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

2 Scopus citations

Abstract

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
Volume71
Issue number6
DOIs
StatePublished - Jun 2023

Bibliographical note

Publisher Copyright:
© 2023 The American Geriatrics Society.

Keywords

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

ASJC Scopus subject areas

  • Geriatrics and Gerontology

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