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Hyperkalemia is common in patients with chronic kidney disease, often asymptomatic, and associated with fatal arrhythmias. Galloway and coauthors developed and validated a deep convolutional neural network for detection of hyperkalemia using 1 576 581 electrocardiograms (ECGs) from 449 380 patients and validated in 61 965 patients with stage 3 or greater chronic kidney disease. Using only 2 ECG leads (leads I and II), a deep-learning model detected hyperkalemia in patients with renal disease with an area under the receiver operating characteristic curve of 0.853 to 0.883. The application of artificial intelligence to the ECG may enable screening for hyperkalemia. Prospective studies of this deep-learning model are warranted.
Highlights. JAMA Cardiol. 2019;4(5):399. doi:10.1001/jamacardio.2018.3195
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