Identifying the optimal conditioning intensity for stem cell transplantation in patients with myelodysplastic syndrome: a machine learning analysis.
Journal
Bone marrow transplantation
ISSN: 1476-5365
Titre abrégé: Bone Marrow Transplant
Pays: England
ID NLM: 8702459
Informations de publication
Date de publication:
02 2023
02 2023
Historique:
received:
24
06
2022
accepted:
01
11
2022
revised:
29
10
2022
pubmed:
15
11
2022
medline:
9
2
2023
entrez:
14
11
2022
Statut:
ppublish
Résumé
A conditioning regimen is an essential prerequisite of allogeneic hematopoietic stem cell transplantation for patients with myelodysplastic syndrome (MDS). However, the optimal conditioning intensity for a patient may be difficult to establish. This study aimed to identify optimal conditioning intensity (reduced-intensity conditioning regimen [RIC] or myeloablative conditioning regimen [MAC]) for patients with MDS. Overall, 2567 patients with MDS who received their first HCT between 2009 and 2019 were retrospectively analyzed. They were divided into a training cohort and a validation cohort. Using a machine learning-based model, we developed a benefit score for RIC in the training cohort. The validation cohort was divided into a high-score and a low-score group, based on the median benefit score. The endpoint was progression-free survival (PFS). The benefit score for RIC was developed from nine baseline variables in the training cohort. In the validation cohort, the hazard ratios of the PFS in the RIC group compared to the MAC group were 0.65 (95% confidence interval [CI]: 0.48-0.90, P = 0.009) in the high-score group and 1.36 (95% CI: 1.06-1.75, P = 0.017) in the low-score group (P for interaction < 0.001). Machine-learning-based scoring can be useful for the identification of optimal conditioning regimens for patients with MDS.
Identifiants
pubmed: 36376472
doi: 10.1038/s41409-022-01871-8
pii: 10.1038/s41409-022-01871-8
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
186-194Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer Nature Limited.
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