Statistical assessment for risk prediction of endoleak formation after tevar based on linear discriminant analysis

Kuniyoshi Hayashi, Fumio Ishioka, Bhargav Raman, Daniel Y. Sze, Hiroshi Suito, Takuya Ueda, Koji Kurihara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 shortto- 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.

Original languageEnglish
Title of host publicationAnalysis and Modeling of Complex Data in Behavioral and Social Sciences
EditorsAkinori Okada, Claus Weihs, Donatella Vicari, Giancarlo Ragozini
PublisherKluwer Academic Publishers
Pages143-151
Number of pages9
ISBN (Print)9783319066912
DOIs
Publication statusPublished - Jan 1 2014
EventJoint international meeting on Japanese Classification Society and the Classification and Data Analysis Group of the Italian Statistical Society, JCS-CLADAG 2012 - Capri Island, Italy
Duration: Sep 3 2012Sep 4 2012

Publication series

NameStudies in Classification, Data Analysis, and Knowledge Organization
Volume49
ISSN (Print)1431-8814

Other

OtherJoint international meeting on Japanese Classification Society and the Classification and Data Analysis Group of the Italian Statistical Society, JCS-CLADAG 2012
CountryItaly
CityCapri Island
Period9/3/129/4/12

Keywords

  • Average discriminant score
  • Quantitative analysis of aortic morphology
  • Sample influence function

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems
  • Information Systems and Management
  • Analysis

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  • Cite this

    Hayashi, K., Ishioka, F., Raman, B., Sze, D. Y., Suito, H., Ueda, T., & Kurihara, K. (2014). Statistical assessment for risk prediction of endoleak formation after tevar based on linear discriminant analysis. In A. Okada, C. Weihs, D. Vicari, & G. Ragozini (Eds.), Analysis and Modeling of Complex Data in Behavioral and Social Sciences (pp. 143-151). (Studies in Classification, Data Analysis, and Knowledge Organization; Vol. 49). Kluwer Academic Publishers. https://doi.org/10.1007/978-3-319-06692-9_16