ArticleDiseases (Basel, Switzerland)2025
Is Global Limb Anatomic Staging System Classification a Useful Tool in Predicting Lower Limb Revascularization Procedures' Success?
Article in Diseases (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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7 authors.
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Abstract
backgroundGLASS (Global Limb Anatomic Staging System) classification is a classification proposed in 2019 by The Lower Extremity Guidelines Committee of the Society for Vascular Surgery, which aims to identify the anatomic substrate that defines the severity of a lower extremity arterial injury and predict the success rate of possible revascularization. The aim of the study is to demonstrate the usefulness of this classification and if it is a reliable tool in predicting the success of the revascularization procedures for patients with chronic limb-threatening ischemia (CLTI).
methodsA retrospective study was conducted on patients undergoing revascularization for CLTI. Glass staging was applied to angiographic data, categorizing them into GLASS 1, 2, or 3 based on the complexity of the femoropopliteal and infrapopliteal lesions. We investigated the clinical characteristics and types of endovascular treatment in correlation with GLASS classification. We also evaluated the technical success of revascularization procedures and the specificity and accuracy of the GLASS classification.
resultsAfter the first testing, we found out that GLASS classification has a sensitivity of 63% and a specificity of 77%. After the second testing, the sensitivity was 82%. of 77% also. The follow-up of this sample was made after 1 year, with no patients lost to follow-up and with an amputation-free survival of 81.3%.
conclusionsGLASS 1 and 2 patients had significantly higher rates of success compared to GLASS 3. GLASS serves as a valuable tool in predicting revascularization success and provides a standardized approach to anatomical complexity, but further studies should integrate more data in order to enhance its predictive capability.
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