BackgroundImmune checkpoint inhibitors (ICI) have emerged as a front-line therapy for a variety of solid tumors. With the widespread use of these agents, immune-associated toxicities are increasingly being recognized, including fatal myocarditis. There are limited data on the outcomes and prognostic utility of biomarkers associated with ICI-associated myocarditis. Our objective was to examine the associations between clinical biomarkers of cardiomyocyte damage and mortality in patients with cancer treated with ICIs.MethodsWe retrospectively studied 23 patients who developed symptomatic and asymptomatic troponin elevations while receiving ICI therapy at a National Cancer Institute-designated comprehensive cancer center. We obtained serial ECGs, troponin I, and creatine kinase-MD (CK-MB), in addition to other conventional clinical biomarkers, and compared covariates between survivors and non-survivors.ResultsAmong patients with myocarditis, higher troponin I (p=0.037) and CK-MB (p=0.034) levels on presentation correlated with progression to severe myocarditis. Higher troponin I (p=0.016), CK (p=0.013), and CK-MB (p=0.034) levels were associated with increased mortality, while the presence of advanced atrioventricular block on presentation (p=0.088) trended toward increased mortality. Weekly troponin monitoring lead to earlier hospitalization for potential myocarditis (p=0.022) and was associated with decreased time to steroid initiation (p=0.053) and improved outcomes.ConclusionsRoutine troponin surveillance may be helpful in predicting mortality in ICI-treated patients with cancer in the early phase of ICI therapy initiation. Early detection of troponin elevation is associated with earlier intervention and improved outcomes in ICI-associated myocarditis. The recommended assessment and diagnostic studies guiding treatment decisions are presented.

Background Serious but rare side effects associated with immunotherapy pose a difficult problem for regulators and practitioners. Immune checkpoint inhibitors (ICIs) have come into widespread use in oncology in recent years and are associated with rare cardiotoxicity, including potentially fatal myocarditis. To date, no comprehensive model of myocarditis progression and outcomes integrating time-series based laboratory and clinical signals has been constructed. In this paper, we describe a time-series neural net (NN) model of ICI-related myocarditis derived using supervised machine learning. Methods We extracted and modeled data from electronic medical records of ICI-treated patients who had an elevation in their troponin. All data collection was performed using an electronic case report form, with approximately 300 variables collected on as many occasions as available, yielding 6000 data elements per patient over their clinical course. Key variables were scored 0–5 and sequential assessments were used to construct the model. The NN model was developed in MatLab and applied to analyze the time course and outcomes of treatments. Results We identified 23 patients who had troponin elevations related to their ICI therapy, 15 of whom had ICI-related myocarditis, while the remaining 8 patients on ICIs had other causes for troponin elevation, such as myocardial infarction. Our model showed that troponin was the most predictive biomarker of myocarditis, in line with prior studies. Our model also identified early and aggressive use of steroid treatment as a major determinant of survival for cases of grade 3 or 4 ICI-related myocarditis. Conclusion Our study shows that a supervised learning NN can be used to model rare events such as ICI-related myocarditis and thus provide clinical insight into drivers of progression and treatment outcomes. These findings direct attention to early detection biomarkers and clinical symptoms as the best means of implementing early and potentially life-saving steroid treatment.