Development of deep learning algorithm for detecting dyskalemia based on electrocardiogram.

ABSTRACT: Dyskalemia is a common electrolyte abnormality. Since dyskalemia can cause fatal arrhythmias and cardiac arrest in severe cases, it is crucial to monitor serum potassium (K⁺) levels on time. We developed deep learning models to detect hyperkalemia (K⁺  ≥ 5.5 mEq/L) and hypokalemia (K⁺  < 3.5 mEq/L) from electrocardiograms (ECGs), which are noninvasive and can be quickly measured. The retrospective cohort study was conducted at two hospitals from 2006 to 2020. The training set, validation set, internal testing cohort, and external validation cohort comprised 310,449, 15,828, 23,849, and 130,415 ECG-K⁺ samples, respectively. Deep learning models demonstrated high diagnostic performance in detecting hyperkalemia (AUROC 0.929, 0.912, 0.887 with sensi

SUBMITTER: An JN 

PROVIDER: S-EPMC11445469 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature