Artikel
Development and validation of the PROPERmed instrument to identify older patients in general practice at risk of hospital admissions: an individual participant data meta-analysis (IPD-MA)
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Autoren
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Andreas Meid
- University Hospital, Department of Clinical Pharmacology and Pharmacoepidemiology, Deutschland
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Ana Isabel González-González
- Goethe University, Institute of General Medicine, Deutschland
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Truc Sophia Nguyen
- Goethe University, Institute of General Medicine, Deutschland
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Jeanet W. Blom
- Leiden University Medical Center, Department of Public Health and Primary Care, Leiden, Niederlande
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Marjan van den Akker
- Goethe University, Institute of General Medicine, Deutschland
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Karin Swart
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, Amsterdam, Niederlande
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Daniela Küllenberg de Gaudry
- University of Freiburg Faculty of Medicine, Institute for Evidence in Medicine (for Cochrane Germany Foundation), Medical Center, Freiburg, Deutschland
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Ulrich Thiem
- Albertinen-Diakoniewerk, Deutschland
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Kym Snell
- Keele University, Centre for Prognosis Research. Research Institute for Primary Care & Health Sciences, Großbritannien
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Walter Emil Haefeli
- University Hospital, Department of Clinical Pharmacology and Pharmacoepidemiology, Deutschland
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Rafael Perera
- University of Oxford, Nuffield Department of Primary Care, Oxford, Großbritannien
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Hans-Joachim Trampisch
- Ruhr University, AMIB, Deutschland
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Henrik Rudolf
- Ruhr University, AMIB, Deutschland
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Jörg Meerpohl
- University of Freiburg Faculty of Medicine, Institute for Evidence in Medicine (for Cochrane Germany Foundation), Medical Center, Freiburg, Deutschland
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Petra Elders
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, Amsterdam, Niederlande
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Frank Verheyen
- Techniker Krankenkasse (TK), Deutschland
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Benno Flaig
- Goethe University, Institute of General Medicine, Deutschland
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Ghainsom Kom
- Techniker Krankenkasse (TK), Deutschland
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Paul P. Glasziou
- Bond University, Centre for Research in Evidence-Based Practice (CREBP), Australien
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Ferdinand Michael Gerlach
- Goethe University, Institute of General Medicine, Deutschland
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Christiane Muth
- Goethe University, Institute of General Medicine, Deutschland
| Veröffentlicht: | 11. September 2019 |
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Gliederung
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Background: Elderly patients with multimorbidity and polypharmacy are at risk of inappropriate prescriptions and undertreatment, which may lead to increased number of hospital admissions (HAs). For designing preventive interventions and applying them to heterogeneous primary care populations, it would be helpful to identify those patients at highest risk of HAs.
Objective: To develop and validate a prognostic model to predict HAs within six-month follow-up in older patients in general practice with ≥1 chronic condition and ≥1 chronic medication.
Methods: We harmonized individual participant data (IPD) from four cluster-randomized trials conducted in the Netherlands and Germany.The model was developed using logistic regression with a stratified-intercept to account for between-study heterogeneity in baseline risk. Variables were selected in complete cases and then refitted in multiply imputed data to obtain the final model equation. Between-study heterogeneity in predictor effects was explored by meta-analytic techniques and interaction terms accounted for it, if indicated. Predictive performance and generalisability were derived by bootstrap internal validation and internal-external cross-validation (IECV), respectively.
Results: We included 3,832 participants with a mean age of 78 years, 60% females, 95% living at home, 3 chronic conditions on average as well as 7 chronic prescriptions. Selected predictors related to demographics (e.g., age), disease and health status (e.g., heart failure, pain), and medication-related risks (internal performance: calibration slope of 0.85 [0.33;1.36] and c-statistics of 0.64 [0.62;0.66]; generalisability: pooled c-statistics in the IECV loop of 0.61). Development sample sizes, event frequencies in validation data and effect heterogeneity may partly explain obtained performance estimates.
Discussion: This very first IPD-based prediction model for HAs in older patients already performed satisfactorily. Nevertheless, it may be further improved, e.g., by considering preventable HAs instead of all-cause HAs.
Take home message for practical use: IPD-based modelling is a promising approach to address the challenging prediction of future HAs in general practice.
