Evidence map›Paper›PMID 41942915›Full record

ArticleBMC geriatrics2026

Development of fall prediction models in community-dwelling older adults: comparison of biological and multidomain models for falls and injurious falls.

Fajr Elbanna, Stephanie Saunders, Cassandra D'Amore, Marla K Beauchamp

Abstract readComparative Study
In one paragraph

Article in BMC geriatrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Fajr ElbannaSchool of Rehabilitation Science, Institute of Applied Health Sciences, McMaster University, Hamilton, ON, L8S 1C7, Canada.
Stephanie SaundersDepartment of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.
Cassandra D'AmoreDepartment of Physical Therapy, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada.
Marla K BeauchampSchool of Rehabilitation Science, Institute of Applied Health Sciences, McMaster University, Hamilton, ON, L8S 1C7, Canada. beaucm1@mcmaster.ca.

Funding

CIHR PJT-165844
6 · The paper itself

Abstract

backgroundFalls are a leading cause of morbidity and loss of independence in older adults. Existing fall prediction models vary widely in design and performance, and the added value of incorporating nonbiological factors remains unclear. This study aimed to evaluate the predictive performance of the biological, sociodemographic, behavioral, and environmental domains proposed by the World Health Organization (WHO) for discriminating fall risk in community-dwelling older adults.

methodsWe conducted a cross-sectional analysis using baseline data from the prospective INITIATE cohort of community-dwelling adults aged ≥ 65 years. LASSO regression with 10-fold cross-validation was used to select biological predictors for any fall and injurious fall outcomes, followed by multivariable logistic regression. Sequential models reflecting sociodemographic, behavioral, and environmental domains were constructed to assess incremental discriminative performance. Model performance was assessed using the area under the ROC curve (AUC), with DeLong’s test used for comparisons. Bootstrap validation (1,000 iterations) was used to assess internal validity.

resultsA total of 433 participants were included (median age 76 years; 64% female). For any falls (n = 194, 44.8%), the reduced biological model (mobility limitation, balance, visual acuity, fear of falling) achieved an AUC of 0.64 (95% CI 0.58–0.69). For injurious falls (n = 40, 28.4%), the reduced biological model (Timed Up and Go [TUG] time, grip strength, executive function, global cognition, and pain interference) achieved an AUC of 0.73 (95% CI 0.64–0.82). Adding sociodemographic, behavioral, and environmental variables produced minimal, nonsignificant improvements for both outcomes (any falls: AUC 0.65, p = 0.46; injurious falls: AUC 0.78, p = 0.30).

conclusionsParsimonious models based primarily on biological measures can provide clinically meaningful discrimination while remaining feasible for community and outpatient use. The distinct risk profiles for any falls and injurious falls highlight the need for outcome-specific screening approaches. Prospective evaluation and external validation are needed prior to clinical implementation.

Indexed as

Accidental FallsIndependent LivingAgedAged, 80 and overAging in PlaceCohort StudiesCross-Sectional StudiesFemaleHumansMalePrediction AlgorithmsProspective StudiesRisk AssessmentRisk FactorsBiological predictorsCommunity-dwellingFall risk assessmentFallsInjurious fallsLASSO regressionModel performanceOlder adultsPrediction models

Identifiers

PMID41942915
PMCPMC13192079

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.