Therapeutic utilization in chronic lymphocytic leukemia: A cross-sectional analysis of the Lymphoma Coalition’s 2022 Global Patient Survey.
Bibliographic record
Abstract
e19026 Background: Chronic Lymphocytic Leukemia (CLL) is a highly prevalent disease with heterogenous prognostic trajectories. With a multitude of treatment options, we sought to explore therapeutic utilization trends from a global perspective in order to gain a more in-depth understanding of how different treatment regimens are utilized in different health systems. Methods: In 2022, The Lymphoma Coalition designed and globally deployed a survey to patients with lymphoma and CLL in order to gather patient reported outcomes and patient reported experience measures. Data were collected and enumerated with relevant contingency analyses performed to determine associations with a particular treatment regimen in the context of CLL. Results: The entire cohort of patients with CLL was 1289. The median age of the cohort was 65 and ranged from 26 to 97 years old. Biological sex was equally distributed with 50.5% of respondent indicating that they were female. Respondents were derived from 29 countries. The top three contributors to each region were China (50%), Australia (25%) and New Zealand (18%) for Asia-Pacific (N = 227); France (50%), Italy (13%) and Sweden (7%) for Europe (N = 950); Canada (75%) and The United States (25%) for North America (N = 112). Of those, 636 indicated that they received at least one therapy. The use of chemo-immunotherapy was most common (42%) followed by targeted therapy (32%), immunotherapy alone (18%), and chemotherapy alone (16%). Neither age nor biological sex was a significant predictor of therapy. Europe had the highest rates of chemo-immunotherapy utilization (45%) with North America and Asia-Pacific indicating 32% respectively (p = 0.01). Targeted therapy was used at a higher rate in North America (45%) but failed to demonstrate a significant difference against Asia-Pacific and Europe (31%); (p=0.10). Both immunotherapy and chemotherapy alone failed to demonstrate significant differences across regions. Conclusions: The data support that the majority of patients with CLL who require treatment are most likely to receive combination chemo-immunotherapy utilizing rituximab. However, we acknowledge that the data presented here is two years old and that the use of targeted molecular therapy is likely increasing. We found it reassuring that we could not identify any biases in the administration of the therapeutic regimens across the demographics of age and biological sex. Our research in this space will continue as we have deployed the 2024 version of the Global Patient Survey and have focused on increasing respondent participation from North America. With this new data, we plan on producing a time-dependent model to investigate how emerging therapies enter and expand in the CLL therapeutic space.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".