Immune checkpoint inhibitor monotherapy is associated with less cardiac toxicity than combination therapy
Bibliographic record
Abstract
BACKGROUND: Treatment options for many cancers include immune checkpoint inhibitor (ICI) monotherapy and combination therapy with impressive clinical benefit across cancers. We sought to define the comparative cardiac risks of ICI combination and monotherapy. METHODS: We used VigiBase, the World Health Organization pharmacovigilance database, to identify cardiac ADRs (cADRs), such as carditis, heart failure, arrhythmia, myocardial infarction, and valvular dysfunction, related to ICI therapy. To explore possible relationships, we used the reporting odds ratio (ROR) as a proxy of relative risk. A lower bound of a 95% confidence interval of ROR > 1 reflects a disproportionality signal that more ADRs are observed than expected due to chance. RESULTS: We found 2278 cADR for ICI monotherapy and 353 for ICI combination therapy. Combination therapy was associated with significantly higher odds of carditis (ROR 6.9, 95% CI: 5.6-8.3) versus ICI monotherapy (ROR 5.0, 95% CI: 4.6-5.4). Carditis in ICI combination therapy was fatal in 23.4% of reported ADRs, compared to 15.8% for ICI monotherapy (P = 0.058). CONCLUSIONS: Using validated pharmacovigilance methodology, we found increased odds of carditis for all ICI therapies, with the highest odds for combination therapy. Given the substantial risk of severe ADR and death, clinicians should consider these findings when prescribing checkpoint inhibitors.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".