title: Identifying frail older people using predictive modeling.
abstract: To determine whether a designation of frailty using the Adjusted Clinical Groups-diagnoses based computerized predictive model (ACG Dx-PM) can identify an elderly population who (1) have the clinical characteristics of frailty and (2) are frail as determined by the validated Vulnerable Elders Survey (VES), and to determine the ability of these tools to predict adverse outcomes.
  
  
  
  Secondary analysis of administrative and survey data.
  
  
  
  Participants over age 65 years (n = 195) in an outpatient comprehensive geriatric assessment study at an Israeli health maintenance organization (HMO) were screened for frailty using the ACG Dx-PM and VES. Administrative and demographic data were also gathered.
  
  
  
  Compared with ACG nonfrail patients, ACG frail patients were older and less likely to be married; had a higher rate of falls, incontinence, and need for personal care; and had a poorer quality of life consistent with a clinical picture of frailty. The ACG frailty tag identified a frail population using the VES frailty determination as the accepted standard with moderate success (area under the curve 0.62). Adjusting for sex and functional status in backward logistic regression, the ACG frailty tag predicted hospitalizations (P <.032) and the VES frailty tool predicted emergency department visits (P <.016).
  
  
  
  The ACG frailty tag identified an elderly population with clinical characteristics of frailty and performed with moderate success compared with the VES. Both tools predicted adverse outcomes in older HMO members. A combined screening approach for frailty using predictive modeling with a function-based survey deserves further study.
authors.affiliation: Department of Geriatric Medicine, Maccabi Healthcare Services, Tel Aviv, Israel. sternb_sh@mac.org.il
authors.forename: Shelley A
authors.initials: SA
authors.lastname: Sternberg
authors.forename: Netta
authors.initials: N
authors.lastname: Bentur
authors.forename: Chad
authors.initials: C
authors.lastname: Abrams
authors.forename: Tal
authors.initials: T
authors.lastname: Spalter
authors.forename: Tomas
authors.initials: T
authors.lastname: Karpati
authors.forename: John
authors.initials: J
authors.lastname: Lemberger
authors.forename: Anthony D
authors.initials: AD
authors.lastname: Heymann
mesh_terms: D000058:Accidental Falls / Q000706:statistics & numerical data; D000368:Aged; D000369:Aged, 80 and over; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D016330:Frail Elderly / Q000706:statistics & numerical data; D015577:Geriatric Assessment / Q000379:methods*; D006306:Health Surveys; D006801:Humans; D017533:Marital Status; D015233:Models, Statistical; D011788:Quality of Life; D014549:Urinary Incontinence / Q000453:epidemiology