Healthcare resource utilization and costs associated with first-line ibrutinib compared to chemoimmunotherapy treatment among Medicare beneficiaries with chronic lymphocytic leukemia
Bibliographic record
Abstract
Objective This retrospective observational study aimed to compare healthcare resource utilization and costs of Medicare beneficiaries with chronic lymphocytic leukemia (CLL)/small lymphocytic lymphoma (SLL) who received ibrutinib versus chemoimmunotherapy (CIT) in first line (1 L).Methods Fee-for-service (FFS) and Medicare Advantage (MA) claims data were used to identify adults with a CLL/SLL diagnosis initiating 1 L ibrutinib single agent or CIT between 4 March 2016 and 30 September 2017 (index date). HRU and costs (Medicare spending) were evaluated during 1 L Oncology Care Model (1 L OCM) episodes (the first six months post-index) and over the observed 1 L duration. Patients' baseline characteristics were balanced using inverse probability of treatment weighting. Mean monthly cost differences (MMCDs) obtained from ordinary least square regressions were used to compare costs between ibrutinib and CIT cohorts.Results In the Medicare FFS dataset (ibrutinib: n = 2014; CIT: n = 2050), ibrutinib patients incurred significantly higher monthly pharmacy costs (1 L OCM: MMCD = $4878, p < .0001; 1 L duration: MMCD= $4892, p < .0001) that were fully offset by lower monthly medical costs (1 L OCM: MMCD= -$8289, p < .0001; 1 L duration: MMCD=-$5888, p < .0001), yielding a monthly total healthcare cost reduction (1 L OCM: MMCD=-$3411, p < .0001; 1 L duration: MMCD=-$996, p < .0001) relative to CIT patients. In the MA dataset (ibrutinib: n = 293; CIT: n = 303), ibrutinib was also associated with a monthly total healthcare cost reduction (1 L OCM: MMCD=-$10,459; 1 L duration: MMCD=-$5492).Conclusions In Medicare patients with CLL/SLL, 1 L ibrutinib single agent was associated with total monthly cost savings relative to 1 L CIT, driven by lower monthly medical costs that fully offset higher monthly pharmacy costs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".