Treatment patterns, overall survival, healthcare resource use and costs in elderly Medicare beneficiaries with chronic myeloid leukemia using second-generation tyrosine kinase inhibitors as second-line therapy
Bibliographic record
Abstract
Objective Though the median age at diagnosis is 64 years, few studies focus on elderly (≥65 years) patients with chronic myeloid leukemia (CML). This study examines healthcare outcomes among elderly Medicare beneficiaries with CML who started nilotinib or dasatinib after imatinib. Research design and methods Patients were identified in the Medicare Research Identifiable Files (2006-2012) and had continuous Medicare Parts A, B, and D coverage. Main outcome measures Treatment patterns, overall survival (OS), monthly healthcare resource utilization and medical costs were measured from the second-line tyrosine kinase inhibitor (TKI) initiation (index date) to end of Medicare coverage. Results Despite similar adherence, dasatinib patients (N = 379) were more likely to start on the recommended dose (74% vs. 53%; p < 0.001), and to have dose reductions (21% vs. 11%, adjusted hazard ratio [HR] = 1.94; p = 0.002) or dose increases (9% vs. 7%; adjusted HR = 1.81; p = 0.048) than nilotinib patients (N = 280). Fewer nilotinib patients discontinued (59% vs. 67%; adjusted HR = 0.80; p = 0.026) or switched to another TKI (21% vs. 29%; adjusted HR = 0.72; p = 0.044) than dasatinib patients. Nilotinib patients had longer median OS (>4.9 years vs. 4.0 years; p = 0.032) and 37% lower mortality risk than dasatinib patients (adjusted HR = 0.63; p = 0.008). Nilotinib patients had 23% fewer inpatient admissions, 30% fewer emergency room visits, 13% fewer outpatient visits (all p < 0.05), and lower monthly medical costs (by $513, p = 0.024) than dasatinib patients. Limitations Lack of clinical assessment (disease phase and response to first-line therapy) and retrospective nature of study (unobservable potential confounding factors, non-randomized treatment choice). Conclusions In the current study of elderly CML patients, initiation of second-line TKIs frequently occurs at doses lower than the recommended starting doses and, despite this, many patients require dose adjustments. Here, nilotinib patients required fewer dose adjustments than dasatinib patients. Further research focusing on elderly CML patients is warranted in order to help define future best clinical practices.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".