Novel bloodless potassium determination using a signal-processed single-lead ECG

Background-hyper- and hypokalemia are clinically silent,common in patients with renal or cardiac disease,and are life threatening. a noninvasive,unobtrusive,blood-free method for tracking potassium would be an important clinical advance. methods and results-two groups of hemodialysis patients (development group,n=26; validation group,n=19) underwent highresolution digital ecg recordings and had 2 to 3 blood tests during dialysis. using advanced signal processing,we developed a personalized regression model for each patient to noninvasively calculate potassiumvalues during the second and third dialysis sessions using only the processed single-channel ecg. in addition,by analyzing the entire development group's first-visit data,we created a global model for all patients that was validated against subsequent sessions in the development group and in a separate validation group. this global model sought to predict potassium,based on the t wave characteristics,with no blood tests required. for the personalized model,we successfully calculated potassium values with an absolute error of 0.36±0.34 mmol/l (or 10% of the measured blood potassium). for the global model,potassium prediction was also accurate,with an absolute error of 0.44±0.47 mmol/l for the training group (or 11% of the measured blood potassium) and 0.5±0.42 for the validation set (or 12% of the measured blood potassium). conclusions-the signal-processed ecg derived from a single lead can be used to calculate potassium values with clinically meaningful resolution using a strategy that requires no blood tests. this enables a cost-effective,noninvasive,unobtrusive strategy for potassium assessment that can be used during remote monitoring. © 2016 the authors.