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, 43 (8), 1105-1122

The Impact of Frailty on Intensive Care Unit Outcomes: A Systematic Review and Meta-Analysis


The Impact of Frailty on Intensive Care Unit Outcomes: A Systematic Review and Meta-Analysis

John Muscedere et al. Intensive Care Med.


Purpose: Functional status and chronic health status are important baseline characteristics of critically ill patients. The assessment of frailty on admission to the intensive care unit (ICU) may provide objective, prognostic information on baseline health. To determine the impact of frailty on the outcome of critically ill patients, we performed a systematic review and meta-analysis comparing clinical outcomes in frail and non-frail patients admitted to ICU.

Methods: We searched the Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE, PubMed, CINAHL, and All study designs with the exception of narrative reviews, case reports, and editorials were included. Included studies assessed frailty in patients greater than 18 years of age admitted to an ICU and compared outcomes between fit and frail patients. Two reviewers independently applied eligibility criteria, assessed quality, and extracted data. The primary outcomes were hospital and long-term mortality. We also determined the prevalence of frailty, the impact on other patient-centered outcomes such as discharge disposition, and health service utilization such as length of stay.

Results: Ten observational studies enrolling a total of 3030 patients (927 frail and 2103 fit patients) were included. The overall quality of studies was moderate. Frailty was associated with higher hospital mortality [relative risk (RR) 1.71; 95% CI 1.43, 2.05; p < 0.00001; I 2 = 32%] and long-term mortality (RR 1.53; 95% CI 1.40, 1.68; p < 0.00001; I 2 = 0%). The pooled prevalence of frailty was 30% (95% CI 29-32%). Frail patients were less likely to be discharged home than fit patients (RR 0.59; 95% CI 0.49, 0.71; p < 0.00001; I 2 = 12%).

Conclusions: Frailty is common in patients admitted to ICU and is associated with worsened outcomes. Identification of this previously unrecognized and vulnerable ICU population should act as the impetus for investigating and implementing appropriate care plans for critically ill frail patients. Registration: PROSPERO (ID: CRD42016053910).

Keywords: Clinical frailty scale; Critically ill; Frail elderly; Frailty; Frailty index; Systematic review.

Conflict of interest statement

Braden Waters, Aditya Varambally, Sean M Bagshaw, Stephanie Sibley, and David Maslove declare that no conflicts of interest exist. J. Gordon Boyd: Dr. Boyd receives a stipend from the Trillium Gift of Life Network to support his role as the Hospital Donation Physician. Kenneth Rockwood: President and Chief Scientific Officer of DGI Clinical, which has contracts with pharma on individualized outcome measurement. In July 2015 he gave a lecture at the Alzheimer Association International Conference in a symposium sponsored by Otsuka and Lundbeck. At that time he presented at an Advisory Board meeting for Nutricia. He plans to attend a 2017 advisory board meeting for Lundbeck. He is a member of the Research Executive Committee of the Canadian Consortium on Neurodegeneration in Aging, which is funded by the Canadian Institutes of Health Research, with additional funding from the Alzheimer Society of Canada and several other charities, as well as from Pfizer Canada and Sanofi Canada. He receives career support from the Dalhousie Medical Research Foundation as the Kathryn Allen Weldon Professor of Alzheimer Research, and research support from the Nova Scotia Health Research Foundation, the Capital Health Research Fund, and the Fountain Family Innovation Fund of the Nova Scotia Health Authority Foundation. John Muscedere. Dr. Muscedere is the Scientific Director for the Canadian Frailty Network which is funded by the government of Canada.


Fig. 1
Fig. 1
Prevalence of frailty in the included studies using all measures of frailty
Fig. 2
Fig. 2
Forest plot of the risk ratio for hospital and long-term mortality (>6 months) in frail and non-frail patients using all measures of frailty
Fig. 3
Fig. 3
Forest plot of the risk ratio for discharge home in frail and non-frail patients
Fig. 4
Fig. 4
Forest plot of the risk ratio for hospital mortality in frail and non-frail patients categorized according to the measure of frailty used

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