Risk of bleeding complications and atrial fibrillation associated with ibrutinib treatment: A systematic review and meta-analysis
Bibliographic record
Abstract
The use of ibrutinib is hampered by major bleeding events and atrial fibrillation. Speculating whether randomized controlled trials might underestimate the risk of adverse events in clinical practice, we conducted a systematic review and meta-analysis studying patients treated in any setting and indication. We systematically searched the literature using MEDLINE and EMBASE databases for case series, cohort studies, or randomized controlled trials and retrieved all data in parallel. Proportions of patients with adverse events were pooled in relevant subgroups using the binominal distribution and Freeman-Tukey double arcsine transformation. Among 2'537 records screened, 85 were finally included, comprising 7'317 patients. Methodological quality according to the Newcastle-Ottawa Scale was rated as moderate to poor with regard to bleeding events and atrial fibrillation; 106 studies were excluded because of missing data at all. Reported events varied substantially between 0 % and 78 % (any bleedings), 0 % and 25 % (major bleedings), and 0 % and 38 % (new-onset atrial fibrillation). Pooled estimates were 28 % (95 % confidence interval 22 %, 34 %), 3 % (2 %, 4 %), and 8 % respectively (7 %, 10 %). The risk of events was higher in studies with an older population, high ibrutinib dosage, thrombocytopenia, antithrombotic treatment, and retrospective studies. In conclusions, reporting of bleeding events and atrial fibrillation varied substantially among studies. These observations, in combination with the estimates obtained, suggest a relevant risk in clinical practice.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.043 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".