Application of Wearable Wireless Pulse Positioning System to Predict the Prognosis and to Avoid Sudden Death of Dialysis Patients

According to the United States Renal Data System (USRDS) 2013 international comparison report, the dialysis prevalence in Taiwan is on the top rank of the world. According to the USRDS report, the prevalence rate of chronic kidney disease (CKD) under dialysis was 2548 per million population in Taiwan in 2011. According to the Wu, et al. study, the number of mortality of dialysis patient was around 6000 to 6500 annually. The first mortality rate of dialytic CKD patients was about 18.9%. The first two-year mortality rate of dialytic CKD patients was 33.3%. According to the Wu, et al. publication in 2014, the mortality rate of the 90-days after discharge of the newly acute kidney injury (AKI) dialysis patients was more than 30%. There are no parameters or systems suitable to predict the sudden death of dialysis patients According to the study of Saravanan and Davidson, sudden cardiac death was the single most common form of death in dialysis patients. Arrhythmia was the most common cause of death in dialysis patients. According to the Jerng et al. study, the most common abnormal clinical alert system for stable admission patients was abnormal heart rate (36.5%). Pulse rate is a rapid response physiological parameter. It can reflex many of the pathological conditions such as fever, hypoxia, ischemia, shock, arrhythmia…etc. The heart rate itself is a good predictor for the prognosis of acute coronary syndrome, heart failure, myocardial infarction and the occurrence of sudden cardiac death in recent studies. Nauman et al study showed proper heart rate variability decreased the incidence of sudden cardiac arrest and ischemic heart attack. We plan to use a new definition of resting pulse rate which is defined to be resting of 3G accelerator. Recently, a wearable light device system was designed and manufactured in Taiwan, it could simultaneously real-time demonstrates large amount data of patients’ pulse rates and locations, by the wireless roaming transmission cloud technology. In this study, we plan to use the system to predict the prognosis of dialysis patients. At the same time, we hope to decrease the occurrence of sudden cardiac arrest for patient during dialysis.