Development and validation of a novel interpretable machine learning model integrating immune-inflammatory indicators for intravenous immunoglobulin resistance in Kawasaki disease.

Background: Children with Kawasaki disease (KD) who are resistant to intravenous immunoglobulin (IVIG) therapy face a substantially increased risk of developing coronary artery lesions (CALs). Developing a robust predictive model to identify pediatric patients at high risk of IVIG resistance is crucial for optimizing clinical decision-making and improving prognosis. This study aimed identify risk predictors for IVIG resistance in children with KD and to establish and validate an interpretable machine learning (ML)-based predictive model for clinical application. Methods: Retrospective analysis was carried out on clinical data sourced from 1,584 KD patients who received initial IVIG treatment during their first hospitalization at Xuzhou Children's Hospital between January 2019 and December 2024. This cohort was randomly allocated into the training (70%) and test (30%) sets. Six distinct ML algorithms-Light Gradient Boosting Machine (LightGBM), Random Forest, eXtreme Gradient Boosting (XGBoost), Neural Network (NeuralNet), Support Vector Machine (SVM), and ElasticNet Logistic Regression-were employed to develop predictive models. Comparative performance was evaluated employing the area under the receiver operating characteristic curve (AUC). Then, SHapley Additive exPlanations (SHAP) were applied to quantify each variable's contribution to the optimal model. Results: The LightGBM model demonstrated superior discriminative performance, attaining an AUC of 0.832 [95% confidence interval (CI): 0.766-0.898] on the independent test set, with a sensitivity of 0.860 and a specificity of 0.639. SHAP summary plots revealed that the five most influential features predicting IVIG resistance were, in descending order: fever duration before initial IVIG, the neutrophil-to-lymphocyte ratio (NLR), interleukin-1β (IL-1β) level, albumin (ALB) level, and aspartate aminotransferase (AST) level. Conclusions: Our analysis identified five pivotal predictors (fever duration before initial IVIG, NLR, IL-1β, ALB, and AST) for IVIG resistance and validated an interpretable LightGBM model with satisfactory performance. This model shows potential for estimating the risk of IVIG resistance, thereby aiding in the personalized therapeutic strategies for children with KD.