Shekhar K. Gadkaree, Daniel Q. Sun, Jin Huang, Ravi Varadhan, Yuri Agrawal

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. We developed a series of fall prediction models progressing in complexity and compared area under the receiver operating characteristic curve (AUC) across models. National Health and Aging Trends Study (NHATS), which surveyed a nationally-representative sample of Medicare enrollees (age ≥65) at baseline (Round 1: 2011-12) and one-year follow-up (Round 2: 2012-3). 6056 community-dwelling individuals who participated in Rounds 1 and 2 of NHATS. Primary outcomes were one-year incidence of "any fall" and "recurrent falls". Prediction models were compared and validated in development and validation sets, respectively. 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% 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 marginal additional predictive value. A simple clinical prediction model that does not include physical performance testing could facilitate routine, widespread falls risk screening in the ambulatory care setting.