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Enregistrement W4389231950 · doi:10.1182/blood-2023-179153

Impact of Age on Outcome in Newly Diagnosed Multiple Myeloma Patients Undergoing Upfront Autologous Hematopoietic Cell Transplantation from the Worldwide Network for Blood and Marrow Transplantation Global Study

2023· article· en· W4389231950 sur OpenAlexaffabout
Shohei Mizuno, Luuk Gras, Laurien Baaij, Linda Köster, Anita D’Souza, Parameswaran Hari, Noel Estrada‐Merly, Wael Saber, Andrew J. Cowan, Minako Iida, Shinichiro Okamoto, Hiroyuki Takamatsu, Koji Kawamura, Yoshihisa Kodera, Nada Hamad, Bor‐Sheng Ko, Christopher Liam, Kim Wah Ho, Ai Sim Goh, S. Keat Tan, Alaa Elhaddad, Ali Bazarbachi, Brig Qamar Un N Chaudhry, Rozan Alfar, Mohamed Amine Bekadja, Malek Benakli, Cristobal Augusto Frutos Ortiz, Eloísa Riva, Sebastián Galeano, Francisca Bass, Hira Mian, Arleigh McCurdy, Feng Rong Wang, Daniel Neumann, Mickey Koh, John A. Snowden, Stefan Schönland, Donal P. McLornan, Patrick Hayden, Anna Maria Sureda Balari, Hildegard Greinix, Mahmoud Aljurf, Yoshiko Atsuta, Dietger Niederwieser, Laurent Garderet

Notice bibliographique

RevueBlood · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueMultiple Myeloma Research and Treatments
Établissements canadiensOttawa HospitalMcMaster University
Organismes subventionnairesnon disponible
Mots-clésMedicineTransplantationMultiple myelomaCumulative incidenceInternal medicinePopulationOncologyProportional hazards model

Résumé

récupéré en direct d'OpenAlex

Background : Induction therapy with proteasome inhibitors and immunomodulatory agents followed by autologous hematopoietic cell transplantation (HCT) is considered standard of care in front line multiple myeloma (MM) treatment. Utilization of autologous HCT is increasing worldwide in younger and older patients in light of improvements in supportive care and infrastructure. However, global perspectives on patterns of patient age and the impact of age on outcomes in this population are scarce. In the current analysis of global registry data, we focused on the age distribution and the association of age with outcomes after HCT worldwide. Methods: Data were provided by the Worldwide Network for Blood and Marrow Transplantation through the European Society for Blood and Marrow Transplantation (EBMT), the Center for International Blood and Marrow Transplantation (CIBMTR), the Australia and New Zealand Transplant and Cellular Therapy Registry (ANZTCTR), the Asian Pacific Blood and Marrow Transplant Group (APBMT), the Eastern Mediterranean Blood and Marrow Transplant Group (EMBMT), the Latin American Bone Marrow Transplant group (LABMT), and the Ottawa hospital myeloma registry. The study included newly diagnosed MM patients transplanted between 2013 and 2017. The primary endpoint was overall survival (OS) and secondary endpoints were progression-free survival (PFS), incidence of relapse, and non-relapse mortality (NRM). The probability of OS and PFS was estimated based on the Kaplan-Meier method and differences were analyzed using the log-rank test. The incidences of relapse and NRM were modeled using the crude cumulative incidence estimator and compared between groups with Gray's test. Multivariate analyses were performed using Cox (cause-specific) proportional hazards models including a random effect for country. Age at HCT was modeled as a categorical variable (18-39, 40-64, 65-69, 70-74, and ≥75 years). Models further included patient sex, year of HCT, stage of disease at HCT, Karnofsky score, myeloma subclassification, conditioning dosage, interval between diagnosis and HCT, HCT comorbidity index, ISS at diagnosis, and cytogenetic risk score. Results: In total, 61,725 patients were included in this study; 37,459 (60.1%), 16,217 (26.3%), 3,164 (5.1%), 3,122 (5.1%), 543 (0.9%), 524 (0.8%), 339 (0.5%), 188 (0.3%), and 169 (0.3%) from EBMT, CIBMTR, ANZTCTR, Japan (APBMT), EMBMT, Taiwan (APBMT), LABMT, Ottawa, and Malaysia (APBMT), respectively. The median age at HCT was 60.8 (interquartile range: 54.6-65.8) years. The percentage of patients in the age groups 18-39, 40-64, 65-69, 70-74, and ≥75 years, varied considerably; 2%, 68.9%, 21.8%, 6.5%, and 0.8%, respectively (Table 1). The proportion of <40 years was higher in Malaysia, LABMT, and EMBMT (4-6%) compared to EBMT, CIBMTR, Japan, and ANZTCTR (2%). In contrast, the proportion of patients ≥65 years was higher in EBMT, CIBMTR, ANZTCTR, and Japan (>20%) compared to Malaysia, LABMT, and EMBMT (7-13%). The following patterns were observed in the age groups 18-39, 40-64, 65-69, 70-74, and ≥75 years, respectively. Melphalan 200 mg/m 2 for conditioning was used more frequently in younger patients (78.4%, 75.4%, 63.2%, 40.6%, and 28.3%, respectively). In 60.7% of the group ≥75 years, a lower dose of melphalan 140 mg/m 2 was chosen. OS was lower with older age (p<0.001) and was 86%, 83%, 81%, 78%, and 75% at 3 years, respectively (Figure and Table 1). PFS was similarly associated with older age (56%, 51%, 50%, 47%, and 45% at 3 years, respectively (p<0.001)). The cumulative incidence of relapse was not significantly different (15%, 16%, 15%, 16%, and 16% at 1 year, respectively (p=0.74)), but the cumulative incidence of NRM was higher with older age (p<0.001) and was 0%, 1%, 2%, 2%, and 4% at 1 year, respectively. On multivariate analysis, older age was associated with lower OS (overall p<0.0001), lower PFS (overall p=0.003), and higher NRM (overall p<0.0001), but not with the risk of relapse (overall p=0.79). Conclusions: There is considerable global variability in the age distribution of patients receiving HCT. Globally, 2% of patients receiving front line HCT for myeloma are aged <45 and 0.8% are >75 years. Advancing age was a significant risk factor for OS and PFS due to differences in NRM, but even in patients >75 years NRM was very low.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,005
Score d'incertitude au seuil0,010

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0000,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,024
Tête enseignante GPT0,304
Écart entre enseignants0,281 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission2
Résumé présentoui

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