Patterns and safety of glucocorticosteroid use following CD19 CAR‐T therapy for large B‐cell lymphoma
Bibliographic record
Abstract
Corticosteroids are commonly used to manage cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS) following chimeric antigen receptor (CAR) T-cell therapy, and yet, their dose-specific impact on outcomes remains uncertain. We retrospectively evaluated 276 adults with large B-cell lymphoma (LBCL) treated with CD19-directed CAR-T therapy (axi-cel, tisa-cel, or liso-cel) between 2016 and 2023 at a single institution. Cumulative corticosteroid dose was defined as the total dose administered within 21 days of infusion. Corticosteroids were administered to 105 patients (38%), initiated at a median of 5 days post-infusion (interquartile range [IQR] 3-7) for CRS (38%), ICANS (15%), or both (47%). Use was more frequent with axi-cel (P < 0.001), although the cumulative dose and duration were similar across products. To assess the impact of corticosteroid exposure, we carried out a 21-day landmark analysis. Corticosteroid exposure, modeled as a time-dependent covariate, was not significantly associated with infection risk, non-relapse mortality, or inferior overall survival (OS) or progression-free survival (PFS). However, in a landmark analysis, patients receiving above-median cumulative corticosteroid doses had a significantly higher risk of infection compared to those receiving below-median corticosteroid doses or no corticosteroids (P = 0.042). In a landmark multivariable analysis, corticosteroid cumulative dose was associated with increased late hematologic toxicity (adjusted hazard ratio [HR] 1.02, 95% CI 1.02-1.03). Finally, in a sensitivity analysis excluding patients with Grade ≥4 CRS/ICANS, corticosteroid cumulative dose remained unassociated with OS or relapse, but was linked to shorter PFS (adjusted HR 1.04, 95% CI 1.01-1.06). These findings support the safe yet judicious use of corticosteroids to manage CAR-T toxicities in LBCL.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".