Evaluation of Ventricular Arrhythmic Predictors in Chronic Renal Failure Patients Under Maintenance Hemodialysis Therapy Using Non-invasive Electrocardiological Methods

• Main Authors: ING-FANG YANG, 楊英芳
• Other Authors: TEN-FANG YANG
• Format: Others
• Language: zh-TW
• Published: 2000
• Online Access: http://ndltd.ncl.edu.tw/handle/21114238351714079193

碩士 === 台北醫學院 === 醫學資訊研究所 === 88 === This dissertation has described series of researches concerning the clinical evaluation of ventricular arrhythmic predictors in the end-stage renal failure (ESRF) patients under maintenance hemodialysis therapy using non-invasive electrocardiological methods. The history of the development of conventional 12-lead electrocardiography (ECG) and signal-averaged ECG (SAECG) are first of all reviewed. Simple and risk free electrocardiological methods, namely 12-lead ECG QT dispersion and SAECG were utilized to detect the life-threatening cardiovascular complications and for their clinical availability and reproducibility were also investigated. The clinical emergence of non-invasive recording techniques for the physiological function of human body is one of the most important recent developments in the biomedical sciences of this decade. Methods used to analyze these biomedical signals of the heart are being developed in several aspects; namely, signal-averaged electrocardiogram (SAECG), heart rate variability, 24 hour ambulatory electrocardiogram (Holter ECG) monitoring, etc. A simple algorithm is that if both 12-leads QT dispersion and the various parameters in SAECG can provides an relatively easy, sensitive and cost effective method for predicting spontaneous or inducible ventricular tachyarrhythmias in high-risk ESRF patients receiving maintenance hemodialysis therapy. The ease of these two techniques to be performed at the bedside for ESRF patients will even make them possible as a routinely used procedure before and after each hemodialysis session in the foreseeable future. Not only as a predictor, but also as a rescuer of life, these automated computing electrocardiological methods will fulfill this task.