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Development and validation of a novel interpretable machine learning model integrating immune-inflammatory indicators
Tongtong Shi1, Fei Wang1, Xinjiang An1
1Department of Cardiology, The Affiliated Xuzhou Children's Hospital of Xuzhou Medical University, Xuzhou, China.
Insights
Identifying children with Kawasaki disease (KD) at high risk for intravenous immunoglobulin (IVIG) resistance is key to preventing coronary artery lesions. A machine learning model using fever duration, neutrophil-to-lymphocyte ratio, IL-1β, albumin, and AST levels accurately predicts resistance.
Area of Science:
- Pediatric Cardiology
- Machine Learning in Medicine
- Immunology
Background:
- Kawasaki disease (KD) poses a risk of coronary artery lesions (CALs) in children resistant to intravenous immunoglobulin (IVIG) therapy.
- Early identification of IVIG resistance is crucial for timely intervention and improved patient outcomes.
- Developing predictive models can aid clinical decision-making for pediatric KD patients.
Purpose of the Study:
- To identify key risk predictors for IVIG resistance in pediatric KD patients.
- To establish and validate an interpretable machine learning (ML) model for predicting IVIG resistance.
- To enhance personalized treatment strategies for children with KD.
Main Methods:
- Retrospective analysis of 1,584 KD patients treated with IVIG.
- Random allocation into training (70%) and testing (30%) datasets.
- Evaluation of six ML algorithms (LightGBM, Random Forest, XGBoost, NeuralNet, SVM, ElasticNet) using AUC, with SHAP for feature importance.
Main Results:
- The LightGBM model achieved an AUC of 0.832 on the test set, demonstrating strong predictive performance.
- Key predictors for IVIG resistance included: fever duration before IVIG, neutrophil-to-lymphocyte ratio (NLR), IL-1β, albumin (ALB), and AST levels.
- The model showed high sensitivity (0.860) and specificity (0.639).
Conclusions:
- Five pivotal predictors for IVIG resistance in KD were identified: fever duration, NLR, IL-1β, ALB, and AST.
- An interpretable LightGBM model was validated for predicting IVIG resistance in children with KD.
- This ML model can assist in risk stratification and guide personalized therapeutic approaches.
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.
