Clinical Characteristics and Outcomes in Immune Checkpoint Inhibitor Therapy-Associated Myocarditis
Bibliographic record
Abstract
Immune checkpoint inhibitor (ICI) therapy has played an important role in the treatment of several groups of cancers. Although a life prolonging treatment, many side effects have been shown with ICI therapy. This study looked at individual level clinical characteristics and outcomes with ICI therapy in patients who developed ICI-related myocarditis. A comprehensive review of the National Library of Medicine PubMed database was performed. Inclusion criteria were all studies that were composed of case reports and case series of individual patients undergoing ICI therapy that developed myocarditis. To appreciate individual patient level data, observational studies, clinical trials, systematic reviews, and meta-analyses were excluded. Our search yielded 333 results with 71 cases reviewed of ICI therapy-related myocarditis. The findings included an average age of 68 years, higher incidence in men, and pretreatment cardiac history of hypertension. Melanoma was the most prevalent malignancy with nivolumab being the most used ICI therapy. Heart failure was the most prevalent adverse event that was co-prevalent with myocarditis. Corticosteroid therapy alone was the most utilized therapy to treat ICI-related myocarditis. Mortality was seen in nearly half of the patient population. Our study reviewed the preexisting literature of prior reported myocarditis secondary to ICI therapy. Periodic surveillance should be performed by the cardio-oncologist and internist. Due to the expanding role of ICI therapy in treating a variety of cancer patients, appreciation of its impact on the development of myocarditis is needed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".