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

Autor: Duprey MS; Department of Health Services, Policy, and Practice, Brown University School of Public Health, Providence, Rhode Island, USA.; Department of Pharmacy Practice and Science, University of Kentucky College of Pharmacy, Lexington, Kentucky, USA., Zullo AR; Department of Health Services, Policy, and Practice, Brown University School of Public Health, Providence, Rhode Island, USA.; Department of Pharmacy, Lifespan Rhode Island Hospital, Providence, Rhode Island, USA.; Department of Epidemiology, Brown University School of Public Health, Providence, Rhode Island, USA.; Center of Innovation in Long-Term Services and Supports, Providence Veterans Affairs Medical Center, Providence, Rhode Island, USA., Gouskova NA; Hinda and Arthur Marcus Institute for Aging Research, Hebrew Senior Life, Roslindale, Massachusetts, USA., Lee Y; Department of Health Services, Policy, and Practice, Brown University School of Public Health, Providence, Rhode Island, USA., Capuano A; Department of Pharmacy, Lifespan Rhode Island Hospital, Providence, Rhode Island, USA., Kiel DP; Hinda and Arthur Marcus Institute for Aging Research, Hebrew Senior Life, Roslindale, Massachusetts, USA.; Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA., Daiello LA; Department of Health Services, Policy, and Practice, Brown University School of Public Health, Providence, Rhode Island, USA., Kim DH; Hinda and Arthur Marcus Institute for Aging Research, Hebrew Senior Life, Roslindale, Massachusetts, USA.; Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA., Berry SD; Hinda and Arthur Marcus Institute for Aging Research, Hebrew Senior Life, Roslindale, Massachusetts, USA.; Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA.
Jazyk: angličtina
Zdroj: Journal of the American Geriatrics Society [J Am Geriatr Soc] 2023 Jun; Vol. 71 (6), pp. 1851-1860. Date of Electronic Publication: 2023 Mar 08.
DOI: 10.1111/jgs.18277
Abstrakt: 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.
(© 2023 The American Geriatrics Society.)
Databáze: MEDLINE