Biomarkers for Clinical Classification and Outcomes of Immune Checkpoint Inhibitor-Related-Related Myocarditis in Lung Cancer

By monitoring the dynamic changes in biomarker combinations at different time points (baseline, day 3, day 7, and before discharge), this study aims to evaluate the differences between the severe/critical group and the mild/subclinical myocardial injury group, and investigate their correlation with disease severity. Independent sample t-tests will be used to assess the differences between the two groups, assuming a moderate effect size (Cohen's d = 0.7) for biomarkers between the severe/critical and subclinical/mild immune checkpoint inhibitor-related myocarditis patients. If significant differences (p < 0.10) in biomarkers are observed between the groups, these differences will serve as key indicators for stratified management of disease severity.

The severity assessment model, constructed based on biomarker combinations, was evaluated for its predictive performance using indicators such as the ROC curve and AUC value. The model demonstrated a predictive performance with an AUC > 0.75 at different time points, indicating a high predictive ability and validating its practical application in clinical risk stratification.

The rate of death occurring within the hospital during a patient's stay.

The 3-month survival rate will be defined as the proportion of patients alive 3 months after enrollment in the study.

Patients' symptom improvement (e.g., fatigue, dyspnea) will be recorded using the New York Heart Association (NYHA) Functional Classification and analyzed in relation to changes in biomarker combinations. This will provide insights into the potential application of biomarkers in predicting symptom improvement and disease severity.

The length of hospital stay will be recorded and analyzed in relation to biomarker combinations and model prediction results, providing additional data to support practical applications in clinical management.