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Dinh TS, Meid AD, Rudolf H, Brueckle MS, González-González AI, Bencheva V, Gogolin M, Snell KIE, Elders PJM, Thuermann PA, Donner-Banzhoff N, Blom JW, van den Akker M, Gerlach FM, Harder S, Thiem U, Glasziou PP, Haefeli WE, Muth C. Anticholinergic burden measures, symptoms, and fall-associated risk in older adults with polypharmacy: Development and validation of a prognostic model. PLoS One 2023; 18:e0280907. [PMID: 36689445 PMCID: PMC9870119 DOI: 10.1371/journal.pone.0280907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 12/22/2022] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND Anticholinergic burden has been associated with adverse outcomes such as falls. To date, no gold standard measure has been identified to assess anticholinergic burden, and no conclusion has been drawn on which of the different measure algorithms best predicts falls in older patients from general practice. This study compared the ability of five measures of anticholinergic burden to predict falls. To account for patients' individual susceptibility to medications, the added predictive value of typical anticholinergic symptoms was further quantified in this context. METHODS AND FINDINGS To predict falls, models were developed and validated based on logistic regression models created using data from two German cluster-randomized controlled trials. The outcome was defined as "≥ 1 fall" vs. "no fall" within a 6-month follow-up period. Data from the RIME study (n = 1,197) were used in model development, and from PRIMUM (n = 502) for external validation. The models were developed step-wise in order to quantify the predictive ability of anticholinergic burden measures, and anticholinergic symptoms. In the development set, 1,015 patients had complete data and 188 (18.5%) experienced ≥ 1 fall within the 6-month follow-up period. The overall predictive value of the five anticholinergic measures was limited, with neither the employed anticholinergic variable (binary / count / burden), nor dose-dependent or dose-independent measures differing significantly in their ability to predict falls. The highest c-statistic was obtained using the German Anticholinergic Burden Score (0.73), whereby the optimism-corrected c-statistic was 0.71 after interval validation using bootstrapping and 0.63 in the external validation. Previous falls and dizziness / vertigo had the strongest prognostic value in all models. CONCLUSIONS The ability of anticholinergic burden measures to predict falls does not appear to differ significantly, and the added value they contribute to risk classification in fall-prediction models is limited. Previous falls and dizziness / vertigo contributed most to model performance.
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Affiliation(s)
- Truc Sophia Dinh
- Institute of General Practice, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Andreas D. Meid
- Department of Clinical Pharmacology and Pharmacoepidemiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Henrik Rudolf
- Department of Medical Informatics, Biometry and Epidemiology, Ruhr-University Bochum, Bochum, Germany
| | - Maria-Sophie Brueckle
- Institute of General Practice, Goethe University Frankfurt, Frankfurt am Main, Germany
| | | | - Veronika Bencheva
- HELIOS University Clinic Wuppertal, Philipp Klee-Institute for Clinical Pharmacology, University of Witten / Herdecke, Witten, Germany
| | - Matthias Gogolin
- HELIOS University Clinic Wuppertal, Philipp Klee-Institute for Clinical Pharmacology, University of Witten / Herdecke, Witten, Germany
| | - Kym I. E. Snell
- Centre for Prognosis Research, School of Medicine, Keele University, Staffordshire, United Kingdom
| | - Petra J. M. Elders
- Amsterdam UMC, General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Petra A. Thuermann
- HELIOS University Clinic Wuppertal, Philipp Klee-Institute for Clinical Pharmacology, University of Witten / Herdecke, Witten, Germany
| | - Norbert Donner-Banzhoff
- Department of General Practice / Family Medicine, Philipps University Marburg, Marburg, Germany
| | - Jeanet W. Blom
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands
| | - Marjan van den Akker
- Institute of General Practice, Goethe University Frankfurt, Frankfurt am Main, Germany
- Department of Family Medicine, Care and Public Health Research Institute, Maastricht University, Maastricht, The Netherlands
- Department of Public Health and Primary Care, Academic Centre of General Practice, KU Leuven, Leuven, Belgium
| | - Ferdinand M. Gerlach
- Institute of General Practice, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Sebastian Harder
- Institute of Clinical Pharmacology, Goethe University Frankfurt, Frankfurt, Germany
| | - Ulrich Thiem
- Department of Geriatrics, Immanuel Albertinen Diakonie, Albertinen-Haus, Hamburg, Germany
- University Clinic Eppendorf, Hamburg, Germany
| | - Paul P. Glasziou
- Faculty of Health Sciences and Medicine, Bond University, Robina, QLD, Australia
| | - Walter E. Haefeli
- Department of Clinical Pharmacology and Pharmacoepidemiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Christiane Muth
- Institute of General Practice, Goethe University Frankfurt, Frankfurt am Main, Germany
- Department of General Practice and Family Medicine, Medical Faculty East-Westphalia, University of Bielefeld, Bielefeld, Germany
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Meid AD, Gonzalez-Gonzalez AI, Dinh TS, Blom J, van den Akker M, Elders P, Thiem U, Küllenberg de Gaudry D, Swart KMA, Rudolf H, Bosch-Lenders D, Trampisch HJ, Meerpohl JJ, Gerlach FM, Flaig B, Kom G, Snell KIE, Perera R, Haefeli WE, Glasziou P, Muth C. Predicting hospital admissions from individual patient data (IPD): an applied example to explore key elements driving external validity. BMJ Open 2021; 11:e045572. [PMID: 34348947 PMCID: PMC8340284 DOI: 10.1136/bmjopen-2020-045572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
OBJECTIVE To explore factors that potentially impact external validation performance while developing and validating a prognostic model for hospital admissions (HAs) in complex older general practice patients. STUDY DESIGN AND SETTING Using individual participant data from four cluster-randomised trials conducted in the Netherlands and Germany, we used logistic regression to develop a prognostic model to predict all-cause HAs within a 6-month follow-up period. A stratified intercept was used to account for heterogeneity in baseline risk between the studies. The model was validated both internally and by using internal-external cross-validation (IECV). RESULTS Prior HAs, physical components of the health-related quality of life comorbidity index, and medication-related variables were used in the final model. While achieving moderate discriminatory performance, internal bootstrap validation revealed a pronounced risk of overfitting. The results of the IECV, in which calibration was highly variable even after accounting for between-study heterogeneity, agreed with this finding. Heterogeneity was equally reflected in differing baseline risk, predictor effects and absolute risk predictions. CONCLUSIONS Predictor effect heterogeneity and differing baseline risk can explain the limited external performance of HA prediction models. With such drivers known, model adjustments in external validation settings (eg, intercept recalibration, complete updating) can be applied more purposefully. TRIAL REGISTRATION NUMBER PROSPERO id: CRD42018088129.
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Affiliation(s)
- Andreas Daniel Meid
- Department of Clinical Pharmacology & Pharmacoepidemiology, Heidelberg University, Heidelberg, Baden-Württemberg, Germany
| | - Ana Isabel Gonzalez-Gonzalez
- Institute of General Practice, Goethe University, Frankfurt am Main, Hessen, Germany
- Red de Investigación en Servicios de Salud en Enfermedades Crónicas (REDISSEC), Madrid, Spain
| | - Truc Sophia Dinh
- Institute of General Practice, Goethe University, Frankfurt am Main, Hessen, Germany
| | - Jeanet Blom
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands
| | - Marjan van den Akker
- Institute of General Practice, Goethe University, Frankfurt am Main, Hessen, Germany
- School of CAPHRI, Department of Family Medicine, Maastricht University, Maastricht, The Netherlands
| | - Petra Elders
- Department of General Practice and Elderly Care Medicine, Amsterdam UMC, Vrije Universiteit, Amstedarm Public Health Research Institute, Amsterdam, The Netherlands
| | - Ulrich Thiem
- Chair of Geriatrics and Gerontology, University Clinic Eppendorf, Hamburg, Germany
| | - Daniela Küllenberg de Gaudry
- Institute for Evidence in Medicine (for Cochrane Germany Foundation), Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Karin M A Swart
- Department of General Practice and Elderly Care Medicine, Amsterdam UMC, Vrije Universiteit, Amstedarm Public Health Research Institute, Amsterdam, The Netherlands
| | - Henrik Rudolf
- Department of Medical Informatics, Biometry and Epidemiology, Ruhr University Bochum, Bochum, Nordrhein-Westfalen, Germany
| | - Donna Bosch-Lenders
- School of CAPHRI, Department of Family Medicine, Maastricht University, Maastricht, The Netherlands
| | - Hans J Trampisch
- Department of Medical Informatics, Biometry and Epidemiology, Ruhr University Bochum, Bochum, Nordrhein-Westfalen, Germany
| | - Joerg J Meerpohl
- Institute for Evidence in Medicine (for Cochrane Germany Foundation), Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Ferdinand M Gerlach
- Institute of General Practice, Goethe University, Frankfurt am Main, Hessen, Germany
| | - Benno Flaig
- Institute of General Practice, Goethe University, Frankfurt am Main, Hessen, Germany
| | | | - Kym I E Snell
- Centre for Prognosis Research, School of Primary Care Research, Community and Social Care, Keele University, Keele, UK
| | - Rafael Perera
- Nuffield Department of Primary Care, University of Oxford, Oxford, UK
| | - Walter Emil Haefeli
- Department of Clinical Pharmacology & Pharmacoepidemiology, Heidelberg University, Heidelberg, Baden-Württemberg, Germany
| | - Paul Glasziou
- Centre for Research in Evidence-Based Practice, Bond University, Robina, Queensland, Australia
| | - Christiane Muth
- Institute of General Practice, Goethe University, Frankfurt am Main, Hessen, Germany
- Department of General Practice and Family Medicine, Medical Faculty OWL, University of Bielefeld, Bielefeld, Germany
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