Treatment Patterns, Healthcare Resource Utilization, and Costs of Patients With Chronic Lymphocytic Leukemia or Small Lymphocytic Lymphoma in the US
Bibliographic record
Abstract
BACKGROUND: Chronic lymphocytic leukemia (CLL) is the most common type of leukemia among US adults and has experienced a rapidly evolving treatment landscape; yet current data on treatment patterns in clinical practice and economic burden are limited. This study aimed to provide an up-to-date description of real-world characteristics, treatments, and costs of patients with CLL or small lymphocytic lymphoma (SLL). MATERIALS AND METHODS: Using retrospective data from the Optum Clinformatics DataMart database (January 2013 to December 2021), adults with diagnosis codes for CLL/SLL on two different dates were selected. An adapted algorithm identified lines of therapy (LOT). Treatment patterns were stratified by the index year pre- and post-2018. Healthcare resource utilization and costs were evaluated per patient-years. RESULTS: A total of 18 418 patients with CLL/SLL were identified, 5226 patients (28%) were treated with ≥1 LOT and 1728 (9%) with ≥2 LOT. Among patients diagnosed with CLL in 2014-2017 and ≥1 LOT (N = 2585), 42% used targeted therapy and 30% used chemoimmunotherapy in first line (1L). The corresponding proportions of patients diagnosed with CLL in 2018-2021 (N = 2641) were 54% and 16%, respectively. Total costs were numerically 3.5 times higher and 4.9 times higher compared with baseline costs among patients treated with 1L+ and 3L+, respectively. CONCLUSION: This study documented the real-world change in CLL treatment landscape and the substantial economic burden of patients with CLL/SLL. Specifically, targeted therapies were increasingly used as 1L treatments and they were part of more than half of 1L regimens in recent years (2018-2021).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".