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). Background: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. Methods:We conducted a retrospective cohort study of adults above 65 undergoing EGS from 2012 to 2018 using Institute for Clinical Evaluative Sciences (ICES) provincial healthcare data in Ontario, Canada. We compared 4 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 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, nonhome discharge, days alive at home, length of stay, and 30-day and 365-day health systems cost. Results:A total of 121,095 EGS patients met inclusion criteria. Of these, 11,422 (9.4%) experienced death 30 days postoperatively. Addition of FI, HFRS, and RAI to the baseline model led to improved discrimination, net reclassification index, and R-2; RAI demonstrated the largest improvements. Conclusions:Adding 4 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.