Pathogenesis, Diagnosis, and Management of Cytokine Release Syndrome in Patients with Cancer: Focus on Infectious Disease Considerations
Bibliographic record
Abstract
Background: Cytokine Release Syndrome (CRS) is a hyperinflammatory state triggered by immune therapies like CAR T-cell therapy and bispecific T-cell engagers (BiTEs). Characterized by excessive cytokine release, CRS often mimics infectious and inflammatory conditions, complicating diagnosis and treatment. Immunosuppressive therapies used for CRS further elevate the risk of secondary infections. Methods: A systematic search of PubMed and EMBASE was conducted using terms related to “cytokine release syndrome”, “cytokine storm”, “infections”, and “management”. Studies were included if they described infectious complications, diagnostic mimics, or therapeutic approaches related to CRS. Results: Of 19,634 studies, 2572 abstracts were reviewed. Infections occurred in up to 23% of patients post-CAR T therapy and 24% post-BiTE therapy. Pathogens included gram-positive and gram-negative bacteria, herpesviruses (e.g., CMV, HSV), fungi (e.g., Candida, Aspergillus), and parasites (e.g., Toxoplasma gondii). CRS mimics also included non-infectious inflammatory syndromes. Differentiation remains challenging, but cytokine profiling and biomarkers (e.g., ferritin, CRP, sIL-2Rα) may aid in diagnosis. Treatments included tocilizumab, corticosteroids, and empiric antimicrobials. Prophylactic strategies were inconsistently reported. Conclusions: Effective CRS management requires early recognition, differentiation from infectious mimics, and collaboration between oncology and infectious disease (ID) specialists. A multidisciplinary, collaborative, and structured approach, including dedicated ID input and pre-treatment evaluation, is essential for optimizing CRS management and patient 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".