A Simple and Accurate Model for Predicting Fall Injuries in Hospitalized Patients: Insights from a Retrospective Observational Study in Japan.

Abstract

BACKGROUND While several predictive models for falls have been reported such as we reported in 2020, those for fall "injury" have been unreported. This study was designed to develop a model to predict fall injuries in adult inpatients using simple predictors available immediately after hospitalization. MATERIAL AND METHODS This was a single-center, retrospective cohort study. We enrolled inpatients aged ≥20 years admitted to an acute care hospital from April 2012 to March 2018. The variables routinely obtained in clinical practice were compared between the patients with fall injury and the patients without fall itself or fall injury. Multivariable analysis was performed using covariables available on admission. A predictive model was constructed using only variables showing significant association in prior multivariable analysis. RESULTS During hospitalization of 17 062 patients, 646 (3.8%) had falls and 113 (0.7%) had fall injuries. Multivariable analysis showed 6 variables that were significantly associated with fall injuries during hospitalization: age (P=0.001), sex (P=0.001), emergency transport (P<0.001), medical referral letter (P=0.041), history of falls (P=0.012), and abnormal bedriddenness ranks (all P≤0.001). The area under the curve of this predictive model was 0.794 and the shrinkage coefficient was 0.955 using the same data set given above. CONCLUSIONS We developed a predictive model for fall injuries during hospitalization using 6 predictors, including bedriddenness ranks from official Activities of Daily Living indicators in Japan, which were all easily available on admission. The model showed good discrimination by internal validation and promises to be a useful tool to assess the risk of fall injuries.