• Maxim A Terekhov, Jesse M Ehrenfeld, and Jonathan P Wanderer.
• From the Departments of Anesthesiology (M.A.T., J.M.E., J.P.W.), Biomedical Informatics (J.M.E., J.P.W.), Surgery (J.M.E.), and Health Policy (J.M.E.), Vanderbilt University Medical Center, Nashville, Tennessee.
• Anesthesiology. 2015 Nov 1;123(5):1059-66.

BackgroundEstimating surgical risk is critical for perioperative decision making and risk stratification. Current risk-adjustment measures do not integrate dynamic clinical parameters along with baseline patient characteristics, which may allow a more accurate prediction of surgical risk. The goal of this study was to determine whether the preoperative Risk Quantification Index (RQI) and Present-On-Admission Risk (POARisk) models would be improved by including the intraoperative Surgical Apgar Score (SAS).MethodsThe authors identified adult patients admitted after noncardiac surgery. The RQI and POARisk were calculated using published methodologies, and model performance was compared with and without the SAS. Relative quality was measured using Akaike and Bayesian information criteria. Calibration was compared by the Brier score. Discrimination was compared by the area under the receiver operating curves (AUROCs) using a bootstrapping procedure for bias correction.ResultsSAS alone was a statistically significant predictor of both 30-day mortality and in-hospital mortality (P < 0.0001). The RQI had excellent discrimination with an AUROC of 0.8433, which increased to 0.8529 with the addition of the SAS. The POARisk had excellent discrimination with an AUROC of 0.8608, which increased to 0.8645 by including the SAS. Similarly, overall performance and relative quality increased.ConclusionsWhile AUROC values increased, the RQI and POARisk preoperative risk models were not meaningfully improved by adding intraoperative risk using the SAS. In addition to the estimated blood loss, lowest heart rate, and lowest mean arterial pressure, other dynamic clinical parameters from the patient's intraoperative course may need to be combined with procedural risk estimate models to improve risk stratification.

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