Venous Thromboembolism Risk in Hematological Malignancies Post-Chimeric Antigen Receptor T-Cell (CAR-T) Therapy: A Meta-Analysis of Phase 2 and Phase 3 Clinical Trials
Bibliographic record
Abstract
Chimeric Antigen Receptor T-cell (CAR-T) therapy uses genetically engineered T-cells with specific binding sites. This therapy allows for tumor specificity and durable treatment responses for patients with hematological malignancies. In this review, we study the risk of venous thromboembolism (VTE) associated with CAR-T therapy. We searched the National Institutes of Health library, Cochrane Library Databases, ClinicalTrials.gov database, and medical literature search engines PubMed and Google Scholar for Phase 2 and Phase 3 drug-efficacy and safety trials to determine the aggregate incidence and risk of VTE treated with CAR-T. Of 1127 search results, nine studies were identified and included in our meta-analysis. Of the 1017 patients who received therapy, 805 patients (79.15%) experienced some degree of CRS, and 122 patients (11.9%) experienced severe CRS (higher than grade 3). Only three out of one thousand and seventeen patients were reported to have experienced venous thromboembolism. Our study did not find a statistically significant association between increased VTE incidence (OR = 0.0005, 95% CI [0.0001, 0.0017]) and CRS/ICANS (p < 0.0001). There was a 0.0050 (95% confidence interval [0.0019, 0.0132]) relative risk for VTE. In our study, we did not find a statistically significantly increased risk of developing VTE despite CRS and underlying malignancy, which have been associated with increased risk of VTE.
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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.054 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".