Comparison of Simple Versus Performance-Based Fall Prediction Models

Abstract

Objective: To compare the predictive ability of standard falls prediction models based on physical performance assessments with more parsimonious prediction models based on self-reported data. Design: We developed a series of fall prediction models progressing in complexity and compared area under the receiver operating characteristic curve (AUC) across models. Setting: National Health and Aging Trends Study (NHATS), which surveyed a nationally representative sample of Medicare enrollees (age ≥65) at baseline (Round 1: 2011-2012) and 1-year follow-up (Round 2: 2012-2013). Participants: In all, 6,056 community-dwelling individuals participated in Rounds 1 and 2 of NHATS. Measurements: Primary outcomes were 1-year incidence of “ any fall” and “ recurrent falls.” Prediction models were compared and validated in development and validation sets, respectively. Results: A prediction model that included demographic information, self-reported problems with balance and coordination, and previous fall history was the most parsimonious model that optimized AUC for both any fall (AUC = 0.69, 95% confidence interval [CI] = [0.67, 0.71]) and recurrent falls (AUC = 0.77, 95% CI = [0.74, 0.79]) in the development set. Physical performance testing provided a marginal additional predictive value. Conclusion: A simple clinical prediction model that does not include physical performance testing could facilitate routine, widespread falls risk screening in the ambulatory care setting.