Cardiovascular magnetic resonance in immune checkpoint inhibitor-associated myocarditis
Bibliographic record
Abstract
AIMS: Myocarditis is a potentially fatal complication of immune checkpoint inhibitors (ICI). Sparse data exist on the use of cardiovascular magnetic resonance (CMR) in ICI-associated myocarditis. In this study, the CMR characteristics and the association between CMR features and cardiovascular events among patients with ICI-associated myocarditis are presented. METHODS AND RESULTS: From an international registry of patients with ICI-associated myocarditis, clinical, CMR, and histopathological findings were collected. Major adverse cardiovascular events (MACE) were a composite of cardiovascular death, cardiogenic shock, cardiac arrest, and complete heart block. In 103 patients diagnosed with ICI-associated myocarditis who had a CMR, the mean left ventricular ejection fraction (LVEF) was 50%, and 61% of patients had an LVEF ≥50%. Late gadolinium enhancement (LGE) was present in 48% overall, 55% of the reduced EF, and 43% of the preserved EF cohort. Elevated T2-weighted short tau inversion recovery (STIR) was present in 28% overall, 30% of the reduced EF, and 26% of the preserved EF cohort. The presence of LGE increased from 21.6%, when CMR was performed within 4 days of admission to 72.0% when CMR was performed on Day 4 of admission or later. Fifty-six patients had cardiac pathology. Late gadolinium enhancement was present in 35% of patients with pathological fibrosis and elevated T2-weighted STIR signal was present in 26% with a lymphocytic infiltration. Forty-one patients (40%) had MACE over a follow-up time of 5 months. The presence of LGE, LGE pattern, or elevated T2-weighted STIR were not associated with MACE. CONCLUSION: These data suggest caution in reliance on LGE or a qualitative T2-STIR-only approach for the exclusion of ICI-associated myocarditis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".