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Open Access Research article

Comparing frailty measures in their ability to predict adverse outcome among older residents of assisted living

David B Hogan12, Elizabeth A Freiheit2, Laurel A Strain3, Scott B Patten2, Heidi N Schmaltz1, Darryl Rolfson4 and Colleen J Maxwell256*

Author affiliations

1 Division of Geriatric Medicine, Faculty of Medicine, University of Calgary, HSC 3330 Hospital Drive NW, Calgary, AB, Canada

2 Department of Community Health Sciences, University of Calgary, 3rd Floor TRW, 3280 Hospital Drive NW, Calgary, AB, Canada

3 Department of Sociology, University of Alberta, 5-21 HM Tory Building, Edmonton, AB, Canada

4 Division of Geriatric Medicine, Faculty of Medicine, University of Alberta, 1-108, 11350-83 Avenue, Edmonton, AB, Canada

5 Schools of Pharmacy and Public Health & Health Systems, University of Waterloo, 200 University Avenue West, Waterloo, ON, Canada

6 Institute for Clinical Evaluative Sciences, Toronto, ON, Canada

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Citation and License

BMC Geriatrics 2012, 12:56  doi:10.1186/1471-2318-12-56

Published: 14 September 2012

Abstract

Background

Few studies have directly compared the competing approaches to identifying frailty in more vulnerable older populations. We examined the ability of two versions of a frailty index (43 vs. 83 items), the Cardiovascular Health Study (CHS) frailty criteria, and the CHESS scale to accurately predict the occurrence of three outcomes among Assisted Living (AL) residents followed over one year.

Methods

The three frailty measures and the CHESS scale were derived from assessment items completed among 1,066 AL residents (aged 65+) participating in the Alberta Continuing Care Epidemiological Studies (ACCES). Adjusted risks of one-year mortality, hospitalization and long-term care placement were estimated for those categorized as frail or pre-frail compared with non-frail (or at high/intermediate vs. low risk on CHESS). The area under the ROC curve (AUC) was calculated for select models to assess the predictive accuracy of the different frailty measures and CHESS scale in relation to the three outcomes examined.

Results

Frail subjects defined by the three approaches and those at high risk for decline on CHESS showed a statistically significant increased risk for death and long-term care placement compared with those categorized as either not frail or at low risk for decline. The risk estimates for hospitalization associated with the frailty measures and CHESS were generally weaker with one of the frailty indices (43 items) showing no significant association. For death and long-term care placement, the addition of frailty (however derived) or CHESS significantly improved on the AUC obtained with a model including only age, sex and co-morbidity, though the magnitude of improvement was sometimes small. The different frailty/risk models did not differ significantly from each other in predicting mortality or hospitalization; however, one of the frailty indices (83 items) showed significantly better performance over the other measures in predicting long-term care placement.

Conclusions

Using different approaches, varying degrees of frailty were detected within the AL population. The various approaches to defining frailty were generally more similar than dissimilar with regard to predictive accuracy with some exceptions. The clinical implications and opportunities of detecting frailty in more vulnerable older adults require further investigation.

Keywords:
Frailty; Predictive accuracy; Agreement; Assisted living