Super Learner Enhances Postoperative Complication Prediction in Colorectal Surgery

Abstract

OBJECTIVE: To determine if a Super Learner (SL) machine learning approach could improve the predictive accuracy of the American College of Surgeons Risk Calculator (ACS-RC) for postoperative complications in patients undergoing colorectal surgery. SUMMARY OF BACKGROUND DATA: Machine learning (ML) has shown significant potential to advance medical fields, including surgical risk prediction. Current tools, like the ACS-RC which uses logistic regression and extreme gradient boosting, are standard but may be enhanced by more advanced ML ensembles. METHODS: This retrospective study analyzed colorectal surgery cases from the 2018-2022 ACS National Surgical Quality Improvement Program (NSQIP) database. An SL model, which combines multiple ML algorithms, was developed to predict fourteen postoperative outcomes. Its performance was compared against traditional logistic regression (LOG) and extreme gradient boosting (XGB) models. Key performance metrics included discrimination (AUROC, AUPRC) and calibration (Brier score, Hosmer-Lemeshow test). RESULTS: The SL model demonstrated superior performance across all predicted complications when compared to both LOG and XGB. It showed superior discrimination for severe outcomes, achieving an AUROC greater than 0.94 for predicting mortality. The SL model was also more accurate in predicting infectious complications and length of stay, and its calibration metrics indicated a better overall fit and accuracy. CONCLUSIONS: The Super Learner model enhances the accuracy of postoperative risk prediction in colorectal surgery. Its superior performance suggests it is a promising tool for improving personalized patient counseling, aiding clinical decision-making, and optimizing resource allocation.

Ähnliche Arbeiten