Using machine learning models based on cardiac magnetic resonance parameters to predict the prognostic in children with myocarditis

作者：Dongliang Hu, M. Cui, Xueke Zhang, Yuanyuan Wu, Yan Liu, Duchang Zhai, Wan‐liang Guo, Shenghong Ju, Guohua Fan, Wu Cai · 发表于：BMC Pediatrics · 年份：2025 · DOI：10.1186/s12887-025-05753-y · 被引用次数：4 · 研究领域：Viral Infections and Immunology Research、Cardiac Imaging and Diagnostics、Pericarditis and Cardiac Tamponade

OBJECTIVE: To develop machine learning (ML) models incorporating explanatory cardiac magnetic resonance (CMR) parameters for predicting the prognosis of myocarditis in pediatric patients. MATERIALS AND METHODS: 77 patients with pediatric myocarditis diagnosed clinically between January 2020 and December 2023 were enrolled retrospectively. All patients were examined by ultrasound, electrocardiogram (ECG), serum biomarkers on admission, and CMR scan to obtain 16 explanatory CMR parameters. All patients underwent follow-up echocardiography and CMR. Patients were divided into two groups according to the occurrence of adverse cardiac events (ACE) during follow-up: the poor prognosis group (n = 23) and the good prognosis group (n = 54). Four models were established, including logistic regression (LR), random forest (RF), support vector machine classifier (SVC), and extreme gradient boosting (XGBoost) model. The performance of each model was evaluated by the area under the receiver operating characteristic curve (AUC). Model interpretation was generated by Shapley additive interpretation (Shap). RESULTS: Among the four models, the three most important features were late gadolinium enhancement (LGE), left ventricular ejection fraction (LVEF), and SAXPeak Global Circumferential Strain (SAXGCS). In addition, LGE, LVEF, SAXGCS, and LAXPeak Global Longitudinal Strain (LAXGLS) were selected as the key predictors for all four models. Four interpretable CMR parameters were extracted, among ...