Validation of the FRAiL model to predict non-vertebral and hip fractures in nursing home residents

TitleValidation of the FRAiL model to predict non-vertebral and hip fractures in nursing home residents
Publication TypeJournal Article
Year of Publication2019
AuthorsBerry SD, Zullo AR, Zhang T, Lee Y, McConeghy KW, Kiel DP
Date Published2019/11/01/
ISBN Number8756-3282
Accession NumberWOS:000493584700017
Keywordship fracture, Non-vertebral fracture, nursing home, Prediction model, Risk Factors

Objective Tools were unavailable to assess fracture risk in nursing homes (NH); therefore, we developed the Fracture Risk Assessment in Long term care (FRAiL) model. The objective of this validation study was to assess the performance of the FRAiL model to predict 2-year risk of non-vertebral and hip fractures in a separate large cohort of NH residents. Methods This retrospective cohort study included most long-stay NH residents in the United States (N = 896,840). Hip and non-vertebral fractures were identified using Medicare claims. The Minimum Data Set (MDS) was used to identify characteristics from the original FRAiL model. Multivariable competing risk regression was used to model risk of fracture. Results Mean age was 83.8 years (±8.2 years) and 70.7% were women. Over a mean follow-up of 1.52 years (SD 0.65), 41,531 residents (4.6%) were hospitalized with non-vertebral fracture (n = 30,356 hip fractures). In the fully adjusted model, 14/15 model characteristics remained significant predictors of non-vertebral fracture. Female sex (HR = 1.55, 95% CI 1.52, 1.59), wandering (HR = 1.30, 95% CI 1.26, 1.34), and falls (HR = 1.28, 95% CI 1.26, 1.31) were strongly associated with non-vertebral fracture rate. Total dependence in ADLs (versus independence) was associated with a decrease in non-vertebral fracture rate (HR = 0.57, 95% CI 0.52, 0.64). Discrimination was moderate in men (C-index = 0.68 for hip, 0.66 for non-vertebral) and women (C-index = 0.68 for hip, 0.65 for non-vertebral), and calibration was excellent. Conclusions Our model comprised entirely from routinely collected data was able to identify NH residents at greatest risk for non-vertebral fracture.