Infection after CD19 chimeric antigen receptor T-cell therapy for large B-cell lymphoma: real-world analysis from CIBMTR
Bibliographic record
Abstract
ABSTRACT: Infection is increasingly recognized as a significant cause of morbidity and mortality in patients with relapsed/refractory (R/R) large B-cell lymphoma (LBCL) receiving CD19 chimeric antigen receptor (CAR) T-cell therapy. The current study analyzed the natural history, risk factors, and outcomes of infection in 3350 patients with R/R LBCL receiving commercial CD19 CAR T-cell therapy (n = 2804 axicabtagene ciloleucel [axi-cel], n = 546 tisagenlecleucel) from December 2017 to June 2022. Infection developed in 834 patients (24.9%) within 100 days after infusion, resulting in an infection density of 0.43 per 100 patient days and a 100-day cumulative incidence of 22%. Bacterial, viral, and fungal infections were recorded in 527 (15.7%), 374 (11.2%), and 108 patients (3.2%), respectively, with corresponding infection densities of 0.23, 0.15, and 0.04 per 100 patient days. After a 24-month median follow-up, 1482 patients (44%) had died, with infection as the primary cause in 173 cases (12%). The 100-day infection-related mortality (IRM) was 1.6% (95% confidence interval, 1.2-2.0). Patients with a Karnofsky performance score of ≤80, infection history before CAR T-cell therapy, axi-cel therapy, severe cytokine release syndrome (grade ≥3), and severe immune effector cell-associated neurotoxicity syndrome (grade ≥3) had increased infection risk. Infections within 100 days were an independent risk factor for inferior overall survival beyond day 100 after CD19 CAR T-cell therapy. In conclusion, study results show a significant incidence of infection and IRM in patients with R/R LBCL treated with CD19 CAR T-cell therapy. Furthermore, results identify patients at a heightened risk of infection, offering insights to guide potential interventions aimed at mitigating infection and improving patient outcomes after CAR T-cell therapy.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".