ArticleBMC cancer2025
Myocarditis prediction in locally advanced or metastatic lung cancer patients with cardiac parameters abnormalities undergoing immunotherapy: development and validation of a risk assessment model.
Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction models for immune checkpoint inhibitor-related cardiovascular toxicity: a systematic review.Frontiers in immunology · 2026Pooled it
- Machine learning approaches to predict early cardiac immune-related adverse events in patients receiving immune checkpoint inhibitors.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Article
- Review
- The role and clinical value of natriuretic peptide receptor family in malignant tumor.Cell death discovery · 2025Review
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6 authors.
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Abstract
backgroundImmune 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.
methodsIn 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.
resultsAmong 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.
conclusionsThis 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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