Intraoperative Features Improve Model Risk Predictions After Coronary Artery Bypass Grafting

Abstract

BackgroundIntraoperative physiologic parameters could offer predictive utility in evaluating risk of adverse postoperative events yet are not included in current standard risk models. This study examined whether the inclusion of continuous intraoperative data improved machine learning model predictions for multiple outcomes after coronary artery bypass grafting, including 30-day mortality, renal failure, reoperation, prolonged ventilation, and combined morbidity and mortality (MM). MethodsThe Society of Thoracic Surgeons (STS) database features and risk scores were combined with retrospectively gathered continuous intraoperative data from patients. Risk models were developed for each outcome by training a logistic regression classifier on intraoperative data using 5-fold cross-validation. STS risk scores were included as offset terms in the models. ResultsCompared with the STS Risk Calculator, models developed using a combination of the intraoperative features and the STS preoperative risk score had improved mean area under the receiver operating characteristic curve for prolonged ventilation (0.750 [95% CI, 0.690-0.809] vs 0.800 [95% CI, 0.750-0.851]) and MM (0.695 [95% CI, 0.644-0.746] vs 0.724 [95% CI, 0.673-0.775]). Additionally, models developed using intraoperative features had improved calibration, measured with Brier score, for prolonged ventilation (0.060 [95% CI, 0.050-0.070] vs 0.055 [95% CI, 0.045-0.065]) and MM (0.092 [95% CI, 0.081-0.103] vs 0.087 [95% CI, 0.075-0.098]). ConclusionsThe inclusion of time series intraoperative data in risk models may improve early postoperative care by identifying patients who require closer monitoring postoperatively.