Predicting First Time Falls: Validating a Novel Algorithm in Long Term Care

To determine the predictive validity of the 1stFall algorithm in long-term care (LTC) residents across four Canadian provinces. This retrospective cohort study included all clients admitted to LTC between 2006-2017 with no history of falls in the past 30 days. The outcome was occurrence of a fall and logistic regression analysis was performed to assess predictive validity. A total of 199,997 LTC residents were studied (71% were >80 years old, 66% women, and 17% had severe cognitive impairment). For the total sample, clients in the 2nd, 3rd, 4th and 5th risk categories had 1.15, 1.58, 2.66, and 3.76 times greater odds of falling than the 1st category, respectively. Similar trends were observed across provinces. 1stFall was developed to predict the risk of a first-time fall event in individuals with no history of a recent fall. 1stFall identified LTC residents at risk of a first-time fall, supporting its use in routine care.