Characterization of atrial fibrillation adverse events reported in ibrutinib randomized controlled registration trials
Bibliographic record
Abstract
The first-in-class Bruton’s tyrosine kinase inhibitor ibrutinib has proven clinical benefit in B-cell malignancies; however, atrial fibrillation (AF) has been reported in 6–16% of ibrutinib patients. We pooled data from 1505 chronic lymphocytic leukemia and mantle cell lymphoma patients enrolled in four large, randomized, controlled studies to characterize AF with ibrutinib and its management. AF incidence was 6.5% [95% Confidence Interval (CI): 4.8, 8.5] for ibrutinib at 16.6-months versus 1.6% (95%CI: 0.8, 2.8) for comparator and 10.4% (95%CI: 8.4, 12.9) at the 36-month follow up; estimated cumulative incidence: 13.8% (95%CI: 11.2, 16.8). Ibrutinib treatment, prior history of AF and age 65 years or over were independent risk factors for AF. Multiple AF events were more common with ibrutinib (44.9%; comparator, 16.7%) among patients with AF. Most (85.7%) patients with AF did not discontinue ibrutinib, and more than half received common anticoagulant/antiplatelet medications on study. Low-grade bleeds were more frequent with ibrutinib, but serious bleeds were uncommon (ibrutinib, 2.9%; comparator, 2.0%). Although the AF rate among older non-trial patients with comorbidities is likely underestimated by this dataset, these results suggest that AF among clinical trial patients is generally manageable without ibrutinib discontinuation (clinicaltrials.gov identifier: 01578707, 01722487, 01611090, 01646021).
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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 | 0.133 | 0.205 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.013 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".