Open surgical treatment options for aortoiliac occlusive disease carry significant peri-operative risks; however, outcome prediction tools remain limited. Using machine learning (ML), we developed automated algorithms that predict 30-day outcomes following open aortoiliac revascularization.
The National Surgical Quality Improvement Program (NSQIP) targeted vascular database was used to identify patients who underwent open aortoiliac revascularization for atherosclerotic disease between 2011-2021. Input features included 38 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. The 30-day secondary outcomes were individual components of the primary outcome, major adverse cardiovascular event (MACE; composite of myocardial infarction [MI], stroke, or death), individual components of MACE, wound complication, bleeding, other morbidity, non-home discharge, and unplanned readmission. 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). Model robustness was evaluated with calibration plot and Brier score. Variable importance scores were calculated to determine the top 10 predictive features. Performance was assessed on subgroups based on age, sex, race, ethnicity, symptom status, procedure type, and urgency.
Overall, 9,649 patients were included. The primary outcome of 30-day MALE or death occurred in 1,021 (10.6%) patients. Our best performing prediction model for 30-day MALE or death was XGBoost, achieving an AUROC (95% CI) of 0.95 (0.94-0.96). In comparison, logistic regression had an AUROC (95% CI) of 0.79 (0.77-0.81). For 30-day secondary outcomes, XGBoost achieved AUROC’s between 0.87-0.97 (untreated loss of patency [0.95], major reintervention [0.88], major amputation [0.96], death [0.97], MACE [0.95], MI [0.88], stroke [0.93], wound complication [0.94], bleeding [0.87], other morbidity [0.96], non-home discharge [0.90], and unplanned readmission [0.91]). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.05. The strongest predictive feature in our algorithm was chronic limb threatening ischemia. Model performance remained robust on all subgroup analyses of specific demographic/clinical populations.
Our ML models accurately predict 30-day outcomes following open aortoiliac revascularization using pre-operative data, performing better than logistic regression. They have potential for important utility in guiding risk-mitigation strategies for patients being considered for open aortoiliac revascularization to improve outcomes.
Copyright © 2023. Published by Elsevier Inc.
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