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van Dellen E. Precision psychiatry: predicting predictability. Psychol Med 2024; 54:1500-1509. [PMID: 38497091 DOI: 10.1017/s0033291724000370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Precision psychiatry is an emerging field that aims to provide individualized approaches to mental health care. An important strategy to achieve this precision is to reduce uncertainty about prognosis and treatment response. Multivariate analysis and machine learning are used to create outcome prediction models based on clinical data such as demographics, symptom assessments, genetic information, and brain imaging. While much emphasis has been placed on technical innovation, the complex and varied nature of mental health presents significant challenges to the successful implementation of these models. From this perspective, I review ten challenges in the field of precision psychiatry, including the need for studies on real-world populations and realistic clinical outcome definitions, and consideration of treatment-related factors such as placebo effects and non-adherence to prescriptions. Fairness, prospective validation in comparison to current practice and implementation studies of prediction models are other key issues that are currently understudied. A shift is proposed from retrospective studies based on linear and static concepts of disease towards prospective research that considers the importance of contextual factors and the dynamic and complex nature of mental health.
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Affiliation(s)
- Edwin van Dellen
- Department of Psychiatry and University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, the Netherlands
- Department of Neurology, UZ Brussel and Vrije Universiteit Brussel, Brussels, Belgium
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Newby D, Taylor N, Joyce DW, Winchester LM. Optimising the use of electronic medical records for large scale research in psychiatry. Transl Psychiatry 2024; 14:232. [PMID: 38824136 DOI: 10.1038/s41398-024-02911-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 04/13/2024] [Accepted: 04/15/2024] [Indexed: 06/03/2024] Open
Abstract
The explosion and abundance of digital data could facilitate large-scale research for psychiatry and mental health. Research using so-called "real world data"-such as electronic medical/health records-can be resource-efficient, facilitate rapid hypothesis generation and testing, complement existing evidence (e.g. from trials and evidence-synthesis) and may enable a route to translate evidence into clinically effective, outcomes-driven care for patient populations that may be under-represented. However, the interpretation and processing of real-world data sources is complex because the clinically important 'signal' is often contained in both structured and unstructured (narrative or "free-text") data. Techniques for extracting meaningful information (signal) from unstructured text exist and have advanced the re-use of routinely collected clinical data, but these techniques require cautious evaluation. In this paper, we survey the opportunities, risks and progress made in the use of electronic medical record (real-world) data for psychiatric research.
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Affiliation(s)
- Danielle Newby
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Centre for Statistics in Medicine, University of Oxford, Oxford, UK
| | - Niall Taylor
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Dan W Joyce
- Department of Primary Care and Mental Health and Civic Health, Innovation Labs, Institute of Population Health, University of Liverpool, Liverpool, UK
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Hammelrath L, Hilbert K, Heinrich M, Zagorscak P, Knaevelsrud C. Select or adjust? How information from early treatment stages boosts the prediction of non-response in internet-based depression treatment. Psychol Med 2024; 54:1641-1650. [PMID: 38087867 DOI: 10.1017/s0033291723003537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
BACKGROUND Internet-based interventions produce comparable effectiveness rates as face-to-face therapy in treating depression. Still, more than half of patients do not respond to treatment. Machine learning (ML) methods could help to overcome these low response rates by predicting therapy outcomes on an individual level and tailoring treatment accordingly. Few studies implemented ML algorithms in internet-based depression treatment using baseline self-report data, but differing results hinder inferences on clinical practicability. This work compares algorithms using features gathered at baseline or early in treatment in their capability to predict non-response to a 6-week online program targeting depression. METHODS Our training and test sample encompassed 1270 and 318 individuals, respectively. We trained random forest algorithms on self-report and process features gathered at baseline and after 2 weeks of treatment. Non-responders were defined as participants not fulfilling the criteria for reliable and clinically significant change on PHQ-9 post-treatment. Our benchmark models were logistic regressions trained on baseline PHQ-9 sum or PHQ-9 early change, using 100 iterations of randomly sampled 80/20 train-test-splits. RESULTS Best performances were reached by our models involving early treatment characteristics (recall: 0.75-0.76; AUC: 0.71-0.77). Therapeutic alliance and early symptom change constituted the most important predictors. Models trained on baseline data were not significantly better than our benchmark. CONCLUSIONS Fair accuracies were only attainable by involving information from early treatment stages. In-treatment adaptation, instead of a priori selection, might constitute a more feasible approach for improving response when relying on easily accessible self-report features. Implementation trials are needed to determine clinical usefulness.
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Affiliation(s)
- Leona Hammelrath
- Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany
| | - Kevin Hilbert
- Department of Psychology, Health and Medical University Erfurt, Erfurt, Germany
| | - Manuel Heinrich
- Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany
| | - Pavle Zagorscak
- Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany
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Guinart D, Fagiolini A, Fusar-Poli P, Giordano GM, Leucht S, Moreno C, Correll CU. On the Road to Individualizing Pharmacotherapy for Adolescents and Adults with Schizophrenia - Results from an Expert Consensus Following the Delphi Method. Neuropsychiatr Dis Treat 2024; 20:1139-1152. [PMID: 38812809 PMCID: PMC11133879 DOI: 10.2147/ndt.s456163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 04/27/2024] [Indexed: 05/31/2024] Open
Abstract
Introduction Schizophrenia is a severe mental illness that usually begins in late adolescence or early adulthood. Current pharmacological treatments, while acceptably effective for many patients, are rarely clinically tailored or individualized. The lack of sufficient etiopathological knowledge of the disease, together with overall comparable effect sizes for efficacy between available antipsychotics and the absence of clinically actionable biomarkers, has hindered the advance of individualized medicine in the treatment of schizophrenia. Nevertheless, some degree of stratification based on clinical markers could guide treatment choices and help clinicians move toward individualized psychiatry. To this end, a panel of experts met to formally discuss the current approach to individualized treatment in schizophrenia and to define how treatment individualization could help improve clinical outcomes. Methods A task force of seven experts iteratively developed, evaluated, and refined questionnaire items, which were then evaluated using the Delphi method. Descriptive statistics were used to summarize and rank expert responses. Expert discussion, informed by the results of a scoping review on personalizing the pharmacologic treatment of adults and adolescents with schizophrenia, ultimately generated recommendations to guide individualized pharmacologic treatment in this population. Results There was substantial agreement among the expert group members, resulting in the following recommendations: 1) individualization of treatment requires consideration of the patient's diagnosis, clinical presentation, comorbidities, previous treatment response, drug tolerability, adherence patterns, and social factors; 2) patient preferences should be considered in a shared decision-making approach; 3) identified barriers to personalized care that need to be overcome include the lack of actionable biomarkers and mechanistic similarities between available treatments, but digital tools should be increasingly used to enhance individualized treatment. Conclusion Individualized care can help provide effective, tailored treatments based on an individual's clinical characteristics, disease trajectory, family and social environment, and goals and preferences.
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Affiliation(s)
- Daniel Guinart
- Institut de Salut Mental, Parc de Salut Mar, Barcelona, Spain
- Hospital Del Mar Research Institute, CIBERSAM, Barcelona, Spain
- Department of Psychiatry, The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Andrea Fagiolini
- Department of Molecular Medicine, University of Siena School of Medicine, Siena, Italy
| | - Paolo Fusar-Poli
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Department of Psychosis Studies, King’s College London, London, UK
- Outreach and Support in South-London (OASIS) Service, South London and Maudsley (Slam) NHS Foundation Trust, London, UK
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University Munich, Munich, Germany
| | | | - Stefan Leucht
- Technical University of Munich, TUM School of Medicine and Health, Department of Psychiatry and Psychotherapy, Munich, Germany
| | - Carmen Moreno
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (LISGM), Madrid, Spain
- Centro de Investigación Biomedica en Red (CIBERSAM), ISCIII, Madrid, Spain
- School of Medicine, Universidad Complutense, Madrid, Spain
| | - Christoph U Correll
- Department of Psychiatry, The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Psychiatry, The Zucker Hillside Hospital, New York, NY, USA
- Department of Child and Adolescent Psychiatry, Charité Universitatsmedizin, Berlin, Germany
- German Center for Mental Health (DZPG), Partner Site, Berlin, Germany
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Salazar de Pablo G, Iniesta R, Bellato A, Caye A, Dobrosavljevic M, Parlatini V, Garcia-Argibay M, Li L, Cabras A, Haider Ali M, Archer L, Meehan AJ, Suleiman H, Solmi M, Fusar-Poli P, Chang Z, Faraone SV, Larsson H, Cortese S. Individualized prediction models in ADHD: a systematic review and meta-regression. Mol Psychiatry 2024:10.1038/s41380-024-02606-5. [PMID: 38783054 DOI: 10.1038/s41380-024-02606-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 04/30/2024] [Accepted: 05/09/2024] [Indexed: 05/25/2024]
Abstract
There have been increasing efforts to develop prediction models supporting personalised detection, prediction, or treatment of ADHD. We overviewed the current status of prediction science in ADHD by: (1) systematically reviewing and appraising available prediction models; (2) quantitatively assessing factors impacting the performance of published models. We did a PRISMA/CHARMS/TRIPOD-compliant systematic review (PROSPERO: CRD42023387502), searching, until 20/12/2023, studies reporting internally and/or externally validated diagnostic/prognostic/treatment-response prediction models in ADHD. Using meta-regressions, we explored the impact of factors affecting the area under the curve (AUC) of the models. We assessed the study risk of bias with the Prediction Model Risk of Bias Assessment Tool (PROBAST). From 7764 identified records, 100 prediction models were included (88% diagnostic, 5% prognostic, and 7% treatment-response). Of these, 96% and 7% were internally and externally validated, respectively. None was implemented in clinical practice. Only 8% of the models were deemed at low risk of bias; 67% were considered at high risk of bias. Clinical, neuroimaging, and cognitive predictors were used in 35%, 31%, and 27% of the studies, respectively. The performance of ADHD prediction models was increased in those models including, compared to those models not including, clinical predictors (β = 6.54, p = 0.007). Type of validation, age range, type of model, number of predictors, study quality, and other type of predictors did not alter the AUC. Several prediction models have been developed to support the diagnosis of ADHD. However, efforts to predict outcomes or treatment response have been limited, and none of the available models is ready for implementation into clinical practice. The use of clinical predictors, which may be combined with other type of predictors, seems to improve the performance of the models. A new generation of research should address these gaps by conducting high quality, replicable, and externally validated models, followed by implementation research.
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Affiliation(s)
- Gonzalo Salazar de Pablo
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Child and Adolescent Mental Health Services, South London and Maudsley NHS Foundation Trust, London, UK
- Institute of Psychiatry and Mental Health. Department of Child and Adolescent Psychiatry, Hospital General Universitario Gregorio Marañón School of Medicine, Universidad Complutense, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), CIBERSAM, Madrid, Spain
| | - Raquel Iniesta
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
- King's Institute for Artificial Intelligence, King's College London, London, UK
| | - Alessio Bellato
- School of Psychology, University of Nottingham, Nottingham, Malaysia
- Centre for Innovation in Mental Health-Developmental Lab, School of Psychology, University of Southampton, Southampton, UK
- School of Psychology, University of Southampton, Southampton, UK
| | - Arthur Caye
- Post-Graduate Program of Psychiatry, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- National Center for Research and Innovation (CISM), University of São Paulo, São Paulo, Brazil
- ADHD Outpatient Program, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | - Maja Dobrosavljevic
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - Valeria Parlatini
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Centre for Innovation in Mental Health-Developmental Lab, School of Psychology, University of Southampton, Southampton, UK
- School of Psychology, University of Southampton, Southampton, UK
- Solent NHS Trust, Southampton, UK
| | - Miguel Garcia-Argibay
- Centre for Innovation in Mental Health-Developmental Lab, School of Psychology, University of Southampton, Southampton, UK
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Lin Li
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Anna Cabras
- Department of Neurology and Psychiatry, University of Rome La Sapienza, Rome, Italy
| | - Mian Haider Ali
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Lucinda Archer
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- National Institute for Health and Care Research (NIHR), Birmingham Biomedical Research Centre, Birmingham, UK
| | - Alan J Meehan
- Department of Psychology, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Yale Child Study Center, Yale School of Medicine, New Haven, CT, USA
| | - Halima Suleiman
- Departments of Psychiatry and of Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, Syracuse, NY, USA
| | - Marco Solmi
- Centre for Innovation in Mental Health-Developmental Lab, School of Psychology, University of Southampton, Southampton, UK
- Department of Psychiatry, University of Ottawa, Ottawa, ON, Canada
- Department of Mental Health, The Ottawa Hospital, Ottawa, ON, Canada
- Hospital Research Institute (OHRI) Clinical Epidemiology Program University of Ottawa, Ontario, ON, Canada
- School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
- Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin, Berlin, Germany
| | - Paolo Fusar-Poli
- Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Outreach and Support in South-London (OASIS) service, South London and Maudsley NHS Foundation Trust, London, UK
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig-Maximilian-University (LMU), Munich, Germany
| | - Zheng Chang
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Stephen V Faraone
- Departments of Psychiatry and of Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, Syracuse, NY, USA
| | - Henrik Larsson
- School of Psychology, University of Southampton, Southampton, UK
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Samuele Cortese
- Centre for Innovation in Mental Health-Developmental Lab, School of Psychology, University of Southampton, Southampton, UK.
- Solent NHS Trust, Southampton, UK.
- Clinical and Experimental Sciences (CNS and Psychiatry), Faculty of Medicine, University of Southampton, Southampton, UK.
- Hassenfeld Children's Hospital at NYU Langone, New York University Child Study Center, New York City, NY, USA.
- DiMePRe-J-Department of Precision and Rigenerative Medicine-Jonic Area, University of Bari "Aldo Moro", Bari, Italy.
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Bouter DC, Ravensbergen SJ, de Neve-Enthoven NGM, Zarchev M, Mulder CL, Hoogendijk WJG, Roza SJ, Grootendorst-van Mil NH. Five-year follow-up of the iBerry Study: screening in early adolescence to identify those at risk of psychopathology in emerging adulthood. Eur Child Adolesc Psychiatry 2024:10.1007/s00787-024-02462-2. [PMID: 38772966 DOI: 10.1007/s00787-024-02462-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 04/30/2024] [Indexed: 05/23/2024]
Abstract
The iBerry Study, a Dutch population-based high-risk cohort (n = 1022) examines the transition from subclinical symptoms to psychiatric disorders in adolescents. Here, we present the first follow-up measurement, approximately 3 years after baseline assessment and 5 years after the screening based on self-reported emotional and behavioral problems (SDQ-Y). We give an update on the data collection, details on the (non)response, and the results on psychopathology outcomes. The first follow-up (2019-2022) had a response rate of 79% (n = 807). Our results at baseline (mean age 15.0 years) have shown the effectiveness of using the SDQ-Y to select a cohort oversampled for the risk of psychopathology. At first follow-up (mean age 18.1 years), the previously administered SDQ-Y remains predictive for selecting adolescents at risk. At follow-up, 47% of the high-risk adolescents showed significant mental health problems based on self- and parent reports and 46% of the high-risk adolescents met the criteria for multiple DSM-5 diagnoses. Compared to low-risk adolescents, high-risk adolescents had a sevenfold higher odds of significant emotional and behavioral problems at follow-up. Comprehensive assessment on psychopathology, substance abuse, psychotic symptoms, suicidality, nonsuicidal self-injury, addiction to social media and/or video gaming, and delinquency, as well as social development, and the utilization of healthcare and social services were conducted. This wave, as well as the ones to follow, track these adolescents into their young adulthood to identify risk factors, elucidate causal mechanisms, and discern pathways leading to both common and severe mental disorders. Results from the iBerry Study will provide leads for preventive interventions.
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Affiliation(s)
- D C Bouter
- Department of Psychiatry, Erasmus MC, University Medical Center Rotterdam, P.O. box 2040, 3000 CA, Rotterdam, The Netherlands
| | - S J Ravensbergen
- Department of Psychiatry, Erasmus MC, University Medical Center Rotterdam, P.O. box 2040, 3000 CA, Rotterdam, The Netherlands
| | - N G M de Neve-Enthoven
- Department of Psychiatry, Erasmus MC, University Medical Center Rotterdam, P.O. box 2040, 3000 CA, Rotterdam, The Netherlands
| | - M Zarchev
- Department of Psychiatry, Erasmus MC, University Medical Center Rotterdam, P.O. box 2040, 3000 CA, Rotterdam, The Netherlands
| | - C L Mulder
- Epidemiological and Social Psychiatric Research Institute (ESPRi), Department of Psychiatry, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Parnassia Psychiatric Institute, Rotterdam, The Netherlands
| | - W J G Hoogendijk
- Department of Psychiatry, Erasmus MC, University Medical Center Rotterdam, P.O. box 2040, 3000 CA, Rotterdam, The Netherlands
| | - S J Roza
- Department of Psychiatry, Erasmus MC, University Medical Center Rotterdam, P.O. box 2040, 3000 CA, Rotterdam, The Netherlands
| | - N H Grootendorst-van Mil
- Department of Psychiatry, Erasmus MC, University Medical Center Rotterdam, P.O. box 2040, 3000 CA, Rotterdam, The Netherlands.
- Epidemiological and Social Psychiatric Research Institute (ESPRi), Department of Psychiatry, Erasmus MC University Medical Center, Rotterdam, The Netherlands.
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Damiani S, La-Torraca-Vittori P, Tarchi L, Tosi E, Ricca V, Scalabrini A, Politi P, Fusar-Poli P. On the interplay between state-dependent reconfigurations of global signal correlation and BOLD fluctuations: An fMRI study. Neuroimage 2024; 291:120585. [PMID: 38527658 DOI: 10.1016/j.neuroimage.2024.120585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 03/10/2024] [Accepted: 03/22/2024] [Indexed: 03/27/2024] Open
Abstract
BACKGROUND The dynamics of global, state-dependent reconfigurations in brain connectivity are yet unclear. We aimed at assessing reconfigurations of the global signal correlation coefficient (GSCORR), a measure of the connectivity between each voxel timeseries and the global signal, from resting-state to a stop-signal task. The secondary aim was to assess the relationship between GSCORR and blood-oxygen-level-dependent (BOLD) activations or deactivation across three different trial-conditions (GO, STOP-correct, and STOP-incorrect). METHODS As primary analysis we computed whole-brain, voxel-wise GSCORR during resting-state (GSCORR-rest) and stop-signal task (GSCORR-task) in 107 healthy subjects aged 21-50, deriving GSCORR-shift as GSCORR-task minus GSCORR-rest. GSCORR-tr and trGSCORR-shift were also computed on the task residual time series to quantify the impact of the task-related activity during the trials. To test the secondary aim, brain regions were firstly divided in one cluster showing significant task-related activation and one showing significant deactivation across the three trial conditions. Then, correlations between GSCORR-rest/task/shift and activation/deactivation in the two clusters were computed. As sensitivity analysis, GSCORR-shift was computed on the same sample after performing a global signal regression and GSCORR-rest/task/shift were correlated with the task performance. RESULTS Sensory and temporo-parietal regions exhibited a negative GSCORR-shift. Conversely, associative regions (ie. left lingual gyrus, bilateral dorsal posterior cingulate gyrus, cerebellum areas, thalamus, posterolateral parietal cortex) displayed a positive GSCORR-shift (FDR-corrected p < 0.05). GSCORR-shift showed similar patterns to trGSCORR-shift (magnitude increased) and after global signal regression (magnitude decreased). Concerning BOLD changes, Brodmann area 6 and inferior parietal lobule showed activation, while posterior parietal lobule, cuneus, precuneus, middle frontal gyrus showed deactivation (FDR-corrected p < 0.05). No correlations were found between GSCORR-rest/task/shift and beta-coefficients in the activation cluster, although negative correlations were observed between GSCORR-task and GO/STOP-correct deactivation (Pearson rho=-0.299/-0.273; Bonferroni-p < 0.05). Weak associations between GSCORR and task performance were observed (uncorrected p < 0.05). CONCLUSION GSCORR state-dependent reconfiguration indicates a reallocation of functional resources to associative areas during stop-signal task. GSCORR, activation and deactivation may represent distinct proxies of brain states with specific neurofunctional relevance.
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Affiliation(s)
- Stefano Damiani
- Department of Brain and Behavioral Sciences, University of Pavia, Via Bassi 21, Pavia, Italy
| | | | - Livio Tarchi
- Psychiatry Unit, Department of Health Sciences, University of Florence, Florence, Italy
| | - Eleonora Tosi
- Department of Brain and Behavioral Sciences, University of Pavia, Via Bassi 21, Pavia, Italy
| | - Valdo Ricca
- Psychiatry Unit, Department of Health Sciences, University of Florence, Florence, Italy
| | - Andrea Scalabrini
- Department of Human and Social Sciences, University of Bergamo, Bergamo, Italy
| | - Pierluigi Politi
- Department of Brain and Behavioral Sciences, University of Pavia, Via Bassi 21, Pavia, Italy
| | - Paolo Fusar-Poli
- Department of Brain and Behavioral Sciences, University of Pavia, Via Bassi 21, Pavia, Italy; Department of Psychosis Studies, King's College London, London, UK; Outreach and Support in South-London (OASIS) service, South London and Maudlsey (SLaM) NHS Foundation Trust, UK; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
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8
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Oliver D. The future of preventive psychiatry is precise and transdiagnostic. Neurosci Biobehav Rev 2024; 160:105626. [PMID: 38492764 DOI: 10.1016/j.neubiorev.2024.105626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 03/11/2024] [Accepted: 03/13/2024] [Indexed: 03/18/2024]
Affiliation(s)
- Dominic Oliver
- Department of Psychiatry, University of Oxford, Oxford, UK; NIHR Oxford Health Biomedical Research Centre, Oxford, UK; OPEN Early Detection Service, Oxford Health NHS Foundation Trust, Oxford, UK.
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Wannan CMJ, Nelson B, Addington J, Allott K, Anticevic A, Arango C, Baker JT, Bearden CE, Billah T, Bouix S, Broome MR, Buccilli K, Cadenhead KS, Calkins ME, Cannon TD, Cecci G, Chen EYH, Cho KIK, Choi J, Clark SR, Coleman MJ, Conus P, Corcoran CM, Cornblatt BA, Diaz-Caneja CM, Dwyer D, Ebdrup BH, Ellman LM, Fusar-Poli P, Galindo L, Gaspar PA, Gerber C, Glenthøj LB, Glynn R, Harms MP, Horton LE, Kahn RS, Kambeitz J, Kambeitz-Ilankovic L, Kane JM, Kapur T, Keshavan MS, Kim SW, Koutsouleris N, Kubicki M, Kwon JS, Langbein K, Lewandowski KE, Light GA, Mamah D, Marcy PJ, Mathalon DH, McGorry PD, Mittal VA, Nordentoft M, Nunez A, Pasternak O, Pearlson GD, Perez J, Perkins DO, Powers AR, Roalf DR, Sabb FW, Schiffman J, Shah JL, Smesny S, Spark J, Stone WS, Strauss GP, Tamayo Z, Torous J, Upthegrove R, Vangel M, Verma S, Wang J, Rossum IWV, Wolf DH, Wolff P, Wood SJ, Yung AR, Agurto C, Alvarez-Jimenez M, Amminger P, Armando M, Asgari-Targhi A, Cahill J, Carrión RE, Castro E, Cetin-Karayumak S, Mallar Chakravarty M, Cho YT, Cotter D, D’Alfonso S, Ennis M, Fadnavis S, Fonteneau C, Gao C, Gupta T, Gur RE, Gur RC, Hamilton HK, Hoftman GD, Jacobs GR, Jarcho J, Ji JL, Kohler CG, Lalousis PA, Lavoie S, Lepage M, Liebenthal E, Mervis J, Murty V, Nicholas SC, Ning L, Penzel N, Poldrack R, Polosecki P, Pratt DN, Rabin R, Rahimi Eichi H, Rathi Y, Reichenberg A, Reinen J, Rogers J, Ruiz-Yu B, Scott I, Seitz-Holland J, Srihari VH, Srivastava A, Thompson A, Turetsky BI, Walsh BC, Whitford T, Wigman JTW, Yao B, Yuen HP, Ahmed U, Byun A(JS, Chung Y, Do K, Hendricks L, Huynh K, Jeffries C, Lane E, Langholm C, Lin E, Mantua V, Santorelli G, Ruparel K, Zoupou E, Adasme T, Addamo L, Adery L, Ali M, Auther A, Aversa S, Baek SH, Bates K, Bathery A, Bayer JMM, Beedham R, Bilgrami Z, Birch S, Bonoldi I, Borders O, Borgatti R, Brown L, Bruna A, Carrington H, Castillo-Passi RI, Chen J, Cheng N, Ching AE, Clifford C, Colton BL, Contreras P, Corral S, Damiani S, Done M, Estradé A, Etuka BA, Formica M, Furlan R, Geljic M, Germano C, Getachew R, Goncalves M, Haidar A, Hartmann J, Jo A, John O, Kerins S, Kerr M, Kesselring I, Kim H, Kim N, Kinney K, Krcmar M, Kotler E, Lafanechere M, Lee C, Llerena J, Markiewicz C, Matnejl P, Maturana A, Mavambu A, Mayol-Troncoso R, McDonnell A, McGowan A, McLaughlin D, McIlhenny R, McQueen B, Mebrahtu Y, Mensi M, Hui CLM, Suen YN, Wong SMY, Morrell N, Omar M, Partridge A, Phassouliotis C, Pichiecchio A, Politi P, Porter C, Provenzani U, Prunier N, Raj J, Ray S, Rayner V, Reyes M, Reynolds K, Rush S, Salinas C, Shetty J, Snowball C, Tod S, Turra-Fariña G, Valle D, Veale S, Whitson S, Wickham A, Youn S, Zamorano F, Zavaglia E, Zinberg J, Woods SW, Shenton ME. Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ): Rationale and Study Design of the Largest Global Prospective Cohort Study of Clinical High Risk for Psychosis. Schizophr Bull 2024; 50:496-512. [PMID: 38451304 PMCID: PMC11059785 DOI: 10.1093/schbul/sbae011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
This article describes the rationale, aims, and methodology of the Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ). This is the largest international collaboration to date that will develop algorithms to predict trajectories and outcomes of individuals at clinical high risk (CHR) for psychosis and to advance the development and use of novel pharmacological interventions for CHR individuals. We present a description of the participating research networks and the data processing analysis and coordination center, their processes for data harmonization across 43 sites from 13 participating countries (recruitment across North America, Australia, Europe, Asia, and South America), data flow and quality assessment processes, data analyses, and the transfer of data to the National Institute of Mental Health (NIMH) Data Archive (NDA) for use by the research community. In an expected sample of approximately 2000 CHR individuals and 640 matched healthy controls, AMP SCZ will collect clinical, environmental, and cognitive data along with multimodal biomarkers, including neuroimaging, electrophysiology, fluid biospecimens, speech and facial expression samples, novel measures derived from digital health technologies including smartphone-based daily surveys, and passive sensing as well as actigraphy. The study will investigate a range of clinical outcomes over a 2-year period, including transition to psychosis, remission or persistence of CHR status, attenuated positive symptoms, persistent negative symptoms, mood and anxiety symptoms, and psychosocial functioning. The global reach of AMP SCZ and its harmonized innovative methods promise to catalyze the development of new treatments to address critical unmet clinical and public health needs in CHR individuals.
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Affiliation(s)
- Cassandra M J Wannan
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Barnaby Nelson
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Jean Addington
- Department of Psychiatry, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Kelly Allott
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, CIBERSAM, Instituto de Salud Carlos III, School of Medicine, Universidad Complutense, Madrid, Spain
| | - Justin T Baker
- Department of Psychiatry, McLean Hospital and Harvard Medical School, Boston, MA, USA
| | - Carrie E Bearden
- Department of Psychiatry, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | - Tashrif Billah
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Sylvain Bouix
- Department of Software Engineering and Information Technology, École de technologie supérieure, Montréal, Canada
| | - Matthew R Broome
- School of Psychology, Institute for Mental Health, University of Birmingham, Birmingham, UK
- Early Intervention for Psychosis Services, Birmingham Women’s and Children’s NHS Foundation Trust, Birmingham, UK
| | - Kate Buccilli
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | | | - Monica E Calkins
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Tyrone D Cannon
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychology, Yale University, New Haven, CT, USA
| | | | - Eric Yu Hai Chen
- Department of Psychiatry, University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Kang Ik K Cho
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jimmy Choi
- Olin Neuropsychiatry Research Center, Hartford Hospital, Hartford, CT, USA
| | - Scott R Clark
- Discipline of Psychiatry, University of Adelaide, Adelaide, SA, Australia
- Basil Hetzel Institute, Woodville, SA, Australia
| | - Michael J Coleman
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | - Philippe Conus
- General Psychiatry Service, Treatment and Early Intervention in Psychosis Program (TIPP–Lausanne), Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Cheryl M Corcoran
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Barbara A Cornblatt
- Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Covadonga M Diaz-Caneja
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, CIBERSAM, Instituto de Salud Carlos III, School of Medicine, Universidad Complutense, Madrid, Spain
| | - Dominic Dwyer
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Bjørn H Ebdrup
- Centre for Neuropsychiatric Schizophrenia Research, CNSR Mental Health Centre, Glostrup, Copenhagen, Denmark
| | - Lauren M Ellman
- Department of Psychology and Neuroscience, Temple University, Philadelphia, PA, USA
| | - Paolo Fusar-Poli
- Department of Psychosis Studies, King’s College London, London, UK
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Liliana Galindo
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Pablo A Gaspar
- Department of Psychiatry, IMHAY, University of Chile, Santiago, Chile
| | - Carla Gerber
- Behavioral Health Services, PeaceHealth Medical Group, Eugene, OR, USA
| | - Louise Birkedal Glenthøj
- Copenhagen Research Centre for Mental Health, Mental Health Copenhagen, University of Copenhagen, Denmark
- Department of Psychology, University of Copenhagen, Copenhagen, Denmark
| | - Robert Glynn
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health and Harvard Medical School, Boston, MA, USA
| | - Michael P Harms
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Leslie E Horton
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - René S Kahn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joseph Kambeitz
- Department of Psychiatry, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Lana Kambeitz-Ilankovic
- Department of Psychiatry, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - John M Kane
- Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Tina Kapur
- Department of Radiology, Brigham and Women’s Hospital, Boston, MA, USA
| | - Matcheri S Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Sung-Wan Kim
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
- Mindlink, Gwangju Bukgu Mental Health Center, Gwangju, Korea
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University of Munich, Munich, Germany
| | - Marek Kubicki
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Jun Soo Kwon
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Korea
| | - Kerstin Langbein
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Kathryn E Lewandowski
- Department of Psychiatry, McLean Hospital and Harvard Medical School, Boston, MA, USA
| | - Gregory A Light
- Department of Psychiatry, University of California, San Diego, CA, USA
- Veterans Affairs San Diego Health Care System, San Diego, CA, USA
| | - Daniel Mamah
- Department of Psychiatry, Washington University Medical School, St. Louis, MO, USA
| | | | - Daniel H Mathalon
- Department of Psychiatry and Behavioral Sciences and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
- Mental Health Service 116D, Veterans Affairs San Francisco Health Care System, San Francisco, CA, USA
| | - Patrick D McGorry
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Vijay A Mittal
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Merete Nordentoft
- Copenhagen Research Centre for Mental Health, Mental Health Copenhagen, University of Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Angela Nunez
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
| | - Ofer Pasternak
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Godfrey D Pearlson
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Olin Neuropsychiatry Research Center, Hartford Hospital, Hartford, CT, USA
| | - Jesus Perez
- CAMEO, Early Intervention in Psychosis Service, Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
- Department of Medicine, Institute of Biomedical Research (IBSAL), Universidad de Salamanca, Salamanca, Spain
| | - Diana O Perkins
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Albert R Powers
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
| | - David R Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Fred W Sabb
- Prevention Science Institute, University of Oregon, Eugene, OR, USA
| | - Jason Schiffman
- Department of Psychological Science, University of California, Irvine, Irvine, CA, USA
| | - Jai L Shah
- PEPP-Montreal, Douglas Research Centre, Montreal, Quebec, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Stefan Smesny
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Jessica Spark
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - William S Stone
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | | | - Zailyn Tamayo
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
| | - John Torous
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
| | - Rachel Upthegrove
- Department of Software Engineering and Information Technology, École de technologie supérieure, Montréal, Canada
- Birmingham Womens and Childrens, NHS Foundation Trust, Birmingham, UK
| | - Mark Vangel
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Swapna Verma
- Department of Psychosis, Institute of Mental Health, Singapore
| | - Jijun Wang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Inge Winter-van Rossum
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Daniel H Wolf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Phillip Wolff
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Stephen J Wood
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
- School of Psychology, University of Birmingham, Edgbaston, UK
| | - Alison R Yung
- Institute of Mental and Physical Health and Clinical Translation (IMPACT), Deakin University, Geelong, VIC, Australia
- School of Health Sciences, University of Manchester, Manchester, UK
| | - Carla Agurto
- IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
| | - Mario Alvarez-Jimenez
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Paul Amminger
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Marco Armando
- Youth Early Detection/Intervention in Psychosis Platform (Plateforme ERA), Service of Child and Adolescent Psychiatry, Department of Psychiatry, Lausanne University Hospital and The University of Lausanne, Lausanne, Switzerland
| | | | - John Cahill
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
| | - Ricardo E Carrión
- Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Eduardo Castro
- IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
| | - Suheyla Cetin-Karayumak
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Youngsun T Cho
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
| | - David Cotter
- Department Psychiatry, Beaumont Hospital, Dublin 9, Ireland
- Department of Psychiatry, Royal College of Surgeons in Ireland, Dublin 2, Ireland
| | - Simon D’Alfonso
- School of Computing and Information Systems, The University of Melbourne, Parkville, VIC, Australia
| | - Michaela Ennis
- Department of Psychiatry, McLean Hospital and Harvard Medical School, Boston, MA, USA
| | - Shreyas Fadnavis
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | - Clara Fonteneau
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
| | - Caroline Gao
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Tina Gupta
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Holly K Hamilton
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Gil D Hoftman
- Department of Psychiatry, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | - Grace R Jacobs
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Johanna Jarcho
- Department of Psychology and Neuroscience, Temple University, Philadelphia, PA, USA
| | - Jie Lisa Ji
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
| | - Christian G Kohler
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Paris Alexandros Lalousis
- School of Psychology, Institute for Mental Health, University of Birmingham, Birmingham, UK
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK
| | - Suzie Lavoie
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Martin Lepage
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Einat Liebenthal
- Program for Specialized Treatment Early in Psychosis (STEP), CMHC, New Haven, CT, USA
| | - Josh Mervis
- Department of Psychology and Neuroscience, Temple University, Philadelphia, PA, USA
| | - Vishnu Murty
- Department of Psychology and Neuroscience, Temple University, Philadelphia, PA, USA
| | - Spero C Nicholas
- Department of Psychiatry, Washington University Medical School, St. Louis, MO, USA
| | - Lipeng Ning
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiology, Brigham and Women’s Hospital, Boston, MA, USA
| | - Nora Penzel
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | - Russell Poldrack
- Department of Psychology, Stanford University, Stanford, CA, USA
| | | | - Danielle N Pratt
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Rachel Rabin
- PEPP-Montreal, Douglas Research Centre, Montreal, Quebec, Canada
| | | | - Yogesh Rathi
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital, Boston, MA, USA
| | - Avraham Reichenberg
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jenna Reinen
- IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
| | - Jack Rogers
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK
| | - Bernalyn Ruiz-Yu
- Department of Psychiatry, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | - Isabelle Scott
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Johanna Seitz-Holland
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Vinod H Srihari
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Program for Specialized Treatment Early in Psychosis (STEP), CMHC, New Haven, CT, USA
| | - Agrima Srivastava
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Andrew Thompson
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Bruce I Turetsky
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Barbara C Walsh
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
| | - Thomas Whitford
- Orygen, Parkville, VIC, Australia
- School of Psychology, University of New South Wales (UNSW), Kensington, NSW, Australia
| | - Johanna T W Wigman
- Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion Regulation, University of Groningen, University Medical Center,Groningen, Netherlands
| | - Beier Yao
- Department of Psychiatry, McLean Hospital and Harvard Medical School, Boston, MA, USA
| | - Hok Pan Yuen
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | | | - Andrew (Jin Soo) Byun
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Yoonho Chung
- Department of Psychiatry, McLean Hospital and Harvard Medical School, Boston, MA, USA
| | - Kim Do
- Department of Psychiatry, Center for Psychiatric Neuroscience, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience King’s College London, London, UK
| | - Larry Hendricks
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Kevin Huynh
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Clark Jeffries
- Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC, USA
| | - Erlend Lane
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Carsten Langholm
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Eric Lin
- Department of Psychiatry, McLean Hospital and Harvard Medical School, Boston, MA, USA
- Medical Informatics Fellowship, Veteran Affairs Boston Healthcare System, Boston, MA, USA
- Food and Drug Administration, Silver Spring, MD, USA
| | - Valentina Mantua
- Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Gennarina Santorelli
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
| | - Kosha Ruparel
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Eirini Zoupou
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Tatiana Adasme
- Department of Psychiatry, IMHAY, University of Chile, Santiago, Chile
| | - Lauren Addamo
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Laura Adery
- Department of Psychiatry, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | - Munaza Ali
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
| | - Andrea Auther
- Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Samantha Aversa
- PEPP-Montreal, Douglas Research Centre, Montreal, Quebec, Canada
| | - Seon-Hwa Baek
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
- Mindlink, Gwangju Bukgu Mental Health Center, Gwangju, Korea
| | - Kelly Bates
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Alyssa Bathery
- Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Johanna M M Bayer
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Rebecca Beedham
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Zarina Bilgrami
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Sonia Birch
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Ilaria Bonoldi
- Department of Psychosis Studies, King’s College London, London, UK
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Owen Borders
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Renato Borgatti
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- IRCCS Mondino Foundation, Pavia, Italy
| | - Lisa Brown
- Department of Radiology, Brigham and Women’s Hospital, Boston, MA, USA
| | - Alejandro Bruna
- Department of Psychiatry, IMHAY, University of Chile, Santiago, Chile
| | - Holly Carrington
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Rolando I Castillo-Passi
- Department of Psychiatry, IMHAY, University of Chile, Santiago, Chile
- Department of Neurology and Psychiatry, Clínica Alemana—Universidad del Desarrollo, Santiago, Chile
| | - Justine Chen
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Nicholas Cheng
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Ann Ee Ching
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Chloe Clifford
- School of Psychology, Institute for Mental Health, University of Birmingham, Birmingham, UK
| | - Beau-Luke Colton
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Pamela Contreras
- Department of Psychiatry, IMHAY, University of Chile, Santiago, Chile
| | - Sebastián Corral
- Department of Psychiatry, IMHAY, University of Chile, Santiago, Chile
| | - Stefano Damiani
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Monica Done
- Department of Psychiatry, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | - Andrés Estradé
- Early Psychosis Detection and Clinical Intervention (EPIC) Lab, Department of Psychosis Studies, King’s College London, London, UK
| | - Brandon Asika Etuka
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Melanie Formica
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Rachel Furlan
- Department of Psychology and Neuroscience, Temple University, Philadelphia, PA, USA
| | - Mia Geljic
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Carmela Germano
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Ruth Getachew
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | | | - Anastasia Haidar
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Jessica Hartmann
- Department of Public Mental Health, Central Institute of Mental Health, Heidelberg Univeristy, Mannheim, Germany
| | - Anna Jo
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
| | - Omar John
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Sarah Kerins
- Early Psychosis Detection and Clinical Intervention (EPIC) Lab, Department of Psychosis Studies, King’s College London, London, UK
| | - Melissa Kerr
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Irena Kesselring
- Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Honey Kim
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
| | - Nicholas Kim
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Kyle Kinney
- Department of Psychology and Neuroscience, Temple University, Philadelphia, PA, USA
| | - Marija Krcmar
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Elana Kotler
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Melanie Lafanechere
- School of Psychology, University of Birmingham, Edgbaston, UK
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK
| | - Clarice Lee
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Joshua Llerena
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | | | | | | | - Aissata Mavambu
- School of Psychology, Institute for Mental Health, University of Birmingham, Birmingham, UK
| | | | - Amelia McDonnell
- Department of Psychology and Neuroscience, Temple University, Philadelphia, PA, USA
| | - Alessia McGowan
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Rebecca McIlhenny
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Brittany McQueen
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Yohannes Mebrahtu
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Martina Mensi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- IRCCS Mondino Foundation, Pavia, Italy
| | | | - Yi Nam Suen
- Department of Psychiatry, University of Hong Kong, Pok Fu Lam, Hong Kong
| | | | - Neal Morrell
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Mariam Omar
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Alice Partridge
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Christina Phassouliotis
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Anna Pichiecchio
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Neuroradiology Department, IRCCS Mondino Foundation, Pavia, Italy
| | - Pierluigi Politi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Christian Porter
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Umberto Provenzani
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Nicholas Prunier
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Jasmine Raj
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Susan Ray
- Northwell Health, Glen Oaks, NY, USA
| | - Victoria Rayner
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Manuel Reyes
- Department of Psychiatry, IMHAY, University of Chile, Santiago, Chile
- Department of Neurology and Psychiatry, Clínica Alemana—Universidad del Desarrollo, Santiago, Chile
| | - Kate Reynolds
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Sage Rush
- Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Cesar Salinas
- Department of Psychiatry, IMHAY, University of Chile, Santiago, Chile
| | - Jashmina Shetty
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Callum Snowball
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Sophie Tod
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | | | - Daniela Valle
- Department of Psychiatry, IMHAY, University of Chile, Santiago, Chile
| | - Simone Veale
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Sarah Whitson
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Alana Wickham
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Sarah Youn
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Francisco Zamorano
- Unidad de imágenes cuantitativas avanzadas, departamento de imágenes, clínica alemana, universidad del Desarrollo, Santiago, Chile
- Facultad de ciencias para el cuidado de la salud, Universidad San Sebastián, Campus Los Leones, Santiago, Chile
| | - Elissa Zavaglia
- PEPP-Montreal, Douglas Research Centre, Montreal, Quebec, Canada
| | - Jamie Zinberg
- Department of Psychiatry, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | - Scott W Woods
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
| | - Martha E Shenton
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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10
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Xie H, Wang B, Hong Y. A deep learning approach for acute liver failure prediction with combined fully connected and convolutional neural networks. Technol Health Care 2024:THC248048. [PMID: 38759076 DOI: 10.3233/thc-248048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/19/2024]
Abstract
BACKGROUND Acute Liver Failure (ALF) is a critical medical condition with rapid development, often caused by viral infections, hepatotoxic drug abuse, or other severe liver diseases. Timely and accurate prediction of ALF occurrence is clinically crucial. However, predicting ALF poses challenges due to the diverse physiological differences among patients and the dynamic nature of the disease. OBJECTIVE This study introduces a deep learning approach that combines fully connected and convolutional neural networks for effective ALF prediction. The goal is to overcome limitations of traditional machine learning methods and enhance predictive model performance and generalization. METHODS The proposed model integrates a fully connected neural network for handling basic patient features and a convolutional neural network dedicated to capturing temporal patterns in patient data. The combination allows automatic learning of complex patterns and abstract features present in highly dynamic medical data associated with ALF. RESULTS The model's effectiveness is demonstrated through comprehensive experiments and performance evaluations. It outperforms traditional machine learning methods, achieving 94.8% accuracy and superior generalization capabilities. CONCLUSIONS The study highlights the potential of deep learning in ALF prediction, emphasizing the importance of considering individualized medical factors. Future research should focus on improving model robustness, addressing imbalanced data, and further exploring personalized features for enhanced predictive accuracy in real-world clinical scenarios.
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Affiliation(s)
- Hefu Xie
- Xiamen University School of Information Science and Technology, Xiamen University Xiang'an Campus, Xiamen, Fujian, China
- Xiamen University School of Information Science and Technology, Xiamen University Xiang'an Campus, Xiamen, Fujian, China
| | - Bingbing Wang
- Department of Thoracic Surgery, The First Hospital Affiliated of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China
- Xiamen University School of Information Science and Technology, Xiamen University Xiang'an Campus, Xiamen, Fujian, China
| | - Yuanzhen Hong
- Hepatology Department's Three Wards, Xiamen Hospital, Beijing University of Chinese Medicine, Xiamen, Fujian, China
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11
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van Houtum LAEM, Baaré WFC, Beckmann CF, Castro-Fornieles J, Cecil CAM, Dittrich J, Ebdrup BH, Fegert JM, Havdahl A, Hillegers MHJ, Kalisch R, Kushner SA, Mansuy IM, Mežinska S, Moreno C, Muetzel RL, Neumann A, Nordentoft M, Pingault JB, Preisig M, Raballo A, Saunders J, Sprooten E, Sugranyes G, Tiemeier H, van Woerden GM, Vandeleur CL, van Haren NEM. Running in the FAMILY: understanding and predicting the intergenerational transmission of mental illness. Eur Child Adolesc Psychiatry 2024:10.1007/s00787-024-02423-9. [PMID: 38613677 DOI: 10.1007/s00787-024-02423-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 03/15/2024] [Indexed: 04/15/2024]
Abstract
Over 50% of children with a parent with severe mental illness will develop mental illness by early adulthood. However, intergenerational transmission of risk for mental illness in one's children is insufficiently considered in clinical practice, nor is it sufficiently utilised into diagnostics and care for children of ill parents. This leads to delays in diagnosing young offspring and missed opportunities for protective actions and resilience strengthening. Prior twin, family, and adoption studies suggest that the aetiology of mental illness is governed by a complex interplay of genetic and environmental factors, potentially mediated by changes in epigenetic programming and brain development. However, how these factors ultimately materialise into mental disorders remains unclear. Here, we present the FAMILY consortium, an interdisciplinary, multimodal (e.g., (epi)genetics, neuroimaging, environment, behaviour), multilevel (e.g., individual-level, family-level), and multisite study funded by a European Union Horizon-Staying-Healthy-2021 grant. FAMILY focuses on understanding and prediction of intergenerational transmission of mental illness, using genetically informed causal inference, multimodal normative prediction, and animal modelling. Moreover, FAMILY applies methods from social sciences to map social and ethical consequences of risk prediction to prepare clinical practice for future implementation. FAMILY aims to deliver: (i) new discoveries clarifying the aetiology of mental illness and the process of resilience, thereby providing new targets for prevention and intervention studies; (ii) a risk prediction model within a normative modelling framework to predict who is at risk for developing mental illness; and (iii) insight into social and ethical issues related to risk prediction to inform clinical guidelines.
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Affiliation(s)
- Lisanne A E M van Houtum
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Centre-Sophia, Rotterdam, The Netherlands
| | - William F C Baaré
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital-Amager and Hvidovre, Copenhagen, Denmark
| | - Christian F Beckmann
- Centre for Functional MRI of the Brain, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, the Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, the Netherlands
| | - Josefina Castro-Fornieles
- Department of Child and Adolescent Psychiatry and Psychology, 2021SGR01319, Institut Clinic de Neurociències, Hospital Clínic de Barcelona, FCRB-IDIBAPS, Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Department of Medicine, Institute of Neuroscience, University of Barcelona, Barcelona, Spain
| | - Charlotte A M Cecil
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Centre-Sophia, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus MC, University Medical Centre Rotterdam, Rotterdam, the Netherlands
| | | | - Bjørn H Ebdrup
- Center for Neuropsychiatric Schizophrenia Research and Centre for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Centre Glostrup, University of Copenhagen, Glostrup, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jörg M Fegert
- President European Society for Child and Adolescent Psychiatry (ESCAP), Brussels, Belgium
- Department of Child and Adolescent Psychiatry/Psychotherapy, University Hospital Ulm, Ulm, Germany
| | - Alexandra Havdahl
- PsychGen Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
- PROMENTA Research Centre, Department of Psychology, University of Oslo, Oslo, Norway
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
| | - Manon H J Hillegers
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Centre-Sophia, Rotterdam, The Netherlands
| | - Raffael Kalisch
- Leibniz Institute for Resilience Research, Mainz, Germany
- Neuroimaging Center (NIC), Focus Program Translational Neuroscience (FTN), Johannes Gutenberg University Medical Center, Mainz, Germany
| | - Steven A Kushner
- Department of Psychiatry, Erasmus MC, University Medical Centre Rotterdam, Rotterdam, The Netherlands
| | - Isabelle M Mansuy
- Laboratory of Neuroepigenetics, Medical Faculty, Brain Research Institute, Department of Health Science and Technology of ETH, University of Zurich and Institute for Neuroscience, Zurich, Switzerland
- Zurich Neuroscience Centre, ETH and University of Zurich, Zurich, Switzerland
| | - Signe Mežinska
- Institute of Clinical and Preventive Medicine, University of Latvia, Riga, Latvia
| | - Carmen Moreno
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, CIBERSAM, ISCIII, School of Medicine, Universidad Complutense, Madrid, Spain
| | - Ryan L Muetzel
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Centre-Sophia, Rotterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Alexander Neumann
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Centre-Sophia, Rotterdam, The Netherlands
| | - Merete Nordentoft
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
| | - Jean-Baptiste Pingault
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Centre-Sophia, Rotterdam, The Netherlands
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Department of Clinical, Educational and Health Psychology, University College London, London, UK
| | - Martin Preisig
- Psychiatric Epidemiology and Psychopathology Research Centre, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Andrea Raballo
- Public Health Division, Department of Health and Social Care, Cantonal Socio-Psychiatric Organization, Repubblica e Cantone Ticino, Mendrisio, Switzerland
- Chair of Psychiatry, Faculty of Biomedical Sciences, Università Della Svizzera Italiana, Lugano, Switzerland
| | - John Saunders
- Executive Director European Federation of Associations of Families of People with Mental Illness (EUFAMI), Louvain, Belgium
| | - Emma Sprooten
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, the Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, the Netherlands
- Department of Human Genetics, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Gisela Sugranyes
- Department of Child and Adolescent Psychiatry and Psychology, 2021SGR01319, Institut Clinic de Neurociències, Hospital Clínic de Barcelona, FCRB-IDIBAPS, Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Department of Medicine, Institute of Neuroscience, University of Barcelona, Barcelona, Spain
| | - Henning Tiemeier
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Centre-Sophia, Rotterdam, The Netherlands
- Department of Social and Behavioural Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Geeske M van Woerden
- Department of Neuroscience, Erasmus University Medical Centre, Rotterdam, The Netherlands
- ENCORE Expertise Center for Neurodevelopmental Disorders, Erasmus University Medical Centre, Rotterdam, The Netherlands
- Department of Clinical Genetics, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Caroline L Vandeleur
- Psychiatric Epidemiology and Psychopathology Research Centre, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Neeltje E M van Haren
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Centre-Sophia, Rotterdam, The Netherlands.
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12
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Hao J, Tiles-Sar N, Habtewold TD, Liemburg EJ, Bruggeman R, van der Meer L, Alizadeh BZ. Shaping tomorrow's support: baseline clinical characteristics predict later social functioning and quality of life in schizophrenia spectrum disorder. Soc Psychiatry Psychiatr Epidemiol 2024:10.1007/s00127-024-02630-4. [PMID: 38456932 DOI: 10.1007/s00127-024-02630-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 01/28/2024] [Indexed: 03/09/2024]
Abstract
PURPOSE We aimed to explore the multidimensional nature of social inclusion (mSI) among patients diagnosed with schizophrenia spectrum disorder (SSD), and to identify the predictors of 3-year mSI and the mSI prediction using traditional and data-driven approaches. METHODS We used the baseline and 3-year follow-up data of 1119 patients from the Genetic Risk and Outcome in Psychosis (GROUP) cohort in the Netherlands. The outcome mSI was defined as clusters derived from combined analyses of thirteen subscales from the Social Functioning Scale and the brief version of World Health Organization Quality of Life questionnaires through K-means clustering. Prediction models were built through multinomial logistic regression (ModelMLR) and random forest (ModelRF), internally validated via bootstrapping and compared by accuracy and the discriminability of mSI subgroups. RESULTS We identified five mSI subgroups: "very low (social functioning)/very low (quality of life)" (8.58%), "low/low" (12.87%), "high/low" (49.24%), "medium/high" (18.05%), and "high/high" (11.26%). The mSI was robustly predicted by a genetic predisposition for SSD, premorbid adjustment, positive, negative, and depressive symptoms, number of met needs, and baseline satisfaction with the environment and social life. The ModelRF (61.61% [54.90%, 68.01%]; P =0.013) was cautiously considered outperform the ModelMLR (59.16% [55.75%, 62.58%]; P =0.994). CONCLUSION We introduced and distinguished meaningful subgroups of mSI, which were modestly predictable from baseline clinical characteristics. A possibility for early prediction of mSI at the clinical stage may unlock the potential for faster and more impactful social support that is specifically tailored to the unique characteristics of the mSI subgroup to which a given patient belongs.
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Affiliation(s)
- Jiasi Hao
- Department of Epidemiology, University Medical Centre Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands.
| | - Natalia Tiles-Sar
- Department of Epidemiology, University Medical Centre Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
- Department of Psychiatry, University Medical Centre Groningen, University Centre for Psychiatry, Rob Giel Research Centre, University of Groningen, Groningen, The Netherlands
| | - Tesfa Dejenie Habtewold
- Department of Epidemiology, University Medical Centre Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
| | - Edith J Liemburg
- Department of Psychiatry, University Medical Centre Groningen, University Centre for Psychiatry, Rob Giel Research Centre, University of Groningen, Groningen, The Netherlands
| | - Richard Bruggeman
- Department of Psychiatry, University Medical Centre Groningen, University Centre for Psychiatry, Rob Giel Research Centre, University of Groningen, Groningen, The Netherlands
- Department of Clinical and Developmental Neuropsychology, Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, The Netherlands
- Department of Rehabilitation, Lentis Psychiatric Institute, Zuidlaren, The Netherlands
| | - Lisette van der Meer
- Department of Clinical and Developmental Neuropsychology, Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, The Netherlands
- Department of Rehabilitation, Lentis Psychiatric Institute, Zuidlaren, The Netherlands
| | - Behrooz Z Alizadeh
- Department of Epidemiology, University Medical Centre Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands.
- Department of Psychiatry, University Medical Centre Groningen, University Centre for Psychiatry, Rob Giel Research Centre, University of Groningen, Groningen, The Netherlands.
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13
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Oliver D, Arribas M, Perry BI, Whiting D, Blackman G, Krakowski K, Seyedsalehi A, Osimo EF, Griffiths SL, Stahl D, Cipriani A, Fazel S, Fusar-Poli P, McGuire P. Using Electronic Health Records to Facilitate Precision Psychiatry. Biol Psychiatry 2024:S0006-3223(24)01107-7. [PMID: 38408535 DOI: 10.1016/j.biopsych.2024.02.1006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 01/30/2024] [Accepted: 02/21/2024] [Indexed: 02/28/2024]
Abstract
The use of clinical prediction models to produce individualized risk estimates can facilitate the implementation of precision psychiatry. As a source of data from large, clinically representative patient samples, electronic health records (EHRs) provide a platform to develop and validate clinical prediction models, as well as potentially implement them in routine clinical care. The current review describes promising use cases for the application of precision psychiatry to EHR data and considers their performance in terms of discrimination (ability to separate individuals with and without the outcome) and calibration (extent to which predicted risk estimates correspond to observed outcomes), as well as their potential clinical utility (weighing benefits and costs associated with the model compared to different approaches across different assumptions of the number needed to test). We review 4 externally validated clinical prediction models designed to predict psychosis onset, psychotic relapse, cardiometabolic morbidity, and suicide risk. We then discuss the prospects for clinically implementing these models and the potential added value of integrating data from evidence syntheses, standardized psychometric assessments, and biological data into EHRs. Clinical prediction models can utilize routinely collected EHR data in an innovative way, representing a unique opportunity to inform real-world clinical decision making. Combining data from other sources (e.g., meta-analyses) or enhancing EHR data with information from research studies (clinical and biomarker data) may enhance our abilities to improve the performance of clinical prediction models.
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Affiliation(s)
- Dominic Oliver
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom; NIHR Oxford Health Biomedical Research Centre, Oxford, United Kingdom; OPEN Early Detection Service, Oxford Health NHS Foundation Trust, Oxford, United Kingdom; Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
| | - Maite Arribas
- Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Benjamin I Perry
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, United Kingdom
| | - Daniel Whiting
- Institute of Mental Health, University of Nottingham, Nottingham, United Kingdom
| | - Graham Blackman
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom; NIHR Oxford Health Biomedical Research Centre, Oxford, United Kingdom
| | - Kamil Krakowski
- Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Aida Seyedsalehi
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Emanuele F Osimo
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, United Kingdom; Imperial College London Institute of Clinical Sciences and UK Research and Innovation MRC London Institute of Medical Sciences, Hammersmith Hospital Campus, London, United Kingdom; South London and the Maudsley National Health Service Foundation Trust, London, United Kingdom
| | - Siân Lowri Griffiths
- Institute for Mental Health, University of Birmingham, Birmingham, United Kingdom; Centre for Human Brain Health, University of Birmingham, Birmingham, United Kingdom
| | - Daniel Stahl
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, London, United Kingdom
| | - Andrea Cipriani
- NIHR Oxford Health Biomedical Research Centre, Oxford, United Kingdom; Department of Psychiatry, University of Oxford, Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, United Kingdom
| | - Seena Fazel
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom; NIHR Oxford Health Biomedical Research Centre, Oxford, United Kingdom
| | - Paolo Fusar-Poli
- Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy; South London and the Maudsley National Health Service Foundation Trust, London, United Kingdom; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University Munich, Munich, Germany
| | - Philip McGuire
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom; NIHR Oxford Health Biomedical Research Centre, Oxford, United Kingdom; OPEN Early Detection Service, Oxford Health NHS Foundation Trust, Oxford, United Kingdom
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14
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Krcmar M, Wannan CMJ, Lavoie S, Allott K, Davey CG, Yuen HP, Whitford T, Formica M, Youn S, Shetty J, Beedham R, Rayner V, Murray G, Polari A, Gawęda Ł, Koren D, Sass L, Parnas J, Rasmussen AR, McGorry P, Hartmann JA, Nelson B. The self, neuroscience and psychosis study: Testing a neurophenomenological model of the onset of psychosis. Early Interv Psychiatry 2024; 18:153-164. [PMID: 37394278 DOI: 10.1111/eip.13448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 04/17/2023] [Accepted: 06/07/2023] [Indexed: 07/04/2023]
Abstract
AIM Basic self disturbance is a putative core vulnerability marker of schizophrenia spectrum disorders. The primary aims of the Self, Neuroscience and Psychosis (SNAP) study are to: (1) empirically test a previously described neurophenomenological self-disturbance model of psychosis by examining the relationship between specific clinical, neurocognitive, and neurophysiological variables in UHR patients, and (2) develop a prediction model using these neurophenomenological disturbances for persistence or deterioration of UHR symptoms at 12-month follow-up. METHODS SNAP is a longitudinal observational study. Participants include 400 UHR individuals, 100 clinical controls with no attenuated psychotic symptoms, and 50 healthy controls. All participants complete baseline clinical and neurocognitive assessments and electroencephalography. The UHR sample are followed up for a total of 24 months, with clinical assessment completed every 6 months. RESULTS This paper presents the protocol of the SNAP study, including background rationale, aims and hypotheses, design, and assessment procedures. CONCLUSIONS The SNAP study will test whether neurophenomenological disturbances associated with basic self-disturbance predict persistence or intensification of UHR symptomatology over a 2-year follow up period, and how specific these disturbances are to a clinical population with attenuated psychotic symptoms. This may ultimately inform clinical care and pathoaetiological models of psychosis.
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Affiliation(s)
- Marija Krcmar
- Orygen, Parkville, Parkville, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Cassandra M J Wannan
- Orygen, Parkville, Parkville, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Suzie Lavoie
- Orygen, Parkville, Parkville, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Kelly Allott
- Orygen, Parkville, Parkville, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Christopher G Davey
- Orygen, Parkville, Parkville, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
- Department of Psychiatry, University of Melbourne, Melbourne, Victoria, Australia
| | - Hok Pan Yuen
- Orygen, Parkville, Parkville, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Thomas Whitford
- School of Psychology, University of New South Wales (UNSW), Kensington, New South Wales, Australia
| | - Melanie Formica
- Orygen, Parkville, Parkville, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Sarah Youn
- Orygen, Parkville, Parkville, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Jashmina Shetty
- Orygen, Parkville, Parkville, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Rebecca Beedham
- Orygen, Parkville, Parkville, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Victoria Rayner
- Orygen, Parkville, Parkville, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Graham Murray
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Andrea Polari
- Orygen, Parkville, Parkville, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Łukasz Gawęda
- Experimental Psychopathology Lab, Institute of Psychology, Polish Academy of Sciences, Warsaw, Poland
| | - Dan Koren
- Psychology Department, University of Haifa, Haifa, Israel
| | - Louis Sass
- Department of Clinical Psychology, GSAPP-Rutgers University, Piscataway, New Jersey, USA
| | - Josef Parnas
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Center for Subjectivity Research, University of Copenhagen, Copenhagen, Denmark
| | - Andreas R Rasmussen
- Orygen, Parkville, Parkville, Victoria, Australia
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Patrick McGorry
- Orygen, Parkville, Parkville, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Jessica A Hartmann
- Department of Public Mental Health, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Barnaby Nelson
- Orygen, Parkville, Parkville, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
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15
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Salazar de Pablo G, Rodriguez V, Besana F, Civardi SC, Arienti V, Maraña Garceo L, Andrés-Camazón P, Catalan A, Rogdaki M, Abbott C, Kyriakopoulos M, Fusar-Poli P, Correll CU, Arango C. Umbrella Review: Atlas of the Meta-Analytical Evidence of Early-Onset Psychosis. J Am Acad Child Adolesc Psychiatry 2024:S0890-8567(24)00006-6. [PMID: 38280414 DOI: 10.1016/j.jaac.2023.10.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 10/13/2023] [Accepted: 01/18/2024] [Indexed: 01/29/2024]
Abstract
OBJECTIVE Early-onset psychosis (EOP) refers to the development of psychosis before the age of 18 years. We aimed to summarize, for the first time, the meta-analytical evidence in the field of this vulnerable population and to provide evidence-based recommendations. METHOD We performed a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-compliant, pre-registered (PROSPERO: CRD42022350868) systematic review of several databases and registers to identify meta-analyses of studies conducted in EOP individuals to conduct an umbrella review. Literature search, screening, data extraction, and quality assessment were carried out independently. Results were narratively reported, clustered across core domains. Quality assessment was performed with the Assessment of Multiple Systematic Reviews-2 (AMSTAR-2) tool. RESULTS A total of 30 meta-analyses were included (373 individual studies, 25,983 participants, mean age 15.1 years, 38.3% female). Individuals with EOP showed more cognitive impairments compared with controls and individuals with adult/late-onset psychosis. Abnormalities were observed meta-analytically in neuroimaging markers but not in oxidative stress and inflammatory response markers. In all, 60.1% of EOP individuals had a poor prognosis. Clozapine was the antipsychotic with the highest efficacy for overall, positive, and negative symptoms. Tolerance to medication varied among the evaluated antipsychotics. The risk of discontinuation of antipsychotics for any reason or side effects was low or equal compared to placebo. CONCLUSION EOP is associated with cognitive impairment, involuntary admissions, and poor prognosis. Antipsychotics can be efficacious in EOP, but tolerability and safety need to be taken into consideration. Clozapine should be considered in EOP individuals who are resistant to 2 non-clozapine antipsychotics. Further meta-analytical research is needed on response to psychological interventions and other prognostic factors. STUDY PREREGISTRATION INFORMATION Early Onset Psychosis: Umbrella Review on Diagnosis, Prognosis and Treatment factors; https://www.crd.york.ac.uk/PROSPERO/; CRD42022350868.
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Affiliation(s)
- Gonzalo Salazar de Pablo
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón School of Medicine, Universidad Complutense, IiSGM, CIBERSAM, Madrid, Spain; Child and Adolescent Mental Health Services, South London and Maudsley NHS Foundation Trust, London, United Kingdom.
| | - Victoria Rodriguez
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | | | | | | | | | - P Andrés-Camazón
- Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón School of Medicine, Universidad Complutense, IiSGM, CIBERSAM, Madrid, Spain
| | - Ana Catalan
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Biobizkaia Health Research Institute. Basurto University Hospital, OSI Bilbao-Basurto, and the University of the Basque Country UPV/EHU. Centro de Investigación en Red de Salud Mental (CIBERSAM), Vizcaya, Spain
| | - Maria Rogdaki
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Francis Crick Institute, London, United Kingdom
| | - Chris Abbott
- Child and Adolescent Mental Health Services, South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Marinos Kyriakopoulos
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Child and Adolescent Mental Health Services, South London and Maudsley NHS Foundation Trust, London, United Kingdom; National and Kapodistrian University of Athens, Athens, Greece
| | - Paolo Fusar-Poli
- University of Pavia, Pavia, Italy; Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; LMU Munich, Munich, Germany; OASIS service, South London and Maudsley NHS Foundation Trust, London, UK; and National Institute for Health Research, Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
| | - Christoph U Correll
- Charité Universitätsmedizin, Berlin, Germany; The Zucker Hillside Hospital, Northwell Health, Glen Oaks, New York; Zucker School of Medicine at Hofstra/ Northwell, Hempstead, New York; Center for Psychiatric Neuroscience, The Feinstein Institutes for Medical Research, Manhasset, New York; and the German Center for Mental Health (DZPG), partner site Berlin, Germany
| | - Celso Arango
- Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón School of Medicine, Universidad Complutense, IiSGM, CIBERSAM, Madrid, Spain
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16
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Deisenhofer AK, Barkham M, Beierl ET, Schwartz B, Aafjes-van Doorn K, Beevers CG, Berwian IM, Blackwell SE, Bockting CL, Brakemeier EL, Brown G, Buckman JEJ, Castonguay LG, Cusack CE, Dalgleish T, de Jong K, Delgadillo J, DeRubeis RJ, Driessen E, Ehrenreich-May J, Fisher AJ, Fried EI, Fritz J, Furukawa TA, Gillan CM, Gómez Penedo JM, Hitchcock PF, Hofmann SG, Hollon SD, Jacobson NC, Karlin DR, Lee CT, Levinson CA, Lorenzo-Luaces L, McDanal R, Moggia D, Ng MY, Norris LA, Patel V, Piccirillo ML, Pilling S, Rubel JA, Salazar-de-Pablo G, Schleider JL, Schnurr PP, Schueller SM, Siegle GJ, Uher R, Watkins E, Webb CA, Wiltsey Stirman S, Wynants L, Youn SJ, Zilcha-Mano S, Lutz W, Cohen ZD. Implementing precision methods in personalizing psychological therapies: Barriers and possible ways forward. Behav Res Ther 2024; 172:104443. [PMID: 38086157 DOI: 10.1016/j.brat.2023.104443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 11/21/2023] [Accepted: 11/27/2023] [Indexed: 12/26/2023]
Affiliation(s)
| | | | | | | | | | | | | | | | - Claudi L Bockting
- AmsterdamUMC, Department of Psychiatry, Research Program Amsterdam Public Health and Centre for Urban Mental Health, University of Amsterdam, the Netherlands
| | | | | | | | | | | | | | - Kim de Jong
- Leiden University, Institute of Psychology, USA
| | | | | | | | | | | | | | - Jessica Fritz
- University of Cambridge, UK; Philipps University of Marburg, Germany
| | | | - Claire M Gillan
- School of Psychology, Trinity College Institute for Neuroscience, And Global Brain Health Institute, Trinity College Dublin, USA
| | | | | | | | | | | | | | | | | | | | | | | | - Mei Yi Ng
- Florida International University, USA
| | | | | | | | | | | | | | - Jessica L Schleider
- Stony Brook University and Feinberg School of Medicine Northwestern University, USA
| | - Paula P Schnurr
- National Center for PTSD and Geisel School of Medicine at Dartmouth, USA
| | | | | | | | | | | | | | | | - Soo Jeong Youn
- Reliant Medical Group, OptumCare and Harvard Medical School, USA
| | | | | | - Zachary D Cohen
- University of California, Los Angeles and University of Arizona, USA.
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17
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Cullen AE, Labad J, Oliver D, Al-Diwani A, Minichino A, Fusar-Poli P. The Translational Future of Stress Neurobiology and Psychosis Vulnerability: A Review of the Evidence. Curr Neuropharmacol 2024; 22:350-377. [PMID: 36946486 PMCID: PMC10845079 DOI: 10.2174/1570159x21666230322145049] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 12/17/2022] [Accepted: 12/27/2022] [Indexed: 03/23/2023] Open
Abstract
Psychosocial stress is a well-established risk factor for psychosis, yet the neurobiological mechanisms underlying this relationship have yet to be fully elucidated. Much of the research in this field has investigated hypothalamic-pituitary-adrenal (HPA) axis function and immuno-inflammatory processes among individuals with established psychotic disorders. However, as such studies are limited in their ability to provide knowledge that can be used to develop preventative interventions, it is important to shift the focus to individuals with increased vulnerability for psychosis (i.e., high-risk groups). In the present article, we provide an overview of the current methods for identifying individuals at high-risk for psychosis and review the psychosocial stressors that have been most consistently associated with psychosis risk. We then describe a network of interacting physiological systems that are hypothesised to mediate the relationship between psychosocial stress and the manifestation of psychotic illness and critically review evidence that abnormalities within these systems characterise highrisk populations. We found that studies of high-risk groups have yielded highly variable findings, likely due to (i) the heterogeneity both within and across high-risk samples, (ii) the diversity of psychosocial stressors implicated in psychosis, and (iii) that most studies examine single markers of isolated neurobiological systems. We propose that to move the field forward, we require well-designed, largescale translational studies that integrate multi-domain, putative stress-related biomarkers to determine their prognostic value in high-risk samples. We advocate that such investigations are highly warranted, given that psychosocial stress is undoubtedly a relevant risk factor for psychotic disorders.
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Affiliation(s)
- Alexis E. Cullen
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, United Kingdom
- Department of Clinical Neuroscience, Division of Insurance Medicine, Karolinska Institutet, Solna, Sweden
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, United Kingdom
| | - Javier Labad
- CIBERSAM, Sabadell, Barcelona, Spain
- Department of Mental Health and Addictions, Consorci Sanitari del Maresme, Mataró, Spain
| | - Dominic Oliver
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, United Kingdom
- Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, United Kingdom
| | - Adam Al-Diwani
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, United Kingdom
| | - Amedeo Minichino
- Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, United Kingdom
| | - Paolo Fusar-Poli
- Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, United Kingdom
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
- OASIS Service, South London and Maudsley NHS Foundation Trust, London, United Kingdom
- National Institute of Health Research Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UK
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18
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Grzenda A, Widge AS. Electronic health records and stratified psychiatry: bridge to precision treatment? Neuropsychopharmacology 2024; 49:285-290. [PMID: 37667021 PMCID: PMC10700348 DOI: 10.1038/s41386-023-01724-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 08/24/2023] [Accepted: 08/27/2023] [Indexed: 09/06/2023]
Abstract
The use of a stratified psychiatry approach that combines electronic health records (EHR) data with machine learning (ML) is one potentially fruitful path toward rapidly improving precision treatment in clinical practice. This strategy, however, requires confronting pervasive methodological flaws as well as deficiencies in transparency and reporting in the current conduct of ML-based studies for treatment prediction. EHR data shares many of the same data quality issues as other types of data used in ML prediction, plus some unique challenges. To fully leverage EHR data's power for patient stratification, increased attention to data quality and collection of patient-reported outcome data is needed.
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Affiliation(s)
- Adrienne Grzenda
- Department of Psychiatry & Biobehavioral Sciences, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA, USA.
- Olive View-UCLA Medical Center, Sylmar, CA, USA.
| | - Alik S Widge
- Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
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19
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Cannon TD. On the Clinical Utility of Individualized Prediction Models for Psychosis in At-Risk Youth. Biol Psychiatry 2023:S0006-3223(23)01696-7. [PMID: 37949345 DOI: 10.1016/j.biopsych.2023.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 11/03/2023] [Accepted: 11/03/2023] [Indexed: 11/12/2023]
Affiliation(s)
- Tyrone D Cannon
- Departments of Psychology and Psychiatry, Yale University, New Haven, Connecticut.
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20
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Jeon SM, Lee DY, Cha S, Kwon JW. Psychiatric Comorbidities and Schizophrenia in Youths With Attention-Deficit/Hyperactivity Disorder. JAMA Netw Open 2023; 6:e2345793. [PMID: 38032637 PMCID: PMC10690465 DOI: 10.1001/jamanetworkopen.2023.45793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Accepted: 10/20/2023] [Indexed: 12/01/2023] Open
Abstract
Importance The association between attention-deficit/hyperactivity disorder (ADHD) and schizophrenia has received increased attention; however, evidence on the association between psychiatric comorbidities and subsequent schizophrenia in patients with ADHD is limited. Objective To investigate the risk of being diagnosed with schizophrenia in children and adolescents with ADHD considering the presence of psychiatric comorbidity. Design, Setting, and Participants This was a population-based, retrospective cohort study using the Health Insurance Review and Assessment claims database from January 1, 2007, to December 31, 2019. Participants were children and adolescents aged 5 to 19 years who received an ADHD diagnosis between January 1, 2010, and December 31, 2018, in the nationwide claims data of Korea. Data were analyzed from January 2010 to December 2019. Interventions or Exposures The presence of psychiatric comorbidity was assessed from diagnosis records within 1 year before ADHD diagnosis. Comorbidities were further categorized according to the number of comorbidities and specific comorbid disorders. Main Outcomes and Measures Cox proportional hazard models were used to estimate hazard ratios (HRs) and 95% CIs, examining the association between psychiatric comorbidities and the risk of being diagnosed with schizophrenia. Furthermore, the occurrence of psychiatric comorbidity during the follow-up period was explored among patients without psychiatric comorbidity at baseline. Results A total of 211 705 patients with newly diagnosed ADHD were included. A total of 157 272 patients (74.3%) were male, and the age of 5 to 9 years showed the highest distribution (115 081 patients [54.4%]). Patients with psychiatric comorbidity had a significantly higher risk of being diagnosed with schizophrenia than those without (adjusted HR, 2.14; 95% CI, 2.05-2.23). The association between schizophrenia and psychiatric comorbidity became progressively greater with the increasing number of comorbidities. Several individual psychiatric disorders showed an association with development of schizophrenia, with ASD, intellectual disability, tic disorder, depression, and bipolar disorder being the top 5 disorders most associated. Furthermore, 3244 patients (73.8%) without psychiatric comorbidities experienced the emergence of other psychiatric disorders before schizophrenia occurrence. Conclusions and Relevance In this retrospective cohort study involving children and adolescents with ADHD, the presence of psychiatric comorbidity in patients with ADHD was associated with an increased risk of being diagnosed with schizophrenia, with an increased risk observed in multiple comorbidities and a wide variety of comorbidities. These findings highlight the significance of assessing and managing psychiatric comorbidities in patients with ADHD to decrease subsequent schizophrenia risk and allow for early intervention.
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Affiliation(s)
- Soo Min Jeon
- College of Pharmacy, Jeju National University, Jeju, South Korea
| | - Dong Yun Lee
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea
| | - SangHun Cha
- Department of Statistics, College of Natural Sciences, Kyungpook National University, Daegu, South Korea
| | - Jin-Won Kwon
- BK21 FOUR Community-Based Intelligent Novel Drug Discovery Education Unit, College of Pharmacy and Research Institute of Pharmaceutical Sciences, Kyungpook National University, Daegu, South Korea
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21
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Derissen M, Majid DSA, Tadayonnejad R, Seiger R, Strober M, Feusner JD. Testing anxiety and reward processing in anorexia nervosa as predictors of longitudinal clinical outcomes. J Psychiatr Res 2023; 167:71-77. [PMID: 37839390 DOI: 10.1016/j.jpsychires.2023.09.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 07/05/2023] [Accepted: 09/13/2023] [Indexed: 10/17/2023]
Abstract
Anorexia nervosa (AN) is a psychiatric disorder with a tenuous longitudinal course marked by a high risk of relapse. Previous studies suggest that aberrant threat perception and reward processing operate in many with AN, and may produce obstacles to treatment engagement; therefore, these could potentially represent predictors for longitudinal clinical outcomes. In this study, anxiety and reward symptoms, behaviors, and neural circuit connectivity were measured in intensively treated AN-restrictive subtype patients (n = 33) and healthy controls (n = 31). Participants underwent an fMRI experiment using a monetary reward task in combination with either overlapping individually tailored anxiety-provoking words or neutral words. Behavioral/psychometric measures consisted of reaction times on the monetary reward task and self-ratings on anxiety symptoms at study entry. We tested multimodal, multivariate models based on neural, behavioral, and psychometric measures of reward and anxiety to predict physiological (Body Mass Index; BMI) and psychological (eating disorder symptom severity) longitudinal outcomes in AN over six months. Our results indicated that higher anxiety symptom psychometric scores significantly predicted BMI reductions at follow-up. Untreated anxiety after intensive treatment could put individuals with AN at heightened risk for weight loss. This represents a potentially modifiable risk factor that could be targeted more aggressively to help reduce the chance of future clinical worsening.
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Affiliation(s)
- M Derissen
- Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - D-S A Majid
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, USA
| | - R Tadayonnejad
- Division of Neuromodulation, Semel Institute for Neuroscience and Human Behavior, University of California at Los Angeles, Los Angeles, USA; Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA
| | - R Seiger
- General Adult Psychiatry and Health Systems, Centre for Addiction and Mental Health, Toronto, Canada
| | - M Strober
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, USA
| | - J D Feusner
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, USA; General Adult Psychiatry and Health Systems, Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada; Department of Women's and Children's Health, Karolinska Hospital, Karolinska Institutet, Stockholm, Sweden.
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22
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Xiao Y, Wu XH, Liu L. From bench to bedside: Challenges in implementing precision psychiatry. Gen Hosp Psychiatry 2023; 85:251-252. [PMID: 37679225 DOI: 10.1016/j.genhosppsych.2023.08.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 08/23/2023] [Accepted: 08/30/2023] [Indexed: 09/09/2023]
Affiliation(s)
- Yu Xiao
- Psychosomatic Medical Center, The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610036, China; Psychosomatic Medical Center, The Fourth People's Hospital of Chengdu, Chengdu 610036, China.
| | - Xiao-Hong Wu
- Nursing Department, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliate Cancer Hospital of University of Electronic Science and Technology of China, Chengdu 610041, China
| | - Liang Liu
- Department of Urology, Baoding No.1 Central Hospital, Baoding 071000, China
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23
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Salazar de Pablo G. Editorial: Short interventions and self-help interventions in child and adolescent mental health. Child Adolesc Ment Health 2023; 28:471-472. [PMID: 37795853 DOI: 10.1111/camh.12678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/05/2023] [Indexed: 10/06/2023]
Affiliation(s)
- Gonzalo Salazar de Pablo
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Child and Adolescent Mental Health Services, South London and Maudsley NHS Foundation Trust, London, UK
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón School of Medicine, Universidad Complutense, IiSGM, CIBERSAM, Madrid, Spain
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24
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Mohsen F, Al-Absi HRH, Yousri NA, El Hajj N, Shah Z. A scoping review of artificial intelligence-based methods for diabetes risk prediction. NPJ Digit Med 2023; 6:197. [PMID: 37880301 PMCID: PMC10600138 DOI: 10.1038/s41746-023-00933-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Accepted: 09/25/2023] [Indexed: 10/27/2023] Open
Abstract
The increasing prevalence of type 2 diabetes mellitus (T2DM) and its associated health complications highlight the need to develop predictive models for early diagnosis and intervention. While many artificial intelligence (AI) models for T2DM risk prediction have emerged, a comprehensive review of their advancements and challenges is currently lacking. This scoping review maps out the existing literature on AI-based models for T2DM prediction, adhering to the PRISMA extension for Scoping Reviews guidelines. A systematic search of longitudinal studies was conducted across four databases, including PubMed, Scopus, IEEE-Xplore, and Google Scholar. Forty studies that met our inclusion criteria were reviewed. Classical machine learning (ML) models dominated these studies, with electronic health records (EHR) being the predominant data modality, followed by multi-omics, while medical imaging was the least utilized. Most studies employed unimodal AI models, with only ten adopting multimodal approaches. Both unimodal and multimodal models showed promising results, with the latter being superior. Almost all studies performed internal validation, but only five conducted external validation. Most studies utilized the area under the curve (AUC) for discrimination measures. Notably, only five studies provided insights into the calibration of their models. Half of the studies used interpretability methods to identify key risk predictors revealed by their models. Although a minority highlighted novel risk predictors, the majority reported commonly known ones. Our review provides valuable insights into the current state and limitations of AI-based models for T2DM prediction and highlights the challenges associated with their development and clinical integration.
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Affiliation(s)
- Farida Mohsen
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar
| | - Hamada R H Al-Absi
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar
| | - Noha A Yousri
- Genetic Medicine, Weill Cornell Medicine-Qatar, Qatar Foundation, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar
- Computer and Systems Engineering, Alexandria University, Alexandria, Egypt
| | - Nady El Hajj
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar
| | - Zubair Shah
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar.
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Johansson Å, Andreassen OA, Brunak S, Franks PW, Hedman H, Loos RJ, Meder B, Melén E, Wheelock CE, Jacobsson B. Precision medicine in complex diseases-Molecular subgrouping for improved prediction and treatment stratification. J Intern Med 2023; 294:378-396. [PMID: 37093654 PMCID: PMC10523928 DOI: 10.1111/joim.13640] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Complex diseases are caused by a combination of genetic, lifestyle, and environmental factors and comprise common noncommunicable diseases, including allergies, cardiovascular disease, and psychiatric and metabolic disorders. More than 25% of Europeans suffer from a complex disease, and together these diseases account for 70% of all deaths. The use of genomic, molecular, or imaging data to develop accurate diagnostic tools for treatment recommendations and preventive strategies, and for disease prognosis and prediction, is an important step toward precision medicine. However, for complex diseases, precision medicine is associated with several challenges. There is a significant heterogeneity between patients of a specific disease-both with regards to symptoms and underlying causal mechanisms-and the number of underlying genetic and nongenetic risk factors is often high. Here, we summarize precision medicine approaches for complex diseases and highlight the current breakthroughs as well as the challenges. We conclude that genomic-based precision medicine has been used mainly for patients with highly penetrant monogenic disease forms, such as cardiomyopathies. However, for most complex diseases-including psychiatric disorders and allergies-available polygenic risk scores are more probabilistic than deterministic and have not yet been validated for clinical utility. However, subclassifying patients of a specific disease into discrete homogenous subtypes based on molecular or phenotypic data is a promising strategy for improving diagnosis, prediction, treatment, prevention, and prognosis. The availability of high-throughput molecular technologies, together with large collections of health data and novel data-driven approaches, offers promise toward improved individual health through precision medicine.
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Affiliation(s)
- Åsa Johansson
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala university, Sweden
| | - Ole A. Andreassen
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopment Research, University of Oslo, Oslo, Norway
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark
- Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, DK-2200 Copenhagen, Denmark
| | - Paul W. Franks
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Sweden
- Novo Nordisk Foundation, Denmark
| | - Harald Hedman
- Department of Medical Biosciences, Umeå University, Umeå, Sweden
| | - Ruth J.F. Loos
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Charles Bronfman Institute for Personalized Medicine at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Benjamin Meder
- Precision Digital Health, Cardiogenetics Center Heidelberg, Department of Cardiology, University Of Heidelberg, Germany
| | - Erik Melén
- Department of Clinical Sciences and Education, Södersjukhuset, Karolinska Institutet, Stockholm
- Sachś Children and Youth Hospital, Södersjukhuset, Stockholm, Sweden
| | - Craig E Wheelock
- Unit of Integrative Metabolomics, Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden
- Department of Respiratory Medicine and Allergy, Karolinska University Hospital, 171 76 Stockholm, Sweden
| | - Bo Jacobsson
- Department of Obstetrics and Gynecology, Institute of Clinical Science, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Obstetrics and Gynaecology, Sahlgrenska University Hospital, Göteborg, Sweden
- Department of Genetics and Bioinformatics, Domain of Health Data and Digitalisation, Institute of Public Health, Oslo, Norway
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Østergaard SD, Nielbo KL. False Responses From Artificial Intelligence Models Are Not Hallucinations. Schizophr Bull 2023; 49:1105-1107. [PMID: 37220915 PMCID: PMC10483440 DOI: 10.1093/schbul/sbad068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Affiliation(s)
- Søren Dinesen Østergaard
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
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Solmi M, Cortese S, Vita G, De Prisco M, Radua J, Dragioti E, Köhler-Forsberg O, Madsen NM, Rohde C, Eudave L, Aymerich C, Pedruzo B, Rodriguez V, Rosson S, Sabé M, Hojlund M, Catalan A, de Luca B, Fornaro M, Ostuzzi G, Barbui C, Salazar-de-Pablo G, Fusar-Poli P, Correll CU. An umbrella review of candidate predictors of response, remission, recovery, and relapse across mental disorders. Mol Psychiatry 2023; 28:3671-3687. [PMID: 37957292 PMCID: PMC10730397 DOI: 10.1038/s41380-023-02298-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 09/21/2023] [Accepted: 10/06/2023] [Indexed: 11/15/2023]
Abstract
We aimed to identify diagnosis-specific/transdiagnostic/transoutcome multivariable candidate predictors (MCPs) of key outcomes in mental disorders. We conducted an umbrella review (protocol link ), searching MEDLINE/Embase (19/07/2022), including systematic reviews of studies reporting on MCPs of response, remission, recovery, or relapse, in DSM/ICD-defined mental disorders. From published predictors, we filtered MCPs, validating MCP criteria. AMSTAR2/PROBAST measured quality/risk of bias of systematic reviews/individual studies. We included 117 systematic reviews, 403 studies, 299,888 individuals with mental disorders, testing 796 prediction models. Only 4.3%/1.2% of the systematic reviews/individual studies were at low risk of bias. The most frequently targeted outcome was remission (36.9%), the least frequent was recovery (2.5%). Studies mainly focused on depressive (39.4%), substance-use (17.9%), and schizophrenia-spectrum (11.9%) disorders. We identified numerous MCPs within disorders for response, remission and relapse, but none for recovery. Transdiagnostic MCPs of remission included lower disease-specific symptoms (disorders = 5), female sex/higher education (disorders = 3), and quality of life/functioning (disorders = 2). Transdiagnostic MCPs of relapse included higher disease-specific symptoms (disorders = 5), higher depressive symptoms (disorders = 3), and younger age/higher anxiety symptoms/global illness severity/ number of previous episodes/negative life events (disorders = 2). Finally, positive trans-outcome MCPs for depression included less negative life events/depressive symptoms (response, remission, less relapse), female sex (response, remission) and better functioning (response, less relapse); for schizophrenia, less positive symptoms/higher depressive symptoms (remission, less relapse); for substance use disorder, marital status/higher education (remission, less relapse). Male sex, younger age, more clinical symptoms and comorbid mental/physical symptoms/disorders were poor prognostic factors, while positive factors included social contacts and employment, absent negative life events, higher education, early access/intervention, lower disease-specific and comorbid mental and physical symptoms/conditions, across mental disorders. Current data limitations include high risk of bias of studies and extraction of single predictors from multivariable models. Identified MCPs can inform future development, validation or refinement of prediction models of key outcomes in mental disorders.
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Affiliation(s)
- Marco Solmi
- Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin Berlin, Berlin, Germany
- Department of Psychiatry, University of Ottawa, Ottawa, ON, Canada
- On Track: The Champlain First Episode Psychosis Program, Department of Mental Health, The Ottawa Hospital, Ottawa, ON, Canada
- Ottawa Hospital Research Institute (OHRI) Clinical Epidemiology Program University of Ottawa, Ottawa, ON, Canada
- School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Samuele Cortese
- Centre for Innovation in Mental Health, School of Psychology, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK
- Clinical and Experimental Sciences (CNS and Psychiatry), Faculty of Medicine, University of Southampton, Southampton, UK
- Solent NHS Trust, Southampton, UK
- Hassenfeld Children's Hospital at NYU Langone, New York University Child Study Center, New York, NY, USA
- Division of Psychiatry and Applied Psychology, School of Medicine, University of Nottingham, Nottingham, UK
- DiMePRe-J-Department of Precision and Regenerative Medicine-Jonic Area, University of Bari "Aldo Moro", Bari, Italy
| | - Giovanni Vita
- WHO Collaborating Centre for Research and Training in Mental Health and Service Evaluation, Department of Neuroscience, Biomedicine, and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy
| | - Michele De Prisco
- Bipolar and Depressive Disorders Unit, Hospìtal Clinic de Barcelona, c. Villarroel, 170, 08036, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), c. Villarroel, 170, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
| | - Joaquim Radua
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Imaging of Mood- and Anxiety-Related Disorders (IMARD), CIBERSAM, University of Barcelona, Barcelona, Spain
| | - Elena Dragioti
- University of Ioannina, Research Laboratory Psychology of Patients, Families & Health Professionals, Department of Nursing, School of Health Sciences, Ioannina, Greece
- Linköping University, Pain and Rehabilitation Centre and Department of Health, Medicine and Caring Sciences, Linköping, Sweden
| | - Ole Köhler-Forsberg
- Psychosis Research Unit, Aarhus University Hospital Psychiatry, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Nanna M Madsen
- Psychosis Research Unit, Aarhus University Hospital Psychiatry, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Christopher Rohde
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
| | - Luis Eudave
- Faculty of Education and Psychology, University of Navarra, Pamplona, Spain
| | - Claudia Aymerich
- Biobizkaia Health Research Institute, Basurto University Hospital, OSI Bilbao-Basurto. University of the Basque Country UPV/EHU. Centro de Investigación en Red de Salud Mental. (CIBERSAM), Instituto de Salud Carlos III. Plaza de Cruces 12, 48903, Barakaldo, Bizkaia, Spain
| | - Borja Pedruzo
- Psychiatry Department, Basurto University Hospital, Bilbao, Spain
| | | | - Stella Rosson
- Mental Health Department, Local Health Unit ULSS3 Serenissima, Venice, Italy
| | - Michel Sabé
- Division of Adult Psychiatry, Department of Psychiatry, University Hospitals of Geneva, 2, Chemin du Petit-Bel-Air, CH-1226, Thonex, Switzerland
| | - Mikkel Hojlund
- Department of Psychiatry Aabenraa, Mental Health Services Region of Southern Denmark, Aabenraa, Denmark
- Clinical Pharmacology, Pharmacy, and Environmental Medicine, Department of Public Health, University of Southern Denmark, Odense, Denmark
- Child and Adolescent Mental Health Centre, Mental Health Services Capital Region of Denmark, Copenhagen, Denmark
| | - Ana Catalan
- Biobizkaia Health Research Institute, Basurto University Hospital, OSI Bilbao-Basurto. University of the Basque Country UPV/EHU. Centro de Investigación en Red de Salud Mental. (CIBERSAM), Instituto de Salud Carlos III. Plaza de Cruces 12, 48903, Barakaldo, Bizkaia, Spain
| | - Beatrice de Luca
- WHO Collaborating Centre for Research and Training in Mental Health and Service Evaluation, Department of Neuroscience, Biomedicine, and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy
| | - Michele Fornaro
- Department of Psychiatry, Federico II of Naples, Naples, Italy
| | - Giovanni Ostuzzi
- WHO Collaborating Centre for Research and Training in Mental Health and Service Evaluation, Department of Neuroscience, Biomedicine, and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy
| | - Corrado Barbui
- WHO Collaborating Centre for Research and Training in Mental Health and Service Evaluation, Department of Neuroscience, Biomedicine, and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy
| | - Gonzalo Salazar-de-Pablo
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Child and Adolescent Mental Health Services, South London and Maudsley NHS Foundation Trust, London, UK
- Institute of Psychiatry and Mental Health. Department of Child and Adolescent Psychiatry, Hospital General Universitario Gregorio Marañón School of Medicine, Universidad Complutense, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), CIBERSAM, Madrid, Spain
| | - Paolo Fusar-Poli
- Department of Psychosis Studies, King's College London, London, UK
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Outreach and Support in South London (OASIS) service, NHS South London and Maudsley Foundation Trust, London, UK
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University Munich, Munich, Germany
| | - Christoph U Correll
- Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin Berlin, Berlin, Germany.
- The Zucker Hillside Hospital, Northwell Health, New York, NY, USA.
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, New York, NY, USA.
- The Feinstein Institute for Medical Research, Center for Psychiatric Neuroscience, Manhasset, NY, USA.
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Sato A, Moriyama T, Watanabe N, Maruo K, Furukawa TA. Development and validation of a prediction model for rehospitalization among people with schizophrenia discharged from acute inpatient care. Front Psychiatry 2023; 14:1242918. [PMID: 37692317 PMCID: PMC10483840 DOI: 10.3389/fpsyt.2023.1242918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 07/25/2023] [Indexed: 09/12/2023] Open
Abstract
Objective Relapses and rehospitalization prevent the recovery of individuals with schizophrenia or related psychoses. We aimed to build a model to predict the risk of rehospitalization among people with schizophrenia or related psychoses, including those with multiple episodes. Methods This retrospective cohort study included individuals aged 18 years or older, with schizophrenia or related psychoses, and discharged between January 2014 and December 2018 from one of three Japanese psychiatric hospital acute inpatient care ward. We collected nine predictors at the time of recruitment, followed up with the participants for 12 months, and observed whether psychotic relapse had occurred. Next, we applied the Cox regression model and used an elastic net to avoid overfitting. Then, we examined discrimination using bootstrapping, Steyerberg's method, and "leave-one-hospital-out" cross-validation. We also constructed a bias-corrected calibration plot. Results Data from a total of 805 individuals were analyzed. The significant predictors were the number of previous hospitalizations (HR 1.42, 95% CI 1.22-1.64) and the current length of stay in days (HR 1.31, 95% CI 1.04-1.64). In model development for relapse, Harrell's c-index was 0.59 (95% CI 0.55-0.63). The internal and internal-external validation for rehospitalization showed Harrell's c-index to be 0.64 (95% CI 0.59-0.69) and 0.66 (95% CI 0.57-0.74), respectively. The calibration plot was found to be adequate. Conclusion The model showed moderate discrimination of readmission after discharge. Carefully defining a research question by seeking needs among the population with chronic schizophrenia with multiple episodes may be key to building a useful model.
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Affiliation(s)
- Akira Sato
- Department of Health Promotion and Human Behavior, Kyoto University Graduate School of Medicine/School of Public Health, Kyoto, Japan
| | | | - Norio Watanabe
- Department of Psychiatry, Soseikai General Hospital, Kyoto, Japan
| | - Kazushi Maruo
- Department of Biostatistics, Institute of Medicine, University of Tsukuba, Tsukuba, Japan
| | - Toshi A. Furukawa
- Department of Health Promotion and Human Behavior, Kyoto University Graduate School of Medicine/School of Public Health, Kyoto, Japan
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Eder J, Dom G, Gorwood P, Kärkkäinen H, Decraene A, Kumpf U, Beezhold J, Samochowiec J, Kurimay T, Gaebel W, De Picker L, Falkai P. Improving mental health care in depression: A call for action. Eur Psychiatry 2023; 66:e65. [PMID: 37534402 PMCID: PMC10486253 DOI: 10.1192/j.eurpsy.2023.2434] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 06/20/2023] [Accepted: 06/29/2023] [Indexed: 08/04/2023] Open
Abstract
Depressive disorders have one of the highest disability-adjusted life years (DALYs) of all medical conditions, which led the European Psychiatric Association to propose a policy paper, pinpointing their unmet health care and research needs. The first part focuses on what can be currently done to improve the care of patients with depression, and then discuss future trends for research and healthcare. Through the narration of clinical cases, the different points are illustrated. The necessary political framework is formulated, to implement such changes to fundamentally improve psychiatric care. The group of European Psychiatrist Association (EPA) experts insist on the need for (1) increased awareness of mental illness in primary care settings, (2) the development of novel (biological) markers, (3) the rapid implementation of machine learning (supporting diagnostics, prognostics, and therapeutics), (4) the generalized use of electronic devices and apps into everyday treatment, (5) the development of the new generation of treatment options, such as plasticity-promoting agents, and (6) the importance of comprehensive recovery approach. At a political level, the group also proposed four priorities, the need to (1) increase the use of open science, (2) implement reasonable data protection laws, (3) establish ethical electronic health records, and (4) enable better healthcare research and saving resources.
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Affiliation(s)
- Julia Eder
- Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany
- Graduate Program “POKAL - Predictors and Outcomes in Primary Care Depression Care” (DFG-GrK 2621), Munich, Germany
| | - Geert Dom
- Collaborative Antwerp Psychiatric Research Institute (CAPRI), University of Antwerp, Antwerp, Belgium
| | - Philip Gorwood
- Université Paris Cité, GHU Paris (Sainte Anne hospital, CMME) & INSERM UMR1266, Paris, France
| | - Hikka Kärkkäinen
- Global Alliance of Mental Illness Advocacy Networks-Europe, Brussels, Belgium
| | - Andre Decraene
- EUFAMI, the European Organisation representing Families of persons affected by severe Mental Ill Health, Leuven, Belgium
| | - Ulrike Kumpf
- Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany
| | - Julian Beezhold
- Norfolk and Suffolk NHS Foundation Trust, Norwich, UK, University of East Anglia, Norwich, UK
| | - Jerzy Samochowiec
- Department of Psychiatry, Pomeranian Medical University, Szczecin, Poland
| | - Tamas Kurimay
- North-Central Buda Center, New Saint John Hospital and Outpatient Clinic, Buda Family Centered Mental Health Centre, Department of Psychiatry and Psychiatric Rehabilitation, Teaching Department of Semmelweis University, Budapest, Hungary
| | - Wolfgang Gaebel
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine-University, Düsseldorf, WHO Collaborating Centre DEU-131, Germany
| | - Livia De Picker
- Collaborative Antwerp Psychiatric Research Institute (CAPRI), University of Antwerp, Antwerp, Belgium
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany
- Graduate Program “POKAL - Predictors and Outcomes in Primary Care Depression Care” (DFG-GrK 2621), Munich, Germany
- Max-Planck Institute of Psychiatry, Munich, Germany
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Svendsen VG, Wijnen BFM, De Vos JA, Veenstra R, Evers SMAA, Lokkerbol J. A roadmap for applying machine learning when working with privacy-sensitive data: predicting non-response to treatment for eating disorders. Expert Rev Pharmacoecon Outcomes Res 2023; 23:933-949. [PMID: 37366051 DOI: 10.1080/14737167.2023.2230368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 06/21/2023] [Accepted: 06/23/2023] [Indexed: 06/28/2023]
Abstract
OBJECTIVES Applying machine-learning methodology to clinical data could present a promising avenue for predicting outcomes in patients receiving treatment for psychiatric disorders. However, preserving privacy when working with patient data remains a critical concern. METHODS In showcasing how machine-learning can be used to build a clinically relevant prediction model on clinical data, we apply two commonly used machine-learning algorithms (Random Forest and least absolute shrinkage and selection operator) to routine outcome monitoring data collected from 593 patients with eating disorders to predict absence of reliable improvement 12 months after entering outpatient treatment. RESULTS An RF model trained on data collected at baseline and after three months made 31.3% fewer errors in predicting lack of reliable improvement at 12 months, in comparison with chance. Adding data from a six-month follow-up resulted in only marginal improvements to accuracy. CONCLUSION We were able to build and validate a model that could aid clinicians and researchers in more accurately predicting treatment response in patients with EDs. We also demonstrated how this could be done without compromising privacy. ML presents a promising approach to developing accurate prediction models for psychiatric disorders such as ED.
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Affiliation(s)
- Vegard G Svendsen
- Center of Economic Evaluation & Machine Learning, Trimbos Institute (Netherlands Institute of Mental Health and Addiction), Utrecht, The Netherlands
- Department of Health Services Research, Care and Public Health Research, CAPHRI, Maastricht University, Maastricht, The Netherlands
- Norwegian Centre for Addiction Research, SERAF, University of Oslo, Oslo, Norway
| | - Ben F M Wijnen
- Center of Economic Evaluation & Machine Learning, Trimbos Institute (Netherlands Institute of Mental Health and Addiction), Utrecht, The Netherlands
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Jan Alexander De Vos
- Human Concern, Centre for Eating Disorders, Human Concern, Amsterdam, The Netherlands
| | - Ravian Veenstra
- Human Concern, Centre for Eating Disorders, Human Concern, Amsterdam, The Netherlands
| | - Silvia M A A Evers
- Center of Economic Evaluation & Machine Learning, Trimbos Institute (Netherlands Institute of Mental Health and Addiction), Utrecht, The Netherlands
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Joran Lokkerbol
- Center of Economic Evaluation & Machine Learning, Trimbos Institute (Netherlands Institute of Mental Health and Addiction), Utrecht, The Netherlands
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Whiting D, Mallett S, Lennox B, Fazel S. Assessing violence risk in first-episode psychosis: external validation, updating and net benefit of a prediction tool (OxMIV). BMJ MENTAL HEALTH 2023; 26:e300634. [PMID: 37316256 PMCID: PMC10335427 DOI: 10.1136/bmjment-2022-300634] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 12/29/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Violence perpetration is a key outcome to prevent for an important subgroup of individuals presenting to mental health services, including early intervention in psychosis (EIP) services. Needs and risks are typically assessed without structured methods, which could facilitate consistency and accuracy. Prediction tools, such as OxMIV (Oxford Mental Illness and Violence tool), could provide a structured risk stratification approach, but require external validation in clinical settings. OBJECTIVES We aimed to validate and update OxMIV in first-episode psychosis and consider its benefit as a complement to clinical assessment. METHODS A retrospective cohort of individuals assessed in two UK EIP services was included. Electronic health records were used to extract predictors and risk judgements made by assessing clinicians. Outcome data involved police and healthcare records for violence perpetration in the 12 months post-assessment. FINDINGS Of 1145 individuals presenting to EIP services, 131 (11%) perpetrated violence during the 12 month follow-up. OxMIV showed good discrimination (area under the curve 0.75, 95% CI 0.71 to 0.80). Calibration-in-the-large was also good after updating the model constant. Using a 10% cut-off, sensitivity was 71% (95% CI 63% to 80%), specificity 66% (63% to 69%), positive predictive value 22% (19% to 24%) and negative predictive value 95% (93% to 96%). In contrast, clinical judgement sensitivity was 40% and specificity 89%. Decision curve analysis showed net benefit of OxMIV over comparison approaches. CONCLUSIONS OxMIV performed well in this real-world validation, with improved sensitivity compared with unstructured assessments. CLINICAL IMPLICATIONS Structured tools to assess violence risk, such as OxMIV, have potential in first-episode psychosis to support a stratified approach to allocating non-harmful interventions to individuals who may benefit from the largest absolute risk reduction.
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Affiliation(s)
- Daniel Whiting
- Institute of Mental Health, University of Nottingham, Nottingham, UK
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Sue Mallett
- Centre for Medical Imaging, University College London, London, UK
| | - Belinda Lennox
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust, Oxford, UK
| | - Seena Fazel
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust, Oxford, UK
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Arshi B, Wynants L, Rijnhart E, Reeve K, Cowley LE, Smits LJ. What proportion of clinical prediction models make it to clinical practice? Protocol for a two-track follow-up study of prediction model development publications. BMJ Open 2023; 13:e073174. [PMID: 37197813 DOI: 10.1136/bmjopen-2023-073174] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/19/2023] Open
Abstract
INTRODUCTION It is known that only a limited proportion of developed clinical prediction models (CPMs) are implemented and/or used in clinical practice. This may result in a large amount of research waste, even when considering that some CPMs may demonstrate poor performance. Cross-sectional estimates of the numbers of CPMs that have been developed, validated, evaluated for impact or utilized in practice, have been made in specific medical fields, but studies across multiple fields and studies following up the fate of CPMs are lacking. METHODS AND ANALYSIS We have conducted a systematic search for prediction model studies published between January 1995 and December 2020 using the Pubmed and Embase databases, applying a validated search strategy. Taking random samples for every calendar year, abstracts and articles were screened until a target of 100 CPM development studies were identified. Next, we will perform a forward citation search of the resulting CPM development article cohort to identify articles on external validation, impact assessment or implementation of those CPMs. We will also invite the authors of the development studies to complete an online survey to track implementation and clinical utilization of the CPMs.We will conduct a descriptive synthesis of the included studies, using data from the forward citation search and online survey to quantify the proportion of developed models that are validated, assessed for their impact, implemented and/or used in patient care. We will conduct time-to-event analysis using Kaplan-Meier plots. ETHICS AND DISSEMINATION No patient data are involved in the research. Most information will be extracted from published articles. We request written informed consent from the survey respondents. Results will be disseminated through publication in a peer-reviewed journal and presented at international conferences. OSF REGISTRATION: (https://osf.io/nj8s9).
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Affiliation(s)
- Banafsheh Arshi
- Department of Epidemiology, CAPHRI School for Public Health and Primary Care, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Laure Wynants
- Department of Epidemiology, CAPHRI School for Public Health and Primary Care, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Eline Rijnhart
- Department of Epidemiology, CAPHRI School for Public Health and Primary Care, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Kelly Reeve
- Department of Epidemiology, Biostatistics and Prevention Institute, Department of Biostatistics, University of Zurich, Hirschengraben 84, CH-8001 Zurich, Switzerland
| | | | - Luc J Smits
- Department of Epidemiology, CAPHRI School for Public Health and Primary Care, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
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Cunha GR, Caye A, Pan P, Fisher HL, Pereira R, Ziebold C, Bressan R, Miguel EC, Salum GA, Rohde LA, Kohrt BA, Mondelli V, Kieling C, Gadelha A. Identifying Depression Early in Adolescence: assessing the performance of a risk score for future onset of depression in an independent Brazilian sample. REVISTA BRASILEIRA DE PSIQUIATRIA (SAO PAULO, BRAZIL : 1999) 2023; 45:242-248. [PMID: 37126861 PMCID: PMC10288471 DOI: 10.47626/1516-4446-2022-2775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 03/23/2023] [Indexed: 05/03/2023]
Abstract
OBJECTIVE The Identifying Depression Early in Adolescence Risk Score (IDEA-RS) was recently developed in Brazil using data from the Pelotas 1993 Birth Cohort to estimate the individualized probability of developing depression in adolescence. This model includes 11 sociodemographic variables and has been assessed in longitudinal studies from four other countries. We aimed to test the performance of IDEA-RS in an independent, community-based, school-attending sample within the same country: the Brazilian High-Risk Cohort. METHODS Standard external validation, refitted, and case mix-corrected models were used to predict depression among 1442 youth followed from a mean age of 13.5 years at baseline to 17.7 years at follow-up, using probabilities calculated with IDEA-RS coefficients. RESULTS The area under the curve was 0.65 for standard external validation, 0.70 for the case mix-corrected model, and 0.69 for the refitted model, with discrimination consistently above chance for predicting depression in the new dataset. There was some degree of miscalibration, corrected by model refitting (calibration-in-the-large reduced from 0.77 to 0). CONCLUSION IDEA-RS was able to parse individuals with higher or lower probability of developing depression beyond chance in an independent Brazilian sample. Further steps should include model improvements and additional studies in populations with high levels of subclinical symptoms to improve clinical decision making.
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Affiliation(s)
- Graccielle R. Cunha
- Laboratório Interdisciplinar de Neurociências Clínicas (LiNC), Departamento de Psiquiatria, Universidade Federal de São Paulo (UNIFESP), São Paulo, SP, Brazil
- Departamento de Psiquiatria, UNIFESP, São Paulo, SP, Brazil
| | - Arthur Caye
- Instituto Nacional de Ciência e Tecnologia de Psiquiatria do Desenvolvimento para Crianças e Adolescentes (INPD), São Paulo, SP, Brazil
- Departamento de Psiquiatria, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil
- Serviço de Psiquiatria da Infância e Adolescência, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brazil
| | - Pedro Pan
- Laboratório Interdisciplinar de Neurociências Clínicas (LiNC), Departamento de Psiquiatria, Universidade Federal de São Paulo (UNIFESP), São Paulo, SP, Brazil
- Departamento de Psiquiatria, UNIFESP, São Paulo, SP, Brazil
- Instituto Nacional de Ciência e Tecnologia de Psiquiatria do Desenvolvimento para Crianças e Adolescentes (INPD), São Paulo, SP, Brazil
| | - Helen L. Fisher
- King’s College London, Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, London, United Kingdom
- ESRC Centre for Society and Mental Health, King’s College London, London, United Kingdom
| | - Rivka Pereira
- Departamento de Psiquiatria, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil
- Serviço de Psiquiatria da Infância e Adolescência, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brazil
| | - Carolina Ziebold
- Laboratório Interdisciplinar de Neurociências Clínicas (LiNC), Departamento de Psiquiatria, Universidade Federal de São Paulo (UNIFESP), São Paulo, SP, Brazil
- Departamento de Psiquiatria, UNIFESP, São Paulo, SP, Brazil
| | - Rodrigo Bressan
- Laboratório Interdisciplinar de Neurociências Clínicas (LiNC), Departamento de Psiquiatria, Universidade Federal de São Paulo (UNIFESP), São Paulo, SP, Brazil
| | - Eurípedes Constantino Miguel
- Instituto Nacional de Ciência e Tecnologia de Psiquiatria do Desenvolvimento para Crianças e Adolescentes (INPD), São Paulo, SP, Brazil
- Departamento e Instituto de Psiquiatria, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Giovanni A. Salum
- Instituto Nacional de Ciência e Tecnologia de Psiquiatria do Desenvolvimento para Crianças e Adolescentes (INPD), São Paulo, SP, Brazil
- Departamento de Psiquiatria, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil
- Child Mind Institute, New York, NY, USA
| | - Luis Augusto Rohde
- Instituto Nacional de Ciência e Tecnologia de Psiquiatria do Desenvolvimento para Crianças e Adolescentes (INPD), São Paulo, SP, Brazil
- ADHD Outpatient Program & Developmental Psychiatry Program, HCPA, UFRGS, Porto Alegre, RS, Brazil
- Grupo UniEduK, Brazil
| | - Brandon A. Kohrt
- Division of Global Mental Health, Department of Psychiatry, School of Medicine and Health Sciences, The George Washington University, Washington, DC, United States
| | - Valeria Mondelli
- Institute of Psychiatry, Psychology, & Neuroscience, Department of Psychological Medicine, King’s College London, London, United Kingdom
- National Institute for Health Research Mental Health Biomedical Research Centre, South London and Maudsley NHS Foundation Trust and King’s College London, London, United Kingdom
| | - Christian Kieling
- Departamento de Psiquiatria, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil
- Serviço de Psiquiatria da Infância e Adolescência, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brazil
| | - Ary Gadelha
- Laboratório Interdisciplinar de Neurociências Clínicas (LiNC), Departamento de Psiquiatria, Universidade Federal de São Paulo (UNIFESP), São Paulo, SP, Brazil
- Departamento de Psiquiatria, UNIFESP, São Paulo, SP, Brazil
- Instituto Nacional de Ciência e Tecnologia de Psiquiatria do Desenvolvimento para Crianças e Adolescentes (INPD), São Paulo, SP, Brazil
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Shamsutdinova D, Das-Munshi J, Ashworth M, Roberts A, Stahl D. Predicting type 2 diabetes prevalence for people with severe mental illness in a multi-ethnic East London population. Int J Med Inform 2023; 172:105019. [PMID: 36787689 DOI: 10.1016/j.ijmedinf.2023.105019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 01/20/2023] [Accepted: 02/03/2023] [Indexed: 02/10/2023]
Abstract
BACKGROUND AND AIMS Prevalence of type two diabetes mellitus (T2DM) in people with severe mental illness (SMI) is 2-3 times higher than in general population. Predictive modelling has advanced greatly in the past decade, and it is important to apply cutting-edge methods to vulnerable groups. However, few T2DM prediction models account for the presence of mental illness, and none seemed to have been developed specifically for people with SMI. Therefore, we aimed to develop and internally validate a T2DM prevalence model for people with SMI. METHODS We utilised a large cross-sectional sample representative of a multi-ethnic population from London (674,000 adults); 10,159 people with SMI formed our analytical sample (1,513 T2DM cases). We fitted a linear logistic regression and XGBoost as stand-alone models and as a stacked ensemble. Age, sex, body mass index, ethnicity, area-based deprivation, past hypertension, cardiovascular diseases, prescribed antipsychotics, and SMI illness were the predictors. RESULTS Logistic regression performed well while detecting T2DM presence for people with SMI: area under the receiver operator curve (ROC-AUC) was 0.83 (95 % CI 0.79-0.87). XGBoost and LR-XGBoost ensemble performed equally well, ROC-AUC 0.83 (95 % CI 0.79-0.87), indicating a negligible contribution of non-linear terms to predictive power. Ethnicity was the most important predictor after age. We demonstrated how the derived models can be utilised and estimated a 2.14 % (95 %CI 2.03 %-2.24 %) increase in T2DM prevalence in East London SMI population in 20 years' time, driven by the projected demographic changes. CONCLUSIONS Primary care data, the setting where prediction models could be most fruitfully used, provide enough information for well-performing T2DM prevalence models for people with SMI. We demonstrated how thorough internal cross-validation of an ensemble of a linear and machine-learning model can quantify the predictive value of non-linearity in the data.
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Affiliation(s)
- Diana Shamsutdinova
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
| | - Jayati Das-Munshi
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, London, United Kingdom; ESRC Centre for Society and Mental Health, King's College London, London, United Kingdom; South London and Maudsley NHS Trust, London, United Kingdom
| | - Mark Ashworth
- ESRC Centre for Society and Mental Health, King's College London, London, United Kingdom
| | - Angus Roberts
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Daniel Stahl
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
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Validation of the Collaborative Outcomes study on Health and Functioning during Infection Times (COH-FIT) questionnaire for adults. J Affect Disord 2023; 326:249-261. [PMID: 36586617 PMCID: PMC9794522 DOI: 10.1016/j.jad.2022.12.022] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 12/06/2022] [Accepted: 12/07/2022] [Indexed: 12/29/2022]
Abstract
BACKGROUND The Collaborative Outcome study on Health and Functioning during Infection Times (COH-FIT; www.coh-fit.com) is an anonymous and global online survey measuring health and functioning during the COVID-19 pandemic. The aim of this study was to test concurrently the validity of COH-FIT items and the internal validity of the co-primary outcome, a composite psychopathology "P-score". METHODS The COH-FIT survey has been translated into 30 languages (two blind forward-translations, consensus, one independent English back-translation, final harmonization). To measure mental health, 1-4 items ("COH-FIT items") were extracted from validated questionnaires (e.g. Patient Health Questionnaire 9). COH-FIT items measured anxiety, depressive, post-traumatic, obsessive-compulsive, bipolar and psychotic symptoms, as well as stress, sleep and concentration. COH-FIT Items which correlated r ≥ 0.5 with validated companion questionnaires, were initially retained. A P-score factor structure was then identified from these items using exploratory factor analysis (EFA) and confirmatory factor analyses (CFA) on data split into training and validation sets. Consistency of results across languages, gender and age was assessed. RESULTS From >150,000 adult responses by May 6th, 2022, a subset of 22,456 completed both COH-FIT items and validated questionnaires. Concurrent validity was consistently demonstrated across different languages for COH-FIT items. CFA confirmed EFA results of five first-order factors (anxiety, depression, post-traumatic, psychotic, psychophysiologic symptoms) and revealed a single second-order factor P-score, with high internal reliability (ω = 0.95). Factor structure was consistent across age and sex. CONCLUSIONS COH-FIT is a valid instrument to globally measure mental health during infection times. The P-score is a valid measure of multidimensional mental health.
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Johnson D, Letchumanan V, Thum CC, Thurairajasingam S, Lee LH. A Microbial-Based Approach to Mental Health: The Potential of Probiotics in the Treatment of Depression. Nutrients 2023; 15:nu15061382. [PMID: 36986112 PMCID: PMC10053794 DOI: 10.3390/nu15061382] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 03/06/2023] [Accepted: 03/10/2023] [Indexed: 03/16/2023] Open
Abstract
Probiotics are currently the subject of intensive research pursuits and also represent a multi-billion-dollar global industry given their vast potential to improve human health. In addition, mental health represents a key domain of healthcare, which currently has limited, adverse-effect prone treatment options, and probiotics may hold the potential to be a novel, customizable treatment for depression. Clinical depression is a common, potentially debilitating condition that may be amenable to a precision psychiatry-based approach utilizing probiotics. Although our understanding has not yet reached a sufficient level, this could be a therapeutic approach that can be tailored for specific individuals with their own unique set of characteristics and health issues. Scientifically, the use of probiotics as a treatment for depression has a valid basis rooted in the microbiota-gut-brain axis (MGBA) mechanisms, which play a role in the pathophysiology of depression. In theory, probiotics appear to be ideal as adjunct therapeutics for major depressive disorder (MDD) and as stand-alone therapeutics for mild MDD and may potentially revolutionize the treatment of depressive disorders. Although there is a wide range of probiotics and an almost limitless range of therapeutic combinations, this review aims to narrow the focus to the most widely commercialized and studied strains, namely Lactobacillus and Bifidobacterium, and to bring together the arguments for their usage in patients with major depressive disorder (MDD). Clinicians, scientists, and industrialists are critical stakeholders in exploring this groundbreaking concept.
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Affiliation(s)
- Dinyadarshini Johnson
- Novel Bacteria and Drug Discovery Research Group (NBDD), Microbiome and Bioresource Research Strength (MBRS), Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bandar Sunway 47500, Malaysia
| | - Vengadesh Letchumanan
- Novel Bacteria and Drug Discovery Research Group (NBDD), Microbiome and Bioresource Research Strength (MBRS), Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bandar Sunway 47500, Malaysia
- Pathogen Resistome Virulome and Diagnostic Research Group (PathRiD), Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bandar Sunway 47500, Malaysia
| | - Chern Choong Thum
- Department of Psychiatry, Hospital Sultan Abdul Aziz Shah, Persiaran Mardi-UPM, Serdang 43400, Malaysia
| | - Sivakumar Thurairajasingam
- Clinical School Johor Bahru, Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Johor Bahru 80100, Malaysia
- Correspondence: (S.T.); or (L.-H.L.)
| | - Learn-Han Lee
- Novel Bacteria and Drug Discovery Research Group (NBDD), Microbiome and Bioresource Research Strength (MBRS), Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bandar Sunway 47500, Malaysia
- Pathogen Resistome Virulome and Diagnostic Research Group (PathRiD), Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bandar Sunway 47500, Malaysia
- Correspondence: (S.T.); or (L.-H.L.)
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van Dee V, Schnack HG, Cahn W. Systematic review and meta-analysis on predictors of prognosis in patients with schizophrenia spectrum disorders: An overview of current evidence and a call for prospective research and open access to datasets. Schizophr Res 2023; 254:133-142. [PMID: 36863229 DOI: 10.1016/j.schres.2023.02.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 01/23/2023] [Accepted: 02/20/2023] [Indexed: 03/04/2023]
Abstract
BACKGROUND Schizophrenia spectrum disorders (SSD) have heterogeneous outcomes. If we could predict individual outcome and identify predictors of outcome, we could personalize and optimize treatment and care. Recent research showed that recovery rates tend to stabilize early in the course of disease. Short- to medium- term treatment goals are most relevant for clinical practice. METHODS We performed a systematic review and meta-analysis to identify predictors of outcome ≤1 year in prospective studies of patients with SSD. For our meta-analysis risk of bias was assessed with the QUIPS tool. RESULTS 178 studies were included for analysis. Our systematic review and meta-analysis showed that the chance of symptomatic remission was lower in males, and in patients with longer duration of untreated psychosis, more symptoms, worse global functioning, more previous hospital admissions and worse treatment adherence. The chance of readmission was higher for patients with more previous admissions. The chance of functional improvement was lower in patients with worse functioning at baseline. For other proposed predictors of outcome, like age at onset and depressive symptoms, limited to no evidence was found. DISCUSSION This study illuminates predictors of outcome of SSD. Level of functioning at baseline was the best predictor of all investigated outcomes. Furthermore, we found no evidence for many predictors proposed in original research. Possible reasons for this include the lack of prospective research, between-study heterogeneity and incomplete reporting. We therefore recommend open access to datasets and analysis scripts, enabling other researchers to reanalyze and pool the data.
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Affiliation(s)
- Violet van Dee
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, the Netherlands.
| | - Hugo G Schnack
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, the Netherlands.
| | - Wiepke Cahn
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, the Netherlands.
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Allesøe RL, Thompson WK, Bybjerg-Grauholm J, Hougaard DM, Nordentoft M, Werge T, Rasmussen S, Benros ME. Deep Learning for Cross-Diagnostic Prediction of Mental Disorder Diagnosis and Prognosis Using Danish Nationwide Register and Genetic Data. JAMA Psychiatry 2023; 80:146-155. [PMID: 36477816 PMCID: PMC9857190 DOI: 10.1001/jamapsychiatry.2022.4076] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Importance Diagnoses and treatment of mental disorders are hampered by the current lack of objective markers needed to provide a more precise diagnosis and treatment strategy. Objective To develop deep learning models to predict mental disorder diagnosis and severity spanning multiple diagnoses using nationwide register data, family and patient-specific diagnostic history, birth-related measurement, and genetics. Design, Setting, and Participants This study was conducted from May 1, 1981, to December 31, 2016. For the analysis, which used a Danish population-based case-cohort sample of individuals born between 1981 and 2005, genotype data and matched longitudinal health register data were taken from the longitudinal Danish population-based Integrative Psychiatric Research Consortium 2012 case-cohort study. Included were individuals with mental disorders (attention-deficit/hyperactivity disorder [ADHD]), autism spectrum disorder (ASD), major depressive disorder (MDD), bipolar disorder (BD), schizophrenia spectrum disorders (SCZ), and population controls. Data were analyzed from February 1, 2021, to January 24, 2022. Exposure At least 1 hospital contact with diagnosis of ADHD, ASD, MDD, BD, or SCZ. Main Outcomes and Measures The predictability of (1) mental disorder diagnosis and (2) severity trajectories (measured by future outpatient hospital contacts, admissions, and suicide attempts) were investigated using both a cross-diagnostic and single-disorder setup. Predictive power was measured by AUC, accuracy, and Matthews correlation coefficient (MCC), including an estimate of feature importance. Results A total of 63 535 individuals (mean [SD] age, 23 [7] years; 34 944 male [55%]; 28 591 female [45%]) were included in the model. Based on data prior to diagnosis, the specific diagnosis was predicted in a multidiagnostic prediction model including the background population with an overall area under the curve (AUC) of 0.81 and MCC of 0.28, whereas the single-disorder models gave AUCs/MCCs of 0.84/0.54 for SCZ, 0.79/0.41 for BD, 0.77/0.39 for ASD, 0.74/0.38, for ADHD, and 0.74/0.38 for MDD. The most important data sets for multidiagnostic prediction were previous mental disorders and age (11%-23% reduction in prediction accuracy when removed) followed by family diagnoses, birth-related measurements, and genetic data (3%-5% reduction in prediction accuracy when removed). Furthermore, when predicting subsequent disease trajectories of the disorder, the most severe cases were the most easily predictable, with an AUC of 0.72. Conclusions and Relevance Results of this diagnostic study suggest the possibility of combining genetics and registry data to predict both mental disorder diagnosis and disorder progression in a clinically relevant, cross-diagnostic setting prior to clinical assessment.
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Affiliation(s)
- Rosa Lundbye Allesøe
- Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark,Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Wesley K. Thompson
- Division of Biostatistics and Department of Radiology, Population Neuroscience and Genetics Lab, University of California, San Diego, La Jolla
| | - Jonas Bybjerg-Grauholm
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark,Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - David M. Hougaard
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark,Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Merete Nordentoft
- Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark,iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark,Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Thomas Werge
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark,Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark,Institute of Biological Psychiatry, Mental Health Centre Sct Hans, Mental Health Services Copenhagen, Roskilde, Denmark
| | - Simon Rasmussen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Michael Eriksen Benros
- Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark,Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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Sathyanarayanan A, Mueller TT, Ali Moni M, Schueler K, Baune BT, Lio P, Mehta D, Baune BT, Dierssen M, Ebert B, Fabbri C, Fusar-Poli P, Gennarelli M, Harmer C, Howes OD, Janzing JGE, Lio P, Maron E, Mehta D, Minelli A, Nonell L, Pisanu C, Potier MC, Rybakowski F, Serretti A, Squassina A, Stacey D, van Westrhenen R, Xicota L. Multi-omics data integration methods and their applications in psychiatric disorders. Eur Neuropsychopharmacol 2023; 69:26-46. [PMID: 36706689 DOI: 10.1016/j.euroneuro.2023.01.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 11/22/2022] [Accepted: 01/02/2023] [Indexed: 01/27/2023]
Abstract
To study mental illness and health, in the past researchers have often broken down their complexity into individual subsystems (e.g., genomics, transcriptomics, proteomics, clinical data) and explored the components independently. Technological advancements and decreasing costs of high throughput sequencing has led to an unprecedented increase in data generation. Furthermore, over the years it has become increasingly clear that these subsystems do not act in isolation but instead interact with each other to drive mental illness and health. Consequently, individual subsystems are now analysed jointly to promote a holistic understanding of the underlying biological complexity of health and disease. Complementing the increasing data availability, current research is geared towards developing novel methods that can efficiently combine the information rich multi-omics data to discover biologically meaningful biomarkers for diagnosis, treatment, and prognosis. However, clinical translation of the research is still challenging. In this review, we summarise conventional and state-of-the-art statistical and machine learning approaches for discovery of biomarker, diagnosis, as well as outcome and treatment response prediction through integrating multi-omics and clinical data. In addition, we describe the role of biological model systems and in silico multi-omics model designs in clinical translation of psychiatric research from bench to bedside. Finally, we discuss the current challenges and explore the application of multi-omics integration in future psychiatric research. The review provides a structured overview and latest updates in the field of multi-omics in psychiatry.
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Affiliation(s)
- Anita Sathyanarayanan
- Queensland University of Technology, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Faculty of Health, Kelvin Grove, Queensland 4059, Australia
| | - Tamara T Mueller
- Institute for Artificial Intelligence and Informatics in Medicine, TU Munich, 80333 Munich, Germany
| | - Mohammad Ali Moni
- Artificial Intelligence and Digital Health Data Science, School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, QLD, 4072, Australia
| | - Katja Schueler
- Clinic for Psychosomatics, Hospital zum Heiligen Geist, Frankfurt am Main, Germany; Frankfurt Psychoanalytic Institute, Frankfurt am Main, Germany
| | - Bernhard T Baune
- Department of Psychiatry and Psychotherapy, University of Münster, Germany; Department of Psychiatry, Melbourne Medical School, University of Melbourne, Australia; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Australia
| | - Pietro Lio
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
| | - Divya Mehta
- Queensland University of Technology, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Faculty of Health, Kelvin Grove, Queensland 4059, Australia.
| | | | - Bernhard T Baune
- Department of Psychiatry and Psychotherapy, University of Münster, Germany; Department of Psychiatry, Melbourne Medical School, University of Melbourne, Australia; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Australia
| | - Mara Dierssen
- Center for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology; Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Bjarke Ebert
- Medical Strategy & Communication, H. Lundbeck A/S, Valby, Denmark
| | - Chiara Fabbri
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Paolo Fusar-Poli
- Early Psychosis: Intervention and Clinical-detection (EPIC) Lab, Department of Psychosis Studies, King's College London, United Kingdom; Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Massimo Gennarelli
- Department of Molecular and Translational Medicine, University of Brescia; Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | | | - Oliver D Howes
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Psychiatric Imaging, Medical Research Council Clinical Sciences Centre, Imperial College London, Hammersmith Hospital Campus, London, United Kingdom
| | | | - Pietro Lio
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
| | - Eduard Maron
- Department of Psychiatry, University of Tartu, Tartu, Estonia; Centre for Neuropsychopharmacology, Division of Brain Sciences, Imperial College London, London, United Kingdom; Documental Ltd, Tallin, Estonia; West Tallinn Central Hospital, Tallinn, Estonia
| | - Divya Mehta
- Queensland University of Technology, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Faculty of Health, Kelvin Grove, Queensland 4059, Australia
| | - Alessandra Minelli
- Department of Molecular and Translational Medicine, University of Brescia; Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Lara Nonell
- MARGenomics, IMIM (Hospital del Mar Research Institute), Barcelona, Spain
| | - Claudia Pisanu
- Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy
| | | | - Filip Rybakowski
- Department of Psychiatry, Poznan University of Medical Sciences, Poznan, Poland
| | - Alessandro Serretti
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy
| | - Alessio Squassina
- Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy
| | - David Stacey
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Roos van Westrhenen
- Parnassia Psychiatric Institute, Amsterdam, the Netherlands; Department of Psychiatry and Neuropsychology, Faculty of Health and Sciences, Maastricht University, Maastricht, the Netherlands; Institute of Psychiatry, Psychology & Neuroscience (IoPPN) King's College London, United Kingdom
| | - Laura Xicota
- Paris Brain Institute ICM, Salpetriere Hospital, Paris, France
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40
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Cohen ZD, DeRubeis RJ, Hayes R, Watkins ER, Lewis G, Byng R, Byford S, Crane C, Kuyken W, Dalgleish T, Schweizer S. The development and internal evaluation of a predictive model to identify for whom Mindfulness-Based Cognitive Therapy (MBCT) offers superior relapse prevention for recurrent depression versus maintenance antidepressant medication. Clin Psychol Sci 2023; 11:59-76. [PMID: 36698442 PMCID: PMC7614103 DOI: 10.1177/21677026221076832] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Depression is highly recurrent, even following successful pharmacological and/or psychological intervention. We aimed to develop clinical prediction models to inform adults with recurrent depression choosing between antidepressant medication (ADM) maintenance or switching to Mindfulness-Based Cognitive Therapy (MBCT). Using data from the PREVENT trial (N=424), we constructed prognostic models using elastic net regression that combined demographic, clinical and psychological factors to predict relapse at 24 months under ADM or MBCT. Only the ADM model (discrimination performance: AUC=.68) predicted relapse better than baseline depression severity (AUC=.54; one-tailed DeLong's test: z=2.8, p=.003). Individuals with the poorest ADM prognoses who switched to MBCT had better outcomes compared to those who maintained ADM (48% vs. 70% relapse, respectively; superior survival times [z=-2.7, p=.008]). For individuals with moderate-to-good ADM prognosis, both treatments resulted in similar likelihood of relapse. If replicated, the results suggest that predictive modeling can inform clinical decision-making around relapse prevention in recurrent depression.
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Affiliation(s)
| | | | - Rachel Hayes
- National Institute for Health Research (NIHR) Applied Research Collaboration (ARC) South West Peninsula, University of Exeter
| | | | - Glyn Lewis
- Division of Psychiatry, Faulty of Brain Sciences, University College London
- Community Primary Care Research Group, University of Plymouth
| | - Richard Byng
- Community Primary Care Research Group, University of Plymouth
- National Institute of Health Research Collaboration for Leadership in Applied Health Research and Care, South West Peninsula, England
| | - Sarah Byford
- Health Service and Population Research Department, Institute of Psychiatry, Psychology and Neuroscience, King’s College London
| | - Catherine Crane
- Department of Psychiatry, Medical Sciences Division, University of Oxford
| | - Willem Kuyken
- Department of Psychiatry, Medical Sciences Division, University of Oxford
| | - Tim Dalgleish
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, England
| | - Susanne Schweizer
- Department of Psychology, University of Cambridge
- School of Psychology, University of New South Wales
- Susanne Schweizer, Department of Psychology, University of Cambridge
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41
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Lorenzi CH, Teixeira Leffa D, Bressan R, Belangero S, Gadelha A, Santoro ML, Salum GA, Rohde LA, Caye A. Replication of a predictive model for youth ADHD in an independent sample from a developing country. J Child Psychol Psychiatry 2023; 64:167-174. [PMID: 35959538 DOI: 10.1111/jcpp.13682] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/21/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Very few predictive models in Psychiatry had their performance validated in independent external samples. A previously developed multivariable demographic model for attention-deficit/hyperactivity disorder (ADHD) accurately predicted young adulthood ADHD using clinical and demographical information collected in childhood in three samples from developed countries, but failed to replicate its performance in a sample from a developing country. Furthermore, consolidated risk factors for ADHD were not included among its predictors. METHODS Participants were 1905 children and adolescents from a community-based sample and followed from ages 6 to 14 years at baseline to ages 14 to 23 years (mean age 18) at follow-up. We applied the intercept and weights of the original model to the data, calculating the predicted probability of each participant according to the set of predictors collected in childhood, and compared the estimates with the actual outcome (ADHD) collected during adolescence and young adulthood. We explored the performance of the original model, and of models including novel predictors (prematurity, family history of ADHD, and polygenic risk score for ADHD). RESULTS The observed area under the curve of the original model was .76 (95% Confidence Interval .70 to .82). The multivariable demographical model outperformed single variable models using only prematurity, family history, or the ADHD PRS. Adding either of these variables, or all at once, did not improve the performance of the original demographical model. CONCLUSIONS Our findings suggest that the originally developed ADHD predictive model is suitable for use in different settings for clinical and research purposes.
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Affiliation(s)
- Cezar H Lorenzi
- ADHD Outpatient Program, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil.,Department of Psychiatry, School of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Douglas Teixeira Leffa
- ADHD Outpatient Program, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil.,Department of Psychiatry, School of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents (INPD), São Paulo, Brazil
| | - Rodrigo Bressan
- National Institute of Developmental Psychiatry for Children and Adolescents (INPD), São Paulo, Brazil.,Post-Graduation Program in Psychiatry and Medical Psychology, UNIFESP, São Paulo, Brazil
| | - Sintia Belangero
- National Institute of Developmental Psychiatry for Children and Adolescents (INPD), São Paulo, Brazil.,Post-Graduation Program in Psychiatry and Medical Psychology, UNIFESP, São Paulo, Brazil.,Genetics Division, Department of Morphology and Genetics, UNIFESP, São Paulo, Brazil
| | - Ary Gadelha
- National Institute of Developmental Psychiatry for Children and Adolescents (INPD), São Paulo, Brazil.,Post-Graduation Program in Psychiatry and Medical Psychology, UNIFESP, São Paulo, Brazil
| | - Marcos L Santoro
- National Institute of Developmental Psychiatry for Children and Adolescents (INPD), São Paulo, Brazil.,Post-Graduation Program in Psychiatry and Medical Psychology, UNIFESP, São Paulo, Brazil.,Genetics Division, Department of Morphology and Genetics, UNIFESP, São Paulo, Brazil
| | - Giovanni A Salum
- National Institute of Developmental Psychiatry for Children and Adolescents (INPD), São Paulo, Brazil
| | - Luis Augusto Rohde
- ADHD Outpatient Program, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil.,Department of Psychiatry, School of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents (INPD), São Paulo, Brazil
| | - Arthur Caye
- ADHD Outpatient Program, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil.,Department of Psychiatry, School of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents (INPD), São Paulo, Brazil
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42
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Spiller TR, Tufan E, Petry H, Böttger S, Fuchs S, Duek O, Ben-Zion Z, Korem N, Harpaz-Rotem I, von Känel R, Ernst J. Delirium screening in an acute care setting with a machine learning classifier based on routinely collected nursing data: A model development study. J Psychiatr Res 2022; 156:194-199. [PMID: 36252349 DOI: 10.1016/j.jpsychires.2022.10.018] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 09/20/2022] [Accepted: 10/03/2022] [Indexed: 12/12/2022]
Abstract
Delirium screening in acute care settings is a resource intensive process with frequent deviations from screening protocols. A predictive model relying only on daily collected nursing data for delirium screening could expand the populations covered by such screening programs. Here, we present the results of the development and validation of a series of machine-learning based delirium prediction models. For this purpose, we used data of all patients 18 years or older which were hospitalized for more than a day between January 1, 2014, and December 31, 2018, at a single tertiary teaching hospital in Zurich, Switzerland. A total of 48,840 patients met inclusion criteria. 18,873 (38.6%) were excluded due to missing data. Mean age (SD) of the included 29,967 patients was 71.1 (12.2) years and 12,231 (40.8%) were women. Delirium was assessed with the Delirium Observation Scale (DOS) with a total score of 3 or greater indicating that a patient is at risk for delirium. Additional measures included structured data collected for nursing process planning and demographic characteristics. The performance of the machine learning models was assessed using the area under the receiver operating characteristic curve (AUC). The training set consisted of 21,147 patients (mean age 71.1 (12.1) years; 8,630 (40.8%) women|) including 233,024 observations with 16,167 (6.9%) positive DOS screens. The test set comprised 8,820 patients (median age 71.1 (12.4) years; 3,601 (40.8%) women) with 91,026 observations with 5,445 (6.0%) positive DOS screens. Overall, the gradient boosting machine model performed best with an AUC of 0.933 (95% CI, 0.929 - 0.936). In conclusion, machine learning models based only on structured nursing data can reliably predict patients at risk for delirium in an acute care setting. Prediction models, using existing data collection processes, could reduce the resources required for delirium screening procedures in clinical practice.
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Affiliation(s)
- Tobias R Spiller
- Department of Consultation-Liaison Psychiatry and Psychosomatic Medicine, University Hospital Zurich (USZ), Zurich, Switzerland; University of Zurich (UZH), Zurich, Switzerland; Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; National Center for PTSD, Clinical Neurosciences Division, VA Connecticut Healthcare System, West Haven, CT, USA.
| | - Ege Tufan
- German Institute for Literature, Leipzig University, Leipzig, Germany
| | - Heidi Petry
- University of Zurich (UZH), Zurich, Switzerland; Center of Clinical Nursing Science, University Hospital Zurich (USZ), Zurich, Switzerland
| | - Sönke Böttger
- University of Zurich (UZH), Zurich, Switzerland; Department of Gastroenterology, University Hospital Zurich (USZ), Zurich, Switzerland
| | - Simon Fuchs
- Department of Consultation-Liaison Psychiatry and Psychosomatic Medicine, University Hospital Zurich (USZ), Zurich, Switzerland; University of Zurich (UZH), Zurich, Switzerland; Psychiatric University Hospital Zurich (PUK), Zurich, Switzerland
| | - Or Duek
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; National Center for PTSD, Clinical Neurosciences Division, VA Connecticut Healthcare System, West Haven, CT, USA
| | - Ziv Ben-Zion
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; National Center for PTSD, Clinical Neurosciences Division, VA Connecticut Healthcare System, West Haven, CT, USA
| | - Nachshon Korem
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; National Center for PTSD, Clinical Neurosciences Division, VA Connecticut Healthcare System, West Haven, CT, USA
| | - Ilan Harpaz-Rotem
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; National Center for PTSD, Clinical Neurosciences Division, VA Connecticut Healthcare System, West Haven, CT, USA; Northeast Program Evaluation Center, VA Connecticut Healthcare System, West Haven, USA; Department of Psychology, Yale University, New Haven, CT, USA
| | - Roland von Känel
- Department of Consultation-Liaison Psychiatry and Psychosomatic Medicine, University Hospital Zurich (USZ), Zurich, Switzerland; University of Zurich (UZH), Zurich, Switzerland
| | - Jutta Ernst
- University of Zurich (UZH), Zurich, Switzerland; Center of Clinical Nursing Science, University Hospital Zurich (USZ), Zurich, Switzerland
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Deforth M, Gebhard CE, Bengs S, Buehler PK, Schuepbach RA, Zinkernagel AS, Brugger SD, Acevedo CT, Patriki D, Wiggli B, Twerenbold R, Kuster GM, Pargger H, Schefold JC, Spinetti T, Wendel-Garcia PD, Hofmaenner DA, Gysi B, Siegemund M, Heinze G, Regitz-Zagrosek V, Gebhard C, Held U. Development and validation of a prognostic model for the early identification of COVID-19 patients at risk of developing common long COVID symptoms. Diagn Progn Res 2022; 6:22. [PMID: 36384641 PMCID: PMC9668400 DOI: 10.1186/s41512-022-00135-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 09/30/2022] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND The coronavirus disease 2019 (COVID-19) pandemic demands reliable prognostic models for estimating the risk of long COVID. We developed and validated a prediction model to estimate the probability of known common long COVID symptoms at least 60 days after acute COVID-19. METHODS The prognostic model was built based on data from a multicentre prospective Swiss cohort study. Included were adult patients diagnosed with COVID-19 between February and December 2020 and treated as outpatients, at ward or intensive/intermediate care unit. Perceived long-term health impairments, including reduced exercise tolerance/reduced resilience, shortness of breath and/or tiredness (REST), were assessed after a follow-up time between 60 and 425 days. The data set was split into a derivation and a geographical validation cohort. Predictors were selected out of twelve candidate predictors based on three methods, namely the augmented backward elimination (ABE) method, the adaptive best-subset selection (ABESS) method and model-based recursive partitioning (MBRP) approach. Model performance was assessed with the scaled Brier score, concordance c statistic and calibration plot. The final prognostic model was determined based on best model performance. RESULTS In total, 2799 patients were included in the analysis, of which 1588 patients were in the derivation cohort and 1211 patients in the validation cohort. The REST prevalence was similar between the cohorts with 21.6% (n = 343) in the derivation cohort and 22.1% (n = 268) in the validation cohort. The same predictors were selected with the ABE and ABESS approach. The final prognostic model was based on the ABE and ABESS selected predictors. The corresponding scaled Brier score in the validation cohort was 18.74%, model discrimination was 0.78 (95% CI: 0.75 to 0.81), calibration slope was 0.92 (95% CI: 0.78 to 1.06) and calibration intercept was -0.06 (95% CI: -0.22 to 0.09). CONCLUSION The proposed model was validated to identify COVID-19-infected patients at high risk for REST symptoms. Before implementing the prognostic model in daily clinical practice, the conduct of an impact study is recommended.
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Affiliation(s)
- Manja Deforth
- Department of Biostatistics at Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.
| | - Caroline E Gebhard
- Intensive Care Unit, Department of Acute Medicine, University Hospital Basel, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Susan Bengs
- Department of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland
- Center for Molecular Cardiology, University of Zurich, Schlieren, Switzerland
| | - Philipp K Buehler
- Institute of Intensive Care Medicine, University Hospital Zurich, Zurich, Switzerland
| | - Reto A Schuepbach
- Institute of Intensive Care Medicine, University Hospital Zurich, Zurich, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Annelies S Zinkernagel
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland
| | - Silvio D Brugger
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland
| | - Claudio T Acevedo
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland
| | - Dimitri Patriki
- Department of Internal Medicine, Cantonal Hospital Baden, Baden, Switzerland
| | - Benedikt Wiggli
- Department of Infectiology and Infection Control, Cantonal Hospital Baden, Baden, Switzerland
| | - Raphael Twerenbold
- Department of Cardiology, University Hospital Basel, Basel, Switzerland
- University Center of Cardiovascular Science & Department of Cardiology, University Heart and Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- German Center for Cardiovascular Research (DZHK) Partner Site Hamburg-Kiel-Lübeck, Berlin, Germany
| | - Gabriela M Kuster
- Department of Cardiology, University Hospital Basel, Basel, Switzerland
| | - Hans Pargger
- Intensive Care Unit, Department of Acute Medicine, University Hospital Basel, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Joerg C Schefold
- Department of Intensive Care Medicine, University Hospital Bern, Bern, Switzerland
| | - Thibaud Spinetti
- Department of Intensive Care Medicine, University Hospital Bern, Bern, Switzerland
| | - Pedro D Wendel-Garcia
- Institute of Intensive Care Medicine, University Hospital Zurich, Zurich, Switzerland
| | - Daniel A Hofmaenner
- Institute of Intensive Care Medicine, University Hospital Zurich, Zurich, Switzerland
| | - Bianca Gysi
- Intensive Care Unit, Department of Acute Medicine, University Hospital Basel, Basel, Switzerland
| | - Martin Siegemund
- Intensive Care Unit, Department of Acute Medicine, University Hospital Basel, Basel, Switzerland
- Department Clinical Research, University of Basel, Basel, Switzerland
| | - Georg Heinze
- Center for Medical Statistics, Informatics and Intelligent Systems, Section for Clinical Biometrics, Medical University of Vienna, Vienna, Austria
| | - Vera Regitz-Zagrosek
- University of Zurich, Zurich, Switzerland
- Charité, University Medicine Berlin, Berlin, Germany
- Department of Cardiology, University Hospital Zurich, Zurich, Switzerland
| | - Catherine Gebhard
- Department of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland
- Center for Molecular Cardiology, University of Zurich, Schlieren, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Ulrike Held
- Department of Biostatistics at Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
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Sato A, Watanabe N, Maruo K, Moriyama T, Furukawa TA. Psychotic relapse in people with schizophrenia within 12 months of discharge from acute inpatient care: protocol for development and validation of a prediction model based on a retrospective cohort study in three psychiatric hospitals in Japan. Diagn Progn Res 2022; 6:20. [PMID: 36324165 PMCID: PMC9629881 DOI: 10.1186/s41512-022-00134-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 08/22/2022] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Schizophrenia is a severe mental illness characterized by recurrent psychoses that typically waxes and wanes through its prodromal, acute, and chronic phases. A large amount of research on individual prognostic factors for relapse in people with schizophrenia has been published, and a few logistic models exist to predict psychotic prognosis for people in the prodromal phase or after the first episode of psychosis. However, research on prediction models for people with schizophrenia, including those in the chronic phase and after multiple recurrences, is scarce. We aim to develop and validate a prediction model for this population. METHODS This is a retrospective cohort study to be undertaken in Japan. We will include participants aged 18 years or above, diagnosed with schizophrenia or related disorders, and discharged between January 2014 and December 2018 from one of the acute inpatient care wards of three geographically distinct psychiatric hospitals. We will collect pre-specified nine predictors at the time of recruitment, follow up the participants for 12 months after discharge, and observe whether our primary outcome of a relapse occurs. Relapse will be considered to have occurred in one of the following circumstances: (1) hospitalization; (2) psychiatrist's judgment that the person needs hospitalization; (3) increasing doses of antipsychotics; or (4) suicidal or homicidal ideation or behavior resulting from such ideation. We will develop a Cox regression model and avoid overfitting by penalizing coefficients using the elastic net. The model will be validated both internally and externally by bootstrapping and "leave-one-hospital-out" cross-validation, respectively. We will evaluate the model's performance in terms of discrimination and calibration. Decision curve analysis will be presented to aid decision-making. We will present a web application to visualize the model for ease of use in daily practice. DISCUSSION This will be the first prediction modeling study of relapse after discharge among people with both first and multiple episodes of schizophrenia using routinely collected data. TRIAL REGISTRATION This study was registered in the UMIN-CTR (UMIN000043345) on February 20, 2021.
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Affiliation(s)
- Akira Sato
- Department of Health Promotion and Human Behavior, Kyoto University Graduate School of Medicine/School of Public Health, Yoshida-konoecho, Sakyo-ku, Kyoto, 606-8501, Japan.
| | - Norio Watanabe
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, 54 Shogoin-kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Kazushi Maruo
- Department of Biostatistics, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan
| | | | - Toshi A Furukawa
- Department of Health Promotion and Human Behavior, Kyoto University Graduate School of Medicine/School of Public Health, Yoshida-konoecho, Sakyo-ku, Kyoto, 606-8501, Japan
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Koutsouleris N, Hauser TU, Skvortsova V, De Choudhury M. From promise to practice: towards the realisation of AI-informed mental health care. THE LANCET DIGITAL HEALTH 2022; 4:e829-e840. [DOI: 10.1016/s2589-7500(22)00153-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 07/14/2022] [Accepted: 07/27/2022] [Indexed: 11/07/2022]
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Salazar de Pablo G, Moreno D, Gonzalez-Pinto A, Paya B, Castro-Fonieles J, Baeza I, Graell M, Arango C, Rapado-Castro M, Moreno C. Affective symptom dimensions in early-onset psychosis over time: a principal component factor analysis of the Young Mania Rating Scale and the Hamilton Depression Rating Scale. Eur Child Adolesc Psychiatry 2022; 31:1715-1728. [PMID: 34052909 DOI: 10.1007/s00787-021-01815-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Accepted: 05/23/2021] [Indexed: 12/11/2022]
Abstract
Early-onset psychosis (EOP) is a complex disorder characterized by a wide range of symptoms, including affective symptoms. Our aim was to (1) examine the dimensional structure of affective symptoms in EOP, (2) evaluate the predominance of the clinical dimensions and (3) assess the progression of the clinical dimensions over a 2-year period. STROBE-compliant prospective principal component factor analysis of Young Mania Rating Scale (YMRS) and Hamilton Depression Rating Scale-21 (HDRS-21) at baseline, 6-months, 1-year and 2-year follow-up. We included 108 EOP individuals (mean age = 15.5 ± 1.8 years, 68.5% male). The factor analysis produced a four-factor model including the following dimensions: mania, depression/anxiety, sleep and psychosis. It explained 47.4% of the total variance at baseline, 60.6% of the total variance at 6-months follow-up, 54.5% of the total variance at 1-year follow-up and 49.5% of the total variance at 2-year follow-up. According to the variance explained, the mania factor was predominant at baseline (17.4%), 6-month follow-up (23.5%) and 2-year follow-up (26.1%), while the depression/anxiety factor was predominant at 1-year follow-up (23.1%). The mania factor was the most stable; 58.3% items that appeared in this factor (with a load > 0.4) at any time point appeared in the same factor at ≥ 3/4 time points. Affective symptoms are frequent and persistent in EOP. Mania seems to be the most predominant and stable affective dimension. However, depression and anxiety may gain predominance with time. A comprehensive evaluation of the dimensional structure and the progression of affective symptoms may offer clinical and therapeutic advantages.
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Affiliation(s)
- Gonzalo Salazar de Pablo
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, School of Medicine, Universidad Complutense, IiSGM, CIBERSAM, C/Ibiza, 43, 28009, Madrid, Spain.,Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Dolores Moreno
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, School of Medicine, Universidad Complutense, IiSGM, CIBERSAM, C/Ibiza, 43, 28009, Madrid, Spain
| | - Ana Gonzalez-Pinto
- Department of Psychiatry, Biomedical Research Networking Centre in Mental Health, BioAraba Research Institute, OSI Araba-University Hospital, University of the Basque Country (EHU/UPV), CIBERSAM, Vitoria, Spain
| | - Beatriz Paya
- Department of Child Psychiatry, Hospital Universitario Marqués de Valdecilla, IDIVAL, Santander, Spain
| | - Josefina Castro-Fonieles
- Department of Child and Adolescent Psychiatry and Psychology, 2017SGR881, Neurosciences Institute, Hospital Clínic of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Spain.,Department of Medicine, University of Barcelona, Barcelona, Spain
| | - Inmaculada Baeza
- Department of Child and Adolescent Psychiatry and Psychology, 2017SGR881, Neurosciences Institute, Hospital Clínic of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Spain
| | - Montserrat Graell
- Department of Child and Adolescent Psychiatry and Psychology, Hospital Infantil Universitario Niño Jesús, School of Medicine, Universidad Autónoma, Madrid, Spain
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, School of Medicine, Universidad Complutense, IiSGM, CIBERSAM, C/Ibiza, 43, 28009, Madrid, Spain
| | - Marta Rapado-Castro
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, School of Medicine, Universidad Complutense, IiSGM, CIBERSAM, C/Ibiza, 43, 28009, Madrid, Spain. .,Department of Psychiatry, Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Victoria, Australia.
| | - Carmen Moreno
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, School of Medicine, Universidad Complutense, IiSGM, CIBERSAM, C/Ibiza, 43, 28009, Madrid, Spain
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The promise of a model-based psychiatry: building computational models of mental ill health. Lancet Digit Health 2022; 4:e816-e828. [PMID: 36229345 PMCID: PMC9627546 DOI: 10.1016/s2589-7500(22)00152-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 07/05/2022] [Accepted: 07/27/2022] [Indexed: 11/07/2022]
Abstract
Computational models have great potential to revolutionise psychiatry research and clinical practice. These models are now used across multiple subfields, including computational psychiatry and precision psychiatry. Their goals vary from understanding mechanisms underlying disorders to deriving reliable classification and personalised predictions. Rapid growth of new tools and data sources (eg, digital data, gamification, and social media) requires an understanding of the constraints and advantages of different modelling approaches in psychiatry. In this Series paper, we take a critical look at the range of computational models that are used in psychiatry and evaluate their advantages and disadvantages for different purposes and data sources. We describe mechanism-driven and mechanism-agnostic computational models and discuss how interpretability of models is crucial for clinical translation. Based on these evaluations, we provide recommendations on how to build computational models that are clinically useful.
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Caye A, Marchionatti LE, Pereira R, Fisher HL, Kohrt BA, Mondelli V, McGinnis E, Copeland WE, Kieling C. Identifying adolescents at risk for depression: Assessment of a global prediction model in the Great Smoky Mountains Study. J Psychiatr Res 2022; 155:146-152. [PMID: 36029626 PMCID: PMC9762325 DOI: 10.1016/j.jpsychires.2022.08.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 07/13/2022] [Accepted: 08/16/2022] [Indexed: 11/18/2022]
Abstract
The Identifying Depression Early in Adolescence Risk Score (IDEA-RS) has been externally assessed in samples from four continents, but North America is lacking. Our aim here was to evaluate the performance of the IDEA-RS in predicting future onset of Major Depressive Disorder (MDD) in an adolescent population-based sample in the United States of America - the Great Smoky Mountains Study (GSMS). We applied the intercept and weights of the original IDEA-RS model developed in Brazil to generate individual probabilities for each participant of the GSMS at age 15 (N = 1029). We then evaluated the performance of such predictions against the diagnosis of MDD at age 19 using simple, case-mix corrected and refitted models. Furthermore, we compared how prioritizing the information provided by parents or by adolescents affected performance. The IDEA-RS exhibited a C-statistic of 0.63 (95% CI 0.53-0.74) to predict MDD in the GSMS when applying uncorrected weights. Case-mix corrected and refitted models enhanced performance to 0.69 and 0.67, respectively. No significant difference was found in performance by prioritizing the reports of adolescents or their parents. The IDEA-RS was able to parse out adolescents at risk for a later onset of depression in the GSMS cohort with above chance discrimination. The IDEA-RS has now showed above-chance performance in five continents.
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Affiliation(s)
- Arthur Caye
- Department of Psychiatry, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil; Child and Adolescent Psychiatry Division, Hospital de Clínicas de Porto Alegre (HCPA), Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
| | - Lauro E Marchionatti
- Department of Psychiatry, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
| | - Rivka Pereira
- Department of Psychiatry, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil; Child and Adolescent Psychiatry Division, Hospital de Clínicas de Porto Alegre (HCPA), Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
| | - Helen L Fisher
- King's College London, Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, London, United Kingdom; ESRC Centre for Society and Mental Health, King's College London, London, United Kingdom
| | - Brandon A Kohrt
- Division of Global Mental Health, Department of Psychiatry, School of Medicine and Health Sciences, The George Washington University, Washington, DC, United States
| | - Valeria Mondelli
- King's College London, Department of Psychological Medicine, Institute of Psychiatry, Psychology, London, United Kingdom; National Institute for Health Research Mental Health Biomedical Research Centre, South London and Maudsley NHS Foundation Trust and King's College London, London, United Kingdom
| | | | | | - Christian Kieling
- Department of Psychiatry, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil; Child and Adolescent Psychiatry Division, Hospital de Clínicas de Porto Alegre (HCPA), Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil.
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The performance of machine learning models in predicting suicidal ideation, attempts, and deaths: A meta-analysis and systematic review. J Psychiatr Res 2022; 155:579-588. [PMID: 36206602 DOI: 10.1016/j.jpsychires.2022.09.050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 08/21/2022] [Accepted: 09/24/2022] [Indexed: 11/21/2022]
Abstract
Research has posited that machine learning could improve suicide risk prediction models, which have traditionally performed poorly. This systematic review and meta-analysis evaluated the performance of machine learning models in predicting longitudinal outcomes of suicide-related outcomes of ideation, attempt, and death and examines outcome, data, and model types as potential covariates of model performance. Studies were extracted from PubMed, Web of Science, Embase, and PsycINFO. A bivariate mixed effects meta-analysis and meta-regression analyses were performed for studies using machine learning to predict future events of suicidal ideation, attempts, and/or deaths. Risk of bias was assessed for each study using an adaptation of the Prediction model Risk Of Bias Assessment Tool. Narrative review included 56 studies, and analyses examined 54 models from 35 studies. The models achieved a very good pooled AUC of 0.86, sensitivity of 0.66 (95% CI [0.60, 0.72)], and specificity of 0.87 (95% CI [0.84, 0.90]). Pooled AUCs for ideation, attempt, and death were similar at 0.88, 0.87, and 0.84 respectively. Model performance was highly varied; however, meta-regressions did not provide evidence that performance varied by outcome, data, or model types. Findings suggest that machine learning has the potential to improve suicide risk detection, with pooled estimates of machine learning performance comparing favourably to performance of traditional suicide prediction models. However, more studies with lower risk of bias are necessary to improve the application of machine learning in suicidology.
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Damiani S, Tarchi L, La-Torraca-Vittori P, Scalabrini A, Castellini G, Ricca V, Fusar-Poli P, Politi P. State-dependent reductions of local brain connectivity in schizophrenia and their relation to performance and symptoms: A functional magnetic resonance imaging study. Psychiatry Res Neuroimaging 2022; 326:111541. [PMID: 36122541 DOI: 10.1016/j.pscychresns.2022.111541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 08/01/2022] [Accepted: 09/04/2022] [Indexed: 11/17/2022]
Abstract
State-dependent reallocation of cognitive resources is impaired in schizophrenia and may be underlined by alterations in brain local-connectivity. Increasing evidence suggests local connectivity reductions from rest to task in healthy individuals, while insufficient information is available for schizophrenia spectrum. Resting-state and stop-signal task fMRI scans of 107 healthy controls and 32 patients with DSM-IV-TR schizophrenia or schizoaffective disorder were analyzed. As primary aim we measured within-group shifts in local-connectivity from rest to task as voxel-wise Regional Homogeneity (ReHo-shift). Secondary aims were to test: i) Between-groups differences in ReHo-rest, ReHo-task and ReHo-shift; ii) ReHo covariations with task performance (=shorter reaction times) and severity of symptoms (SAPS/SANS scores). Age, sex, and education were accounted for as covariates. Motion, global-signal-regression, antipsychotic dosage and smoothing associations with ReHo were evaluated. Rest-to-task ReHo reductions occurred in both groups on a whole-brain level (False-Discovery-Rate p=0.05). Trends of greater ReHo reductions in patients versus controls were observed. Controls performed better than patients (p<0.001). ReHo negatively correlated with performance in both groups. ReHo-shift predicted worse performance in controls, but better performance in patients (uncorrected p=0.05). ReHo reductions correlated with severity of symptoms. State-dependent reconfigurations in local-connectivity provide new links between neurobiology and behavioral/clinical features of the schizophrenia spectrum.
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Affiliation(s)
- Stefano Damiani
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, PV, Italy.
| | - Livio Tarchi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, PV, Italy; Psychiatry Unit, Department of Health Sciences, University of Florence, Florence, FI, Italy
| | | | - Andrea Scalabrini
- Department of Human and Social Sciences, University of Bergamo, Bergamo, BG, Italy
| | - Giovanni Castellini
- Psychiatry Unit, Department of Health Sciences, University of Florence, Florence, FI, Italy
| | - Valdo Ricca
- Psychiatry Unit, Department of Health Sciences, University of Florence, Florence, FI, Italy
| | - Paolo Fusar-Poli
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, PV, Italy; Department of Psychosis Studies, Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK; National Institute for Health Research, Maudsley Biomedical Research Centre, London, UK
| | - Pierluigi Politi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, PV, Italy
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