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Record 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 on 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

Bibliographic record

VenueBlood · 2024
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsOttawa HospitalMcMaster University
Fundersnot available
KeywordsMultiple myelomaMedicineTransplantationInternal medicineOncologyBone marrow transplantationHematologic Neoplasms

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.287
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2024
Admission routes1
Has abstractyes

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