and employment) as well as adverse clinical outcomes. Results obtained from the free text were broadly in line with those obtained using the HoNOS subscale, and shed light on additional associations, notably related to attention and social impairments for patients with higher education. Conclusions: Our findings demonstrate that cognitive problems are common in patients with schizophrenia, can be reliably extracted from clinical records using natural language processing, and are associated with adverse clinical outcomes. Harvesting the free text from medical records provides a larger coverage in contrast to neurocognitive batteries or rating scales, and access to additional socio-demographic and clinical variables. Text mining tools can therefore facilitate large scale patient screening and early symptoms detection, and ultimately help inform clinical decisions.
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Affiliation(s)
- Aurelie Mascio
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Robert Stewart
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley National Health Service (NHS) Foundation Trust and King's College London, London, United Kingdom
- Health Data Research UK, London, United Kingdom
| | - Riley Botelle
- GKT School of Medical Education, King's College London, London, United Kingdom
| | - Marcus Williams
- GKT School of Medical Education, King's College London, London, United Kingdom
| | - Luwaiza Mirza
- GKT School of Medical Education, King's College London, London, United Kingdom
| | - Rashmi Patel
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Thomas Pollak
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Richard Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley National Health Service (NHS) Foundation Trust and King's College London, London, United Kingdom
- Health Data Research UK, London, United Kingdom
| | - Angus Roberts
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley National Health Service (NHS) Foundation Trust and King's College London, London, United Kingdom
- Health Data Research UK, London, United Kingdom
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Brakenhoff TB, Franks B, Goodale BM, van de Wijgert J, Montes S, Veen D, Fredslund EK, Rispens T, Risch L, Dowling AV, Folarin AA, Bruijning P, Dobson R, Heikamp T, Klaver P, Cronin M, Grobbee DE. A prospective, randomized, single-blinded, crossover trial to investigate the effect of a wearable device in addition to a daily symptom diary for the remote early detection of SARS-CoV-2 infections (COVID-RED): a structured summary of a study protocol for a randomized controlled trial. Trials 2021; 22:412. [PMID: 34158099 PMCID: PMC8218271 DOI: 10.1186/s13063-021-05241-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 03/30/2021] [Indexed: 11/10/2022] Open
Abstract
OBJECTIVES It is currently thought that most-but not all-individuals infected with SARS-CoV-2 develop symptoms, but that the infectious period starts on average two days before the first overt symptoms appear. It is estimated that pre- and asymptomatic individuals are responsible for more than half of all transmissions. By detecting infected individuals before they have overt symptoms, wearable devices could potentially and significantly reduce the proportion of transmissions by pre-symptomatic individuals. Using laboratory-confirmed SARS-CoV-2 infections (detected via serology tests [to determine if there are antibodies against the SARS-CoV-2 in the blood] or SARS-CoV-2 infection tests such as polymerase chain reaction [PCR] or antigen tests) as the gold standard, we will determine the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for the following two algorithms to detect first time SARS-CoV-2 infection including early or asymptomatic infection: the algorithm using Ava bracelet data when coupled with self-reported Daily Symptom Diary data (Wearable + Symptom Data Algo; experimental condition) the algorithm using self-reported Daily Symptom Diary data alone (Symptom Only Algo; control condition) In addition, we will determine which of the two algorithms has superior performance characteristics for detecting SARS-CoV-2 infection including early or asymptomatic infection as confirmed by SARS-CoV-2 virus testing. TRIAL DESIGN The trial is a randomized, single-blinded, two-period, two-sequence crossover trial. All subjects will participate in an initial Learning Phase (varying from 2 weeks to 3 months depending on enrolment date), followed by two contiguous 3-month test phases, Period 1 and Period 2. Each subject will undergo the experimental condition (the Wearable + Symptom Data Algo) in one of these periods and the control condition (Symptom Only Algo) in the other period. The order will be randomly assigned, resulting in subjects being allocated 1:1 to either Sequence 1 (experimental condition first) or Sequence 2 (control condition first). Based on demographics, medical history and/or profession, each subject will be stratified at baseline into a high-risk and normal-risk group within each sequence. PARTICIPANTS The trial will be conducted in the Netherlands. A target of 20,000 subjects will be enrolled. Based on demographics, medical history and/or profession, each subject will be stratified at baseline into a high-risk and normal-risk group within each sequence. This results in approximately 6,500 normal-risk individuals and 3,500 high-risk individuals per sequence. Subjects will be recruited from previously studied cohorts as well as via public campaigns and social media. All data for this study will be collected remotely through the Ava COVID-RED app, the Ava bracelet, surveys in the COVID-RED web portal, and self-sampling serology and PCR kits. During recruitment, subjects will be invited to visit the COVID-RED web portal ( www.covid-red.eu ). After successfully completing the enrolment questionnaire, meeting eligibility criteria and indicating interest in joining the study, subjects will receive the subject information sheet and informed consent form. Subjects can enrol in COVID-RED if they comply with the following inclusion and exclusion criteria. INCLUSION CRITERIA Resident of the Netherlands At least 18 years old Informed consent provided (electronic) Willing to adhere to the study procedures described in the protocol Must have a smartphone that runs at least Android 8.0 or iOS 13.0 operating systems and is active for the duration of the study (in the case of a change of mobile number, study team should be notified) Be able to read, understand and write Dutch Exclusion criteria: Previous positive SARS-CoV-2 test result (confirmed either through PCR/antigen or antibody tests; self-reported) Previously received a vaccine developed specifically for COVID-19 or in possession of an appointment for vaccination in the near future (self-reported) Current suspected (e.g., waiting for test result) COVID-19 infection or symptoms of a COVID-19 infection (self-reported) Participating in any other COVID-19 clinical drug, vaccine, or medical device trial (self-reported) Electronic implanted device (such as a pacemaker; self-reported) Pregnant at time of informed consent (self-reported) Suffering from cholinergic urticaria (per the Ava bracelet's User Manual; self-reported) Staff involved in the management or conduct of this study INTERVENTION AND COMPARATOR: All subjects will be instructed to complete the Daily Symptom Diary in the Ava COVID-RED app daily, wear their Ava bracelet each night and synchronise it with the app each day for the entire period of study participation. Provided with wearable sensor and/or self-reported symptom data within the last 24 hours, the Ava COVID-RED app's underlying algorithms will provide subjects with a real-time indicator of their overall health and well-being. Subjects will see one of three messages, notifying them that: no seeming deviations in symptoms and/or physiological parameters have been detected; some changes in symptoms and/or physiological parameters have been detected and they should self-isolate; or alerting them that deviations in their symptoms and/or physiological parameters could be suggestive of a potential COVID-19 infection and to seek additional testing. We will assess intraperson performance of the algorithms in the experimental condition (Wearable + Symptom Data Algo) and control conditions (Symptom Only Algo). MAIN OUTCOMES The trial will evaluate the use and performance of the Ava COVID-RED app and Ava bracelet, which uses sensors to measure breathing rate, pulse rate, skin temperature, and heart rate variability for the purpose of early and asymptomatic detection and monitoring of SARS-CoV-2 in general and high-risk populations. Using laboratory-confirmed SARS-CoV-2 infections (detected via serology tests, PCR tests and/or antigen tests) as the gold standard, we will determine the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for each of the following two algorithms to detect first-time SARS-CoV-2 infection including early or asymptomatic infection: the algorithm using Ava Bracelet data when coupled with the self-reported Daily Symptom Diary data, and the algorithm using self-reported Daily Symptom Diary data alone. In addition, we will determine which of the two algorithms has superior performance characteristics for detecting SARS-CoV-2 infection including early or asymptomatic infection as confirmed by SARS-CoV-2 virus testing. The protocol contains an additional seventeen secondary outcomes which address infection incidence rates, health resource utilization, symptoms reported by SARS-CoV-2 infected participants, and the rate of breakthrough and asymptomatic SARS-CoV-2 infections among individuals vaccinated against COVID-19. PCR or antigen testing will occur when the subject receives a notification from the algorithm to seek additional testing. Subjects will be advised to get tested via the national testing programme, and report the testing result in the Ava COVID-RED app and a survey. If they cannot obtain a test via the national testing programme, they will receive a nasal swab self-sampling kit at home, and the sample will be tested by PCR in a trial-affiliated laboratory. In addition, all subjects will be asked to take a capillary blood sample at home at baseline (Month 0), and at the end of the Learning Phase (Month 3), Period 1 (Month 6) and Period 2 (Month 9). These samples will be used for SARS-CoV-2-specific antibody testing in a trial-affiliated laboratory, differentiating between antibodies resulting from a natural infection and antibodies resulting from COVID-19 vaccination (as vaccination will gradually be rolled out during the trial period). Baseline samples will only be analysed if the sample collected at the end of the Learning Phase is positive, and samples collected at the end of Period 1 will only be analysed if the sample collected at the end of Period 2 is positive. When subjects obtain a positive PCR/antigen or serology test result during the study, they will continue to be in the study but will be moved into a so-called "COVID-positive" mode in the Ava COVID-RED app. This means that they will no longer receive recommendations from the algorithms but can still contribute and track symptom and bracelet data. The primary analysis of the main objective will be executed using data collected in Period 2 (Month 6 through 9). Within this period, serology tests (before and after Period 2) and PCR/antigen tests (taken based on recommendations by the algorithms) will be used to determine if a subject was infected with SARS-CoV-2 or not. Within this same time period, it will be determined if the algorithms gave any recommendations for testing. The agreement between these quantities will be used to evaluate the performance of the algorithms and how these compare between the study conditions. RANDOMISATION All eligible subjects will be randomized using a stratified block randomization approach with an allocation ratio of 1:1 to one of two sequences (experimental condition followed by control condition or control condition followed by experimental condition). Based on demographics, medical history and/or profession, each subject will be stratified at baseline into a high-risk and normal-risk group within each sequence, resulting in equal numbers of high-risk and normal-risk individuals between the sequences. BLINDING (MASKING) In this study, subjects will be blinded as to study condition and randomization sequence. Relevant study staff and the device manufacturer will be aware of the assigned sequence. The subject will wear the Ava bracelet and complete the Daily Symptom Diary in the Ava COVID-RED app for the full duration of the study, and they will not know if the feedback they receive about their potential infection status will only be based on data they entered in the Daily Symptom Diary within the Ava COVID-RED app or based on both the data from the Daily Symptom Diary and the Ava bracelet. NUMBERS TO BE RANDOMISED (SAMPLE SIZE) 20,000 subjects will be recruited and randomized 1:1 to either Sequence 1 (experimental condition followed by control condition) or Sequence 2 (control condition followed by experimental condition), taking into account their risk level. This results in approximately 6,500 normal-risk and 3,500 high-risk individuals per sequence. TRIAL STATUS Protocol version: 1.2, dated January 22nd, 2021 Start of recruitment: February 22nd, 2021 End of recruitment (estimated): April 2021 End of follow-up (estimated): December 2021 TRIAL REGISTRATION: The trial has been registered at the Netherlands Trial Register on the 18th of February, 2021 with number NL9320 ( https://www.trialregister.nl/trial/9320 ) FULL PROTOCOL: The full protocol is attached as an additional file, accessible from the Trials website (Additional file 1). In the interest in expediting dissemination of this material, the familiar formatting has been eliminated; this Letter serves as a summary of the key elements of the full protocol.
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Affiliation(s)
| | | | | | - Janneke van de Wijgert
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
| | | | - Duco Veen
- Julius Clinical, Zeist, the Netherlands.,Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands.,Optentia Research Program, North-West University, Potchefstroom, South Africa
| | | | | | - Lorenz Risch
- Labormedizinisches zentrum Dr. Risch, Vaduz, Liechtenstein.,Faculty of Medical Sciences, Private Universität im Fürstentum Liechtenstein, Triesen, Liechtenstein.,Center of Laboratory Medicine, University Institute of Clinical Chemistry, University of Bern, Bern, Switzerland
| | - Ariel V Dowling
- Data Sciences Institute, Takeda Pharmaceuticals U.S.A. Inc., Cambridge, MA, USA
| | - Amos A Folarin
- National Institute for Health Research Maudsley Biomedical Research Centre, King's College London, London, UK.,Institute of Health Informatics, University College London, London, UK.,South London and Maudsley NHS Foundation Trust, London, UK
| | - Patricia Bruijning
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Richard Dobson
- Institute of Health Informatics, University College London, London, UK
| | | | | | | | - Diederick E Grobbee
- Julius Clinical, Zeist, the Netherlands.,Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
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El-Medany A, Sunderland N, Dobson R, Nisbet A. Catheter ablation for atrial arrhythmias in adults with congenital heart disease: recurrence rates and predictors of acute procedural success. Europace 2021. [DOI: 10.1093/europace/euab116.088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Funding Acknowledgements
Type of funding sources: None.
Background
Heart rhythm disorders are an important cause of morbidity and emergency hospitalisation in patients with adult congenital heart disease (ACHD), and this is due to a combination of surgical scar, residual haemodynamic lesions, and cardiac chamber dilatation. The most effective available treatment is catheter ablation, although this can be extremely challenging owing to abnormal anatomy and problems accessing intra cardiac sites critical to the arrhythmia mechanism. However, outcomes of catheter ablation and analysis of factors which may predict recurrence of arrhythmia remain poorly defined.
Purpose
To define the cohort of ACHD patients undergoing catheter ablation for atrial arrhythmia in a large tertiary centre, characterise outcomes, and determine factors associated with arrhythmia recurrence.
Methods
Retrospective study of all catheter ablations for atrial arrhythmias in ACHD patients between April 13, 2016 and December 16, 2019 at our institution. Patients were identified using a field search through a centralised database; and pre-specified clinical and procedural data of interest, and time from ablation to recurrence were determined from the computerised electronic record. Binary logistical regression and cox regression analysis were used to determine potential predictors of acute procedural success and arrhythmia recurrence respectively.
Results
Among 90 patients (mean age 43 ± 15 years) who underwent catheter ablation for atrial arrhythmia, 39 (43%) were treated for macro-reentrant atrial tachycardia, 19 (21%) for focal atrial tachycardia, 9 (10%) for multifocal atrial tachycardia, 10 (10%) for atrial fibrillation, 7 (8%) for atrioventricular nodal reentrant tachycardia, and 6 (7%) for atrioventricular reentrant tachycardia. 35 (39%) of patients had "severe" complexity ACHD as per the Bethesda classification. 35 (39%) experienced recurrent arrhythmia with a median time to recurrence of 120 days. Age, gender, body mass index, complexity of congenital heart disease, and previous surgical repair were not identified as being significantly associated with recurrence, however univariate cox regression analysis showed a significantly longer time to recurrence in cases utilising electroanatomical mapping and demonstrating non-inducibility of arrhythmia in the lab post ablation (p < 0.001). There was 1 case of post-ablation bradycardia requiring pacemaker implantation, but no other complications.
Conclusion
Catheter ablation for atrial arrhythmia in ACHD patients is safe and effective, with a majority of patients achieving multiple arrhythmia-free months. Non-inducibility of arrhythmia post procedure and use of electroanatomical mapping are predictors of freedom from recurrence of atrial arrhythmia, suggesting effective characterisation and ablation of the arrhythmia mechanism is more important than the underlying substrate. These findings may aid management decisions for recurrent arrhythmia in ACHD patients.
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Affiliation(s)
- A El-Medany
- Bristol Heart Institute, Bristol, United Kingdom of Great Britain & Northern Ireland
| | - N Sunderland
- Bristol Heart Institute, Bristol, United Kingdom of Great Britain & Northern Ireland
| | - R Dobson
- Bristol Heart Institute, Bristol, United Kingdom of Great Britain & Northern Ireland
| | - A Nisbet
- Bristol Heart Institute, Bristol, United Kingdom of Great Britain & Northern Ireland
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46
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Allen-Philbey K, Stennett A, Begum T, Johnson AC, Dobson R, Giovannoni G, Gnanapavan S, Marta M, Smets I, Turner BP, Baker D, Mathews J, Schmierer K. Experience with the COVID-19 AstraZeneca vaccination in people with multiple sclerosis. Mult Scler Relat Disord 2021; 52:103028. [PMID: 34049216 PMCID: PMC8129799 DOI: 10.1016/j.msard.2021.103028] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 05/04/2021] [Accepted: 05/10/2021] [Indexed: 02/09/2023]
Abstract
Background Some people with multiple sclerosis (pwMS) are at increased risk of severe Coronavirus disease 19 (COVID-19) and should be rapidly vaccinated. However, vaccine supplies are limited, and there are concerns about side-effects, particularly with the ChAdOx1nCoV-19 (AstraZeneca) vaccine. Objectives To report our first experience of pwMS receiving the AstraZeneca vaccine. Methods Service evaluation. pwMS using the MS service at Barts Health NHS Trust were sent questionnaires to report symptoms following vaccination. Results Thirty-three responses were returned, 29/33 pwMS received a first dose of AstraZeneca vaccine, the remaining four received a first dose of BioNTech/Pfizer vaccine. All but two patients (94%) reported any symptoms including a sore arm (70%), flu-like symptoms (64%), fever (21%), fatigue (27%), and headache (21%). In more than 2/3 patients, symptoms lasted up to 48 hours, and with the exception of two pwMS reporting symptom duration of 10 and 12 days, respectively, symptoms in the remainder resolved within seven days. No severe adverse effects occurred. Conclusions pwMS report transient symptoms following AstraZeneca vaccination, characteristics of which were similar to those reported in the non-MS population. Symptoms may be more pronounced in pwMS due to the temperature-dependent delay in impulse propagation (Uhthoff's phenomenon) due to demyelination.
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Affiliation(s)
- K Allen-Philbey
- The Blizard Institute, Centre for Neuroscience, Surgery & Trauma, Queen Mary University of London, Barts and The London School of Medicine & Dentistry, London, United Kingdom; Clinical Board Medicine (Neuroscience), The Royal London Hospital, Barts Health NHS Trust, London, United Kingdom
| | - A Stennett
- The Blizard Institute, Centre for Neuroscience, Surgery & Trauma, Queen Mary University of London, Barts and The London School of Medicine & Dentistry, London, United Kingdom; Clinical Board Medicine (Neuroscience), The Royal London Hospital, Barts Health NHS Trust, London, United Kingdom
| | - T Begum
- Clinical Board Medicine (Neuroscience), The Royal London Hospital, Barts Health NHS Trust, London, United Kingdom
| | - A C Johnson
- Clinical Board Medicine (Neuroscience), The Royal London Hospital, Barts Health NHS Trust, London, United Kingdom
| | - R Dobson
- Clinical Board Medicine (Neuroscience), The Royal London Hospital, Barts Health NHS Trust, London, United Kingdom; Preventive Neurology Unit, Wolfson Institute of Preventive Medicine, Queen Mary University of London, Barts and The London School of Medicine & Dentistry, London, United Kingdom
| | - G Giovannoni
- The Blizard Institute, Centre for Neuroscience, Surgery & Trauma, Queen Mary University of London, Barts and The London School of Medicine & Dentistry, London, United Kingdom; Clinical Board Medicine (Neuroscience), The Royal London Hospital, Barts Health NHS Trust, London, United Kingdom; Preventive Neurology Unit, Wolfson Institute of Preventive Medicine, Queen Mary University of London, Barts and The London School of Medicine & Dentistry, London, United Kingdom
| | - S Gnanapavan
- The Blizard Institute, Centre for Neuroscience, Surgery & Trauma, Queen Mary University of London, Barts and The London School of Medicine & Dentistry, London, United Kingdom; Clinical Board Medicine (Neuroscience), The Royal London Hospital, Barts Health NHS Trust, London, United Kingdom
| | - M Marta
- The Blizard Institute, Centre for Neuroscience, Surgery & Trauma, Queen Mary University of London, Barts and The London School of Medicine & Dentistry, London, United Kingdom; Clinical Board Medicine (Neuroscience), The Royal London Hospital, Barts Health NHS Trust, London, United Kingdom
| | - I Smets
- The Blizard Institute, Centre for Neuroscience, Surgery & Trauma, Queen Mary University of London, Barts and The London School of Medicine & Dentistry, London, United Kingdom; Clinical Board Medicine (Neuroscience), The Royal London Hospital, Barts Health NHS Trust, London, United Kingdom
| | - B P Turner
- The Blizard Institute, Centre for Neuroscience, Surgery & Trauma, Queen Mary University of London, Barts and The London School of Medicine & Dentistry, London, United Kingdom; Clinical Board Medicine (Neuroscience), The Royal London Hospital, Barts Health NHS Trust, London, United Kingdom
| | - D Baker
- The Blizard Institute, Centre for Neuroscience, Surgery & Trauma, Queen Mary University of London, Barts and The London School of Medicine & Dentistry, London, United Kingdom
| | - J Mathews
- Pharmacy, The Royal London Hospital, Barts Health NHS Trust, London, United Kingdom
| | - K Schmierer
- The Blizard Institute, Centre for Neuroscience, Surgery & Trauma, Queen Mary University of London, Barts and The London School of Medicine & Dentistry, London, United Kingdom; Clinical Board Medicine (Neuroscience), The Royal London Hospital, Barts Health NHS Trust, London, United Kingdom.
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47
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Coats T, Bean D, Vatopoulou T, Vijayavalli D, El‐Bashir R, Panopoulou A, Wood H, Wimalachandra M, Coppell J, Medd P, Furtado M, Tucker D, Kulasakeraraj A, Pawade J, Dobson R, Ireland R. An open-source, expert-designed decision tree application to support accurate diagnosis of myeloid malignancies. EJHaem 2021; 2:261-265. [PMID: 35845286 PMCID: PMC9175663 DOI: 10.1002/jha2.182] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 03/01/2021] [Indexed: 11/08/2022]
Abstract
Accurate, reproducible diagnoses can be difficult to make in haemato-oncology due to multi-parameter clinical data, complex diagnostic criteria and time-pressured environments. We have designed a decision tree application (DTA) that reflects WHO diagnostic criteria to support accurate diagnoses of myeloid malignancies. The DTA returned the correct diagnoses in 94% of clinical cases tested. The DTA maintained a high level of accuracy in a second validation using artificially generated clinical cases. Optimisations have been made to the DTA based on the validations, and the revised version is now publicly available for use at http://bit.do/ADAtool.
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Affiliation(s)
- Thomas Coats
- Department of HaematologyRoyal Devon and Exeter NHS Foundation TrustExeterUK
| | - Daniel Bean
- Biostatistics and Health InformaticsKing's College LondonLondonUK
- Health Data Research UK LondonUniversity College LondonLondonUK
| | - Theodora Vatopoulou
- Department of HaematologySt George's University Hospitals NHS Foundation TrustLondonUK
| | - Dhanapal Vijayavalli
- Department of HaematologyMedway NHS Foundation TrustKentUK
- Department of Haematological MedicineKing's College Hospital NHS Foundation TrustLondonUK
| | | | - Aikaterini Panopoulou
- Department of HaematologyDarent Valley HospitalKentUK
- Department of HaematologyRoyal Marsden NHS Foundation TrustLondonUK
| | - Henry Wood
- Department of Haematological MedicineKing's College Hospital NHS Foundation TrustLondonUK
| | | | - Jason Coppell
- Department of HaematologyRoyal Devon and Exeter NHS Foundation TrustExeterUK
| | - Patrick Medd
- Department of HaematologyDerriford HospitalPlymouthUK
| | | | - David Tucker
- Department of HaematologyRoyal Cornwall NHS TrustTruroUK
| | - Austin Kulasakeraraj
- Department of Haematological MedicineKing's College Hospital NHS Foundation TrustLondonUK
| | - Joya Pawade
- Department of PathologyNorth Bristol NHS TrustBristolUK
| | - Richard Dobson
- Biostatistics and Health InformaticsKing's College LondonLondonUK
- Health Data Research UK LondonUniversity College LondonLondonUK
| | - Robin Ireland
- Department of Haematological MedicineKing's College Hospital NHS Foundation TrustLondonUK
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48
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Lord J, Jermy B, Green R, Wong A, Xu J, Legido-Quigley C, Dobson R, Richards M, Proitsi P. Mendelian randomization identifies blood metabolites previously linked to midlife cognition as causal candidates in Alzheimer's disease. Proc Natl Acad Sci U S A 2021; 118:e2009808118. [PMID: 33879569 PMCID: PMC8072203 DOI: 10.1073/pnas.2009808118] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 02/23/2021] [Indexed: 12/29/2022] Open
Abstract
There are currently no disease-modifying treatments for Alzheimer's disease (AD), and an understanding of preclinical causal biomarkers to help target disease pathogenesis in the earliest phases remains elusive. Here, we investigated whether 19 metabolites previously associated with midlife cognition-a preclinical predictor of AD-translate to later clinical risk, using Mendelian randomization (MR) to tease out AD-specific causal relationships. Summary statistics from the largest genome-wide association studies (GWASs) for AD and metabolites were used to perform bidirectional univariable MR. Bayesian model averaging (BMA) was additionally performed to address high correlation between metabolites and identify metabolite combinations that may be on the AD causal pathway. Univariable MR indicated four extra-large high-density lipoproteins (XL.HDL) on the causal pathway to AD: free cholesterol (XL.HDL.FC: 95% CI = 0.78 to 0.94), total lipids (XL.HDL.L: 95% CI = 0.80 to 0.97), phospholipids (XL.HDL.PL: 95% CI = 0.81 to 0.97), and concentration of XL.HDL particles (95% CI = 0.79 to 0.96), significant at an adjusted P < 0.009. MR-BMA corroborated XL.HDL.FC to be among the top three causal metabolites, in addition to total cholesterol in XL.HDL (XL.HDL.C) and glycoprotein acetyls (GP). Both XL.HDL.C and GP demonstrated suggestive univariable evidence of causality (P < 0.05), and GP successfully replicated within an independent dataset. This study offers insight into the causal relationship between metabolites demonstrating association with midlife cognition and AD. It highlights GP in addition to several XL.HDLs-particularly XL.HDL.FC-as causal candidates warranting further investigation. As AD pathology is thought to develop decades prior to symptom onset, expanding on these findings could inform risk reduction strategies.
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Affiliation(s)
- Jodie Lord
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 5AF, United Kingdom
| | - Bradley Jermy
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, United Kingdom
- National Institute for Health Research Maudsley Biomedical Research Centre, South London and Maudsley National Health Service (NHS) Foundation Trust, London, SE5 8AF, United Kingdom
| | - Rebecca Green
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 5AF, United Kingdom
- National Institute for Health Research Maudsley Biomedical Research Centre, South London and Maudsley National Health Service (NHS) Foundation Trust, London, SE5 8AF, United Kingdom
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, WC1E 7HB, United Kingdom
| | - Jin Xu
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 5AF, United Kingdom
- Institute of Pharmaceutical Science, King's College London, London, SE1 9NH, United Kingdom
| | - Cristina Legido-Quigley
- Institute of Pharmaceutical Science, King's College London, London, SE1 9NH, United Kingdom
- Systems Medicine, Steno Diabetes Centre Copenhagen, 2820 Gentofte, Denmark
| | - Richard Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF, United Kingdom
- National Institute for Health Research Biomedical Research at South London and Maudsley NHS Foundation Trust and King's College London, London, SE5 8AF, United Kingdom
- Health Data Research UK London, University College London, London, NW1 2DA, United Kingdom
- Institute of Health Informatics, University College London, London, NW1 2DA, United Kingdom
- National Institute for Health Research Biomedical Research Centre at University College London Hospitals NHS Foundation Trust, London, NW1 2DA, United Kingdom
| | - Marcus Richards
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, WC1E 7HB, United Kingdom;
| | - Petroula Proitsi
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 5AF, United Kingdom;
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Wu H, Zhang H, Karwath A, Ibrahim Z, Shi T, Zhang X, Wang K, Sun J, Dhaliwal K, Bean D, Cardoso VR, Li K, Teo JT, Banerjee A, Gao-Smith F, Whitehouse T, Veenith T, Gkoutos GV, Wu X, Dobson R, Guthrie B. Ensemble learning for poor prognosis predictions: A case study on SARS-CoV-2. J Am Med Inform Assoc 2021; 28:791-800. [PMID: 33185672 PMCID: PMC7717299 DOI: 10.1093/jamia/ocaa295] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 11/11/2020] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE Risk prediction models are widely used to inform evidence-based clinical decision making. However, few models developed from single cohorts can perform consistently well at population level where diverse prognoses exist (such as the SARS-CoV-2 [severe acute respiratory syndrome coronavirus 2] pandemic). This study aims at tackling this challenge by synergizing prediction models from the literature using ensemble learning. MATERIALS AND METHODS In this study, we selected and reimplemented 7 prediction models for COVID-19 (coronavirus disease 2019) that were derived from diverse cohorts and used different implementation techniques. A novel ensemble learning framework was proposed to synergize them for realizing personalized predictions for individual patients. Four diverse international cohorts (2 from the United Kingdom and 2 from China; N = 5394) were used to validate all 8 models on discrimination, calibration, and clinical usefulness. RESULTS Results showed that individual prediction models could perform well on some cohorts while poorly on others. Conversely, the ensemble model achieved the best performances consistently on all metrics quantifying discrimination, calibration, and clinical usefulness. Performance disparities were observed in cohorts from the 2 countries: all models achieved better performances on the China cohorts. DISCUSSION When individual models were learned from complementary cohorts, the synergized model had the potential to achieve better performances than any individual model. Results indicate that blood parameters and physiological measurements might have better predictive powers when collected early, which remains to be confirmed by further studies. CONCLUSIONS Combining a diverse set of individual prediction models, the ensemble method can synergize a robust and well-performing model by choosing the most competent ones for individual patients.
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Affiliation(s)
- Honghan Wu
- Institute of Health Informatics, University College London,
London, United Kingdom
- Health Data Research UK, University College London, London,
United Kingdom
| | - Huayu Zhang
- Centre for Medical Informatics, Usher Institute, University of
Edinburgh, Edinburgh, United Kingdom
| | - Andreas Karwath
- Institute of Cancer and Genomic Sciences, University of
Birmingham, Birmingham, United Kingdom
- Health Data Research UK, University of Birmingham, Birmingham,
United Kingdom
| | - Zina Ibrahim
- Health Data Research UK, University College London, London,
United Kingdom
- Department of Biostatistics and Health Informatics, Institute of Psychiatry,
Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Ting Shi
- Centre for Global Health, Usher Institute, University of
Edinburgh, Edinburgh, United Kingdom
| | - Xin Zhang
- Department of Pulmonary and Critical Care Medicine, People’s Liberation Army
Joint Logistic Support Force 920th Hospital, Kunming, China
| | - Kun Wang
- Department of Pulmonary and Critical Care Medicine, Shanghai East Hospital,
Tongji University, Shanghai, China
| | - Jiaxing Sun
- Department of Pulmonary and Critical Care Medicine, Shanghai East Hospital,
Tongji University, Shanghai, China
| | - Kevin Dhaliwal
- Centre for Inflammation Research, Queens Medical Research Institute, University
of Edinburgh, Edinburgh, United
Kingdom
| | - Daniel Bean
- Department of Biostatistics and Health Informatics, Institute of Psychiatry,
Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Victor Roth Cardoso
- Institute of Cancer and Genomic Sciences, University of
Birmingham, Birmingham, United Kingdom
- Health Data Research UK, University of Birmingham, Birmingham,
United Kingdom
| | - Kezhi Li
- Institute of Health Informatics, University College London,
London, United Kingdom
| | - James T Teo
- Department of Stroke and Neurology, King’s College Hospital NHS Foundation
Trust, London, United Kingdom
| | - Amitava Banerjee
- Institute of Health Informatics, University College London,
London, United Kingdom
| | - Fang Gao-Smith
- Department of Intensive Care Medicine, Queen Elizabeth Hospital
Birmingham, Birmingham, United Kingdom
- Birmingham Acute Care Research, University of Birmingham,
Birmingham, United Kingdom
| | - Tony Whitehouse
- Department of Intensive Care Medicine, Queen Elizabeth Hospital
Birmingham, Birmingham, United Kingdom
- Birmingham Acute Care Research, University of Birmingham,
Birmingham, United Kingdom
| | - Tonny Veenith
- Department of Intensive Care Medicine, Queen Elizabeth Hospital
Birmingham, Birmingham, United Kingdom
- Birmingham Acute Care Research, University of Birmingham,
Birmingham, United Kingdom
| | - Georgios V Gkoutos
- Institute of Cancer and Genomic Sciences, University of
Birmingham, Birmingham, United Kingdom
- Health Data Research UK, University of Birmingham, Birmingham,
United Kingdom
- Institute of Translational Medicine, University Hospitals Birmingham NHS
Foundation Trust, Birmingham, United
Kingdom
| | - Xiaodong Wu
- Department of Pulmonary and Critical Care Medicine, Shanghai East Hospital,
Tongji University, Shanghai, China
- Department of Pulmonary and Critical Care Medicine, Taikang Tongji
Hospital, Wuhan, China
| | - Richard Dobson
- Institute of Health Informatics, University College London,
London, United Kingdom
- Health Data Research UK, University College London, London,
United Kingdom
- Department of Biostatistics and Health Informatics, Institute of Psychiatry,
Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Bruce Guthrie
- Centre for Population Health Sciences, Usher Institute, University of
Edinburgh, Edinburgh, United Kingdom
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Dobson R, Siddiqi K, Ferdous T, Huque R, Lesosky M, Balmes J, Semple S. Diurnal variability of fine-particulate pollution concentrations: data from 14 low- and middle-income countries. Int J Tuberc Lung Dis 2021; 25:206-214. [PMID: 33688809 PMCID: PMC7948758 DOI: 10.5588/ijtld.20.0704] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND: Scientific understanding of indoor air pollution is predominately based on research carried out in cities in high-income countries (HICs). Less is known about how pollutant concentrations change over the course of a typical day in cities in low- and middle-income countries (LMICs).OBJECTIVE: To understand how concentrations of fine particulate matter smaller than 2.5 microns in diameter (PM2.5) change over the course of the day outdoors (across a range of countries) and indoors (using measurements from Dhaka, Bangladesh).DESIGN: Data on PM2.5 concentrations were gathered from 779 households in Dhaka as part of the MCLASS II (Muslim Communities Learning About Second-hand Smoke in Bangladesh) project, and compared to outdoor PM2.5 concentrations to determine the temporal variation in exposure to air pollution. Hourly PM2.5 data from 23 cities in 14 LMICs, as well as London (UK), Paris (France) and New York (NY, USA), were extracted from publicly available sources for comparison.RESULTS: PM2.5 in homes in Dhaka demonstrated a similar temporal pattern to outdoor measurements, with greater concentrations at night than in the afternoon. This pattern was also evident in 19 of 23 LMIC cities.CONCLUSION: PM2.5 concentrations are greater at night than during the afternoon in homes in Dhaka. Diurnal variations in PM2.5 in LMICs is substantial and greater than in London, Paris or New York. This has implications for public health community approaches to health effects of air pollution in LMICs.
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Affiliation(s)
- R. Dobson
- Institute for Social Marketing and Health, University of Stirling, Stirling, Scotland
| | - K. Siddiqi
- Department of Health Sciences, University of York, York, UK
| | - T. Ferdous
- Advancement through Research and Knowledge Foundation Bangladesh, Dhaka, Bangladesh
| | - R. Huque
- Advancement through Research and Knowledge Foundation Bangladesh, Dhaka, Bangladesh
| | - M. Lesosky
- Division of Epidemiology & Biostatistics, School of Public Health & Family Medicine, University of Cape Town, Cape Town, South Africa
| | - J. Balmes
- Department of Medicine, University of California, San Francisco, CA
,School of Public Health, University of California, Berkeley, CA, USA
| | - S. Semple
- Institute for Social Marketing and Health, University of Stirling, Stirling, Scotland
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