Comparative efficacy of Bruton tyrosine kinase inhibitors in high-risk relapsed/refractory CLL: a network meta-analysis
Bibliographic record
Abstract
ABSTRACT: Bruton tyrosine kinase inhibitors (BTKis) have led to changes in the treatment algorithm for patients with high-risk relapsed/refractory (R/R) chronic lymphocytic leukemia (CLL), defined based on the presence of genetic mutations. Given the lack of head-to-head trials comparing next-generation BTKis used to treat high-risk R/R disease, a network meta-analysis (NMA) was performed to estimate their relative efficacy. High-risk populations were defined based on the prespecified definitions within each trial, including patients with del(17p) and/or TP53 mutations in the ALPINE (n = 150) and ASCEND (n = 86) trials, and del(17p)/del(11q) in the ELEVATE-RR (n = 533) trial. Bayesian NMAs found zanubrutinib to be the most efficacious treatment for high-risk patients, with significantly reduced risk of progression or death compared with ibrutinib (hazard ratio [HR], 0.49; 95% credible interval [CrI], 0.31-0.78), acalabrutinib (HR, 0.55; 95% CrI, 0.32-0.94), and bendamustine + rituximab or idelalisib + rituximab (BR/IR; HR, 0.12; 95% CrI, 0.05-0.26). Differences in overall survival demonstrated a numerical trend favoring zanubrutinib (probability better than ≥80%) compared with ibrutinib (HR, 0.59; 95% CrI, 0.31-1.11), acalabrutinib (HR, 0.72; 95% CrI, 0.35-1.50), and BR/IR (HR, 0.65; 95% CrI, 0.23-1.75). Rates of response also demonstrated trends favoring zanubrutinib compared with acalabrutinib, with significant results compared with ibrutinib. The NMA suggests that the most efficacious BTKi for patients with high-risk R/R CLL is zanubrutinib.
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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 | 0.023 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.042 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".