Chronic Myeloid Leukemia: Part I—Real-World Treatment Patterns, Healthcare Resource Utilization, and Associated Costs in Later Lines of Therapy in the United States
Bibliographic record
Abstract
Background: Despite advances in tyrosine kinase inhibitor (TKI) therapy for chronic myeloid leukemia in chronic phase (CML-CP), a sizeable proportion of patients with CML-CP remains refractory or intolerant to these agents. Objectives: Treatment patterns, healthcare resource utilization (HRU), and costs were evaluated among patients with CML who received third or later lines of therapy (3L+), a clinical population that has not been previously well-studied, with unmet treatment needs as TKI therapy has repeatedly failed. Methods: Adult patients with CML who received 3L+ were identified in the IBM® MarketScan® Databases (January 1, 2001–June 30, 2019) and the SEER-Medicare–linked database (January 1, 2006–December 31, 2016). Treatment patterns were observed from CML diagnosis. HRU and direct healthcare costs (payer’s perspective, 2019 USD) were measured in a 3L+ setting. Results: Among 296 commercially insured patients with 3L+ (median age, 58.5 years; female, 49.7%), the median duration of first-line (1L), second-line (2L), and 3L therapy was 8.5, 4.2, and 8.3 months, respectively. The annual incidence rate during 3L+ was 3.4 for inpatient days, 30.8 for days with outpatient services, and 1.2 for emergency department visits. Mean per-patient-per-month (PPPM) total healthcare costs (pharmacy + medical costs) were $18 784 in 3L+, $15 206 in 3L, and $19 546 in 4L, with inpatient costs driving most of the difference between 3L and 4L (mean [3L] = $2528 PPPM, mean [4L] = $6847 PPPM). Among 53 Medicare-insured patients with 3L+ (median age, 72.0 years; female, 39.6%), the median duration of 1L, 2L, and 3L therapy was 9.7, 5.0, and 7.0 months, respectively. During 3L+, the annual incidence rate was 10.3 for inpatient days, 61.9 for days with outpatient services, and 1.5 for emergency department visits. Mean PPPM total healthcare costs were $14 311 in 3L+, $15 100 in 3L, and $16 062 in 4L. Discussion: Patients with CML receiving 3L+ rapidly cycled through multiple lines. Costs increased from 3L to 4L; in commercially insured patients, inpatient costs were responsible for most of the cost increase between 3L and 4L, underlying these patients’ continued need for care. Conclusions: These findings support the need for better treatment options in patients with CML undergoing later lines of therapy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".