New Predictive Models for Falls in Acute Care Setting Classified Using An Activities of Daily Living Scale in Japan: A Retrospective Cohort Study
Abstract Background In several current fall prediction models, the reported predictors vary from one model to another. We developed and validated a new fall prediction model for patients admitted to an acute care hospital by identifying predictors of falls considering a combination of background factors and one crucial stratum. Methods We conducted a retrospective cohort study of patients admitted to Shizuoka General Hospital from April 2019 to September 2020, aged 20 years or older. We developed and validated a new fall prediction model by identifying predictors of falls stratified by essential activities of daily living (ADL) indicators and integrating these models. Results A total of 22,988 individuals were included in the analysis, with 653 (2.8 %) experiencing all falls and 400 (1.7 %) experiencing falls with medical resources during the study period. Multivariate analysis was performed after one stratification level, using bedridden rank (ability to move around in daily life) as a stratifying variable, a clinically important variable and highly correlated with 17 other variables. The results of multivariate analysis showed that the risk factors for falls (high risk) were age (high), sex (men), and ambulance transport (yes) for rank J (independence/autonomy); age (high),) and sex (men) for rank A (house-bound); There were no predictors for rank B (chair-bound); and there was ophthalmologic disease (no) for rank C (bed-bound). The c-index indicating the prediction model’s performance for falls within 28 days of hospitalisation was 0.705 (95 % CI, 0.664–0.746). Hosmer-Lemeshow goodness-of-fit statistics were significant (χ2 = 192.06; 8 degrees of freedom; p < 0.001). The c-index for the entire unstratified sample was 0.703 (95 % CI, 0.661–0.746), indicating that the predictive model stratified by bedriddenness rank was accurate (p < 0.001). Conclusion We identified predictors of falls using important ADLs (bedriddenness rank) and developed a more accurate prediction model in acute care hospital settings. This predictive model is an essential tool for fall prevention.