title: Comparison of simple vs. performance-based fall prediction models: data from the National Health and Aging Trends Study.
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.
  
  
  METHODS
  We developed a series of fall prediction models progressing in complexity and compared area under the receiver operating characteristic curve (AUC) across models.
  
  
  METHODS
  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).
  
  
  METHODS
  6056 community-dwelling individuals who participated in Rounds 1 and 2 of NHATS.
  
  
  METHODS
  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 bothany fall(AUC=0.69, 95% CI 0.67-0.71) andrecurrent falls(AUC=0.77, 95% CI 0.74-0.79) in the development set. Physical performance testing provided marginal additional predictive value.
  
  
  CONCLUSIONS
  A simple clinical prediction model that does not include physical performance testing could facilitate routine, widespread falls risk screening in the ambulatory care setting.
authors.affiliation: Department of Otolaryngology - Head & Neck Surgery Johns Hopkins University School of Medicine Baltimore, MD.
authors.forename: Shekhar K
authors.initials: SK
authors.lastname: Gadkaree
authors.affiliation: Department of Otolaryngology - Head & Neck Surgery Johns Hopkins University School of Medicine Baltimore, MD.
authors.forename: Daniel Q
authors.initials: DQ
authors.lastname: Sun
authors.affiliation: The Center on Aging and Health, Johns Hopkins University School of Medicine, Baltimore, MD.
authors.forename: Jin
authors.initials: J
authors.lastname: Huang
authors.affiliation: The Center on Aging and Health, Johns Hopkins University School of Medicine, Baltimore, MD ; Division of Biostatistics and Bioinformatics, Department of Oncology, Sidney Kimmel Comprehensive Care Center, Johns Hopkins University.
authors.forename: Ravi
authors.initials: R
authors.lastname: Varadhan
authors.affiliation: Department of Otolaryngology - Head & Neck Surgery Johns Hopkins University School of Medicine Baltimore, MD.
authors.forename: Yuri
authors.initials: Y
authors.lastname: Agrawal
keywords: Falls; fall risk; recurrent falls