• Ben Li, Naomi Eisenberg, Derek Beaton, Douglas S Lee, Badr Aljabri, Raj Verma, Duminda N Wijeysundera, Ori D Rotstein, Charles de Mestral, Muhammad Mamdani, Graham Roche-Nagle, and Mohammed Al-Omran.
• Department of Surgery, University of Toronto, Toronto, ON, Canada.
• Ann. Surg. 2024 Apr 1; 279 (4): 705713705-713.

ObjectiveTo develop machine learning (ML) algorithms that predict outcomes after infrainguinal bypass.BackgroundInfrainguinal bypass for peripheral artery disease carries significant surgical risks; however, outcome prediction tools remain limited.MethodsThe Vascular Quality Initiative database was used to identify patients who underwent infrainguinal bypass for peripheral artery disease between 2003 and 2023. We identified 97 potential predictor variables from the index hospitalization [68 preoperative (demographic/clinical), 13 intraoperative (procedural), and 16 postoperative (in-hospital course/complications)]. The primary outcome was 1-year major adverse limb event (composite of surgical revision, thrombectomy/thrombolysis, or major amputation) or death. Our data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, we trained 6 ML models using preoperative features. The primary model evaluation metric was the area under the receiver operating characteristic curve (AUROC). The top-performing algorithm was further trained using intraoperative and postoperative features. Model robustness was evaluated using calibration plots and Brier scores.ResultsOverall, 59,784 patients underwent infrainguinal bypass, and 15,942 (26.7%) developed 1-year major adverse limb event/death. The best preoperative prediction model was XGBoost, achieving an AUROC (95% CI) of 0.94 (0.93-0.95). In comparison, logistic regression had an AUROC (95% CI) of 0.61 (0.59-0.63). Our XGBoost model maintained excellent performance at the intraoperative and postoperative stages, with AUROCs (95% CI's) of 0.94 (0.93-0.95) and 0.96 (0.95-0.97), respectively. Calibration plots showed good agreement between predicted and observed event probabilities with Brier scores of 0.08 (preoperative), 0.07 (intraoperative), and 0.05 (postoperative).ConclusionsML models can accurately predict outcomes after infrainguinal bypass, outperforming logistic regression.Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.

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