Predictors of Cytokine Release Syndrome and Neurotoxicity in Patients with Large B-Cell Lymphoma and Their Impact on Survival
Bibliographic record
Abstract
Background: Autologous CAR-T therapy targeting CD19 is an effective treatment for relapsed or refractory large B-cell lymphoma (LBCL). However, morbidity and mortality related to CAR-T toxicity remain significant concerns. This study aims to evaluate the patterns, risk factors, and implications of cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS) following CD19-CAR-T therapy in LBCL. Methods: This retrospective analysis includes 1916 LBCL patients treated with CD19-CAR-T cell therapy (axicabtagene-ciloleucel [axi-cel] 75% and tisagenlecleucel 25%) between 2018 and 2020 and reported to the Center for International Blood and Marrow Transplant Research (CIBMTR). Patient demographics, baseline characteristics, treatment-related variables, and clinical outcomes were collected. The incidence, severity and timing of onset of CRS and ICANS (according to median time to onset) were analyzed, factors associated with these outcomes and their impact on overall survival as time dependent covariates were done using Cox Regression multivariate analyses. Results: The median age of the patients was 64 years, with a majority having a Karnofsky performance status (KPS) below 90% (61%) and not receiving bridging therapy (67%). CRS was observed in 75.1% of patients, with 9% experiencing severe CRS (grades ≥ 3). Similarly, 43% of patients experienced ICANS, with 20% having severe ICANS (grade ≥ 3). The median time to toxicity was shorter for CRS (4 days) compared to ICANS (7 days). Nearly all the patients who developed ICANS also experienced CRS (98%; ), and the correlation between these two events and respective severities are shown in the Figure 1a. In the multivariate analyses, factors associated with development of CRS were gender (women, Odds ratio [OR] 1.39, 95% confidence interval [CI] 1.10- 1.76, p=0.006), high LDH at time of infusion (OR 1.56; 95% CI 1.22 - 2.00, p=0.002) and product (axi-cel OR 4.60, 95% CI 3.56 -5.81, p<0.001). Factors associated with the development of ICANS included age (> 65 years, OR 1.83; 1.50-2.24, p<0.001), KPS (<80%, OR 2.48; 1.88-3.27, p<0.001), disease status at infusion (treatment resistant disease, OR 1.98; 1.09-3.58, p=0.024; and untreated relapse OR 2.62, 1.35-5.10, p=0.004) and product (axi-cel, OR 5.85; 4.47-7.64, p<0.0001). Factors associated with severe and early onset CRS and ICANS are listed in the table below. The median follow-up of 14.2 months, the estimated 12-month overall survival, progression-free survival (PFS), and treatment-related mortality were 61.6% (95% CI: 59.4-63.9), 42.2% (39.9-44.5), and 4.3% (3.4-5.3), respectively. Overall CRS, early onset CRS and overall ICANS were not associated with higher mortality, however grade >3 CRS (HR 1.79; 1.41-2.28, p<0.001), grade >3 ICANS (hazard ratio [HR] 1.34; 95% CI 1.13-1.59, p=0.002) and early onset ICANS (<7 days, HR 1.23; 1.05-1.44, p=0.01) were associated with higher mortality. The impact of severe CRS on survival are shown in the landmark analysis at 30 days from infusion in the figure 1B. Conclusion: In the largest analysis of CD19-CAR-T cell therapy toxicities to date, we observed a high burden of CRS and ICANS among LBCL patients. The CAR T cell product type was consistently associated with higher incidence, shorter onset and higher severity of both toxicities. High LDH, older age and low performance score also influence the severity and onset of these outcomes. Additionally, patients who developed higher grades of CRS/ICANS and earlier onset ICANS had a higher mortality, stressing the need to develop novel strategies to mitigate the incidence and optimize management of these patients to improve their outcomes.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".