TY - JOUR
T1 - Novel bloodless potassium determination using a signal-processed single-lead ECG
AU - Attia, Zachi I.
AU - DeSimone, Christopher V.
AU - Dillon, John J.
AU - Sapir, Yehu
AU - Somers, Virend K.
AU - Dugan, Jennifer L.
AU - Bruce, Charles J.
AU - Ackerman, Michael J.
AU - Asirvatham, Samuel J.
AU - Striemer, Bryan L.
AU - Bukartyk, Jan
AU - Scott, Christopher G.
AU - Bennet, Kevin E.
AU - Ladewig, Dorothy J.
AU - Gilles, Emily J.
AU - Sadot, Dan
AU - Geva, Amir B.
AU - Friedman, Paul A.
N1 - Publisher Copyright:
© 2016 The Authors.
PY - 2016/1/1
Y1 - 2016/1/1
N2 - 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.
AB - 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.
KW - Electrophysiology
KW - Potassium
KW - Waves
UR - http://www.scopus.com/inward/record.url?scp=84997787387&partnerID=8YFLogxK
U2 - 10.1161/JAHA.115.002746
DO - 10.1161/JAHA.115.002746
M3 - Article
C2 - 26811164
AN - SCOPUS:84997787387
SN - 2047-9980
VL - 5
JO - Journal of the American Heart Association
JF - Journal of the American Heart Association
IS - 1
M1 - e002746
ER -