Management of Upper Extremity Superficial Vein Thrombosis in Patients with Cancer: A Retrospective Observational Cohort Study
Bibliographic record
Abstract
Introduction: Venous thromboembolism (VTE) including superficial vein thromboses are common in patients with cancer. Although clinical studies have reported that anticoagulant therapy may be beneficial in some patients with lower extremity superficial vein thrombosis, the management of upper extremity superficial vein thrombosis (UESVT), especially in patient with cancer, remains unclear. Patients with cancer are at higher risk of both recurrent VTE and bleeding complications while receiving anticoagulant therapy. Hence, the risks and benefits of anticoagulant therapy are unknown in this patient population. We sought to assess the efficacy and safety of anticoagulant therapy in patients with cancer and UESVT. Methods: A single center retrospective observational cohort study which included consecutive patients with cancer and UESVT assessed between 2019 and 2023 was conducted. The primary outcome was the extension of the UESVT or recurrence of VTE during a 90-day follow-up period. Secondary outcomes included major bleeding and clinically relevant non-major bleeding (CRNMB), and overall mortality. Major bleeding and CRNMB were defined according to the International Society on Thrombosis and Haemostasis criteria. Outcomes were reported as proportions along with their 95% confidence intervals (CI) during the follow-up period. Results: Overall, 86 patients were included in the analyses. The median age was 79±48 years, and 47.7% were female. The most common locations for cancer types were lung (16.3%) and breast (11.6%), and 57.0% in stage 3 or 4. A majority of patients had a central venous catheter in place. A total of 64 (74.4%) patients received anticoagulant therapy, including 27 patients (31.4%) at a therapeutic dosage. The overall rate of UESVT or VTE recurrence was 17.4% (95% CI: 10.1-27.1) over the 90-day follow-up period. There were 2 major bleeding events (2.3%, 95% CI: 0.2-8.2) and 5 CRNMB (5.8%, 95% CI: 1.9-13.1) during the follow-up period. The overall mortality rate was 24.4%. Conclusion: In patients with cancer and UESVT, the risk of superficial vein thrombosis extension or recurrent VTE is high despite anticoagulant therapy. Future prospective studies are needed to derive the optima management strategy for this patient 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".