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 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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".