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Incidence and risk factors for venous thromboembolism after CAR T-cell therapy: A systematic review and meta-analysis.

2025· review· en· W4410803628 on OpenAlexaff
Adrian Bailey, Shi Qi Zhou, Vicky Tagalakis

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typereview
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsJewish General HospitalMcGill University
Fundersnot available
KeywordsMedicineVenous thromboembolismMeta-analysisIncidence (geometry)OncologySurgeryInternal medicineThrombosis

Abstract

fetched live from OpenAlex

e24085 Background: Chimeric antigen receptor (CAR) T-cell therapy has revolutionized hematologic cancer treatment; however, the incidence of venous thromboembolism (VTE) after infusion remains understudied. As indications for CAR T expand into solid tumors and more oncologists oversee care, recognizing VTE risk is crucial to mitigate morbidity and mortality in this growing population. Methods: This systematic review and meta-analysis was pre-registered on PROSPERO. Following PRISMA guidelines, we searched Medline, Embase, and CENTRAL (Cochrane Central Register of Controlled Trials) through January 1, 2024, for studies enrolling >20 adults treated with any FDA-approved CAR T-cell product (axicabtagene ciloleucel, tisagenlecleucel, brexucabtagene autoleucel, lisocabtagene maraleucel, idecabtagene vicleucel, or ciltacabtagene autoleucel) that reported VTE events (pulmonary embolism, deep vein thrombosis, cerebral vein thrombosis, hepatic vein thrombosis, splenic vein thrombosis, or portal vein thrombosis) post infusion. Studies not reporting VTE were excluded. All statistical analyses were performed in R software (version R version 4.4.0) using the DerSimonian-Laird random-effects model. Heterogeneity was assessed using the Cochrane Q-statistic and quantified via I 2 ; I 2 >50% indicated substantial heterogeneity. Publication bias was evaluated with funnel plots, Egger’s test, and trim-and-fill method. P<0.05 indicated significance in all analyses. Results: From 7,579 records, 24 studies (2,945 patients) met inclusion. The overall VTE proportion post-CAR T infusion was 6.16% (95% CI 4.58–8.24%), and 4.64% (95% CI 3.41–6.29%) were ≥ grade 3 based on the Common Terminology Criteria for Adverse Events (CTCAE). Most events occurred within 50 days post-infusion. In two studies with control arms, VTE risk did not differ between CAR T recipients and standard-of-care (risk ratio [RR]=0.47, 95% CI 0.08–2.70; P=0.39). Grade >2 cytokine release syndrome (CRS; RR=4.12, 95% CI 2.94–11.63), immune effector cell-associated neurotoxicity syndrome (ICANS; RR=3.43, 95% CI 1.55–11.61), and Eastern Cooperative Oncology Group (ECOG) performance status >1 (RR=4.43, 95% CI 1.91–10.29) significantly increased VTE risk. No major bleeding was observed among patients on therapeutic anticoagulation. Publication bias was detected (Egger’s test, P<0.001), and trim-and-fill analysis yielded an adjusted proportion of 7.82% (95% CI 5.83–10.43%). Conclusions: Approximately 6–8% of patients develop VTE after CAR T-cell therapy, with higher risk among those who have grade >2 CRS, ICANS, or ECOG >1. Given CAR T’s expanding role, these findings underscore the need for vigilant VTE assessment and management. Future investigations should evaluate prophylactic anticoagulation strategies to reduce VTE risk in this setting.

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)0.011
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.026
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0160.042
Bibliometrics0.0070.009
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.285
GPT teacher head0.544
Teacher spread0.259 · 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 designMeta-analysis
Domainnot available
GenreReview

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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Citations0
Published2025
Admission routes1
Has abstractyes

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