title: Implementing frailty into clinical practice: a cautionary tale.
abstract: BACKGROUND
  Little is known about the contribution of frailty in improving patient-level prediction beyond readily available clinical information. The objective of this study is to compare the predictive ability of 129 combinations of seven frailty markers (cognition, energy, mobility, mood, nutrition, physical activity, and strength) and quantify their contribution to predictive accuracy beyond age, sex, and number of chronic diseases.
  
  
  METHODS
  Two cohorts from the Established Populations for Epidemiologic Studies of the Elderly were used. The model with the best predictive fit in predicting 6-year incidence of disability was determined using the Akaike Information Criterion. Predictive accuracy was measured by the C statistic.
  
  
  RESULTS
  Incident disability was 23% in one cohort and 20% in the other cohort. The "best model" in each cohort was found to be a model including between five and seven frailty markers including cognition, mobility, nutrition, physical activity, and strength. Predictive accuracy of the 129 models ranged from 0.73 to 0.77 across both cohorts. Adding frailty markers to age, sex, and chronic disease increased predictive accuracy by up to 3% in both cohorts (p < .001). The contribution of frailty increased up to 9% in the oldest age group.
  
  
  CONCLUSIONS
  Adding frailty markers provided a modest increase in patient-level prediction of disability. Such a modest increase may still be worthwhile because while age, sex, and the number of chronic diseases are not modifiable, frailty may be. Further studies examining the contribution of frailty in improving prediction are needed before adopting frailty as a prognostic tool.
authors: *These authors contributed equally to the design of the study and to the preparation of the article.
authors: Nadia
authors: N
authors: Sourial
authors: Howard
authors: H
authors: Bergman
authors: Sathya
authors: S
authors: Karunananthan
authors: Christina
authors: C
authors: Wolfson
authors: Hélène
authors: H
authors: Payette
authors: Luis Miguel
authors: LM
authors: Gutierrez-Robledo
authors: François
authors: F
authors: Béland
authors: John D
authors: JD
authors: Fletcher
authors: Jack
authors: J
authors: Guralnik
mesh_terms: D000368:Aged; D002170:Canada / Q000453:epidemiology; D002908:Chronic Disease / Q000453:epidemiology*; D003627:Data Interpretation, Statistical; D004185:Disability Evaluation; D006233:Persons with Disabilities / Q000706:statistics & numerical data*; D005260:Female; D016330:Frail Elderly / Q000706:statistics & numerical data*; D015577:Geriatric Assessment / Q000379:methods / Q000706:statistics & numerical data; D006801:Humans; D015994:Incidence; D008297:Male; D015233:Models, Statistical; D015596:Nutrition Assessment; D011379:Prognosis; D011446:Prospective Studies; D012307:Risk Factors; D012737:Sex Factors; D014481:United States / Q000453:epidemiology
keywords: Epidemiology; Frailty; Functional performance; Outcomes.; Risk factors
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