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Real-world barriers to CAR-T access: A Canadian referral center perspective.

2024· article· en· W4399481328 on OpenAlexaffabout
Steven Shi, Eva Laverdure, Sandra R. A. Cohen, Olivier Veilleux, Silvy Lachance, Luigina Mollica, Isabelle Fleury

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBiosimilars and Bioanalytical Methods
Canadian institutionsUniversité de MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsMedicineReferralPerspective (graphical)Family medicineMedical emergency

Abstract

fetched live from OpenAlex

e19002 Background: CD19 chimeric antigen receptor T-cell (CAR-T) is the standard of care for patients with relapsed or refractory large B-cell lymphoma (LBCL) beyond second line therapy. Access to CAR-T remains a significant barrier for patients and is limited by the numbers of CAR-T centers, patient comorbidities and lymphoma kinetics. This study aims to define clinical features associated with CAR-T ineligibility and associated outcomes in a real-world setting. Methods: We conducted a single center retrospective study of all adult patients with R/R LBCL referred at Hôpital Maisonneuve-Rosemont in Montreal, Canada, for CAR-T between July 2019 and December 2023. Comprehensive data were gathered from electronic medical records. This study was approved by the institutional ethics committee. Results: A total of 235 patients were evaluated: 133 (57%) were infused, 10 (4%) were apheresed but not infused and 92 (39%) patients were deemed ineligible and further analyzed. The main reasons for ineligibility were ECOG performance score more than 1 (n=23), comorbidities (n=18), not R/R to two prior lines of therapy (LOT) (n=15) and rapid disease progression (n=15). Prohibitive comorbidities included: creatinine clearance <45ml/min (n=13), left ventricular ejection fraction <45% (n=2), unstable coronary artery disease (n=1), severe valvular disease (n=1) and Child B cirrhosis (n=1). 10 declined CAR-T and 10 were diagnosed or had lymphoma subtypes not covered by CAR-T reimbursement criteria: grade 3B follicular lymphoma (n=5), T-cell lymphoma (n=2), Hodgkin lymphoma (n=1), Richter from chronic lymphocytic leukemia (n=1) and transformed marginal zone lymphoma (n=1). 33 had more than one exclusion criteria. Median age at referral was 66 years (range: 25-87). Median IPI at diagnosis (available in 74%) was 3 (range: 0-5). At CAR-T evaluation, 77 patients had a stage 3 or 4 disease and 7 had CNS involvement. Median LOT for LBCL were 2 (range: 0-5), 46 were primary refractory and 23 relapsed within 12 months. 18 patients had undergone prior high dose therapy and autologous stem cell transplant (HDT-ASCT). 6 were exposed to novel therapies (polatuzumab=6, mosunetuzumab=2). Median follow-up after CAR-T exclusion was 4.25 months (range: 0-49.2). 50 (68% of patients with B-cell lymphoma R/R to at least 2 LOT) were deceased at time of analysis. Data on further LOT was available for 22 patients: 10 received novel therapies (tafasitamab=5, polatuzumab=3, mosunetuzumab=1, alectinib=1), 6 HDT-ASCT, 3 allogeneic stem cell transplant, 4 conventional chemotherapies and 2 radiation therapy. Conclusions: 39% of evaluated patients with R/R LBCL were ineligible for CAR-T mainly due to poor performance status, comorbidities and prohibitive progressive disease. Our study did not capture patients not referred due to geographical considerations but highlights dismal outcomes for patients ineligible for CAR-T. Widespread access to novel therapies is awaited.

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)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.945
Threshold uncertainty score0.401

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.006
Science and technology studies0.0050.002
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0140.001

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 head0.161
GPT teacher head0.515
Teacher spread0.354 · 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 designQualitative
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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Citations2
Published2024
Admission routes2
Has abstractyes

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