Using Machine Learning (XGBoost) to Predict Outcomes following Infrainguinal Bypass for Peripheral Artery Disease.

OBJECTIVE: To develop machine learning (ML) algorithms that predict outcomes following infrainguinal bypass.

SUMMARY BACKGROUND DATA: Infrainguinal bypass for peripheral artery disease (PAD) carries significant surgical risks; however, outcome prediction tools remain limited.

METHODS: The Vascular Quality Initiative (VQI) database was used to identify patients who underwent infrainguinal bypass for PAD between 2003-2023. We identified 97 potential predictor variables from the index hospitalization (68 pre-operative [demographic/clinical], 13 intra-operative [procedural], and 16 post-operative [in-hospital course/complications]). The primary outcome was 1-year major adverse limb event (MALE; 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 pre-operative features. The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). The top-performing algorithm was further trained using intra- and post-operative 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 MALE/death. The best pre-operative 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 intra- and post-operative stages, with AUROC's (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 (pre-operative), 0.07 (intra-operative), and 0.05 (post-operative).

CONCLUSIONS: ML models can accurately predict outcomes following infrainguinal bypass, outperforming logistic regression.