PB0928 Competing Risks Analysis of Recurrent Venous Thromboembolism (VTE) and Bleeding on Anticoagulation in Patients with Cancer- Associated VTE
Bibliographic record
Abstract
Background: Chimeric antigen receptor (CAR) T-cell therapy is established for patients with hematologic malignancies.Emerging toxicities associated with CAR T-cell therapy include systemic coagulopathy and potentially increased risk of bleeding and thrombosis.Aims: We aimed to determine the rates of hemorrhagic and thrombotic events in patients receiving CAR T-cell therapy.Methods: We performed a retrospective cohort study of patients with hematologic malignancies treated with CAR T-cell therapy at an academic center from 2016-22.Data was manually extracted from CAR T-cell administration through 180 days post-infusion.Bleeding and thrombotic events were reviewed and categorized based on ISTH criteria, stratified at 30 and 180 days.We calculated cumulative incidence and 95% confidence intervals (CI) for thrombotic and bleeding events at 30 and 180 days postinfusion, with death as a competing risk.Results: The study cohort included 102 patients with a median age of 67 years.Primary malignancies included diffuse large B-cell lymphoma (55.9%) and multiple myeloma (25.5%).Of 99 patients who had coagulopathy testing, 77 (77.8%) developed prothrombin time prolongation and 54 (54.5%) developed partial thromboplastin time prolongation.Forty-eight of 95 patients (50.5%) developed hypofibrinogenemia.Cumulative incidence of thrombotic events at 30 days and 180 days post-infusion was 3.8% (95% CI: 0.2-7.4) and 5.7% (95% CI: 1.3-9.9),respectively.The cumulative incidence of bleeding events at 30 days and 180 days post-infusion was 4.8% (95% CI: 0.7-8.7)and 16.2% (95% CI: 9.7-22.2),respectively (Figure 1).Of the 7 thrombotic events during the 180-day study period, 5 were venous events.Four of the 20 bleeding events were categorized as major hemorrhage, including 2 intracranial bleeds (Table 1).
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".