Koji Kurihara

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.

Finding upper-level sets in cellular surface data using echelons and saTScan

Across a spectrum of contemporary contexts from public health to landscape ecology and natural resources, there is need for objective determination of elevated occurrence in phenomena such as disease incidence and biodiversity. Occurrences of such phenomena constitute response surfaces, but data regarding the surface is typically acquired in a cellular framework. The cells may comprise a regular grid, or may be of irregular shapes such as counties in which statistics are collected. Echelons are a topologically based approach to systematic determination of spatial structure in a step surface. Spatial scan statistics are a probability-based approach to the same issue when interest lies in a rate variable. Here we examine the use of echelons both separately and in conjunction with the SaTScan implementation of spatial scan statistics for purposes of determination and visualization of upper-level sets. Consideration is given to both conventional geographic space and to the cellular pseudo-space of contingency tables for ordered categorical variables.

Detecting a change point using statistical sensitivity analysis based on the influence function

In the field of statistics, when we construct prediction and decision-making models on the basis of a statistical approach, we usually employ previous data to do so. Statistical sensitivity analysis plays an important role in the assessment of these statistical models because it can detect influential observations for the target models, which can enhance their accuracy. However, thus far, it appears that many researchers have developed statistical sensitivity analysis with the assumption that the population parameters for the target data remain flat. Therefore, if the population parameters are not static, a traditional statistical sensitivity analysis cannot exactly evaluate the influence of each observation for target statistical models or parameters. Under these conditions, we must pay attention to not only the influential data point, given as an outlier, but also the change point, which is the point in time when the population parameters of the target data change. In this paper, we propose a sequential statistical approach for detecting a change point by extending the existing statistical sensitivity analysis based on influence functions. Through some numerical simulation studies, we demonstrate the performance of our diagnostic approach.

Assessment of the Relationship Between Native Thoracic Aortic Curvature and Endoleak Formation After TEVAR Based on Linear Discriminant Analysis

In the field of surgery treatment, thoracic endovascular aortic repair has recently gained popularity, but this treatment often causes an adverse clinical side effect called endoleak. The risk prediction of endoleak is essential for pre-operative planning (Nakatamari et al., J Vasc Interv Radiol 22(7):974–979, 2011). In this study, we focus on a quantitative curvature in the morphology of a patient’s aorta, and predict the risk of endoleak formation through linear discriminant analysis. Here, we objectively evaluate the relationship between the side effect after stent-graft treatment for thoracic aneurysm and a patient’s native thoracic aortic curvature. In addition, based on the sample influence function for the average of discriminant scores in linear discriminant analysis, we also perform statistical diagnostics on the result of the analysis. We detected the influential training samples to be deleted to realize improved prediction accuracy, and made subsets of all of their possible combinations. Furthermore, by considering the minimum misclassification rate based on leave-one-out cross-validation in Hastie et al. (The elements of statistical learning. Springer, New York, 2001, pp. 214–216) and the minimum number of training samples to be deleted, we deduced the subset to be excluded from training data when we develop the target classifier. From this study, we detected an important part of the native thoracic aorta in terms of risk prediction of endoleak occurrence, and identified influential patients for the result of the discrimination.

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

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.

An Airline Scheduling Model and Solution Algorithms

The rapid development of airlines, has made airports busier and more complicated. The assignment of scheduled to available gates is a major issue for daily airline operations. We consider the over-constrained airport gate assignment problem(AGAP) where the number of flights exceeds the number of available gates, and where the objectives are to minimize the number of ungated flights and the total walking distance or connection times. The procedures used in this project are to create a mathematical model formulation to identify decision variables to identify, constraints and objective functions. In addition, we will consider in the AGAP the size of each gate in the terminal and also the towing process for the aircraft. We will use a greedy algorithm to solve the problem. The greedy algorithm minimizes ungated flights while providing initial feasible solutions that allow flexibility in seeking good solutions, especially in case when flight schedules are dense in time. Experiments conducts give good results.