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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.

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posted on 09.06.2022, 16:39 by Bob van de Loo, Lotta J Seppala, Nathalie van der Velde, Stephanie Medlock, Michael Denkinger, Lisette CPGM de Groot, Rose-Anne Kenny, Frank MoriartyFrank Moriarty, Dietrich Rothenbacher, Bruno Stricker, André Uitterlinden, Ameen Abu-Hanna, Martijn W Heymans, Natasja van Schoor

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 two Dutch (LASA, B-PROOF) and one German cohort (ActiFE Ulm) of adults aged ≥ 65 years were used to fit two logistic regression models: one for predicting any fall and another for predicting recurrent falls over one year. Model generalizability was assessed using internal-external cross-validation.

Results: Data of 5722 participants were included in the analyses, of whom 1868 (34.7%) endured at least one 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.

Conclusions: 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 is warranted.

Funding

The Netherlands Organization for Health Research and Development (ZonMw, Grant 848017004), The Hague

The Netherlands Ministry of Health, Welfare and Sports, Directorate of Long-Term Care

The Netherlands Organization for Scientific Research (NWO) in the framework of the project “New Cohorts of young old in the 21st century” (File Number 480-10-014)

Netherlands Organization for Health Research and Development (ZonMw, Grant 6130.0031), The Hague

NZO (Dutch Dairy Association), Zoetermeer

Wageningen University, Wageningen

European Union (No. 2005121)

Ministry of Science, Baden-Württemberg, and the German Research Foundation (RO2606/14-1, DE2674/1-1)

Erasmus MC University Medical Center and Erasmus University Rotterdam

The Netherlands Organization for Scientific Research (NWO)

The Netherlands Organization for Health Research and Development (ZonMw)

The Research Institute for Diseases in the Elderly (RIDE)

The Netherlands Genomics Initiative (NGI)

The Ministry of Education, Culture and Science

The Ministry of Health, Welfare and Sports

The European Commission (DG XII)

The Municipality of Rotterdam

Government of Ireland through the Office of the Minister for Health and Children, by Atlantic Philanthropies and Irish Life

Orthica, Almere

Netherlands Consortium Healthy Ageing (NCHA), Leiden/Rotterdam

Ministry of Economic Affairs, Agriculture and Innovation (project KB-15-004-003) The Hague

VUmc, Amsterdam

Erasmus Medical Center, Rotterdam

History

Comments

The original article is available at https://academic.oup.com/

Published Citation

van de Loo B. et al. 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. J Gerontol A Biol Sci Med Sci. 2022:glac080

Publication Date

4 April 2022

PubMed ID

35380638

Department/Unit

  • School of Pharmacy and Biomolecular Sciences

Research Area

  • Population Health and Health Services

Publisher

Oxford University Press (OUP)

Version

  • Published Version (Version of Record)