Propensity Score Matching Analysis Comparing Reduced Intensity Versus Myeloablative Conditioning in AML/MDS Patients Transplanted with Fludarabine-Busulfan-Low Dose Total Body Irradiation Based Regimen
Notice bibliographique
Résumé
Abstract Background: Allogeneic hematopoietic cell transplantation (HCT) is an effective therapy in Acute Myeloid Leukemia and Myelodysplastic Syndrome (AML/MDS). There is controversy over whether reduced intensity conditioning (RIC) results in similar outcomes to myeloablative conditioning (MAC), especially regarding relapse risk. It is difficult to identify the specific cause of the transplant failure rate in RIC patients amongst the multiple possible factors including relapse risk due to disease characteristics of older pts with AML/MDS, or multiple comorbidities in the population receiving RIC, resulting in higher morbidity and mortality, versus expected lower risk of regimen-related toxicity. In order to overcome this, we used a propensity score matching analysis in this study. Methods: A total of 248 patients transplanted for AML or MDS at the Princess Margret Cancer Center between 2009 and 2013 were included in this analysis. Inclusion was restricted to patients receiving Fludarabine/Busulfan plus low dose total body irradiation (TBI) with either RIC conditioning (Fludarabine 30mg/m2/day for 4 days, Busulfan 3.2mg/kg/day for 2 days and TBI 200 cGy n=121) or MAC conditioning (Fludarabine 50mg/m2/day for 4 days, Busulfan 3.2mg/kg/day for 4 days and TBI 400 cGy; n=127). The RIC and MAC groups were compared for overall survival (OS), non-relapse mortality (NRM) and relapse. Propensity score matching (PSM) analysis is used to adjust for the risk factors which affect the choice of treatment between different treatment options. Using PSM analysis, we performed a case-control study with well-balanced pairs of RIC and MAC patients. Pre-transplant variables included in the PSM were age at HCT, HCT-Comorbidity Index (HCT-CI), complete remission status (CR) at HCT, diagnosis (AML vs MDS), cytogenetic risk group (high-risk vs others), donor type (related vs unrelated) and period effect (transplant year). A total of 39 case-control pairs were selected within 0.2 of a difference in propensity score. Paired analysis was adopted throughout the PSM analysis for survival. RESULTS: With a median follow-up of 18 months among survivors in the overall population (n=248), the 2-year OS, NRM and relapse incidence rates were 48.0±3.6%, 34.6±3.6% and 24.8±3.5% respectively There was no difference between the 2 groups in OS (45.2±5.0% in RIC vs 51.7±5.2% in MAC at 2 years; p=0.541) or NRM (32.9±5.2% in RIC vs 35.7±4.9% in MAC at 2 years; p=0.504). However, there was a higher incidence of relapse in the RIC group (31.5±5.1% in RIC vs 18.2±4.8% in MAC at 2 years; p=0.033) Demographic and transplant characteristics were imbalanced between the 2 groups within the overall population, including older age (P=<0.001), higher HCT-CI score (p=0.002) and more related donors in the RIC group (p=0.02). However, no differences were observed in CR status at HCT (p=0.110), subtype of diagnosis (AML vs MDS, p=0.174), or cytogenetic risk group (p=0.278). To overcome baseline imbalances we used a PSM analysis, and 39 case-control pairs (n=78) were selected. All pre-transplant variables became well balanced after propensity score matching, i.e. there were no differences in age (p=0.537), HCT-CI (p=0.931), CR status at HCT (p=0.655), diagnosis (p=0.774), cytogenetic risk group (p=0.784), donor type (p=0.496) or period effect (p=0.984). In the propensity score matched patients, there were no differences in OS (58.0±8.8% in RIC vs 50.9±8.1% in MAC at 2 years; p=0.554), NRM (28.0±8.2% in RIC vs 32.8±7.8% in MAC at 2 years; p=0.688), or relapse (17.8±6.7% in RIC vs 18.0±6.8% in MAC at 2 years; p=0.635). Conclusion: These results suggest, based on a propensity score matching analysis, that the outcomes of a Fludarabine/Busulfan plus low dose TBI based RIC HCT for AML/MDS are equivalent to a Fludarabine/Busulfan plus low dose TBI based MAC with regards to the risk of relapse, NRM, and OS. Disclosures No relevant conflicts of interest to declare.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 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) | 0,003 | 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 source (Gemma direct ou Codex distillé), 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 ».