Impact of anticoagulation on recurrent thrombosis and bleeding after hematopoietic cell transplantation
Bibliographic record
Abstract
History of venous thromboembolism (VTE) is prevalent among patients undergoing hematopoietic cell transplantation (HCT). Management of anticoagulation is particularly challenging as most patients will have chemotherapy-induced thrombocytopenia while awaiting engraftment post-HCT. We conducted a retrospective study of autologous and allogeneic HCT recipients with prior VTE from 2006-2015 to 1) compare anticoagulant strategies on short-term VTE recurrence and bleeding and 2) assess predictors for VTE recurrence beyond 30 days. Patients with VTE were allocated to two cohorts based on anticoagulant strategy at thrombocytopenia onset and underwent inverse probability weighting to assess primary outcomes of VTE recurrence and bleeding within 30 days post-HCT. Subsequently, multivariable logistic regression model was used to assess the association of 100-day VTE recurrence by the HIGH-2-LOW VTE risk assessment score and whether patients resumed anticoagulation at platelet recovery. Thirteen percent of recipients had VTE prior to HCT; of those meeting inclusion criteria, 227 continued anticoagulation and 113 temporarily discontinued it. Anticoagulant strategy was not significantly associated with decreased risk of VTE recurrence within 30 days (3% vs 4%, p = 0.61); however, risk of overall bleeding was non-significantly higher in those who continued vs discontinued anticoagulation (41% vs 31%, p = 0.08). In a subgroup of 250 allogeneic HCT patients, every one-point increase of HIGH-2-LOW score was significantly associated with VTE recurrence at 100 days (OR 1.57 [95% CI 1.10-2.23]), while anticoagulation resumption upon platelet engraftment was associated with lower recurrent risk (OR 0.48 [0.20-1.14]). Temporarily withholding anticoagulation during thrombocytopenia may optimize risk-benefit tradeoffs, though additional strategies are essential to prevent VTE recurrence after hematopoietic recovery.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".