Development and validation of the fall-related injury risk in nursing homes (INJURE-NH) prediction tool

Matthew S. Duprey, Andrew R. Zullo, Natalia A. Gouskova, Yoojin Lee, Alyssa Capuano, Douglas P. Kiel, Lori A. Daiello, Dae Hyun Kim, Sarah D. Berry

Research output: Contribution to journalArticlepeer-review


Background: Existing 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. Methods: Retrospective 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. Results: Mean (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. Conclusions: We 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.

Original languageEnglish
Pages (from-to)1851-1860
Number of pages10
JournalJournal of the American Geriatrics Society
Issue number6
StatePublished - Jun 2023

Bibliographical note

Funding Information:
This work was funded by the National Institute on Aging award RF1AG061221. Dr. Zullo is also funded in part by awards R21AG061632 and R01AG065722 from the National Institute on Aging and U54GM115677 from the National Institute of General Medical Sciences.

Publisher Copyright:
© 2023 The American Geriatrics Society.


  • fall-related injuries
  • fracture
  • functional assessment
  • long-term care
  • risk prediction

ASJC Scopus subject areas

  • Geriatrics and Gerontology


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