Real-world characterization of ibrutinib therapy for chronic lymphocytic leukemia and small lymphocytic lymphoma in British Columbia
Bibliographic record
Abstract
Background: Ibrutinib therapy has quickly become standard of care in Canada for CLL/SLL patients, due to its proven survival benefits. Real-world studies, however, have unveiled that ibrutinib therapy is also associated with higher rates of discontinuations and toxicities in patients. It is predicted that patients on ibrutinib therapy incur high costs to the healthcare system due to these toxicities. One potential solution includes testing for immunoglobulin heavy chain variable region (IGHV) mutations. IGHV has the potential to inform treatment-decisions, allowing for the appropriate administration of ibrutinib and improved outcomes in patients. Objectives: The global objective was to complete a comprehensive real-world population-level observational study to characterize ibrutinib treatment in CLL/SLL patients in BC. The specific aims were as follows: i) to assess the rates of adverse events, discontinuations, dose modifications, and survival outcomes ii) to characterize the healthcare utilization and associated costs of ibrutinib therapy, and iii) to assess the impact of IGHV genomic testing, costs, resource use, and challenges of this testing and impact on survival. Methods: Provincial cancer and administrative databases were used to collate the data required. Each objective was addressed using three unique cohorts. Specific inclusion and exclusion criteria were set, and strict data cleaning procedures were followed. Results: Ibrutinib therapy led to good survival outcomes in the first-line and relapse/refractory settings; however, a high proportion of patients (35.1%) discontinued their ibrutinib treatment due to toxicities or disease progression. Survival outcomes were not maintained for patients who discontinued treatment regardless of reason. In the first 3 years of its use, ibrutinib cost a mean of $68,266.31 per patient per year. From time of diagnosis, patients with unmutated IGHV had worse overall survival (OS) compared to patients with mutated IGHV (P<0.001). However, when IGHV testing was used to inform treatment selection, there was no difference in survival, with 24-month OS 88.7% vs 91.3% for patients with unmutated and mutated IGHV status respectively, (P=0.785). Conclusions: Ibrutinib and IGHV testing have the potential to improve survival outcomes of CLL/SLL patients overall, however the drug cost of ibrutinib is a major burden to the healthcare system.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".