Ibrutinib in Elderly Patients with Chronic Lymphocytic Leukemia: Adverse Event Incidence, Management, and Outcomes in a Canadian Real-World Setting
Bibliographic record
Abstract
BACKGROUND: Long-term clinical trials and real-world data have established a comprehensive risk-benefit profile for ibrutinib, informing adverse event (AE) management strategies to optimize safety and efficacy. METHODS: We retrospectively assessed the incidence of AEs of special interest and management strategies in all patients treated with ibrutinib for chronic lymphocytic leukemia (CLL) in Saskatchewan, Canada, since 2014. RESULTS: Among 187 patients (median age 75.7 years, 63% male), the median time from ibrutinib treatment initiation to data cutoff was 3.1 years. Approximately two-thirds of patients received ibrutinib for relapsed CLL (33.7% second-line and 32.6% third-line and beyond), with 33.7% receiving it first-line. All patients initiated ibrutinib as monotherapy at 420 mg. AEs of interest were observed in 81.3% of patients, with 42.8% experiencing ≥2 AEs. No grade 5 AEs were reported. Among the 284 first-onset AEs observed in 152 patients, 90.8% were successfully managed, allowing treatment continuation. The median time to successful management ranged from 27.0 days (range: 12.5-73.0) for infections to 84.0 days (range: 55.0-141.0) for hypertension. Both AE and discontinuation rates were comparable or favourable to previous reports. CONCLUSION: This real-world analysis suggests that ibrutinib may be safely used in the majority of CLL patients encountered in routine 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".