What does this research mean for the field?

A Random Forest machine learning model utilizing 18 preoperative predictors, including weight and RACHS-1 category, accurately predicts critical outcomes such as mortality and ventilator dependence in pediatric congenital heart disease surgery. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The study aims to develop a machine learning model for precise preoperative risk stratification in pediatric congenital heart disease surgery.

June 7, 2026Open Access

An interpretable machine learning model for preoperative risk stratification in paediatric congenital heart disease surgery: a retrospective cohort study

YZYu-zhen ZhangKunming Children's Hospital YSYong-yu SiKunming Medical University JOJie OuyangHong Kong Baptist University

Key Points

The study aims to develop a machine learning model for precise preoperative risk stratification in pediatric congenital heart disease surgery.
Retrospective dual-centre cohort analysis of 1,363 CHD surgical cases
Feature selection using Boruta algorithm and LASSO regression to identify 18 key predictors
Construction and validation of nine machine learning models, including Random Forest and XGBoost, assessed by AUC and F1-score.
Random Forest achieved the highest AUC of 0.861 and F1-score of 0.631 for the primary outcome.
Mortality prediction showed excellent discrimination with an AUC of 0.948, while prolonged hospital stay predicted with an AUC of 0.714.
Key predictors included weight, RACHS-1 category, and monocyte-lymphocyte ratio for different adverse outcomes.

Abstract

BACKGROUND: Congenital heart disease (CHD), one of the most common birth defects, poses challenges to preoperative risk stratification due to its anatomical complexity and developmental vulnerability. Existing tools inadequately predict critical outcomes, including mortality and ventilator dependence. We developed a machine learning-based clinical tool to enable precise preoperative risk assessment. METHODS: This retrospective dual-centre cohort study analysed 1,363 CHD surgical cases (1,033 from Kunming Children's Hospital and 330 from Fuwai Yunnan Cardiovascular Hospital), with 78 preoperative variables collected. Feature selection using the Boruta algorithm and LASSO regression identified key predictors. Nine machine learning models, including Random Forest and XGBoost, were constructed to predict a primary composite outcome and four secondary adverse events. Model performance was assessed using AUC and F1-score with threshold optimisation, and external validation was performed using the independent cohort. RESULTS: Eighteen predictors were selected. For the primary outcome, Random Forest achieved the highest AUC (0.861) and F1-score (0.631). Secondary outcomes showed divergent performance: mortality prediction demonstrated excellent discrimination (AUC 0.948), while prolonged hospital stay was predicted moderately (AUC 0.714). SHAP analysis revealed outcome-specific drivers: weight and RACHS-1 category dominated primary outcome risk, whereas mortality was associated with diastolic interventricular septum thickness and the AST/platelet ratio. Mechanical ventilation dependency correlated strongly with the monocyte-lymphocyte ratio. CONCLUSION: Five interpretable Random Forest models were developed and deployed as a web-based tool for preoperative risk stratification in CHD surgery. External validation demonstrated stable predictive performance, and future multicentre prospective studies are planned to further refine model generalisability and clinical utility.

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