This study aims to investigate the clinical classification and outcome-related biomarkers of immune checkpoint inhibitor (ICI)-related myocarditis in patients with lung cancer.A total of 50 patients with ICI-related myocarditis will be enrolled, including 25 with severe/critical myocarditis and 25 with subclinical/mild myocarditis. Blood samples will be collected at baseline and at follow-up time points (3 days, 7 days, and before discharge). Traditional myocardial injury markers, iron metabolism-related markers, and immunological markers will be measured and compared between groups. Changes in biomarkers after treatment will also be assessed. Clinical information such as in-hospital mortality and 3-month survival rates will be integrated to develop a severity assessment model. This model aims to evaluate disease severity and prognostic risk accurately by combining biomarkers, enhancing their application in clinical management.
Study Type
OBSERVATIONAL
Enrollment
50
Blood samples will be collected at baseline and at follow-up time points (3 days, 7 days, and before discharge). Traditional myocardial injury biomarkers, iron metabolism-related biomarkers, and immunological biomarkers will be tested.
Shanghai Chest Hospital
Shanghai, China
RECRUITINGThe correlation between the dynamic changes in biomarker combinations and disease severity.
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.
Time frame: Up to 3 months
Predictive performance of the severity assessment model
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.
Time frame: Up to 3 months
In-hospital mortality
The rate of death occurring within the hospital during a patient's stay.
Time frame: Up to 3 months
3-month survival rate
The 3-month survival rate will be defined as the proportion of patients alive 3 months after enrollment in the study.
Time frame: Up to 3 months
Improvement in patients' symptoms.
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.
Time frame: Up to 3 months
Length of hospital stay.
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.
Time frame: Up to 3 months
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