P581: A MARKOV DECISION ANALYSIS OF AZACITIDINE AND VENETOCLAX VERSUS INDUCTION CHEMOTHERAPY FOR THE TREATMENT OF MEDICALLY-FIT PATIENTS WITH ACUTE MYELOID LEUKEMIA
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».