Key result
Random Forest model accurately predicts postoperative hyperlactatemia with ~0.82 AUC using eight perioperative variables.
Why the study?
Postoperative hyperlactatemia is common after pediatric cardiac surgery, but its perioperative risk factors remain unclear.
Can an interpretable machine learning model accurately predict postoperative hyperlactatemia in young children following congenital heart surgery?
Observational (n=3,224)
No
Can an interpretable machine learning model accurately predict postoperative hyperlactatemia in young children following congenital heart surgery?
Effect estimate: AUC 0.821 (95% CI 0.787-0.854)
An interpretable Random Forest model integrating both established and novel predictors successfully estimated the risk of postoperative hyperlactatemia in young children, offering a tool for proactive perioperative risk stratification.
May support early risk stratification after pediatric cardiac surgery; leaves open need for prospective validation before clinical use.
Objectives: Postoperative hyperlactatemia (POHL) is a common complication after pediatric cardiac surgery, yet its perioperative risk factors remain unclear. This study developed and internally validated an interpretable machine learning (ML) model to identify young children at risk for POHL. Methods: We retrospectively analyzed 3224 children aged 0 to 36 months from 2018 to 2023. Four ML models, including logistic regression (LR), random forest (RF), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost), were trained and validated. Model performance was assessed using discrimination, calibration, and classification metrics, and decision curve analysis evaluated clinical utility. SHapley Additive exPlanation (SHAP) provided both global and local interpretability. Results: Of the 3224 children, 731 (22.7%) developed POHL, with a median age of 5 months. The RF model performed best (AUC, 0.821; 95% CI, 0.787–0.854; sensitivity, 69.7%; specificity, 84.1%; Brier score, 0.146). SHAP analysis identified 8 key predictors of POHL. Established factors included cardiopulmonary bypass duration, lowest bypass temperature, epinephrine dose, and RACHS-1 category. Novel contributors comprised low body weight, reduced left ventricular end-diastolic diameter, plasma transfusion, and continued mechanical ventilation within the first 24 postoperative hours. Conclusions: We developed and internally validated an interpretable RF model that integrates established and novel predictors to estimate POHL risk in young children after cardiac surgery. Pending external validation, it may support earlier risk recognition and more personalized perioperative management in this high-risk pediatric population.
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Li et al. (2026) conducted an observational in Congenital heart disease requiring cardiac surgery with cardiopulmonary bypass (n=3,224). Random Forest machine learning risk prediction model vs. Logistic regression, SVM, and XGBoost models was evaluated on Model discrimination (AUC) for predicting postoperative hyperlactatemia (AUC 0.821, 95% CI 0.787-0.854). A Random Forest machine learning model utilizing 8 perioperative variables accurately predicted the risk of postoperative hyperlactatemia with an AUC of 0.821.
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