Kuspinar A, et al. - Researchers used decision tree analysis to construct and validate an algorithm to predict first-time falls (1^stFall) among home care clients who had not fallen in the past 90 days. Participants were 88,690 Ontario home care clients who were evaluated with the Resident Assessment Instrument-Home Care between 2002 and 2014. They sought to validate this algorithm among home care clients in Ontario (n = 38,013), Manitoba (n = 2738), Alberta (n = 1226) and British Columbia (n = 9566). The identification of six categories from low to high risk was enabled by the 1^stFall algorithm that incorporated the utilization of assistive devices, unsteady gait, age, cognition, pain and incontinence. This algorithm allowed the prediction of future falls in individuals who had not fallen in the past 90 days. In 4 independent samples, the predictive validity was displayed by six distinct risk categories.