Predictors of ibrutinib‐associated atrial fibrillation: 5‐year follow‐up of a prospective study
Bibliographic record
Abstract
Abstract Ibrutinib‐associated atrial fibrillation (IRAF) emerged among the adverse events of major interests in ibrutinib‐treated patients as real‐world studies showed a higher incidence compared to clinical trials. We prospectively analyzed predictors of IRAF in 43 single‐center consecutive patients affected by chronic lymphocytic leukemia that started therapy with ibrutinib between 2015 and 2017. Key secondary endpoints were to describe the management of IRAF and survival outcomes. During a median follow‐up period of 52 months, we registered 45 CV events, with a total of 23 AF events in 13 patients (CI 30.0% (95% CI: 16.5–43.9)). Pre‐existent cardiovascular risk factors, in particular hypertension, a previous history of AF and a high Shanafelt risk score emerged as predictors of IRAF. Baseline echocardiographic evaluation of left atrial (LA) dimensions confirmed to predict IRAF occurrence and cut‐off values were identified in our cohort: 32 mm for LA diameter and 18 cm 2 for LA area. No difference in progression free survival and overall survival emerged in patients experiencing IRAF. Following AF, anticoagulation was started in all eligible patients, and cardioactive therapy was accordingly modified. Echocardiography represents a highly reproducible and widespread tool to be included in the work‐up of ibrutinib candidates; the identification of IRAF predictors represents a useful guide to 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".