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Abstract
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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: 2011-12) and one-year follow-up (Round 2: 2012-3). PARTICIPANTS: 6056 community-dwelling individuals who participated in Rounds 1 and 2 of NHATS. MEASUREMENTS: Primary outcomes were one-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% 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.
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.
Language: en |