Prediction of the Future Risk of Endoleak Complications Based on Statistical Method
Fumio Ishioka1, Hazuki Nakatamari2, Hiroshi Suito3, Takuya Ueda2, Koji Kurihara3
1School of Law, Okayama University, Okayama, Japan; 2Chiba University Hospital; 3Okayama University

Recently, a forecast which is obtained by extracting the casual relationships from statistical models has been expected in clinical practice. However, quantitative medical diagnosis based on statistical perspectives is still not enough, and it is usually diagnosed on the basis of doctor's experience or flair.

In this study, we apply a statistical method to quantitative assessment of thoracic aortic morphology. That is, using a discriminant model which predicts a risk of endoleak after thoracic endovascular aortic repair (TEVAR). While the TEVAR has become accepted as an effective treatment for atherosclerotic aortic aneurysms (Chu, M.W. et el, 2007; Svensson, L.G. et el, 2008), it can cause a side effect called endoleak which is a kind of complication. Although many investigators have recognized the contribution of aortic morphology, the specific relationship between aortic curvature and risk of endoleaks after TEVAR has not been quantitatively defined.

Our model created by the quantification of pattern of native aortic curvature, the size and the location of aortic aneurysms predicted the risk of endoleaks with 100% sensitivity and 76% specificity. In addition, we considered a reliability for the discriminant model by using the cross validation. In result, the accuracy got 73.8%. In particular, a diagnosis of endoleak(+) could get a high reliability of 84%. It would be useful to assess the impact of native thoracic aortic morphology on the subsequent development of endoleaks after TEVAR.


Chu MW, Forbes TL, Kirk Lawlor D, Harris KA, Derose G. Endovascular repair of thoracic aortic disease: early and midterm experience. Vasc Endovascular Surg 2007; 41:186-191.

Svensson LG, Kouchoukos NT, Miller DC et al. Expert consensus document on the treatment of descending thoracic aortic disease using endovascular stent-grafts. Ann Thorac Surg 2008; 85:S1-41.

Keywords: Medical data analysis; Discriminant analysis; Cross validation

Biography: Professional Preparation: BSc. 2002. Environmental Science and Technology, Okayama University.

MSc. 2004. Graduate School of Natural Science and Technology, Okayama University.

DSc. 2007. Graduate School of Natural Science and Technology, Okayama University.

Job history: Postdoctoral fellow, Graduate School of Environmental Science, Okayama University, Japan (October 2007 - March 2008).

Assistant Professor, School of Law, Okayama University, Japan (April 2008 -).

Executive Committee: JKSC2010 (Joint Meeting of Japan-Korea Special Conference of Statistics and The 2nd Japan-Korea Statistics Conference of Young Researchers).