Cost of treatment failure in patients with chronic lymphocytic leukemia: Results of a large U.S. observational study.
Bibliographic record
Abstract
22 Background: Patients with chronic lymphocytic leukemia (CLL) who eventually experience disease progression are offered a limited choice of treatments. This retrospective observational study assesses healthcare resource utilization and costs in patients with CLL who have received one prior therapy and experience treatment failure (TF). Methods: Adult patients with ≥1 diagnosis of CLL and ≥1 claim for a medication used to treat CLL were identified in the IMS PharMetrics Plus database (01/2008 – 09/2013). Patients were excluded if they had evidence of a non-hematologic malignancy, used a non-CLL antineoplastic agent, or received a stem cell transplant during the 12-month baseline period. TF was identified based on earliest occurrence of one of the following events: initiation of a new treatment for CLL that was not part of the 1st-line therapy, resumption of any CLL treatment following a minimum of 3-month break in treatment, stem cell transplant, radiotherapy, hospital mortality, or hospice care. Resource utilization was reported as monthly incidence rates, and costs were reported in 2013 $US per patient per month (PPPM), comparing patients with and without TF. Results: A total of 6,015 patients with CLL were identified (mean patient age: 63 years old; proportion female: 36%), of which 2,734 (45%) experienced TF. Patients with TF tended to require more OP visits (3.2 vs. 2.5). Average total cost PPPM was $7,850 for patients with TF and $4,555 for patients without TF. The main cost drivers were outpatient (OP) costs ($4,355 for patients with TF; $3,022 for patients without TF) and hospitalization costs ($2,659 for patients with TF; $1,038 for patients without TF). Once adjusted for baseline characteristics, average total cost difference between patients with and without TF was $3,757 PPPM. This difference was largely due to hospitalization (45%) and to OP costs (46%). Conclusions: Patients with CLL experiencing TF appear to require more OP visits and to be associated with higher OP and hospitalization costs PPPM compared to those without TF. These data help in our understanding of the healthcare resource utilization and costs associated with the treatment of patients with CLL.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".