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Enregistrement W4405039302 · doi:10.1182/blood-2024-200071

Validation of the EBMT Multiple Myeloma Early Relapse Score within Worldwide Network for Blood and Marrow Transplantation (WBMT) Global Study

2024· article· en· W4405039302 sur OpenAlexaff
Meral Beksac, S. Iacobelli, Luuk Gras, Linda Köster, Laurien Baaij, Nada Hamad, Anita D’Souza, Noel Estrada‐Merly, Parameswaran Hari, Andrew J. Cowan, Wael Saber, Minako Iida, Shinichiro Okamoto, Hiroyuki Takamatsu, Shohei Mizuno, Koji Kawamura, Yoshihisa Kodera, Bor‐Sheng Ko, Christopher Liam, Kim Wah Ho, A Sim Goh, S Keat Tan, Alaa Elhaddad, Ali Bazarbachi, Qamar-Un-Nisa 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, Meng Lv, Daniel Neumann, Mickey Koh, John A. Snowden, Stefan Schönland, Donal P. McLornan, Patrick Hayden, Damiano Rondelli, Hildegard Greinix, Mahmoud Aljurf, Yoshiko Atsuta, Ana Sureda Balari, Dietger Niederwieser, Laurent Garderet

Notice bibliographique

RevueBlood · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueMultiple Myeloma Research and Treatments
Établissements canadiensOttawa HospitalMcMaster University
Organismes subventionnairesnon disponible
Mots-clésMultiple myelomaMedicineTransplantationInternal medicineOncologyBone marrow transplantationHematologic Neoplasms

Résumé

récupéré en direct d'OpenAlex

Rationale: Early relapse (ER) within 12 months of Autologous Hematopoietic Stem Cell Transplantation (AHCT) is currently accepted as functional high risk among patients diagnosed with Multiple Myeloma (MM). Efforts to predict ER has led to different risk scores developed by CIBMTR, GIMEMA and EBMT. CIBMTR and GIMEMA score integrates parameters not always available (bone marrow plasma cell percentage prior to AHCT, lambda light chain, FISH and LDH), whereas the EBMT score includes very simple factors ISS (at diagnosis), performance and disease status (prior to AHCT) (Beksac et al BMT 2023). EBMT ER score was developed using data obtained from patients aged 40-70 with a first AHCT in mainly European transplantation centers. This retrospective study aims to validate the EBMT ER score using data from a worldwide cohort of AHCT MM patients. This large population differs from the one used in the original paper, by age range, the number of countries and ethnicities, features in favor of suitability for external validation. Methods: We selected patients from the WBMT study, a collaboration of five MM transplantation registries around the world. Only registries with data available on all components of the EBMT score were selected. Patients included in WAUSTIM had a first AHCT between 2013-2017. EBMT data within this current study belonged to only 2013 which was not included in the original study. We used Cox proportional hazards regression and the c-index to measure the predictive discriminative performance of the EBMT ER score. Results: In total 14,924 patients (EBMT (4.1%), CIBMTR (79%), Australia and New Zealand (2.8.%), Japan (14%) and EMBMT (0.2%) were included. Median age at AHCT was 61(25-83) years. IgG (56%), IgA (21%) and light chain (21%). Disease status at the time of AHCT was CR (16%), VGPR (39%), PR (39%), SD/MR (6%), Rel/Prog (0.3%): ISS at diagnosis was I/II/III: 38/36/27%; Karnofsky score prior to AHCT-1: ≤70/80/90/100: 12/29/41/18 %. Cytogenetic risk was standard/high (t(4;14),t(14;16),17pdel) in 69%/31% of those with data available (not available in 17%). 81%/16% received Mel200/Mel140. Maintenance treatment was unknown for 89%. EBMT ER score distribution was: 0/1/2/3/4: 18%/32%/31%/14%/3.5%. Within the Mel200 only population restricted to the 40-70 age limit (as in the original study), after a median FU of 48 months 12-month PFS (PFS-12) was 84% (95% CI 83-84%) with an ER incidence of 14.7%. which is similar to that observed in the original EBMT study (Mel200 training: 14.7%, Mel200 validation: 11.6%, Mel140: 16.9%). The score 0/1/2/3/4 distribution was: 19%/34%/31%/13%/3.2%. Thus the prevalence of scores 0-4 within the original and the current study are similar as well. PFS-12 (95% CI) according to scores were as follows: score 0: 90 (89-91); score 1: 86 (85-87); score 2: 83 (82-84); score 3: 78 (76-80); score 4: 69 (64-74) resulting with HRs vs score 0: score 1: 1.42; score 2: 1.75; score 3: 2.32; score 4: 3.69 (all p values<0.001. The c-index was 0.58 without and 0.61 with cytogenetic risk included. EBMT ER score acts similarly within this worldwide population with a clear separation of curves between scores 0-4. Cytogenetic high risk vs standard risk HR: 1.88 (95% CI: 1.69-2.09) among Mel200 and 1.81(95% CI: 1.65-1.98) were among the whole population(p-value<0.001). Comparison of the original study HRs and score points with the current analysis will be presented at the meeting. Similar analysis performed among the whole population not limited to age or conditioning regimen intensity, resulted with highly similar PFS-12 ranging between 90-69% for scores 0-4 with HRs 1.39-3.49 (scores 1-4, all p-values <0.001; high vs standard risk HR: 1.81; C index: 0.58 without cytogenetic, and 0.61 with cytogenetic). Conclusion: In a population from various parts of the world, we have been able to validate the EBMT ER score among both Mel200 and Mel140 conditioned myeloma patients with a wider range of age at AHCT-1. Although the cytogenetic risk score provided additional predictive value in the original and current study, it had no modification effect on the EBMT ER score. Due to high rate of maintenance data missingness, we could not investigate the role of the score when patients were continued to be treated after AHCT. Based on our findings, EBMT ER score is a predictive tool for recognition of functional high risk MM patients at the time of AHCT-1.

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)0,008
score de la tête « metaresearch » (Gemma)0,010
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 enseignants0,008
Score d'incertitude au seuil0,042

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0080,010
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,002
Études des sciences et des technologies0,0000,001
Communication savante0,0010,001
Science ouverte0,0010,002
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,001

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 Opus0,022
Tête enseignante GPT0,287
Écart entre enseignants0,265 · 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
Publié2024
Routes d'admission1
Résumé présentoui

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