ArticleAnnals of surgical treatment and research2024
Validation of the Vascular Study Group of New England (VSGNE) risk prediction model for abdominal aortic aneurysm repair in Korea: a single-center retrospective study.
Article in Annals of surgical treatment and research, 2024. 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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6 authors.
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Abstract
Purpose: The Vascular Study Group of New England (VSGNE) risk prediction model is a simple method for estimating risk for elective abdominal aortic aneurysm (AAA) repair. The model considers both treatment methods and the physical characteristics of the aneurysm type as well as comorbidities. This research aimed to validate its effectiveness by analyzing retrospective data on Korean patients. Methods: Our single-center retrospective analysis included 1,227 patients who underwent elective open repair surgery (ORS) or endovascular aortic repair (EVAR) from 2005 to 2021. We assessed the discrimination of the risk score and the effects of several risk factors. Results: Most patients (66.7%) were classified as low risk in the model, with only 5.6% considered high risk. The mean risk score was 2.81, significantly lower than reported in previous studies. The actual 30-day mortality was only 0.7%, less than the predicted 1.1%. The accuracy of the model in predicting 30-day mortality was statistically significant (area under the curve, 0.822). Patients with high scores were associated with significantly increased mortality (odds ratio, 3.9; P < 0.001). Factors such as advanced age, cerebrovascular disease, and elevated creatinine levels were influential in mortality outcomes. However, a significant difference was not found in short-term mortality between ORS and EVAR. Conclusion: Although the VSGNE model is an objective tool for assessing death risk in elective AAA repair, the actual risk scores in our patient population were lower than predicted. To create a more representative tool for the Korean population, we suggest developing a novel model based on multicenter data collection.
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