Comparison of Healthcare Utilization and Costs Between Nilotinib and Dasatinib as Second Line Therapies in Chronic Myeloid Leukemia.
Bibliographic record
Abstract
Abstract Abstract 4286 Introduction Dasatinib and nilotinib are both indicated to treat chronic myeloid leukemia (CML) patients resistant or intolerant to imatinib. This study compared retrospectively the healthcare resource utilization and costs associated with dasatinib versus nilotinib treatment as second line therapies in CML patients. Patients and Methods Two large administrative claims databases were combined (MarketScan and Ingenix Impact, 01/2002-12/2008) to identify patients diagnosed with CML (ICD-9 code 205.1x) and treated with a tyrosine kinase inhibitor (TKI) second line therapy. Patients with at least 1 prescription of dasatinib or nilotinib and no prior use of TKI other than imatinib were selected to form the dasatinib second line therapy group and nilotinib second line therapy group, respectively. The index date was defined as the first prescription for dasatinib or nilotinib. Only patients with an index date on or after the date of nilotinib FDA approval (10/27/2007) and continuously enrolled at least 1 month prior to and 1 month after the index date were included. Patients were followed for up to 6 months from the index date to the earliest of the termination of healthcare plan enrollment, or end of data availability. Patient total medical visits, as well as outpatient visits and hospitalization days, were compared between the two groups using incidence rate ratios (IRR). Multivariate negative binomial regression models were applied to estimate IRR while adjusting for baseline differences of the two groups. Patient total costs, pharmacy costs, and medical service costs (including costs associated with outpatient visits, inpatient admissions, emergency room visits, and other medical services) were compared between the nilotinib and dasatinib group. Unadjusted and adjusted cost differences were estimated for each cost component using generalized linear models (GLM) or two-part models. Multivariate regression models to compare patient utilization and costs controlled for potential differences in age, gender, and cancer complexity (Darkow 2007) between the two groups. Costs were adjusted for inflation to 2008 U.S. dollars. Results A total of 230 CML patients treated with a second line TKI met the selection criteria; 186 patients treated with dasatinib and 44 patients treated with nilotinib were identified. Average age was similar between the two groups: 56.9 ± 16.3 in dasatinib patients and 54.1 ± 12.4 in nilotinib patients (p=.366) and the ratio of females was not statistically different: 44.1% v 56.8% (p=.128). Comorbidity burden, measured by the Charlson comorbidity index, was also similar between the two groups: 3.12 ± 1.90 for dasatinib patients vs. 3.07 ± 1.95 for nilotinib patients (p=.638), as was the proportion of patients with moderate and severe CML complexity: 53.8% vs 61.4% (p=.362) and 27.4% vs 22.7% (p=.526) for dasatinib and nilotinib treated patients, respectively. Mean duration of prior imatinib treatment for both groups was not statistically significant (p=0.189), 662.1 days vs 583.8 days, for nilotinib Vs dasatinib, respectively. Over the follow-up period, dasatinib patients had significantly more medical visits (IRR=1.32, p=.028), as well as outpatient visits (IRR=1.31, p=.033). Dasatinib patients also had 36% more hospital days but the difference was not statistically significant (IRR=1.36, p=0.664). Over the 6 months following the initiation of the second line therapy, compared to patients on nilotinib, patients on dasatinib incurred $18,328 (p<.001) more in total medical services and $6,367 (p=0.04) less in pharmacy costs, resulting in a higher net total healthcare cost of $12,039 (p=.035). The difference in medical costs was mainly explained by the difference of inpatient costs ($12,480 higher for dasatinib patients; p=<.001) and outpatient costs ($5,035 higher for dasatinib patients; p=.001). Conclusion This preliminary analysis of total cost of treatment data showed that among CML patients treated with a second line TKIs, those treated with dasatinib were associated with higher total healthcare costs and more frequent health care resource utilization than patients treated with nilotinib. Results may be updated when more data on nilotinib patients becomes available. 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. Tsaneva:Novartis: Consultancy, I am working for Analysis Group Inc and Analysis Group Inc received funds from Novartis to conduct the analysis. Williams: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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".