The ability of three different models of frailty to predict all-cause mortality: results from the European Male Aging Study (EMAS)

Arch Gerontol Geriatr. 2013 Nov-Dec;57(3):360-8. doi: 10.1016/j.archger.2013.06.010. Epub 2013 Jul 18.

Abstract

Few studies have directly compared the ability of the most commonly used models of frailty to predict mortality among community-dwelling individuals. Here, we used a frailty index (FI), frailty phenotype (FP), and FRAIL scale (FS) to predict mortality in the EMAS. Participants were aged 40-79 years (n=2929) at baseline and 6.6% (n=193) died over a median 4.3 years of follow-up. The FI was generated from 39 deficits, including self-reported health, morbidities, functional performance and psychological assessments. The FP and FS consisted of five phenotypic criteria and both categorized individuals as robust when they had 0 criteria, prefrail as 1-2 criteria and frail as 3+ criteria. The mean FI increased linearly with age (r(2)=0.21) and in Cox regression models adjusted for age, center, smoking and partner status the hazard ratio (HR) for death for each unit increase of the FI was 1.49. Men who were prefrail or frail by either the FP or FS definitions, had a significantly increased risk of death compared to their robust counterparts. Compared to robust men, those who were FP frail at baseline had a HR for death of 3.84, while those who were FS frail had a HR of 3.87. All three frailty models significantly predicted future mortality among community-dwelling, middle-aged and older European men after adjusting for potential confounders. Our data suggest that the choice of frailty model may not be of paramount importance when predicting future risk of death, enabling flexibility in the approach used.

Keywords: Aging; Frailty index; Frailty phenotype; Male health; Mortality; Population-based.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Activities of Daily Living
  • Adult
  • Age Factors
  • Aged
  • Europe / epidemiology
  • Frail Elderly / statistics & numerical data*
  • Health Status
  • Humans
  • Kaplan-Meier Estimate
  • Male
  • Marital Status / statistics & numerical data
  • Middle Aged
  • Models, Statistical
  • Mortality*
  • Proportional Hazards Models
  • Risk Factors
  • Smoking / mortality
  • Surveys and Questionnaires
  • Survival Analysis