Assessing the Burden of Adverse Events in a Head-to-Head Trial of Acalabrutinib Versus Ibrutinib in Previously Treated Chronic Lymphocytic Leukemia (CLL)
Bibliographic record
Abstract
Background: In a previously reported phase 3 head-to-head trial of the Bruton tyrosine kinase (BTK) inhibitors acalabrutinib (Acala) and ibrutinib (Ibr) (NCT02477696), Acala demonstrated noninferiority to Ibr and an improved safety profile with statistically fewer atrial fibrillation/flutter (afib/flutter) events (9% vs 16%) and numerically fewer discontinuations due to adverse events (AEs; 15% vs 21%) vs Ibr in patients (pts) with previously treated CLL (Byrd et al. J Clin Oncol 2021;39:3441-52). To further characterize differences in the safety profile between Acala and Ibr, we conducted a post hoc analysis of AEs using a recently developed, novel statistical method (Ruppert et al. Leukemia 2021;35:2854-61), which considers measures not captured by incidence alone, such as event duration, recurrence, and grade weighting. Methods: Pts with previously treated CLL and del(17p) or del(11q) received oral Acala 100 mg twice daily or Ibr 420 mg daily until disease progression or unacceptable toxicity. Any-grade AE incidence was reported overall and for AEs commonly associated with BTK inhibitors. An AE burden score was adapted from Ruppert et al and calculated as the sum of the products of the AE duration and AE severity grade (weight) recorded for each all-cause AE, divided by duration of the evaluable treatment-emergent period for each pt. Grade 1-4 AEs were weighted according to their CTCAE severity grades; grade 5 AEs were weighted as 10 to reflect the fatal outcome. Pts with no AE had a score of 0. Separate scores were calculated for grade 1-4 AEs and grade 1-5 AEs. Results: In total, 529 pts (Acala, n=266; Ibr, n=263) received study treatment; median number of prior therapies was 2 in both arms. Median total treatment exposures were 38.3 and 35.5 mo, respectively, at the September 15, 2020 data cutoff. While the frequency of experiencing any AE was similar between Acala (98%) and Ibr (97%), the overall AE burden score considering all AEs was higher for Ibr vs Acala, with (P=0.0006) or without (P=0.0016) grade 5 events included (Figure). The overall AE burden score was higher with Ibr vs Acala in certain pt subgroups, including age <65 y, age ≥65 y, and 1-3 prior therapies; there was no statistical difference among pts with ≥4 prior therapies (Figure). AE incidences and AE burden scores for events of clinical interest (ECIs) and selected AEs are shown in the Table. Among ECIs, the afib/flutter AE burden score P=0.0201and event incidence (P) were higher with Ibr than Acala. The AE burden score for cardiac events overall was numerically higher for Ibr vs Acala, even when including grade 5 events; the incidence rate was also numerically higher for Ibr. For hypertension and hemorrhage, AE burden scores (P<0.0001 and P=0.0007, respectively) and event incidences (P<0.0001 and P=0.0020, respectively) were higher with Ibr; no difference was observed for major hemorrhage. Neither the incidence nor AE burden score for infections was different between treatment arms. Among notable symptomatic AEs, the incidence and AE burden score of fatigue was similar between Acala and Ibr. Diarrhea was more frequent among Ibr-treated pts (P=0.0075); however, the AE burden score was higher with Acala (P=0.0114). Headache was more frequent with Acala (P=0.0002), with a higher AE burden score vs Ibr (P=0.0002). The AE burden score for musculoskeletal events was higher with Ibr (P=0.0229). Conclusions: The results of this novel analysis using methodology that incorporates event duration and severity weighting further support the superior tolerability of Acala compared with Ibr and confirm the safety differences that have been reported previously between the two drugs. The AE burden score was lower with Acala vs Ibr overall as well as for afib/flutter, hypertension, hemorrhage, and musculoskeletal events, while it was greater with Acala for diarrhea and headache. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".