Impact of ibrutinib dose adjustment on TTNT in first-line CLL/SLL: a real-world analysis using target trial emulation
Bibliographic record
Abstract
Ibrutinib, a once-daily Bruton tyrosine kinase inhibitor, is a standard-of-care first-line (1L) treatment for patients with chronic lymphocytic leukemia (CLL)/small lymphocytic lymphoma (SLL). Dosing flexibility (adjustment to daily dose of <420 mg/d) with ibrutinib can help prevent recurrence or worsening of adverse events while maintaining long-term efficacy. This study compared time to next treatment among patients with CLL/SLL in the United States initiating 1L single-agent ibrutinib at 420 mg/d (index date) and staying on this dose vs patients with dose adjustment (DA) within 3 to 12 months. Two databases were used: Komodo claims (a majority from community practices) and Acentrus electronic medical records (from academic and nonteaching hospital systems). To account for immortal time bias (patients with DA survived on 1L therapy until DA) and overlap between follow-up time and definition of treatment strategies, a target trial emulation approach was used, in which patients were cloned at index date and contributed follow-up to both treatment strategy arms until deviation from the strategy. Among 3343 patients in Komodo (mean age: 67.5 years; 37.6% female) and 1171 patients in Acentrus (mean age: 70.4 years; 34.6% female) who initiated 1L single-agent ibrutinib 420 mg/d, 18.0% and 19.6%, respectively, had a DA. DA was not associated with an increased risk of having a next treatment in both databases (adjusted hazard ratio [95% confidence interval]: Komodo: 0.95 [0.80-1.14], Acentrus: 1.14 [0.80-1.62]). These findings suggest that a flexible dosing approach with ibrutinib may be effective in allowing patients to achieve optimal outcomes while remaining on long-term continuous 1L treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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 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".