A Markov analysis of azacitidine and venetoclax vs induction chemotherapy for medically fit patients with AML
Bibliographic record
Abstract
ABSTRACT: Although induction chemotherapy (IC) is the standard of care in medically fit patients with newly diagnosed acute myeloid leukemia (AML), limited retrospective data indicate that patients at adverse-risk may benefit from azacytidine and venetoclax (aza-ven). Our goal was to perform a Markov decision analysis to determine whether IC or aza-ven is the optimal induction regimen in this population. Using the TreeAge software, Markov models were created for adverse-risk and intermediate-risk cohorts. A systematic review of the literature informed the transition probabilities and utilities included in the analyses. Our primary outcome was quality-adjusted life years (QALYs) gained over 5 years after diagnosis. Overall, patients at adverse risk treated with IC gained 1.4 QALYs, compared with 2.0 QALYs in patients treated with aza-ven. Patients at adverse risk treated with IC and allogeneic stem cell transplantation (allo-SCT), IC, aza-ven and allo-SCT, or aza-ven gained 2.1, 1.5, 3.0, and 1.9 QALYs, respectively. Meanwhile, patients at intermediate risk treated with IC gained 2.0 QALY, compared with 1.7 QALY in patients treated with aza-ven. Patients at intermediate risk treated with IC and allo-SCT, IC, aza-ven and allo-SCT, and aza-ven gained 2.7, 2.3, 2.6, and 1.8 QALYs, respectively. We have demonstrated that medically fit patients with newly diagnosed adverse-risk AML may benefit from treatment with aza-ven over those treated with IC, whereas IC remains the preferred approach for patients at intermediate risk. Our work challenges the use of the European LeukemiaNet risk classification for patients treated with aza-ven and highlights the need for prospective investigation into aza-ven as induction therapy for medically fit patients.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".