Predicting 30-Day Cardiotoxicity in Patients Receiving Immune Checkpoint Inhibitors: An Observational Study Utilizing XGBoost.

Abstract

Immune Checkpoint Inhibitor (ICI)-related cardiotoxicity has a high mortality rate, making early prediction crucial for improving patient prognosis. However, early prediction models are currently lacking in clinical practice. This study aims to develop an early prediction model for ICI-related cardiotoxicity using the eXtreme Gradient Boosting (XGBoost) algorithm. Retrospective analysis was conducted on patients who received ICI therapy between January 2020 and December 2023. The population was categorized into a cardiotoxicity group and a non-cardiotoxicity group based on the presence of cardiac biomarkers and electrocardiogram abnormalities that could not be attributed to other diseases within 30 days after initiation ICI therapy. The dataset was split into training (70%) and testing (30%) sets. Logistic Regression (LR), Random Forest (RF), and XGBoost models were constructed in Python, with variables selected based on each model's characteristics. The models were compared based on predictive performance, which was measured by area under the curve (AUC) and decision curve analysis (DCA). The best model was explained using SHapley Additive exPlanation (SHAP). A total of 419 patients were included. The XGBoost model demonstrated the highest predictive performance with an AUC of 0.83, outperforming LR (AUC: 0.80) and RF (AUC: 0.74) models. DCA confirmed the XGBoost model's superior net benefit. Among the selected predictors, cardiac troponin T (cTnT) emerged as the most important variable, demonstrating the highest feature importance. The XGBoost model proposed could assist clinicians in personalized risk stratification for patients on ICI therapy, facilitating precise monitoring of cardiotoxicity and tailored treatment strategies.