Implementing frailty into clinical practice: a cautionary tale

J Gerontol A Biol Sci Med Sci. 2013 Dec;68(12):1505-11. doi: 10.1093/gerona/glt053. Epub 2013 May 2.


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.

Keywords: Epidemiology; Frailty; Functional performance; Outcomes.; Risk factors.

Publication types

  • Comparative Study
  • Multicenter Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Canada / epidemiology
  • Chronic Disease / epidemiology*
  • Data Interpretation, Statistical
  • Disability Evaluation
  • Disabled Persons / statistics & numerical data*
  • Female
  • Frail Elderly / statistics & numerical data*
  • Geriatric Assessment* / methods
  • Geriatric Assessment* / statistics & numerical data
  • Humans
  • Incidence
  • Male
  • Models, Statistical
  • Nutrition Assessment
  • Prognosis
  • Prospective Studies
  • Risk Factors
  • Sex Factors
  • United States / epidemiology