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Measuring the predictive accuracy of preoperative clinical frailty instruments applied to electronic health data in older patients having emergency general surgery: a retrospective cohort study


Objective — To compare predictive accuracy of frailty instruments operationalizable in electronic data for prognosticating outcomes among older adults undergoing emergency general surgery (EGS).

Summary Background Information
— Older patients undergoing EGS are at higher risk of perioperative morbidity and mortality. Preoperative frailty is a common and strong perioperative risk factor in this population. Despite this, existing barriers preclude routine preoperative frailty assessment.

— We conducted a retrospective cohort study of adults >65 undergoing EGS from 2012-2018 using ICES provincial healthcare data in Ontario, Canada. We compared four frailty instruments: Frailty Index(FI), Hospital Frailty Risk Score(HFRS), Risk Analysis Index-Administrative(RAI), ACG Frailty-defining diagnoses indicator(ACG). We compared predictive accuracy beyond baseline risk models (age, sex, American Society of Anesthesiologists’ score, procedural risk). Predictive performance was measured using discrimination, calibration, explained variance, net reclassification index (NRI) and Brier score (binary outcomes); using explained variance, root mean squared error and mean absolute prediction error (continuous outcomes). Primary outcome was 30-day mortality. Secondary outcomes were 365-day mortality, non-home discharge, days alive at home, length of stay, and 30- and 365-day health systems cost.

— 121,095 EGS patients met inclusion criteria. Of these, 11,422 (9.4%) experienced death 30 days post-operatively. Addition of FI, HFRS and RAI to the baseline model led to improved discrimination, NRI, and R2; RAI demonstrated the largest improvements.

— Adding four frailty instruments to typically assessed preoperative risk factors demonstrated strong predictive performance in accurately prognosticating perioperative outcomes. These findings can be considered in developing automated risk stratification systems among older EGS patients.



Grudzinski AL, Aucoin S, Talarico R, Moloo H, Lalu MM, McIsaac DI. Ann Surg. 2023; 278(2):e341-8. Epub 2022 Sep 21.

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