Do we need prophylactic anticoagulation in ambulatory patients with lung cancer? A review.
Evangelos DimakakosElias KotteasGeorgia GomatouTheodora KatsarouVassilis VlahakosIoannis VathiotisSofia TalaganiIoannis DimitroulisKonstantinos SyrigosPublished in: Vascular medicine (London, England) (2020)
Venous thromboembolism is a common complication of malignancy. Lung cancer is considered one of the most thrombogenic cancer types. Primary thromboprophylaxis is not currently recommended for all ambulatory patients with active cancer. In the present narrative review we aim to summarize recent data on the safety and efficacy of primary thromboprophylaxis as well as on venous thromboembolism risk assessment, focusing on ambulatory patients with lung cancer. A potential benefit from prophylactic anticoagulation with low molecular weight heparins in terms of venous thromboembolism risk reduction and increased overall survival in patients with lung cancer, without a significant increase in bleeding risk, has been reported in several studies. Recent studies also reveal promising results of direct oral anticoagulants regarding their efficacy as primary thromboprophylaxis in patients with cancer, including those with lung cancer. However, the use of different study methodologies and the heterogeneity of study populations among the trials limit the extraction of definite results. More randomized, controlled trials, restricted to a well-characterized population of patients with lung cancer, are greatly anticipated. The use of risk assessment tools for stratification of venous thromboembolic risk is warranted. The development of an accurate and practical risk assessment model for patients with lung cancer represents an unmet need.
Keyphrases
- venous thromboembolism
- direct oral anticoagulants
- risk assessment
- atrial fibrillation
- blood pressure
- randomized controlled trial
- human health
- papillary thyroid
- squamous cell carcinoma
- heavy metals
- clinical trial
- high resolution
- systematic review
- squamous cell
- single cell
- machine learning
- lymph node metastasis
- artificial intelligence
- childhood cancer