VTE management in primary central nervous system neoplasms : A retrospective cohort study in a Canadian tertiary center between 2013 and 2023
Bibliographic record
Abstract
Abstract Background : Patients with primary central nervous system (CNS) neoplasms are at increased risk of venous thromboembolism (VTE) but also face a higher risk of intracranial bleeding due to the tumor itself and the associated treatments. Therefore, the safest anticoagulation treatment for these patients remains unclear. Objective : To describe the therapeutic management of VTE (pulmonary embolism (PE) and lower limb deep vein thrombosis (DVT)) in patients aged 18 years or older with a malignant primary central nervous system neoplasm in a Canadian tertiary center. Methods : We conducted a unicentric retrospective cohort study between Jan 1st, 2013 and December 31, 2023. Data was collected from local clinical database. Adults with primary CNS neoplasm and radiologically proven diagnosis of VTE during active cancer were included. Results : 73 patients with primary CNS tumors and VTE were included in this study. The most common cancer was high grade glioblastoma (61.6%; 45/73). Most patients were treated with low molecular weight heparin (LMWH) (67.1%; 49/73), 23.3% (17/73) were treated with direct oral anticoagulants, 9.6% (7/73) were not treated with a full dose of anticoagulation due to contraindications, and 9 patients had an inferior vena cava filter inserted. During follow up (IQR 5.75 [2.4 - 19.2] months), treatment of VTE was complicated with bleeding in 19.2% (14/73, 5 intra-cranial), with a non-significant increased risk of bleeding in the LMWH group compared to DOACs [24.5% vs 5.9%, HR 4,48 (IC95% 0.58-34.8), p =0.265]. VTE recurrence occurred in 15.1% of patients (11/73; 7 PE, 4 DVT), with a non-significant increased risk for DOACs compared with LMWH [29.5% vs 12.2%, HR 2,38 (IC95% 0,59-9,61), p=0.172]. These complications led to modification of anticoagulation treatment in 78.6% of patients who experienced bleeding, and 100% who had VTE recurrence. Conclusion : In conclusion, preferred treatment for the patients with both a primary CNS neoplasm and a VTE in this cohort was LMWH. Our results, though not statistically significant, suggest a slightly higher rate of bleeding with LMWH, while DOACs seems to be associated with a modest increase in incidence of VTE recurrence. These exploratory results highlight the need for further prospective studies to guide treatment decisions in this high-risk population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".