Real-World Patterns of Targeted Therapy Use in Chronic Lymphocytic Leukemia and Small Lymphocytic Lymphoma in the United States: A Longitudinal Study
Bibliographic record
Abstract
Introduction: Clinical guidelines and practice have shifted toward increasing use of novel Bruton's tyrosine kinase inhibitors (BTKi) or B-cell lymphoma 2 inhibitor (BCL2i) for treatment of chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL). To highlight recent changes in clinical practice, this study evaluated real-world treatment sequences, with a focus on sequential novel targeted therapy, among patients with CLL/SLL in the US over time. Methods: Adult patients with ≥2 medical claims for CLL/SLL diagnosis on distinct days and ≥1 claim for CLL/SLL therapy were identified in the Optum Clinformatics DataMart database (2014-2021). The index date was the date of initiation of the first observed line of therapy (LOT) following the first observed CLL/SLL diagnosis. The observation period spanned from index to the earliest of end of eligibility, data availability, or death. LOTs were identified using a claims-based algorithm adapted from published literature during the observation period. Each LOT spanned from the initiation of a new agent to discontinuation of all agents in the LOT (i.e., 90-day gap without any dispensing), a switch to another agent, or the addition of a new agent. The proportion of patients receiving sequential targeted therapy in the first 2 LOTs (i.e., BTKi/BCL2i as first-line [1L] and second-line [2L] treatments in any order) was described. For sequential BTKi in 1L and 2L, treatments were also described by agent (i.e., ibrutinib, acalabrutinib, zanubrutinib, and combinations thereof). To assess longitudinal patterns, Sankey plots were created for 2 temporal data cohorts: patients with a 1L in 2014-2017 and patients with a 1L in 2018-2021. Results: We identified 7,146 eligible patients, 40.6% female, median age 73 years, with a median follow-up of 2 years after 1L initiation. The most common 1L treatment was BTKi (36.8%), then chemoimmunotherapy (CIT; 27.4%), CD20 (22.2%), chemotherapy (7.6%), and BCL2i (5.4%). In the first 2 LOTs, 22.3% of patients had sequential targeted therapy, comprising 70.0% BTKi→BTKi (i.e., BTKi retreated), 23.8% BTKi→BCL2i, 3.6% BCL2i→BCL2i, and 2.6% BCL2i→BTKi. The most common BTKi sequences by agent were ibrutinib→ibrutinib (61.6%) and ibrutinib→acalabrutinib (28.4%). Among patients with a 1L in 2014-2017 (N=2,612; median follow-up=3 years; Figure A), the most common 1L treatment class was CIT (44.6%), followed by CD20 (25.2%), BTKi (21.7%), chemotherapy (7.6%), and BCL2i (0.3%). For those receiving a 2L treatment (N=905), the 3 most common treatment classes were BTKi (37.6%), CD20 (30.2%), and CIT (17.2%). The proportion of patients receiving sequential targeted therapy in the first 2 LOTs was 11.2%; among these patients, 80.2% had BTKi→BTKi; 17.8% had BTKi→BCL2i; 2.0% had BCL2i→BCL2i; and 0% had BCL2i→BTKi. The most common BTKi sequences by agent were ibrutinib→ibrutinib (81.5%) and ibrutinib→acalabrutinib (17.3%). Among patients with a 1L in 2018-2021 (N=4,534; median follow-up=1 year; Figure B), the most common 1L treatment class was BTKi (45.5%), followed by CD20 (20.4%), CIT (17.5%), BCL2i (8.3%), and chemotherapy (7.6%). For 2L (N=833), the 3 most common treatment classes were BTKi (40.9%), CD20 (24.4%), and BCL2i (14.9%). The proportion of patients receiving sequential targeted therapy in the first 2 LOTs was 34.3%, comprising 66.4% BTKi→BTKi; 25.9% BTKi→BCL2i; 4.2% BCL2i→BCL2i; and 3.5% BCL2i→BTKi). Among those receiving sequential BTKi, the most common sequences by agent were ibrutinib→ibrutinib (53.2%), ibrutinib→acalabrutinib (33.2%), and acalabrutinib→acalabrutinib (5.3%). Conclusions: This longitudinal real-world study found a substantial increase in patients with CLL/SLL receiving targeted therapies in 1L over time. In particular, the proportion receiving BTKi in 1L doubled in 2018-2021 relative to 2014-2017 and accounted for almost half of the recent 1L CLL/SLL initiations. A third of patients with 1L observed in 2018-2021 also received a targeted agent in 2L, relative to about 1 in 10 patients in 2014-2017, with BTKi→BTKi being the most common sequence. Surprisingly, patients who received a BTKi in 1L were more likely to receive a BTKi in 2L than they were to switch to a BCL2i. Future studies should assess clinical outcomes to determine optimal sequences for CLL/SLL as well as the reasons for re-treatment with BTKi.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".