Exploratory Analysis for Development Predictive Models of Immune Checkpoint Inhibitor-Induced Myocarditis Using a Nationwide Claims Database

Abstract

Immune checkpoint inhibitors (ICIs), essential in cancer therapy, can cause severe immune-related adverse events (irAEs), including myocarditis with a high fatality rate. Currently, the pathogenesis, biomarkers, and risk factors of ICI-induced myocarditis (ICIM) are not fully understood. This exploratory study aimed to develop machine learning-based models to predict the onset of ICIM within 3 months of starting ICI therapy, using a large health insurance database. The models were constructed using the Light Gradient Boosting Machine (LightGBM) and Random Forest algorithms, incorporating clinical variables such as comorbidities and prior medication classifications. In this study, a strategy combining undersampling and bagging was used to minimize the impact of highly imbalanced datasets. The Random Forest model demonstrated superior performance compared with the LightGBM model, and the SHapley Additive exPlanations (SHAP) analysis for the Random Forest model revealed that the concurrent use of ICIs was the most important variable for predictions. Although predictive performance remains limited (AUROC ≈ 0.63), this exploratory framework demonstrates the feasibility of developing data-driven risk prediction models for ICIM. Future studies with expanded datasets and integration of laboratory parameters are warranted to improve predictive accuracy and potential clinical applicability.