Development of the ADFICE_IT Models for Predicting Falls and Recurrent Falls in Community-Dwelling Older Adults: Pooled Analyses of European Cohorts With Special Attention to Medication

Author:

van de Loo Bob123ORCID,Seppala Lotta J23ORCID,van der Velde Nathalie23ORCID,Medlock Stephanie34,Denkinger Michael56,de Groot Lisette CPGM7,Kenny Rose-Anne8,Moriarty Frank79ORCID,Rothenbacher Dietrich10ORCID,Stricker Bruno11,Uitterlinden André1112,Abu-Hanna Ameen34,Heymans Martijn W13,van Schoor Natasja13

Affiliation:

1. Amsterdam UMC location Vrije Universiteit Amsterdam, Epidemiology and Data Science , De Boelelaan 1117, Amsterdam , Netherlands

2. Amsterdam UMC location University of Amsterdam, Internal Medicine, Section of Geriatric Medicine , Meibergdreef 9, Amsterdam , Netherlands

3. Amsterdam Public Health research institute , Amsterdam , The Netherlands

4. Amsterdam UMC location University of Amsterdam, Department of Medical Informatics , Meibergdreef 9, Amsterdam , Netherlands

5. Institute for Geriatric Research, Ulm University at Agaplesion Bethesda Clinic , Ulm , Germany

6. Geriatric Center Ulm , Ulm , Germany

7. Division of Human Nutrition and Health, Wageningen University , Wageningen , The Netherlands

8. TILDA, Department of Medical Gerontology, Trinity College , Dublin , Ireland

9. School of Pharmacy and Biomolecular Sciences, Royal College of Surgeons in Ireland , Dublin , Ireland

10. Institute of Epidemiology and Medical Biometry, Ulm University , Ulm , Germany

11. Department of Epidemiology, Erasmus University Medical Center , Rotterdam , The Netherlands

12. Department of Internal Medicine, Erasmus University Medical Center , Rotterdam , The Netherlands

Abstract

Abstract Background Use of fall prevention strategies requires detection of high-risk patients. Our goal was to develop prediction models for falls and recurrent falls in community-dwelling older adults and to improve upon previous models by using a large, pooled sample and by considering a wide range of candidate predictors, including medications. Methods Harmonized data from 2 Dutch (LASA, B-PROOF) and 1 German cohort (ActiFE Ulm) of adults aged ≥65 years were used to fit 2 logistic regression models: one for predicting any fall and another for predicting recurrent falls over 1 year. Model generalizability was assessed using internal–external cross-validation. Results Data of 5 722 participants were included in the analyses, of whom 1 868 (34.7%) endured at least 1 fall and 702 (13.8%) endured a recurrent fall. Positive predictors for any fall were: educational status, depression, verbal fluency, functional limitations, falls history, and use of antiepileptics and drugs for urinary frequency and incontinence; negative predictors were: body mass index (BMI), grip strength, systolic blood pressure, and smoking. Positive predictors for recurrent falls were: educational status, visual impairment, functional limitations, urinary incontinence, falls history, and use of anti-Parkinson drugs, antihistamines, and drugs for urinary frequency and incontinence; BMI was a negative predictor. The average C-statistic value was 0.65 for the model for any fall and 0.70 for the model for recurrent falls. Conclusion Compared with previous models, the model for recurrent falls performed favorably while the model for any fall performed similarly. Validation and optimization of the models in other populations are warranted.

Funder

ZonMw

New Cohorts of young old in the 21st century

Netherlands Consortium Healthy Ageing

Ministry of Economic Affairs, Agriculture and Innovation

European Union

Ministry of Science, Baden-Württemberg, and the German Research Foundation

European Commission

Office of the Minister for Health and Children

Publisher

Oxford University Press (OUP)

Subject

Geriatrics and Gerontology,Aging

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