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Prognostic risk factors and a survival prediction model for immune checkpoint inhibitor related myocarditis in patients with lung cancer: a multicenter study

  • 0Department of Pulmonary and Critical Care Medicine, The Third XiangyaHospital of Central South University, Changsha, 410013, China.
Scientific Reports +

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October 10, 2025

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October 10, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study identifies key factors influencing survival in lung cancer patients with immune checkpoint inhibitor (ICI)-related myocarditis. A validated nomogram model aids in predicting survival outcomes for this patient group.

Area Of Science

  • Oncology
  • Cardiology
  • Immunology

Background

  • Immune checkpoint inhibitor (ICI)-related myocarditis is a rare but serious adverse event in lung cancer patients.
  • Limited data exists on multivariate prognostic factors for ICI-related myocarditis severity, major adverse cardiac events (MACE), and survival.

Purpose Of The Study

  • To identify risk factors for severity, MACE, and survival in lung cancer patients with ICI-related myocarditis.
  • To develop and validate a survival prediction model for this population.

Main Methods

  • Retrospective analysis of 70 lung cancer patients with ICI-related myocarditis (training set) and 40 (validation set).
  • Cox and Logistic regression models were used to determine factors associated with survival, severe myocarditis, and MACE.
  • A nomogram model was constructed and validated for survival prediction.

Main Results

  • Combined radiotherapy and longer ICI treatment duration (≥132 days) were protective factors against all-cause death.
  • Low-dose glucocorticoid use in severe myocarditis and angiogenesis inhibitor use were risk factors for severe myocarditis.
  • Coronary artery disease history was a risk factor for MACE, while left ventricular ejection fraction was protective.

Conclusions

  • A validated nomogram model incorporating radiotherapy, ICI treatment duration, and glucocorticoid use demonstrates good discriminative ability for predicting survival.
  • The model aids in survival prediction for lung cancer patients experiencing ICI-related myocarditis.
  • Identified risk factors for severe myocarditis and MACE can inform clinical management.

Keywords:

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