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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".