Non-Vitamin K Antagonist Oral Anticoagulants versus Low Molecular Weight Heparin for Cancer-Related Venous Thromboembolic Events: Individual Patient Data Meta-Analysis.
Chun En YauChen Ee LowNatasha Yixuan OngSounak RanaLucas Jun Rong ChewSara Moiz TyeballyPing ChaiTiong-Cheng YeoMark Y ChanMatilda Xinwei LeeLi-Ling TanChieh Yang KooAinsley Ryan Yan Bin LeeChing-Hui SiaPublished in: Cancers (2023)
Venous thromboembolism (VTE) is a leading cause of morbidity and mortality in cancer patients. Low molecular weight heparin (LMWH) has been the standard of care but new guidelines have approved the use of non-vitamin K antagonist oral anticoagulants (NOAC). By conducting an individual patient data (IPD) meta-analysis of randomised controlled trials (RCTs) comparing the outcomes of NOAC versus LMWH in cancer patients, we aim to determine an ideal strategy for the prophylaxis of VTE and prevention of VTE recurrence. Three databases were searched from inception until 19 October 2022. IPD was reconstructed from Kaplan-Meier curves. Shared frailty, stratified Cox and Royston-Parmar models were fit to compare the outcomes of venous thromboembolism recurrence and major bleeding. For studies without Kaplan-Meier curves, aggregate data meta-analysis was conducted using random-effects models. Eleven RCTs involving 4844 patients were included. Aggregate data meta-analysis showed that administering NOACs led to a significantly lower risk of recurrent VTE (RR = 0.65; 95%CI: 0.50-0.84) and deep vein thrombosis (DVT) (RR = 0.60; 95%CI: 0.40-0.90). In the IPD meta-analysis, NOAC when compared with LMWH has an HR of 0.65 (95%CI: 0.49-0.86) for VTE recurrence. Stratified Cox and Royston-Parmar models demonstrated similar results. In reducing risks of recurrent VTE and DVT among cancer patients, NOACs are superior to LMWHs without increased major bleeding.
Keyphrases
- venous thromboembolism
- oral anticoagulants
- atrial fibrillation
- systematic review
- direct oral anticoagulants
- meta analyses
- case control
- electronic health record
- big data
- end stage renal disease
- healthcare
- chronic kidney disease
- case report
- type diabetes
- ejection fraction
- newly diagnosed
- free survival
- metabolic syndrome
- prognostic factors
- clinical practice
- artificial intelligence
- chronic pain
- deep learning
- health insurance