Machine learning predicts postoperative mortality in a Latin American cardiovascular surgery multicenter cohort: Toward precision risk stratification.

OBJECTIVE

A total of 28% of global cardiac surgeries are performed in Latin America; however, surgeons there are faced with many preventable deaths, partly as the result of the lack of accurate, region-specific risk assessment tools. We developed an explainable machine learning (ML) model tailored to a multicenter Latin American cohort that outperformed European System for Cardiac Operative Risk Evaluation II and Society of Thoracic Surgeons scores in predicting postoperative mortality.

METHODS

We analyzed 8680 adult cardiovascular surgery cases (2010-2025) from 5 Latin American countries. Sixty perioperative variables trained 5 ML models-random forest, neural network, support vector machine, eXtreme Gradient Boosting, and logistic regression-in R. The models were trained with repeated 10-fold cross-validation (80%) and tested (20%). Performance (area under the receiver operating characteristic curve, sensitivity, specificity, calibration) was compared with traditional scores. Shapley Additive exPlanations values explained predictions at both the cohort and individual levels.

RESULTS

The eXtreme Gradient Boosting model showed the greatest accuracy (area under the receiver operating characteristic curve, 0.80; sensitivity, 75.4%, specificity, 73.6%). Shapley Additive exPlanations analysis identified creatinine clearance (0.142) and mean arterial pressure (0.134) as top predictors. Risk factors included chronic kidney disease (+0.25) and malnutrition (+0.22); protective features were mean arterial pressure >80 mm Hg (-0.25) and high-volume centers (-0.20). Low-volume centers (<500 surgeries/year) had greater mortality (6.2% vs 4.2%, P < .001). Synergistic risk factors amplified effects, whereas protective factors mitigated them. High-risk patients were elderly (72.6 ± 10.4 years), with renal impairment (creatinine clearance, 48.3 mL/min) or emergency cases (23% mortality). Predicted high-risk profiles correlated strongly with observed outcomes (83%, P < .001)

CONCLUSIONS

This study presents the first explainable ML tool for Latin American cardiovascular surgery, which outperforms existing models and reveals actionable risk factors to guide preoperative care and policy.