Myocarditis in Patients Starting Combination Checkpoint Inhibitor Therapy: Analysis of a Commercial Claims Database
Bibliographic record
Abstract
BACKGROUND: Immune checkpoint inhibitors have improved the clinical outcomes of several cancers but have also been associated with a greater risk of immune-related adverse effects, especially when combined. The objective of this study was to investigate the incidence of myocarditis in relation to the use of dual concurrent versus single immune checkpoint inhibitors therapies. METHODS AND RESULTS: A cohort study was conducted using medical and pharmacy claims data (2011-2022) from a large US commercial insurer. Cox regression quantified the comparative risks of myocarditis or heart failure in patients with cancer receiving treatment with combination therapy (nivolumab and ipilimumab) in comparison to taking a single immune checkpoint inhibitor only. Mean follow-up time in 53 018 patients was 226 days (interquartile range, 93-495 days). There were 148 cases of myocarditis (0.3%), 33 (0.7%) in patients on combination therapy, and 115 (0.2%) in patients on monotherapy. The risk of myocarditis per 1000 patients was 7.40 in the combination therapy group and 2.37 in the monotherapy group (risk ratio, 3.12 [95% CI, 2.12-4.60]). Using multivariable regression analysis, the hazard ratio for myocarditis in the combination therapy group was 2.38 (1.57-3.63). No difference in the risk of heart failure was found between combination and single therapy. CONCLUSIONS: Therapy with 2 immune checkpoint inhibitors was associated with an increased risk of myocarditis compared with monotherapy, with most cases occurring in the first 6 months of therapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".