P581: A MARKOV DECISION ANALYSIS OF AZACITIDINE AND VENETOCLAX VERSUS INDUCTION CHEMOTHERAPY FOR THE TREATMENT OF MEDICALLY-FIT PATIENTS WITH ACUTE MYELOID LEUKEMIA
Bibliographic record
Abstract
Background: Induction chemotherapy (IC) for acute myeloid leukemia (AML) is intensive and carries a significant risk of morbidity and mortality. In the absence of allogenic bone marrow transplant (AlloBMT), relapse is common and survival is truncated. Older and unfit patients are typically offered less intensive treatment regimens, with higher complete remission (CR) and overall survival rates offered by azacitidine and venetoclax (Aza-Ven) over azacitidine alone (14.7 versus 9.6 months). Aza-Ven may also be a useful bridge to AlloBMT. There is limited retrospective data comparing IC to Aza-Ven as a means of achieving CR and AlloBMT. This is especially true for younger, medically fit patients. RCTs comparing these regimens are ongoing, with no results expected until 2026. A propensity-matched analysis comparing IC to Aza-Ven found adverse-risk AML favoured Aza-Ven. While further propensity-matched data will likely be made available in the interim, there will be a significant gap in the literature for the foreseeable future. Aims: To leverage the predictive potential of Markov decision analysis to calculate the optimal treatment regimen in a hypothetical 60-year-old medically-fit patient with ELN 2017 intermediate and adverse risk disease. To integrate real-time disease-free survival, relapse, and refractory disease rates over five years with health state utility values to determine quality adjusted life years (QALY) gained. Methods: Using the TreeAge Healthcare Pro software, separate Markov models were created for intermediate-risk patients and adverse-risk patients. The health states included in the Markov analysis were early death from any cause, CR with AlloBMT, CR without AlloBMT, relapse, CR2, and refractory disease. A systematic review of the literature was conducted to inform the probabilities of each branch of both Markov analyses. Five-year overall survival and relapse-free survival curves were digitized to allow for accurate year-to-year variability in the risk of death and relapse. QALY gained will be the primary outcome. Results: In the adverse-risk group, our model favoured Aza-Ven. Overall, patients treated with IC gained 1.47 QALY, compared to 1.90 QALY in patients treated with Aza-Ven. On subgroup analysis, patients who achieved CR with IC followed by AlloBMT gained 2.12 QALY, while those who did not receive AlloBMT gained 1.70 QALY. Those in CR treated with Aza-Ven followed by AlloBMT gained 2.88 QALY, while those who did not receive AlloBMT gained 1.92 QALY. There was not a clear difference between IC and Aza-Ven in the intermediate-risk group. On the whole, patients treated with IC gained 2.02 QALY, compared to 1.91 QALY in patients treated with Aza-Ven. Patients who achieved CR with IC followed by AlloBMT gained 2.78 QALY, while those who did not receive AlloBMT gained 2.26 QALY. Those in CR treated with Aza-Ven followed by AlloBMT gained 2.93 QALY, while those who did not receive AlloBMT gained 1.98 QALY. Summary/Conclusion: Our Markov decision analysis agrees with previous retrospective data indicating that adverse-risk disease favours treatment with Aza-Ven over IC. QALY gained was similar between the two treatments in the intermediate-risk group. AlloBMT provides increased QALY, with intermediate-risk patients being treated with Aza-Ven receiving the greatest benefit. Our data support further investigation into the role of Aza-Ven in younger patients, particularly those with adverse-risk disease.Keywords: Venetoclax, Induction chemotherapy, Hypomethylating agents, Acute myeloid leukemia
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".