Propensity Score Matching Analysis Demonstrates the Use of Statin Enhances Chance of Achieving MR4.5 in Chronic Myeloid Leukemia Patients in Chronic Phase Following Imatinib Therapy Regardless of Other Clinical Features Including Age of the Patients
Bibliographic record
Abstract
Abstract BACKGROUND: HMG-CoAreducatase inhibitor, or statin was suggested to increase therapeutic efficacy of anti-cancer therapy, to improve response rate of chemotherapy and to improve survival of cancer patients. Statin family of drugs is known to trigger tumor specific apoptosis and to result in growth arrest in leukemias (Penn, Leukemia 2002). Our previous study (Kim, ASH 2014) suggested the use of statin could enhance chance of achieving MR4.5 with imatinib therapy by 78.5% in 503 chronic myeloid leukemia (CML) patients in chronic phase. A concern has remained regarding that the clinical features of patients receiving statins are more related to the adherence to medication including age. For example, elderly patient who is known to be more adherent to medication dosing, is more likely to take statins. Thus clinical and demographic characteristics of patients should be taken account into the interpretation of enhancing therapeutic effect of statin in combination with imatinib. Propensity score matching (PSM) analysis is a statistical method to adjust for the clinical factors which affect the choice of treatment between different treatment options. Using PSM analysis, clinical and demographic characteristics between the groups with vs without statin can be balanced out, thus mimicking randomized controlled prospective trial. METHODS: Out of 408 patients treated with imatinib at 400mg daily dose as a frontline for CML in CP, 88 patients was identified as "statin" group, while remaining 320 as "non-statin" group. The statin group was defined as those on statin for cholesterol control at the time of imatinib commencement and remaining on statin while on at least 3 years or longer. There was significant difference in age between the statin vs non-statin group (median 62 vs 49, p<0.001). Other clinical factors did not show an difference between the 2 groups including gender, sokal risk group or additional cytogenetic abnormalities at the time of imatinib commencement. Using PSM analysis, we performed a case-control study with well-balanced pairs of patients treated with vs without statin. Pre-treatment variables included in the PSM were age, gender, Sokal risk group and additional cytogenetic abnormalities. A total of 84 case-control pairs were selected within 0.05 of a difference in propensity score. Paired analysis was adopted throughout the PSM analysis. Treatment outcomes were evaluated for the response to TKI therapy with respect to complete cytogenetic response (CCyR), major molecular response (MMR), molecular response at 4.5 (MR4.5) and for long-term outcomes including treatment failure, progression free- (PFS) and overall survival (OS). Cumulative incidence method considering competing risk was adopted to calculate the incidences of MCyR, CCyR, MMR and MR4.5. Discontinuation of imatinib was accounted as competing risk in the analysis. Treatment failure, PFS and OS was also evaluated. RESULT: After the PSM matching, each of 84 patients was selected, thus a total of 168 patients were included in the final analysis. With a median follow-up duration of 6 years (range 3 months to 14 years), clinical and demographic characteristics between the 2 groups did not show any differences including age (p=0.813), gender (p=0.440), Sokal risk group (p=0.888), and additional cytogenetic abnormalities (p=0.682), thus balancing all the confounding factors related to the use of statin. The statin group showed a higher MR4.5 rate than non-statin groups (p=0.019): 56.8±11.9% vs 47.0±11.6%, MR4.5 at 5 years. The use of statin increased the chance of achieving MR4.5 by 64.3% (hazard ratio 1.643 [1.080-2.501]). There is a trend of better MMR at 18 months in statin group compared to non-statin group: 68.2±10.7% vs 53.1±11.1% (p=0.072). However, the use of statin was not found to be associated with improvement in treatment failure (p=0.953), PFS (p=0.938) or OS (p=0.734). CONCLUSION: The use of statin is suggested to improve deeper molecular response following imatinib therapy in CML-CP patients. After taking account for potential confounding clinical variables using PSM analysis, we confirmed that independent of age or other clinical variables, the use of statin could enhance deeper molecular response following imatinib therapy in CML-CP patients. This approach of adopting statin in CML treatment could potentially increase the proportion of patients eligible for TKI discontinuation attempt. Disclosures Kim: Novartis Pharmaceuticals: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding. Lipton:Teva: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Ariad: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Novartis Pharmaceuticals: Consultancy, Research Funding.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".