Publications by authors named "Nobumori Yagi"
Depressed cardiac systolic function in hemodialysis patients occurs for a variety of reasons and is a clinical problem. Beta-blockers are a key drug in the treatment of heart failure; however, hypotension may occur, particularly in dialysis patients, thereby complicating dialysis. Ivabradine has the unique property of a negative chronotropic effect only, without the negative inotropic effect.
View Article and Find Full Text PDFBackground: Nursing school is a stressful environment that demands high performance both professionally and academically. Interpersonal mindfulness training has shown promise for its stress-reducing capacity in other contexts; however, few descriptions or tests of this method in nursing training settings exist in the literature.
Purpose: This pilot study examined effects of a brief interpersonal mindfulness program embedded in a 4-week psychiatric nursing practicum in Thailand.
Article Synopsis
- - The study focuses on developing an AI-enhanced ECG model to detect elevated pulmonary artery pressure (ePAP), a critical sign of pulmonary hypertension, to enable early diagnosis and treatment.
- - Using data from over 41,000 patients, the AI model demonstrated strong accuracy (sensitivity 81.0% and specificity 79.6%) in identifying ePAP, regardless of age, sex, or comorbidities.
- - Results indicate that patients identified by the AI as having ePAP faced significantly higher cardiovascular mortality risks, underscoring the model's potential for improving early intervention and patient survival.
Background: Concealed left ventricular hypertrophy (LVH) is a prevalent condition that is correlated with a substantial risk of cardiovascular events and mortality, especially in young to middle-aged adults. Early identification of LVH is warranted. In this work, we aimed to develop an artificial intelligence (AI)-enabled model for early detection and risk stratification of LVH using 12-lead ECGs.
View Article and Find Full Text PDFBackground: Brugada syndrome is a major cause of sudden cardiac death in young people and has distinctive electrocardiographic (ECG) features. We aimed to develop a deep learning-enabled ECG model for automatic screening for Brugada syndrome to identify these patients at an early point in time, thus allowing for life-saving therapy.
Methods: A total of 276 ECGs with a type 1 Brugada ECG pattern (276 type 1 Brugada ECGs and another randomly retrieved 276 non-Brugada type ECGs for 1:1 allocation) were extracted from the hospital-based ECG database for a 2-stage analysis with a deep learning model.
Background: The aim of this study was to investigate the different substrate characteristics of repetitive premature ventricular complexed (PVC) trigger sites by the non-contact mapping (NCM).
Methods: Thirty-five consecutive patients, including 14 with arrhythmogenic right ventricular cardiomyopathy/dysplasia (ARVC) and 21 with idiopathic right ventricular outflow tract tachycardia (RVOT VT), were enrolled for electrophysiological study and catheter ablation guided by the NCM. Substrate and electrogram (Eg) characteristics of the earliest activation (EA) and breakout (BO) sites of PVCs were investigated, and these were confirmed by successful PVC elimination.