Lung Cancer, Myocarditis Due to Drug
Conditions
Keywords
Lung Cancer, Myocarditis, Immune Checkpoint Inhibitors
Brief summary
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.
Interventions
DIAGNOSTIC_TESTBiomarker Analysis for Severity Assessment
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.
Sponsors
Shanghai Chest Hospital
Study design
Observational model
CASE_ONLY
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Pathologically confirmed lung cancer and having received at least one dose of immune checkpoint inhibitor therapy; * Clinically diagnosed with immune checkpoint inhibitor-related myocarditis; * Aged 18 years or older; * Voluntarily signed informed consent after being fully informed.
Exclusion criteria
* Pregnancy or breastfeeding; * Presence of severe underlying cardiovascular diseases or recent acute cardiac events (e.g., myocardial infarction, severe arrhythmia); * Concurrent other malignancies, immunosuppressive diseases, or autoimmune diseases; * Inability to complete the required examinations and follow-ups specified in the study.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| The correlation between the dynamic changes in biomarker combinations and disease severity. | Up to 3 months | 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. |
| Predictive performance of the severity assessment model | Up to 3 months | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| In-hospital mortality | Up to 3 months | The rate of death occurring within the hospital during a patient's stay. |
| 3-month survival rate | Up to 3 months | The 3-month survival rate will be defined as the proportion of patients alive 3 months after enrollment in the study. |
| Improvement in patients' symptoms. | Up to 3 months | 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. |
| Length of hospital stay. | Up to 3 months | 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. |
Countries
China
Contacts
Primary ContactYanbin Kuang, PhD
Outcome results
None listed