Machine Learning-based Mortality Prediction for Pediatric Fulminant Myocarditis Using Cytokine Profiles

BackgroundFulminant myocarditis (FM) is a rare but life-threatening pediatric condition that rapidly progresses to cardiogenic shock and fatal arrhythmias. Early identification of prognostic biomarkers is vital for timely intervention and better outcomes. Although inflammatory cytokines contribute to FM pathogenesis, their prognostic value remains unclear. This study aimed to identify mortality-associated markers by integrating cytokine profiles and clinical variables through a machine learning approach. MethodsWe retrospectively analyzed 21 pediatric FM cases from two tertiary centers (2012-2022). At admission, 37 cytokines and 14 clinical parameters were assessed. Partial least squares discriminant analysis was employed to identify prognostic features, with variable importance in projection scores quantifying their contribution. Model performance was evaluated using leave-one-out cross-validation. Statistical significance was determined via the Benjamini-Hochberg method at a false discovery rate of 0.05. ResultsOf the 51 features analyzed, 23 emerged as key predictors with variable importance in projection scores above 1.0, including 20 cytokines and three clinical parameters. Six cytokines (TNF-, M-CSF, MIP-1, IL-8, IL-6, and IL-15) were both statistically significant and highly important. Elevated CK-MB and lactate levels and lower pH were also linked to poor outcomes. The model performed robustly, with an AUC of 0.92, 85.7% accuracy, 92.9% sensitivity, and 71.4% specificity. ConclusionsTNF- emerged as a key cytokine linked to mortality in pediatric FM, supporting its role as a prognostic biomarker and potential therapeutic target.