Key result
Risk model combining cardiac biomarkers and Caprini score predicts ICI-related myocarditis with ~0.84 AUC.
Why the study?
Immune checkpoint inhibitor-related myocarditis poses clinical challenges, creating a need for a predictive model using cardiac biomarkers to identify patients prone to myocarditis during immunotherapy.
Does a risk assessment model integrating cardiac biomarkers and Caprini score predict immune checkpoint inhibitor-related myocarditis in patients with locally advanced or metastatic lung cancer and abnormal baseline cardiac parameters?
Cohort (n=1,838)
No
Does a risk assessment model integrating cardiac biomarkers and Caprini score predict immune checkpoint inhibitor-related myocarditis in patients with locally advanced or metastatic lung cancer and abnormal baseline cardiac parameters?
A novel nomogram integrating baseline cardiac biomarkers and the Caprini risk score accurately predicts the risk of immune checkpoint inhibitor-related myocarditis in lung cancer patients with pre-existing cardiac abnormalities.
May support myocarditis risk stratification in ICI-treated lung cancer; leaves open prospective validation before practice change.
BACKGROUND: Immune checkpoint inhibitors (ICIs) have revolutionized treatment for advanced lung cancer, yet their cardiotoxicity, particularly immune checkpoint inhibitor-related myocarditis, poses significant clinical challenges. This study aims to create a predictive model using cardiac biomarkers to identify patients prone to myocarditis during treatment, thereby enhancing clinical decision-making and patient outcomes. METHODS: In this retrospective cohort study, 1,838 patients with locally advanced and metastatic lung cancer and abnormal baseline cardiac parameters receiving immunotherapy from June 2018 to August 2024 were analyzed, with a follow-up date cutoff of September 20, 2024. Patients were randomly divided into training (70%) and validation (30%) cohorts. Logistic regression analysis was conducted on demographic information, clinical characteristics, treatments, and cardiac parameters of these patients prior to immunotherapy. A nomogram was constructed via multivariable logistic regression, and AUC and Hosmer-Lemeshow tests were performed to verify the accuracy of the model. RESULTS: Among 1,838 patients, 89 (4.84%) developed myocarditis. Independent predictors included α-HBDH > 910 U/L (OR = 10.57, 95%CI: 2.47-45.22, P = 0.001), CK-MB > 15 ng/mL (OR = 3.87, 95%CI: 1.06-14.11, P = 0.040), hs-cTnT elevation (14-28 pg/mL: OR = 4.19; 28-42 pg/mL: OR = 13.10; >42 pg/mL: OR = 25.43, P < 0.001), NT-proBNP > 3× age-adjusted upper limit (OR = 9.72, 95%CI: 1.09-86.73, P = 0.042), and Caprini score ≥ 4 (OR = 4.49, 95%CI: 2.26-8.90, P < 0.001). The nomogram demonstrated strong discrimination ability, with an AUC of 0.831 in the training cohort (sensitivity: 0.842, specificity: 0.717) and an AUC of 0.844 in the validation cohort. CONCLUSIONS: This study establishes a validated risk assessment model integrating cardiac biomarkers (α-HBDH, CK-MB, hs-cTnT, NT-proBNP) and Caprini risk score to predict ICI-related myocarditis in lung cancer patients with cardiac abnormalities. The tool facilitates early identification of high-risk patients, enabling tailored monitoring and preemptive management. These findings underscore the critical role of baseline cardiac profiling in optimizing immunotherapy safety.
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Li et al. (2025) conducted a cohort in Locally advanced or metastatic lung cancer with abnormal baseline cardiac parameters (n=1,838). Risk assessment model (cardiac biomarkers and Caprini score) was evaluated on Prediction of ICI-related myocarditis (Training cohort) (AUC 0.831, 95% CI 0.772-0.890, p=<0.001). A risk assessment model integrating cardiac biomarkers and Caprini score accurately predicted immune checkpoint inhibitor-related myocarditis in lung cancer patients (validation AUC 0.844).
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