International Cardiovascular Research Journal
An annual journal in scientific affiliation with the Cardiovascular Research Center, Shiraz University of Medical Sciences
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Outline
Prevention of Venous Thromboembolism in Major Orthopedic Surgery: Bayesian Network Meta-Analysis of 21 Randomized Trials Evaluating Unfractionated Heparins, Low-Molecular Weight Heparins, and New Oral Anticoagulants
Authors
Abstract
Background: In major orthopedic surgery, prevention of venous thromboembolism has been based on Unfractionated Heparins (UFHs) over the past decades, then on Low-Molecular Weight Heparins (LMWHs), and on New Oral Anticoagulants (NOACs) more recently. To summarize the comparative effectiveness of UFHs, LMWHs, and NOACs in this clinical indication, we applied Bayesian network meta-analysis to the clinical material (randomized studies) published in two previous reviews focused on this issue.
Objectives: Our end-point was a composite of venous thromboembolism and pulmonary embolism.
Materials and Methods: Our analysis was based on standard Bayesian network meta-analysis (random-effect model).
Results: The analysis included 21 randomized trials for a total of 21,805 patients. Our results showed that the degree of effectiveness did not differ among UFHs, LMWHs, and NOACs. Although some trends emerged from an in-depth analysis of these data (e.g. according to the histogram of rankings), no significant differences were found (P > 0.05). Moreover, two agents among LMWHs proved to be adequately supported by randomized trials (enoxaparin and dalteparin), while limited evidence was available for other agents of this class.
Conclusions: Our synthesis of the effectiveness data can be useful as an overall reference in this area and can also contribute to defining the place of further innovative treatments for this clinical indication.
Highlights
References
- 1.The references are available in PDF.
Copyright
© 2017, Author(s). This open-access article is available under the Creative Commons Attribution 4.0 (CC BY 4.0) International License (https://creativecommons.org/licenses/by/4.0/), which allows for unrestricted use, distribution, and reproduction in any medium, provided that the original work is properly cited.
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