No abstract

BackgroundA frailty paradigm would be useful in primary care to identify older people at risk, but appropriate metrics at that level are lacking. We created and validated a simple instrument for frailty screening in Europeans aged ≥50. Our study is based on the first wave of the Survey of Health, Ageing and Retirement in Europe (SHARE, http://www.share-project.org), a large population-based survey conducted in 2004-2005 in twelve European countries.MethodsSubjects: SHARE Wave 1 respondents (17,304 females and 13,811 males). Measures: five SHARE variables approximating Fried's frailty definition. Analyses (for each gender): 1) estimation of a discreet factor (DFactor) model based on the frailty variables using LatentGOLD®. A single DFactor with three ordered levels or latent classes (i.e. non-frail, pre-frail and frail) was modelled; 2) the latent classes were characterised against a biopsychosocial range of Wave 1 variables; 3) the prospective mortality risk (unadjusted and age-adjusted) for each frailty class was established on those subjects with known mortality status at Wave 2 (2007-2008) (11,384 females and 9,163 males); 4) two web-based calculators were created for easy retrieval of a subject's frailty class given any five measurements.ResultsFemales: the DFactor model included 15,578 cases (standard R2 = 0.61). All five frailty indicators discriminated well (p < 0.001) between the three classes: non-frail (N = 10,420; 66.9%), pre-frail (N = 4,025; 25.8%), and frail (N = 1,133; 7.3%). Relative to the non-frail class, the age-adjusted Odds Ratio (with 95% Confidence Interval) for mortality at Wave 2 was 2.1 (1.4 - 3.0) in the pre-frail and 4.8 (3.1 - 7.4) in the frail. Males: 12,783 cases (standard R2 = 0.61, all frailty indicators had p < 0.001): non-frail (N = 10,517; 82.3%), pre-frail (N = 1,871; 14.6%), and frail (N = 395; 3.1%); age-adjusted OR (95% CI) for mortality: 3.0 (2.3 - 4.0) in the pre-frail, 6.9 (4.7 - 10.2) in the frail.ConclusionsThe SHARE Frailty Instrument has sufficient construct and predictive validity, and is readily and freely accessible via web calculators. To our knowledge, SHARE-FI represents the first European research effort towards a common frailty language at the community level.

The CFS may help predict in-patient mortality and target specialist geriatric resources within the hospital. Usual hospital metrics such as mortality and LOS should take into account measurable patient complexity.