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Record W4399419913 · doi:10.1016/j.jtct.2024.06.001

Trends in Outcomes After Upfront Autologous Transplant for Multiple Myeloma Over Three Decades

2024· article· en· W4399419913 on OpenAlexaff
Oren Pasvolsky, Curtis Marcoux, Jianliang Dai, Denái R. Milton, Mark R. Tanner, Naureen Syed, Qaiser Bashir, Samer A. Srour, Neeraj Saini, Paul Lin, Jeremy Ramdial, Yago Nieto, Guilin Tang, Yosra Aljawai, Hans C. Lee, Mahmoud Gaballa, Krina K. Patel, Partow Kebriaei, Sheeba K. Thomas, Robert Z. Orlowski, Elizabeth J. Shpall, Richard E. Champlin, Muzaffar H. Qazilbash

Bibliographic record

VenueTransplantation and Cellular Therapy · 2024
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsDalhousie University
FundersNational Cancer InstituteNational Institutes of HealthDr. Miriam and Sheldon G. Adelson Medical Research FoundationPaula and Rodger Riney FoundationUniversity of Texas MD Anderson Cancer CenterLeukemia and Lymphoma Society
KeywordsLenalidomideMedicineMultiple myelomaCarfilzomibInternal medicineBortezomibOncologyTransplantationAutologous stem-cell transplantationHematopoietic stem cell transplantationSurgery

Abstract

fetched live from OpenAlex

Upfront autologous stem cell transplantation (auto-SCT) remains standard of care for eligible patients with newly diagnosed multiple myeloma (NDMM), although recently its role has been questioned. The aim of the study was to evaluate trends in patient characteristics, treatment, and outcomes of NDMM who underwent upfront auto-SCT over three decades. We conducted a single-center retrospective analysis of patients with NDMM who underwent upfront auto-SCT at MD Anderson Cancer Center between 1988 to 2021. Primary end points were progression-free survival (PFS) and overall survival (OS). Patients were grouped by the year of auto-SCT: 1988-2000 (n = 249), 2001-2005 (n = 373), 2006-2010 (n = 568), 2011-2015 (n = 815) and 2016-2021 (n = 1036). High-risk cytogenetic abnormalities were defined as del (17p), t (4;14), t (14;16), and 1q21 gain or amplification by fluorescence in situ hybridization. We included 3041 MM patients in the analysis. Median age at auto-SCT increased from 52 years (1988-2000) to 62 years (2016-2021), as did the incidence of high-risk cytogenetics from 15% to 40% (P < .001). Comorbidity burden, as measured by a Hematopoietic Cell Transplantation-Specific Comorbidity Index (HCT-CI) of >3, increased from 17% (1988-2000) to 28% (2016-2021) (P < .001). Induction regimens evolved from predominantly chemotherapy to immunomodulatory drug (IMiD) and proteasome inhibitor (PI) based regimens, with 74% of patients receiving IMiD-PI triplets in 2016-2021 (39% bortezomib, lenalidomide and dexamethasone (VRD) and 35% carfilzomib, lenalidomide and dexamethasone [KRD]). Response rates prior to auto-SCT steadily increased, with 4% and 10% achieving a ≥CR and ≥VGPR compared to 19% and 65% between 1988-2000 and 2016-2021, respectively. Day 100 response rates post auto-SCT improved from 24% and 49% achieving ≥CR and ≥VGPR between 1988-2000 to 41% and 81% between 2016-2021, respectively. Median PFS improved from 22.3 months between 1988-2000 to 58.6 months between 2016-2021 (HR 0.42, P < .001). Among patients with high-risk cytogenetics, median PFS increased from 13.7 months to 36.8 months (HR 0.32, P < .001). Patients aged ≥65 years also had an improvement in median PFS from 33.6 months between 2001 and 2005 to 52.8 months between 2016-2021 (HR 0.56, P = .001). Median OS improved from 55.1 months between 1988-2000 to not reached (HR 0.41, P < .001). Patients with high-risk cytogenetics had an improvement in median OS from 32.9 months to 66.5 months between 2016-2021 (HR 0.39, P < .001). Day 100 non-relapse mortality from 2001 onwards was ≤1%. Age-adjust rates of second primary malignancies were similar in patients transplanted in different time periods. Despite increasing patient age and comorbidity burden, this large real-world study demonstrated significant improvements in the depth of response and survival outcomes in patients with NDMM undergoing upfront auto-SCT over the past three decades, including those with high-risk disease.

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)0.005
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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.029
GPT teacher head0.316
Teacher spread0.287 · 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".

Quick stats

Citations8
Published2024
Admission routes1
Has abstractno

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