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Derivation and validation of a generalizable preoperative frailty index using population-based health administrative data


Objective — To develop and validate a preoperative frailty index (pFI) for use in population-based health administrative (HA) data.

Summary Background Data — Frailty is a robust predictor of adverse postoperative outcomes. Population-level frailty measures used in surgical studies have significant methodological limitations. Frailty indices (FIs) are a well-defined approach to measuring frailty with well-described methods for development and evaluation. An appropriate preoperative FI in HA data has not been derived or evaluated.

Methods — Retrospective cohort study using linked HA data in Canada. We identified people >65 years (2002-2015) who had major elective or emergency surgery. Standardized methods were used to construct a 30-variable pFI. Unadjusted and multilevel, multivariable adjusted models were used to measure the association of the pFI with 1-year mortality and institutional discharge. Elective patients were the derivation cohort, emergency patients were the validation cohort. Prespecified sensitivity analyses were performed.

Results — We identified 415,704 elective, and 95,581 emergency patients. The elective 1-year mortality rate was 4.7%. Thirty percent of population-level deaths occurred in people with frailty. Every 0.1-unit increase in the pFI was associated with a 2.20-fold increase in the adjusted odds of mortality (95% CI 2.15-2.26; c-statistic 0.81), and a 1.70-fold increase in institutional discharge (95% CI 1.59-1.80; c-statistic 0.71). pFI performance was similar in emergency patients, and was robust to changes in index composition.

Conclusions — A preoperative FI derived from HA data is a robust method to measure frailty in elective and emergency patients. Generalizable FIs should be considered a standard approach to population-level study of surgical frailty.



McIsaac DI, Wong CA, Huang A, Moloo H, van Walraven C. Ann Surg. 2019; 270(1):102-8. Epub 2018 Apr 18.

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