Key result
An XGBoost machine learning model accurately predicted 30-day major adverse limb event or death following lower extremity open revascularization with an AUROC of 0.93.
Why the study?
Lower extremity open revascularization for peripheral artery disease carries significant peri-operative risks, but outcome prediction tools remain limited.
Can machine learning models accurately predict 30-day major adverse limb events or death in patients undergoing lower extremity open revascularization for chronic atherosclerotic disease?
Cohort (n=24,309)
Yes
Can machine learning models accurately predict 30-day major adverse limb events or death in patients undergoing lower extremity open revascularization for chronic atherosclerotic disease?
Effect estimate: AUROC 0.93 (95% CI 0.92-0.94)
ML models may aid perioperative risk prediction in PAD revascularization; leaves open prospective validation before clinical use.
Lower extremity open revascularization is a treatment option for peripheral artery disease that carries significant peri-operative risks; however, outcome prediction tools remain limited. Using machine learning (ML), we developed automated algorithms that predict 30-day outcomes following lower extremity open revascularization. The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent lower extremity open revascularization for chronic atherosclerotic disease between 2011 and 2021. Input features included 37 pre-operative demographic/clinical variables. The primary outcome was 30-day major adverse limb event (MALE; composite of untreated loss of patency, major reintervention, or major amputation) or death. Our data were split into training (70%) and test (30%) sets. Using tenfold cross-validation, we trained 6 ML models. Overall, 24,309 patients were included. The primary outcome of 30-day MALE or death occurred in 2349 (9.3%) patients. Our best performing prediction model was XGBoost, achieving an area under the receiver operating characteristic curve (95% CI) of 0.93 (0.92-0.94). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.08. Our ML algorithm has potential for important utility in guiding risk mitigation strategies for patients being considered for lower extremity open revascularization to improve outcomes.
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Li et al. (2024) conducted a cohort in Peripheral artery disease requiring lower extremity open revascularization (n=24,309). XGBoost machine learning model vs. Logistic regression was evaluated on 30-day major adverse limb event (MALE) or death (AUROC 0.93, 95% CI 0.92-0.94). An XGBoost machine learning model accurately predicted 30-day major adverse limb event or death following lower extremity open revascularization with an AUROC of 0.93.
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