Treatment Response and Predictors of Response Among Patients with Chronic Myeloid Leukemia: A Retrospective Medical Record Review
Bibliographic record
Abstract
Abstract Abstract 4450 Objective: To assess treatment responses and predictors of response among patients with chronic myeloid leukemia (CML) in Australia, Canada, and South Korea. Methods: Oncologists and hematologists abstracted data from medical records of patients diagnosed with CML between 1/1/2005 and 9/30/2010 via a web-based data collection tool. Patients included were at least 18 years old; had chronic phase, Philadelphia chromosome and/or BCR-ABL positive CML at the time of diagnosis; received imatinib as first-line therapy; and were not enrolled in a clinical trial between the date of CML diagnosis and the end of available chart data. A subset of patients received second- and/or third-line therapy with dasatinib or nilotinib. We assessed rates of complete hematological response (CHR), complete cytogenetic response (CCyR), and complete or major molecular response (MMR) at 3, 6, 12, and 18 months. Multivariable logistic regression models were fitted to assess predictors of response (CHR, CCyR, and MMR) to 1stline therapy with imatinib. The independent and control variables incorporated into the models were age, gender, employment status, Charlson comorbidity index (CCI) score, time to initiation of therapy, imatinib dose at treatment initiation, and presence of treatment-related side effects. Follow-up was truncated if the patient had a change in dose. Only those still in chronic phase at the time of imatinib initiation who had non-missing data on the variables of interest (n=387) were included in the regression analyses. Results: Ninety-three physicians (31 in Australia, 28 in Canada, and 34 in S. Korea) provided data on 609 patients (203 in Australia, 199 in Canada, and 207 in S. Korea). The average patient age was 57 years, and 59% of the patients were male. At 3 months after imatinib initiation, CHR was attained by 67%, 83%, and 86% of patients in Australia, S. Korea, and Canada respectively. Over 85% of all patients maintained CHR at 6, 12, and 18 months. Cytogenetics were not commonly evaluated at 3 and 6 months in the three countries studied. At month 12, 48% of patients achieved CCyR (42% in Australia and Canada, 60% in S. Korea), and a similar proportion of patients had CCyR at 18 months. Overall, MMR was achieved by 59% of patients at month 12, increasing to 70% at month 18. Multivariable regressions showed that receipt of previous treatment (odds ratio [OR]=1.88, 95% CI=1.08–3.25; reference [ref]: no previous treatment), and low Sokal score (OR=2.04, 95% CI=1.13–3.69; ref: intermediate) were associated with a greater likelihood of CHR at 3 months while initial dose > 400mg (OR=0.37 95% CI=0.13–1.03; ref: initial dose <= 400 mg), CCI score of 2 or higher (OR=0.23, 95% CI=0.11–0.51; ref: CCI score 0), and presence of treatment-related side effects (OR=0.41, 95% CI=0.18–0.95; ref: no side effects) predicted a lower likelihood of CHR at 3 months. High Sokal score (OR=0.39, 95% CI=0.18–0.87; ref: intermediate) was found to be associated with a lower likelihood of achieving CCyR at 12 months while the presence of treatment-related side effects (OR=1.86, 95% CI=1.04 – 3.33; ref: no side effects) was found to be associated with a higher probability of achieving CCyR at 12 months. Low Sokal score (OR=1.85, 95% CI=1.10–3.10; ref: intermediate) was the only significant prognostic indicator that was associated with a greater likelihood of MMR at 18 months while age >65 years was the only significant adverse prognostic indicator of MMR by 18 months (OR=0.42, 95% CI=0.20–0.86; ref: age 45–65). Conclusions: The favorable prognostic indicators of response among patients with chronic phase CML included previous treatment, low Sokal score, lower initial imatinib dose (< 400 mg), and no comorbidities. Better understanding of predictors of response may enable physicians to better tailor therapy to optimize patient outcomes. Disclosures: Whiteley: Pfizer Inc: Employment, Equity Ownership. Mitra:Pfizer Inc: Research Funding. Iyer:Pfizer Inc: Employment. Candrilli:Pfizer Inc: Research Funding. Kaye:Pfizer Inc: 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".