An XGBoost machine learning model accurately predicted 1-year major adverse limb events or death after infrainguinal bypass, outperforming logistic regression (AUROC 0.94; 95% CI 0.93-0.95).
Cohort (n=59,784)
Does a machine learning model (XGBoost) improve prediction of 1-year major adverse limb events or death compared to logistic regression in patients undergoing infrainguinal bypass for peripheral artery disease?
Machine learning models, specifically XGBoost, demonstrate excellent accuracy in predicting 1-year major adverse limb events or death after infrainguinal bypass, significantly outperforming traditional logistic regression.
Effect estimate: AUROC 0.94 (95% CI 0.93-0.95)
OBJECTIVE: To develop machine learning (ML) algorithms that predict outcomes after infrainguinal bypass. BACKGROUND: Infrainguinal bypass for peripheral artery disease carries significant surgical risks; however, outcome prediction tools remain limited. METHODS: The 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. RESULTS: Overall, 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). CONCLUSIONS: ML models can accurately predict outcomes after infrainguinal bypass, outperforming logistic regression.
Li et al. (Wed,) conducted a cohort in Peripheral artery disease (n=59,784). XGBoost machine learning model vs. Logistic regression was evaluated on 1-year major adverse limb event (composite of surgical revision, thrombectomy/thrombolysis, or major amputation) or death (AUROC 0.94, 95% CI 0.93-0.95). An XGBoost machine learning model accurately predicted 1-year major adverse limb events or death after infrainguinal bypass, outperforming logistic regression (AUROC 0.94; 95% CI 0.93-0.95).
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