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Janssen A, Smalbil L, Bennis FC, Cnossen MH, Mathôt RAA. A Generative and Causal Pharmacokinetic Model for Factor VIII in Hemophilia A: A Machine Learning Framework for Continuous Model Refinement. Clin Pharmacol Ther 2024; 115:881-889. [PMID: 38372445 DOI: 10.1002/cpt.3203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 01/25/2024] [Indexed: 02/20/2024]
Abstract
In rare diseases, such as hemophilia A, the development of accurate population pharmacokinetic (PK) models is often hindered by the limited availability of data. Most PK models are specific to a single recombinant factor VIII (rFVIII) concentrate or measurement assay, and are generally unsuited for answering counterfactual ("what-if") queries. Ideally, data from multiple hemophilia treatment centers are combined but this is generally difficult as patient data are kept private. In this work, we utilize causal inference techniques to produce a hybrid machine learning (ML) PK model that corrects for differences between rFVIII concentrates and measurement assays. Next, we augment this model with a generative model that can simulate realistic virtual patients as well as impute missing data. This model can be shared instead of actual patient data, resolving privacy issues. The hybrid ML-PK model was trained on chromogenic assay data of lonoctocog alfa and predictive performance was then evaluated on an external data set of patients who received octocog alfa with FVIII levels measured using the one-stage assay. The model presented higher accuracy compared with three previous PK models developed on data similar to the external data set (root mean squared error = 14.6 IU/dL vs. mean of 17.7 IU/dL). Finally, we show that the generative model can be used to accurately impute missing data (< 18% error). In conclusion, the proposed approach introduces interesting new possibilities for model development. In the context of rare disease, the introduction of generative models facilitates sharing of synthetic data, enabling the iterative improvement of population PK models.
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Affiliation(s)
- Alexander Janssen
- Department of Clinical Pharmacology, Hospital Pharmacy, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Louk Smalbil
- Quantitative Data Analytics Group, Department of Computer Science, VU Amsterdam, Amsterdam, The Netherlands
| | - Frank C Bennis
- Follow Me & Emma Neuroscience Group, Emma Children's Hospital, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development, Amsterdam, The Netherlands
| | - Marjon H Cnossen
- Department of Pediatric Hematology, Erasmus MC Sophia Children's Hospital, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Ron A A Mathôt
- Department of Clinical Pharmacology, Hospital Pharmacy, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
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Kooijmans ECM, Hoogendijk EO, Pokladníková J, Smalbil L, Szczerbińska K, Barańska I, Ziuziakowska A, Fialová D, Onder G, Declercq A, Finne-Soveri H, Hoogendoorn M, van Hout HPJ, Joling KJ. The prevalence of non-pharmacological interventions in older homecare recipients: an overview from six European countries. Eur Geriatr Med 2024; 15:243-252. [PMID: 37792242 PMCID: PMC10876758 DOI: 10.1007/s41999-023-00868-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 09/13/2023] [Indexed: 10/05/2023]
Abstract
PURPOSE Non-pharmacological interventions (NPIs) play an important role in the management of older people receiving homecare. However, little is known about how often specific NPIs are being used and to what extent usage varies between countries. The aim of the current study was to investigate the prevalence of NPIs in older homecare recipients in six European countries. METHODS This is a cross-sectional study of older homecare recipients (65+) using baseline data from the longitudinal cohort study 'Identifying best practices for care-dependent elderly by Benchmarking Costs and outcomes of community care' (IBenC). The analyzed NPIs are based on the interRAI Home Care instrument, a comprehensive geriatric assessment instrument. The prevalence of 24 NPIs was analyzed in Belgium, Germany, Finland, Iceland, Italy and the Netherlands. NPIs from seven groups were considered: psychosocial interventions, physical activity, regular care interventions, special therapies, preventive measures, special aids and environmental interventions. RESULTS A total of 2884 homecare recipients were included. The mean age at baseline was 82.9 years and of all participants, 66.9% were female. The intervention with the highest prevalence in the study sample was 'emergency assistance available' (74%). Two other highly prevalent interventions were 'physical activity' (69%) and 'home nurse' (62%). Large differences between countries in the use of NPIs were observed and included, for example, 'going outside' (range 7-82%), 'home health aids' (range 12-93%), and 'physician visit' (range 24-94%). CONCLUSIONS The use of NPIs varied considerably between homecare users in different European countries. It is important to better understand the barriers and facilitators of use of these potentially beneficial interventions in order to design successful uptake strategies.
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Affiliation(s)
- Eline C M Kooijmans
- Department of General Practice, Amsterdam UMC, Location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands.
- Amsterdam Public Health, Ageing and Later Life, Amsterdam, The Netherlands.
| | - Emiel O Hoogendijk
- Department of General Practice, Amsterdam UMC, Location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
- Amsterdam Public Health, Ageing and Later Life, Amsterdam, The Netherlands
- Department of Epidemiology and Data Science, Amsterdam UMC, Location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Jitka Pokladníková
- Department of Social and Clinical Pharmacy, Faculty of Pharmacy in Hradec Králové, Charles University, Prague, Czech Republic
| | - Louk Smalbil
- Department of Computer Science, Vrije Universiteit Amsterdam, De Boelelaan 1111, 1081 HV, Amsterdam, The Netherlands
| | - Katarzyna Szczerbińska
- Chair of Epidemiology and Preventive Medicine, Laboratory for Research on Aging Society, Medical Faculty, Jagiellonian University Medical College, ul. Skawińska 8, Kraków, Poland
| | - Ilona Barańska
- Chair of Epidemiology and Preventive Medicine, Laboratory for Research on Aging Society, Medical Faculty, Jagiellonian University Medical College, ul. Skawińska 8, Kraków, Poland
| | - Adrianna Ziuziakowska
- Chair of Epidemiology and Preventive Medicine, Laboratory for Research on Aging Society, Medical Faculty, Jagiellonian University Medical College, ul. Skawińska 8, Kraków, Poland
| | - Daniela Fialová
- Department of Social and Clinical Pharmacy, Faculty of Pharmacy in Hradec Králové, Charles University, Prague, Czech Republic
- Department of Geriatrics and Gerontology, 1st Faculty of Medicine in Prague, Charles University, Prague, Czech Republic
| | - Graziano Onder
- Fondazione Policlinico Gemelli IRCCS and Università Cattolica del Sacro Cuore, Rome, Italy
| | - Anja Declercq
- LUCAS-Center for Care Research and Consultancy and Ceso-Center for Sociological Research, KU Leuven, Leuven, Belgium
| | | | - Mark Hoogendoorn
- Department of Computer Science, Vrije Universiteit Amsterdam, De Boelelaan 1111, 1081 HV, Amsterdam, The Netherlands
| | - Hein P J van Hout
- Department of General Practice, Amsterdam UMC, Location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
- Amsterdam Public Health, Ageing and Later Life, Amsterdam, The Netherlands
| | - Karlijn J Joling
- Amsterdam Public Health, Ageing and Later Life, Amsterdam, The Netherlands
- Department of Medicine for Older People, Amsterdam UMC, Location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
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Hoogendijk EO, Onder G, Smalbil L, Vetrano DL, Hirdes JP, Howard EP, Morris JN, Fialová D, Szczerbińska K, Kooijmans EC, Hoogendoorn M, Declercq A, De Almeida Mello J, Leskelä RL, Häsä J, Edgren J, Ruppe G, Liperoti R, Joling KJ, van Hout HP. Optimising the care for older persons with complex chronic conditions in home care and nursing homes: design and protocol of I-CARE4OLD, an observational study using real-world data. BMJ Open 2023; 13:e072399. [PMID: 37385750 PMCID: PMC10314651 DOI: 10.1136/bmjopen-2023-072399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 06/08/2023] [Indexed: 07/01/2023] Open
Abstract
INTRODUCTION In ageing societies, the number of older adults with complex chronic conditions (CCCs) is rapidly increasing. Care for older persons with CCCs is challenging, due to interactions between multiple conditions and their treatments. In home care and nursing homes, where most older persons with CCCs receive care, professionals often lack appropriate decision support suitable and sufficient to address the medical and functional complexity of persons with CCCs. This EU-funded project aims to develop decision support systems using high-quality, internationally standardised, routine care data to support better prognostication of health trajectories and treatment impact among older persons with CCCs. METHODS AND ANALYSIS Real-world data from older persons aged ≥60 years in home care and nursing homes, based on routinely performed comprehensive geriatric assessments using interRAI systems collected in the past 20 years, will be linked with administrative repositories on mortality and care use. These include potentially up to 51 million care recipients from eight countries: Italy, the Netherlands, Finland, Belgium, Canada, USA, Hong Kong and New Zealand. Prognostic algorithms will be developed and validated to better predict various health outcomes. In addition, the modifying impact of pharmacological and non-pharmacological interventions will be examined. A variety of analytical methods will be used, including techniques from the field of artificial intelligence such as machine learning. Based on the results, decision support tools will be developed and pilot tested among health professionals working in home care and nursing homes. ETHICS AND DISSEMINATION The study was approved by authorised medical ethical committees in each of the participating countries, and will comply with both local and EU legislation. Study findings will be shared with relevant stakeholders, including publications in peer-reviewed journals and presentations at national and international meetings.
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Affiliation(s)
- Emiel O Hoogendijk
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of General Practice, De Boelelaan 1117, Amsterdam, The Netherlands
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Epidemiology and Data Science, De Boelelaan 1117, Amsterdam, The Netherlands
- Amsterdam Public Health research institute, Ageing and later life research program, Amsterdam, The Netherlands
| | - Graziano Onder
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - Louk Smalbil
- Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Davide L Vetrano
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden
- Stockholm Gerontology Research Center, Stockholm, Sweden
| | - John P Hirdes
- School of Public Health Sciences, University of Waterloo, Waterloo, Ontario, Canada
| | - Elizabeth P Howard
- Connell School of Nursing, Boston College, Chestnut Hill, Boston, MA, USA
- The Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, MA, USA
| | - John N Morris
- The Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, MA, USA
| | - Daniela Fialová
- Department of Social and Clinical Pharmacy, Faculty of Pharmacy, Charles University, Hradec Králové, Czech Republic
- Department of Geriatrics and Gerontology, 1st Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Katarzyna Szczerbińska
- Laboratory for Research on Aging Society, Chair of Epidemiology and Preventive Medicine, Medical Faculty, Jagiellonian University Medical College, Kraków, Poland
| | - Eline Cm Kooijmans
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of General Practice, De Boelelaan 1117, Amsterdam, The Netherlands
- Amsterdam Public Health research institute, Ageing and later life research program, Amsterdam, The Netherlands
| | - Mark Hoogendoorn
- Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Anja Declercq
- LUCAS, Center for Care Research and Consultancy, KU Leuven, Leuven, Belgium
- Center for Sociological Research, KU Leuven, Leuven, Belgium
| | | | | | - Jokke Häsä
- Data and Analytics Unit, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Johanna Edgren
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Georg Ruppe
- European Geriatric Medicine Society (EUGMS), Vienna, Austria
| | - Rosa Liperoti
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - Karlijn J Joling
- Amsterdam Public Health research institute, Ageing and later life research program, Amsterdam, The Netherlands
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Medicine for Older People, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Hein Pj van Hout
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of General Practice, De Boelelaan 1117, Amsterdam, The Netherlands
- Amsterdam Public Health research institute, Ageing and later life research program, Amsterdam, The Netherlands
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