Neural Net Modeling of Checkpoint Inhibitor Related Myocarditis and Steroid Response

Abstract

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.

Keywords: NN modeling; checkpoint inhibitor; dose and timing; myocarditis; steroid response.

Figure 1

Biomarker time course in 11 patients with ICI myocarditis and lived. Time course of selected biomarkers in 11 patients with elevated troponin caused by ICI associated myocarditis. Time zero is onset of myocarditis, shown by change in myocarditis severity score from zero. Myocarditis was assigned a clinical severity score from 0–5 with 1 corresponding to symptomatic disease, 2 to symptomatic disease + abnormal biomarkers, 3 to myocarditis-related hospitalization, 4 to ICU care, and 5 assigned at the time of death. All 11 patients survived the ICI myocarditis episode displayed in this figure, although a score of 4 was reached by patient 1, 4, 8 and 9. Notable is the close association between prednisone equivalent dose score and the decline of myocarditis score as well as both laboratory and clinical indices of myocarditis. Among the important biomarkers that track with use of steroids, the QRS rose before prednisone score and declined rapidly afterwards. Lymphocyte count declined with myocarditis severity score and rose after it resolved.

Figure 2

Biomarker time course in 4 patients with fatal ICI myocarditis. Time course of selected biomarkers in 4 patients with elevated troponin caused by ICI-related myocarditis. Time zero is onset of myocarditis, shown by change in myocarditis severity score from zero. Myocarditis was assigned a clinical severity score from 0–5 with 1 corresponding to symptomatic disease, 2 to symptomatic disease + abnormal biomarkers, 3 to myocarditis-related hospitalization, 4 to ICU care, and 5 assigned at the time of death. These 4 patients died primarily as a result of ICI myocarditis or immediate sequelae. Notable is the close association between steroid use and dose (represented in prednisone equivalents by “PrednisoneEq_Score”) and the decline of myocarditis score as well as both laboratory and clinical indices of myocarditis. However, in most cases prednisone dose was too low and started too late to be lifesaving. QRS rose before prednisone score but did not decline with inadequate prednisone dose. Lymphocyte count declined with myocarditis and did not recover with inadequate prednisone dose.

Figure 3

Biomarker time course in 8 ICI treated patients with other cardiotoxicity. Time course of selected biomarkers in 8 patients with elevated troponin that was determined not to be caused by ICI associated myocarditis. Two patients had a rise in troponin associated with worsening of CHF, 5 patients had coronary artery events primarily ischemic, and one patient had tumor hyper-progression with obstructing cardiopulmonary disease. Time zero here is the onset of troponin rise, shown by the change in troponin score. Although 3 of these cases did receive prednisone, there was seldom any evident change in the biomarkers of myocarditis, identifying these cases as not having an inflammatory cause in the heart.

Figure 4

Prednisone vs myocarditis score. The averaged prednisone equivalent dose and myocarditis score are shown for patients with a response (left) and patients who did respond to treatment (right). In patients who responded, they received treatment on average 2-days earlier than patients who did not respond, and received higher doses earlier. Patients recovered from myocarditis often required prolonged high dose steroids before myocarditis scores returned to baseline.