Real‐world treatment patterns and outcomes with oral azacitidine maintenance therapy in patients with acute myeloid leukemia
Bibliographic record
Abstract
INTRODUCTION: This study describes baseline and clinical characteristics, treatment patterns, survival, and safety outcomes of patients with acute myeloid leukemia (AML) who received oral azacitidine (oral-AZA) maintenance therapy in Canada following its approval in 2021. METHODS: A retrospective, observational medical record review was conducted of patients with AML in remission after induction therapy and who initiated treatment with oral-AZA between March 2021 and July 2023 in Canada. Real-world relapse-free survival and overall survival outcomes were estimated using Kaplan-Meier methodology. RESULTS: Data from 119 patients were analyzed. The median age at oral-AZA initiation was 62.5 years. Most patients had favorable (39.5%) or intermediate (39.5%) genetic risk per the 2017/2022 European LeukemiaNet classification. Nearly all patients (99.2%) received cytarabine-based induction regimens. A total of 55.5% of patients received consolidation therapy, with a median of two cycles. After a median follow-up of 9.4 months, 68.1% of all patients were still receiving oral-AZA at last follow-up. After oral-AZA treatment, 21.0% of patients relapsed. Rates of real-world relapse-free survival and overall survival at 12 months from oral-AZA initiation were 66.9% and 74.5%, respectively. During oral-AZA treatment, 67.2% of patients experienced ≥1 adverse event. Concomitant antiemetic treatment was received by 78.2% of patients. CONCLUSION: These findings provide real-world evidence further supporting the use of oral-AZA as a standard-of-care maintenance therapy in current routine clinical practice for patients with AML in remission who do not receive hematopoietic stem cell transplantation. These results may inform a broader clinical audience because of the inclusion of patients with diverse demographic and clinical characteristics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".