Predicting mortality risk following major lower extremity amputation using machine learning

OBJECTIVE: Major lower extremity amputation for advanced vascular disease involves significant perioperative risks. Although outcome prediction tools could aid in clinical decision-making, they remain limited. To address this, we developed machine learning (ML) algorithms capable of predicting 1-year mortality following major lower extremity amputation. METHODS: The Vascular Quality Initiative (VQI) database was queried to identify patients who underwent major lower extremity amputation for non-traumatic and non-malignant causes between 2012 and 2024. A total of 75 features were collected from the index hospitalization, including 52 preoperative (demographic/clinical), five intraoperative (procedural), and 18 postoperative (in-hospital course/complications) variables. The primary outcome was 1-year all-cause mortality. The data was split into training (70%) and test (30%) sets. Six ML models were trained using preoperative features, employing 10-fold cross-validation, which included Extreme Gradient Boosting (XGBoost), random forest, Naïve Bayes classifier, support vector machine, artificial neural network, and logistic regression. The primary model evaluation metric was the area under the receiver operating characteristic curve (AUROC). The best-performing model was then further trained using intra- and postoperative features. Model robustness was evaluated through calibration plots and Brier scores. Model performance was assessed across various subgroups based on age, sex, race, ethnicity, rurality, median Area Deprivation Index, prior ipsilateral minor amputation, prior ipsilateral open/endovascular revascularization, level of amputation, indication for amputation, and urgency. RESULTS: A total of 22,828 patients underwent major lower extremity amputation during the study period, with 5842 (25.6%) experiencing 1-year mortality. Patients who reached the primary endpoint were older with more comorbidities, had poorer functional status, and were more likely to undergo higher-level amputations. Despite having elevated cardiovascular risk, these patients were less likely to receive cardiovascular risk reduction medications. The best preoperative prediction model was XGBoost, which achieved an AUROC of 0.88 (95% confidence interval [CI], 0.87-0.89). In comparison, logistic regression showed an AUROC of 0.70 (95% CI, 0.68-0.72). The XGBoost model maintained excellent performance at the intra- and postoperative stages, with AUROCs of 0.88 (95% CI, 0.87-0.89) and 0.94 (95% CI, 0.93-0.95), respectively. Calibration plots indicated strong agreement between predicted/observed event probabilities, with Brier scores of 0.12 (preoperative), 0.11 (intraoperative), and 0.09 (postoperative). Among the top 10 predictors, 6 were preoperative features, including the level of and indication for amputation, comorbidities, and functional status. Model performance remained robust across all subgroups. CONCLUSIONS: We developed ML models that can accurately predict 1-year mortality following major lower extremity amputation, outperforming logistic regression. These algorithms have potential for important utility in guiding patient selection, counseling, goals of care discussions, and clinical decision-making to support patient-centered care for a high-risk population.

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