A machine-learning-derived online prediction model for coronary artery aneurysm risk in children with kawasaki disease: a retrospective cohort study

Abstract

Coronary artery aneurysm (CAA) is the most serious complication of Kawasaki disease (KD) and is associated with long-term cardiovascular morbidity. Early identification of children at high risk of CAA remains challenging. We aimed to develop and temporally validate a machine learning–based prediction model for individualized CAA risk assessment in children with KD. In this retrospective cohort study, 1,930 children diagnosed with KD at The Second Affiliated Hospital of Wenzhou Medical University between January 2018 and March 2025 were included. Among them, 1,378 patients diagnosed between January 2018 and December 2023 constituted the development cohort and were randomly divided into a training set and an internal test set. A further 552 patients diagnosed between January 2024 and March 2025 were used as an independent temporal validation cohort. Forty-two demographic, clinical, laboratory, and composite biomarker variables were initially considered. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for feature selection after preprocessing. Ten machine-learning algorithms were developed and compared. The model was designed to be applied after the initial IVIG response had been determined. Model performance was evaluated using discrimination, calibration, and decision curve analysis. Model interpretability was assessed using Shapley Additive Explanations (SHAP). 12 predictors were retained in the final model, including age, intravenous immunoglobulin (IVIG) resistance, IVIG administration time, oral mucosal changes, albumin, hematocrit, platelet count, prothrombin time, D-dimer, NT-proBNP, prognostic nutritional index, and C-reactive protein–to–albumin ratio. Among the ten algorithms, the Random Forest model showed the best overall performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.899 (95% CI, 0.870–0.928) in the internal test set and 0.847 (95% CI, 0.812–0.882) in the temporal validation cohort. Calibration and decision curve analyses indicated good agreement and clinical utility. We developed and temporally validated a machine-learning model for predicting CAA risk in children with KD. The model has been deployed as an online tool to support early risk stratification. Further prospective multicenter validation is needed before routine clinical implementation.