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Record W4304690353 · doi:10.1016/j.jtct.2022.10.003

Impact of Implementing a Bendamustine-Based Conditioning Regimen on Outcomes of Autologous Stem Cell Transplantation in Lymphoma while Novel Cellular Therapies Emerge

2022· article· en· W4304690353 on OpenAlexaff
Silvy Lachance, Alex Bourguignon, Josie-Anne Boisjoly, Philippe Bouchard, Imran Ahmad, Nadia M. Bambace, Léa Bernard, Sandra Cohen, Jean‐Sébastien Delisle, Isabelle Fleury, Thomas Kiss, Luigina Mollica, Denis‐Claude Roy, Guy Sauvageau, Olivier Veilleux, Justine Zehr, Miguel Chagnon, Jean Roy

Bibliographic record

VenueTransplantation and Cellular Therapy · 2022
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsCentre Hospitalier de l’Université de MontréalCentre Hospitalier Universitaire de SherbrookeUniversité de MontréalHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsMedicineBendamustineRegimenCarmustineMucositisTransplantationAutologous stem-cell transplantationOncologyLymphomaInternal medicineSurgeryChemotherapyRituximabCyclophosphamide

Abstract

fetched live from OpenAlex

With the advent of new cellular and targeted therapies, treatment options for relapsed and refractory (r/R) lymphomas have multiplied, and the optimal approach offering the best outcomes remains a matter of passionate debate. High-dose chemotherapy followed by autologous stem cell transplantation (ASCT) is still considered a treatment option for patients with chemosensitive lymphoma when cure is the expected goal. The myeloablative conditioning regimen preceding the stem cell infusion is considered the effective component of this approach. Carmustine (BCNU)-based preparative regimens, such as BEAM and BEAC, are considered the standard of care and have shown efficacy and low nonrelapse mortality (NRM). Comparative studies between conditioning regimens have failed to identify a better option. After a BCNU drug shortage in Canada followed by a steep increase in price, we elected to substitute BCNU for bendamustine (benda) in the preparative regimen. The purpose of this substitution was to improve response while preserving safety and controlling costs. From May 2015 to May 2018, a total of 131 consecutive lymphoma patients received benda-EAM conditioning. These patients were compared with 96 consecutive patients who received BCNU-based conditioning from January 2012 to May 2015. Apart from conditioning, supportive care measures were the same in the 2 groups. Patients receiving benda were older (55.7 years versus 51.1 years; P = .002). The development of grade ≥3 mucositis was more frequent with benda conditioning (39.5% versus 7.8%; P < .001) leading to a greater requirement for parenteral nutrition (48.9% versus 21.9%; P < .001). A transient creatinine increase >1.5 times the upper limit of normal (15.3% versus 4.2%; P < .008) and intensive care unit admission (6.9% versus 1.1%; P < .029) were more frequent with benda; however, there were no between-group differences in cardiac, pulmonary, or liver toxicity and NRM. With a median follow-up of 48 months for the benda group and 60 months for the BCNU group, benda was associated with significantly better progression-free survival (71% versus 61%; P = .040; hazard ratio [HR], 1.6; 95% confidence interval [CI], 1.0 to 2.7) and overall survival (86% vs 71%; P = .0066; HR, 2.6; 95% CI, 1.3 to 5.4) compared with BCNU-based conditioning regimens. While novel therapies emerge, our study demonstrates that benda-EAM is safe and effective and should be considered a valid alternative to BCNU conditioning to improve outcomes of patients with chemosensitive r/R lymphomas undergoing ASCT.

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)0.002
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.276
Teacher spread0.252 · 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

Citations10
Published2022
Admission routes1
Has abstractno

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