ArticleJournal of the American Geriatrics Society2023
Development and validation of the fall-related injury risk in nursing homes (INJURE-NH) prediction tool.
Article in Journal of the American Geriatrics Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 2 syntheses or guidelines pooled it, 8 citations in OpenAlex.
- A Systematic Review of Multivariate Models for Predicting Fall-Related Injuries in Older Adults.Journal of nursing management · 2026Pooled it
- Interventions for preventing falls in older people in care facilities.The Cochrane database of systematic reviews · 2025Pooled it
- Exposure to Drugs Related to Injurious Falls Risk and to Bisphosphonates in Nursing Home Residents With Recent Fracture.Journal of the American Medical Directors Association · 2026Article
- The interplay of risk factors for future falls and fall-related injuries among community-dwelling older adults: a Bayesian network analysis.BMC public health · 2026Article
- Fall Risk Assessment and Prevention Strategies in Nursing Homes: A Narrative Review.Healthcare (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
9 authors at 5 institutions in 1 country.
Funding
Abstract
backgroundExisting models to predict fall-related injuries (FRI) in nursing homes (NH) focus on hip fractures, yet hip fractures comprise less than half of all FRIs. We developed and validated a series of models to predict the absolute risk of FRIs in NH residents.
methodsRetrospective cohort study of long-stay US NH residents (≥100 days in the same facility) between January 1, 2016 and December 31, 2017 (n = 733,427) using Medicare claims and Minimum Data Set v3.0 clinical assessments. Predictors of FRIs were selected through LASSO logistic regression in a 2/3 random derivation sample and tested in a 1/3 validation sample. Sub-distribution hazard ratios (HR) and 95% confidence intervals (95% CI) were estimated for 6-month and 2-year follow-up. Discrimination was evaluated via C-statistic, and calibration compared the predicted rate of FRI to the observed rate. To develop a parsimonious clinical tool, we calculated a score using the five strongest predictors in the Fine-Gray model. Model performance was repeated in the validation sample.
resultsMean (Q1, Q3) age was 85.0 (77.5, 90.6) years and 69.6% were women. Within 2 years of follow-up, 43,976 (6.0%) residents experienced ≥1 FRI. Seventy predictors were included in the model. The discrimination of the 2-year prediction model was good (C-index = 0.70), and the calibration was excellent. Calibration and discrimination of the 6-month model were similar (C-index = 0.71). In the clinical tool to predict 2-year risk, the five characteristics included independence in activities of daily living (ADLs) (HR 2.27; 95% CI 2.14-2.41) and a history of non-hip fracture (HR 2.02; 95% CI 1.94-2.12). Performance results were similar in the validation sample.
conclusionsWe developed and validated a series of risk prediction models that can identify NH residents at greatest risk for FRI. In NH, these models should help target preventive strategies.
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