Non-Adherence to Imatinib in Chronic Myeloid Leukemia Patients Is Associated with a Short Term and Long Term Negative Impact On Healthcare Utilization and Costs.
Bibliographic record
Abstract
Abstract Abstract 4270 Introduction This study compared the healthcare resource utilization and costs associated with long-term imatinib treatment adherence versus non-adherence in patients with chronic myelogenous leukemia (CML). Methods Two large administrative claims databases were combined (MarketScan and Ingenix Impact, 01/2002-07/2008) to identify patients diagnosed with CML (ICD-9 code 205.1x). Patients with ≥2 imatinib prescriptions and continuous enrollment ≥6 months prior to and ≥1month following the first observed imatinib prescription filled (i.e., the index date) were selected. Patients were followed for up to 3 years from the index date to the earliest of the termination of healthcare plan enrollment, end of data availability, imatinib treatment discontinuation for ≥90 consecutive days, switch to another drug (i.e., dasatinib or nilotinib), or a CML remission diagnosis (ICD-9 code 205.11). A longitudinal retrospective open-cohort design was used to measure patients' adherence to imatinib repeatedly over time. Imatinib treatment periods were divided into 90-day intervals. Using the medication possession ratio (MPR), treatment intervals were categorized as adherent (MPR≥85%) or non-adherent (MPR<85%). Patients' healthcare utilization and costs were compared between adherent and non-adherent intervals. Multivariate regression models were used to compare rates of inpatient admissions, outpatient visits, emergency room visits, and total urgent care visits. Regression models controlled for age, gender, CML complexity, treatment duration, prior chemotherapies, prior adverse events, Charlson comorbidity index, and prior resource utilization. Additional regression models including past cumulative MPR were used to assess the long term impact of non-adherence. Results For the 1,877 CML patients who met the selection criteria, there were 6,175 adherent and 3,163 non-adherent intervals. Only 34% of patients were completely adherent throughout their observation period. During non-adherent intervals, patients incurred significantly more frequent total urgent care visits (IRR=1.82, p<.001), including inpatient visits (IRR=2.76, p<.001) and emergency room visits (IRR=1.25, p=.021), and more frequent outpatient visits (IRR=1.09 p=.001) compared to adherent intervals. Though non-adherence was associated with lower pharmacy cost by $3,053 (p<.001) over 90 days, this difference was outweighed by a $4,531 higher medical cost (p<.001), resulting in a net cost increase of $1,477 (p<.001) over adherent intervals. Patients who were adherent throughout their observation period incurred an average cost of $11,759 per quarter, compared to $13,773 for patients who were not always adherent. When extrapolated to the 3-year study, health care costs were $24,168 less per patient for patients who were adherent at each of the studied quarters. In models where both the current adherence status and the long-term cumulative impact of past adherence was taken into account, for patients who had always been adherent (past cumulative MPR≥85%), total cost was $883 (p=.084) higher in a non-adherent interval (current MPR<85%) compared to an adherent interval (current MPR≥85%). In patients who had not always been adherent (past cumulative MPR<85%) an adherent interval cost (current MPR≥85%) $1,239 (p=.002) more, while another non-adherent interval (current MPR<85%) cost $2,122 (p<.001) more compared to an adherent interval in patients who had always been adherent (both current and past cumulative MPR≥85%). Conclusions Our analysis indicates that imatinib non-adherence is associated with significant negative economic consequences, while continuous adherence to imatinib in CML patients was associated with lower healthcare resource utilization and costs. Disclosures: Wu: Novartis: Consultancy, I am working for Analysis Group Inc and Analysis Group Inc received funds from Novartis to conduct the analysis. Bollu:Novartis Oncology: Employment. Guo:Novartis Pharmaceuticals Corporation: Employment. Guerin:Novartis: Consultancy, I am working for Analysis Group Inc and Analysis Group Inc received funds from Novartis to conduct the analysis. Yu:Novartis: Consultancy, I am working for Analysis Group Inc and Analysis Group Inc received funds from Novartis to conduct the analysis. Sirulnik:Novartis Pharmaceutical Corporation: Employment. Griffin:Novartis Pharmaceutical Corporation: Consultancy, I have.
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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 | 0.001 |
| 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".