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Record 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 on 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

Bibliographic record

VenueBlood · 2023
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsOttawa HospitalMcMaster University
Fundersnot available
KeywordsMedicineTransplantationMultiple myelomaCumulative incidenceInternal medicinePopulationOncologyProportional hazards model

Abstract

fetched live from 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.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.304
Teacher spread0.281 · 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
Published2023
Admission routes2
Has abstractyes

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