Association between anticoagulant-related bleeding and mortality in patients with hematological malignancies and cancer-associated venous thromboembolism
Bibliographic record
Abstract
INTRODUCTION: Patients with hematological malignancies are at an increased risk of severe bleeding. Anticoagulant (AC) therapy further increases this risk. Mortality after these bleeds is unclear and may differ by bleeding site. Aim To evaluate the association between bleeding and mortality in patients with hematological malignancies prescribed AC therapy for cancer-associated venous thromboembolism (VTE). METHODS: In a nationwide cohort of US Veterans (2012-2020), we identified patients with hematological malignancies and cancer-associated VTE prescribed AC therapy. Bleeding events were identified by a previously validated algorithm using hospitalization International Classification of Disease (ICD) codes. Within 12 months of AC therapy initiation, we evaluated the association between bleeding and mortality using multivariate Cox regression models, with AC-related bleeding analyzed as a time-varying covariate. RESULTS: The cohort included 1825 patients. Within 12 months of starting AC therapy, 123 (6.7 %) had bleeding events and 162 (8.9 %) patients died. Patients with bleeding events were more likely to have anemia, history of bleeding, aspirin use, chemotherapy use, and frailty. A multivariable Cox proportional-hazard model showed that AC-related bleeding was associated with tripled mortality rate (aHR 3.26, 95 % CI 1.96-5.45). When stratified by bleeding site, intracranial bleeding was associated with the highest risk of death (aHR 13.41, 95 % CI 4.13-43.48), followed by gastrointestinal bleeding (aHR 4.55, 95 % CI 2.48-8.35). CONCLUSION: In this cohort of patients with hematological malignancies and newly diagnosed VTE initiated on AC therapy, bleeding was associated with an increased risk of mortality within 12 months. Association was highest with intracranial and gastrointestinal bleeding.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".