Statistical Assessment for Risk Prediction of Endoleak Formation After TEVAR Based on Linear Discriminant Analysis

Abstract

Over the past decade, therapy for thoracic aneurysms involving the use of a stent-graft has gained popularity as an alternate therapy for surgical treatment. This therapy is considered to be safe and efficient, and realizes satisfactory short-to-midterm results. However, a clinical side effect called endoleak has often been observed after alternate therapy. Based on the empirical findings of doctors, if a stent-graft is inserted into the part of the large curvature on the aortic angiography of a patient, it is believed that there is an increased risk of endoleak formation. To understand the relationship between the risk and the aortic curvature, we set a two-class discriminant problem involving no-endoleak and endoleak groups, and apply linear discriminant analysis to the two-class discriminant problem with a quantitative dataset that is associated with the curvature of aortic angiography and the insertion position of a stent-graft. Next, we propose a procedure for the diagnostics based on the sign of the sample influence function for the average discriminant score in each class. In addition, we apply our proposed diagnostic procedure to the prediction result of the two-class linear discriminant analysis, and detect large influential individuals for the improvement of the prediction accuracy for endoleak groups. With our approach, we determine the relation between the curvature of the aorta and the risk of endoleak formation.