Real-world selection of fixed-duration versus continuous therapies for treatment-naïve chronic lymphocytic leukemia/small lymphocytic lymphoma
Bibliographic record
Abstract
Abstract Introduction: The therapeutic landscape for treatment-naïve (TN) chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL) has evolved substantially with the introduction of novel targeted agents. Continuous treatment with BTK inhibitors (BTKi) and fixed-duration (FD) regimens such as venetoclax-obinutuzumab (Ven-O) or ibrutinib-venetoclax (I+V) are now standard options. In the absence of head-to-head trials, treatment selection between targeted FD and continuous therapies is frequently individualized. However, the factors influencing this decision-making process in real-world clinical practice remain incompletely characterized. Methods: This retrospective, population-based study included consecutive patients aged ≥18 years who initiated first-line treatment for CLL/SLL between January 1 and December 31, 2024 in the province of Alberta, Canada. During this period, Ven-O was universally funded via provincial health insurance for TN patients deemed ineligible for fludarabine, while first-line BTKi therapy was reimbursed for those with high-risk genetic features (e.g. del(17p), TP53 mutation, or unmutated IGHV) or contraindications to intravenous therapy. I+V was available in select cases via special access request or private insurance. Legacy chemoimmunotherapy regimens—including fludarabine, cyclophosphamide, and rituximab (FCR); bendamustine-rituximab (BR); and chlorambucil-obinutuzumab (Chl-O)—remained publicly funded for TN CLL/SLL. The study objectives were to determine the real-world distribution of first-line treatment strategies and to identify clinical or demographic factors associated with treatment selection. Clinical trial therapies were excluded. Statistical comparisons were conducted using Fisher's exact test. Results: This study included 148 patients with TN CLL/SLL with a median age of 71 years (range 43-93) at initiation of first-line therapy. IGHV mutation status was unmutated in 73 (49%), mutated in 38 (26%), indeterminate in 18 (12%), and not performed in 19 (13%) patients. FISH analysis for del(17p) was positive in 19 (13%), negative in 110 (74%), and not performed in 19 (13%) patients. NGS detected a TP53 mutation in 1/7 (14%) evaluable patients. Targeted FD therapies were selected for 75 (51%) patients, including Ven-O (n=73, 49%)and I+V (n=2, 1%). Continuous BTKi therapy was administered to 65 (44%) patients, including zanubrutinib (n=39, 26%), acalabrutinib(n=25, 17%), and ibrutinib (n=1, 1%). Despite the availability of funded targeted therapies, 8 (5%) patients received chemotherapy-based regimens, including BR (n=5, 4%), Chl-O (n=1, 1%), or chlorambucil monotherapy (n=2, 1%), due to patient frailty and/or logistical considerations (n=4), physician preference (n=3), or patient preference (n=1). Among the 138 patients treated with either continuous BTKi or Ven-O, BTKi was more frequently selected than Ven-O for patients with del(17p)/TP53 mutation (84% versus 16%, p=0.0002) and age >75 years (66% versus 34%, p=0.0051). No association was observed between treatment selection and other clinical or demographic factors, such as sex, treatment centre, urban versus rural residence, cardiovascular comorbidities, use of anticoagulants or antiplatelets, prior malignancy, and IGHV mutation status. A discussion of >1 treatment option was documented for 98 (66%) patients. Of the 87 patients with a documented rationale for treatment choice, the most common reasons included patient preference for FD treatment in 30 (34%), logistical considerations (e.g. appointment burden, travel time, convenience) in 27 (31%), toxicity profile in 17 (20%), genetic risk (e.g. del(17p)) in 16 (18%), and medical fitness (e.g. age, frailty, comorbidities) in 13 (15%) patients. Conclusions: This real-world, population-based study reveals that FD and continuous targeted therapies are selected with similar frequency as first-line treatments for CLL/SLL. Treatment selection is primarily influenced by age, presence of del(17p)/TP53 mutations, patient preferences, and logistical considerations, emphasizing the role of individualized care and shared decision-making. These findings illustrate the heterogeneity of real-world practice and the complexity of first-line treatment decisions in the era of targeted therapies for CLL/SLL, and emphasize the need for further research to guide optimal therapy selection including head-to-head comparative trials.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".