Differential Performance of Machine Learning Models in Prediction of Procedure-Specific Outcomes

Background: Procedure-specific complications can have devastating consequences. Machine learning–based tools have the potential to outperform traditional statistical modeling in predicting their risk and guiding decision-making. We sought to develop and compare deep neural network (NN) models, a type of machine learning, to logistic regression (LR) for predicting anastomotic leak after colectomy, bile leak after hepatectomy, and pancreatic fistula after pancreaticoduodenectomy (PD). Methods: The colectomy, hepatectomy, and PD National Surgical Quality Improvement Program (NSQIP) databases were analyzed. Each dataset was split into training, validation, and testing sets in a 60/20/20 ratio, with fivefold cross-validation. Models were created using NN and LR for each outcome. Models were evaluated primarily with area under the receiver operating characteristic curve (AUROC). Results: A total of 197,488 patients were included for colectomy, 25,403 for hepatectomy, and 23,333 for PD. For anastomotic leak, AUROC for NN was 0.676 (95% 0.666–0.687), compared with 0.633 (95% CI 0.620–0.647) for LR. For bile leak, AUROC for NN was 0.750 (95% CI 0.739–0.761), compared with 0.722 (95% CI 0.698–0.746) for LR. For pancreatic fistula, AUROC for NN was 0.746 (95% CI 0.733–0.760), compared with 0.713 (95% CI 0.703–0.723) for LR. Variables related to intra-operative information, such as surgical approach, biliary reconstruction, and pancreatic gland texture were highly important for model predictions. Discussion: Machine learning showed a marginal advantage over traditional statistical techniques in predicting procedure-specific outcomes. However, models that included intra-operative information performed better than those that did not, suggesting that NSQIP procedure-targeted datasets may be strengthened by including relevant intra-operative information.