Discriminant analysis of native thoracic aortic curvature: risk prediction for endoleak formation after thoracic endovascular aortic repair.
PURPOSE: To determine the association of native thoracic aortic curvature measured from computed tomographic (CT) angiography categorized by discriminant analysis with the development of endoleaks after thoracic endovascular aortic repair (EVAR). MATERIALS AND METHODS: Forty patients (28 men, 12 women; mean age, 74 y; range, 40-89 y) with aortic diseases treated with thoracic EVAR were evaluated. Diseases treated included atherosclerotic aneurysm (n = 27), penetrating atherosclerotic ulcer (n = 4), intramural hematoma (n = 3), mycotic aneurysm (n = 3), and anastomotic pseudoaneurysm (n = 3). Quantitative analysis of native aortic morphology was performed on preprocedural CT angiograms with an original customized computer program, and regional curvature indices in each anatomic segment of the aorta were calculated. Patterns of native thoracic aortic morphology were analyzed by discriminant analysis. The association between the morphologic pattern of the aorta and the presence and type of endoleak was assessed. RESULTS: After leave-one-out cross-validation methods had been applied, the sensitivity, specificity, and accuracy to detect endoleak formation in a new population group by discriminant analysis of the patterns of native aortic curvature were estimated as 84.0%, 58.8%, and 73.8%, respectively. Compared with the no-endoleak group, the type Ia endoleak group had greater curvature at the aortic arch, the type Ib endoleak group had greater curvature at the thoracoabdominal junction, and the type III endoleak group had greater curvature in the midportion of the descending aorta. CONCLUSIONS: Discriminant analysis of native thoracic aortic morphology measured from CT angiography is a useful tool to predict the risk of endoleak formation after thoracic EVAR and should be implemented during treatment planning and follow-up.
Nakatamari, H; Ueda, T; Ishioka, F; Raman, B; Kurihara, K; Rubin, GD; Ito, H; Sze, DY
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