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A Randomized, Double-Blind, Parallel Group Study on the Effect of Continuous Consumption of Nutritionally Balanced "Bento" on Psychosomatic Disorders - A Randomized, Double-Blind, Parallel Group Study on the Effect of Continuous Consumption of Nutritionally Balanced "Bento" on Psychosomatic Disorders

Start Date16 Jul 2024

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Hokkaido Information University

JPRN-UMIN000054576

/ SuspendedNot ApplicableIIT

Effect of Ingestion of the Test Food A and Test Food B on Body Composition: An Exploratory Study - Effect of Ingestion of the Test Food A and Test Food B on Body Composition: An Exploratory Study

Start Date07 Jun 2024

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Hokkaido Information University

100 Clinical Results associated with Hokkaido Information University

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Literatures (Medical) associated with Hokkaido Information University

01 Feb 2025·Annals of Vascular Surgery

Predicting Short-Term Mortality after Endovascular Aortic Repair Using Machine Learning–Based Decision Tree Analysis

Article

Author: Iwahashi, Toru ; Fukuda, Shoji ; Iwasa, Tsuyoshi ; Kano, Masaki ; Nishibe, Masayasu ; Nishibe, Toshiya ; Koizumi, Jun ; Akiyama, Shinobu

BACKGROUNDEndovascular aneurysm repair (EVAR) has revolutionized the treatment of abdominal aortic aneurysms by offering a less invasive alternative to open surgery. Understanding the factors that influence patient outcomes, particularly for high-risk patients, is crucial. The aim of this study was to determine whether machine learning (ML)-based decision tree analysis (DTA), a subset of artificial intelligence, could predict patient outcomes by identifying complex patterns in data.METHODSThis study analyzed 169 patients who underwent EVAR to identify predictors of short-term mortality (within 3 years) using DTA. Data included 23 variables such as age, gender, nutritional status, comorbidities, and surgical details. The Python 3.7 was used as the programming language, and the scikit-learn toolkit was used to complete the derivation and verification of the decision tree classifier.RESULTSDTA identified poor nutritional status as the most significant predictor, followed by chronic kidney disease, chronic obstructive pulmonary disease, and advanced age (octogenarian). The decision tree identified 6 terminal nodes with a risk of short-term mortality ranging from 0% to 79.9%. This model had 68.7% accuracy, 65.7% specificity, and 79.0% sensitivity.CONCLUSIONSML-based DTA is promising in predicting short-term mortality after EVAR, highlighting the need for comprehensive preoperative assessment and individualized management strategies.

01 Feb 2025·Journal of Surgical Research

Prediction of Aneurysm Sac Shrinkage After Endovascular Aortic Repair Using Machine Learning-Based Decision Tree Analysis

Article

Author: Akiyama, Shinobu ; Kano, Masaki ; Fukuda, Shoji ; Iwasa, Tsuyoshi ; Matsuda, Seiji ; Nishibe, Toshiya ; Nishibe, Masayasu ; Koizumi, Jun ; Dardik, Alan

INTRODUCTIONA simple risk stratification model to predict aneurysm sac shrinkagein patients undergoing endovascular aortic repair (EVAR) for abdominal aortic aneurysms (AAA) was developed using machine learning-based decision tree analysis.METHODSOne hundred nineteen patients with AAA who underwent elective EVAR at Tokyo Medical University Hospital between November 2013 and July 2019 were included in the study. Predictors of aneurysm sac shrinkage identified in univariable analysis (P < 0.05) were entered into the decision tree analysis.RESULTSUnivariable analysis revealed significant differences between patients with and without aneurysm sac shrinkage in the variables of age (<75 y or ≥75 y), current smoking, operative type II endoleak, and preoperative pulse wave velocity (PWV) (<1800 cm/s or ≥1800 cm/s). The decision tree showed that preoperative PWV was the most relevant predictor, followed by operative type II endoleak and current smoking, and identified 6 terminal nodes with likelihoods of aneurysm sac shrinkage ranging from 5.6% to 63.6%.CONCLUSIONSWe established a decision tree model with 3 variables (preoperative PWV, operative type II endoleak, and current smoking) to predict aneurysm sac shrinkage in patients undergoing EVAR for AAA. This classification model may help identify patients with a high or low likelihood of aneurysm sac shrinkage.

01 Jan 2025·Annals of Vascular Diseases

Brachial-Ankle Pulse Wave Velocity Reflects Regional Arterial Stiffness and Distensibility in Patients with Abdominal Aortic Aneurysm