Omar Z Yasin1, Zachi Attia2, John J Dillon3, Christopher V DeSimone4, Yehu Sapir5, Jennifer Dugan1, Virend K Somers1, Michael J Ackerman1, Samuel J Asirvatham1, Christopher G Scott6, Kevin E Bennet7, Dorothy J Ladewig8, Dan Sadot5, Amir B Geva5, Paul A Friedman9. 1. Department of Internal Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN, USA; Department of Cardiovascular Diseases, Mayo Clinic, 200 First Street SW, Rochester, MN, USA. 2. Department of Internal Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN, USA; Department of Cardiovascular Diseases, Mayo Clinic, 200 First Street SW, Rochester, MN, USA; Electrical and Computer Engineering, Ben-Gurion University of the Negev, P.O. Box 653, Beer Sheva, Israel. 3. Nephrology and Hypertension, Mayo Clinic, 200 First Street SW, Rochester, MN, USA. 4. Department of Cardiovascular Diseases, Mayo Clinic, 200 First Street SW, Rochester, MN, USA. 5. Electrical and Computer Engineering, Ben-Gurion University of the Negev, P.O. Box 653, Beer Sheva, Israel. 6. Biomedical Statistics and Informatics, Mayo Clinic, 200 First Street SW, Rochester, MN, USA. 7. Division of Engineering, Mayo Clinic, 200 First Street SW, Rochester, MN, USA. 8. Mayo Clinic Ventures, 200 First Street SW, Rochester, MN, USA. 9. Department of Internal Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN, USA; Department of Cardiovascular Diseases, Mayo Clinic, 200 First Street SW, Rochester, MN, USA. Electronic address: pfriedman@mayo.edu.
Abstract
OBJECTIVE: We have previously used a 12-lead, signal-processed ECG to calculate blood potassium levels. We now assess the feasibility of doing so with a smartphone-enabled single lead, to permit remote monitoring. PATIENTS AND METHODS: Twenty-one hemodialysis patients held a smartphone equipped with inexpensive FDA-approved electrodes for three 2min intervals during hemodialysis. Individualized potassium estimation models were generated for each patient. ECG-calculated potassium values were compared to blood potassium results at subsequent visits to evaluate the accuracy of the potassium estimation models. RESULTS: The mean absolute error between the estimated potassium and blood potassium 0.38±0.32 mEq/L (9% of average potassium level) decreasing to 0.6 mEq/L using predictors of poor signal. CONCLUSIONS: A single-lead ECG acquired using electrodes attached to a smartphone device can be processed to calculate the serum potassium with an error of 9% in patients undergoing hemodialysis. SUMMARY: A single-lead ECG acquired using electrodes attached to a smartphone can be processed to calculate the serum potassium in patients undergoing hemodialysis remotely.
OBJECTIVE: We have previously used a 12-lead, signal-processed ECG to calculate blood potassium levels. We now assess the feasibility of doing so with a smartphone-enabled single lead, to permit remote monitoring. PATIENTS AND METHODS: Twenty-one hemodialysis patients held a smartphone equipped with inexpensive FDA-approved electrodes for three 2min intervals during hemodialysis. Individualized potassium estimation models were generated for each patient. ECG-calculated potassium values were compared to blood potassium results at subsequent visits to evaluate the accuracy of the potassium estimation models. RESULTS: The mean absolute error between the estimated potassium and blood potassium 0.38±0.32 mEq/L (9% of average potassium level) decreasing to 0.6 mEq/L using predictors of poor signal. CONCLUSIONS: A single-lead ECG acquired using electrodes attached to a smartphone device can be processed to calculate the serum potassium with an error of 9% in patients undergoing hemodialysis. SUMMARY: A single-lead ECG acquired using electrodes attached to a smartphone can be processed to calculate the serum potassium in patients undergoing hemodialysis remotely.
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