Artificial Intelligence-Enabled Electrocardiography for Monitoring Serum Potassium Dynamics in Patients With Severe Hypokalemia.

Published in American journal of kidney diseases : the official journal of the National Kidney Foundation. Abstract: Severe hypokalemia requires prompt management and close surveillance. Although artificial intelligence-enabled electrocardiography (AI-ECG) rapidly detects severe hypokalemia, its application for monitoring serum potassium (K + ) dynamics during treatment remains unexplored. This study assessed AI-ECG performance in monitoring K + changes during supplementation. Multicenter retrospective cohort study. 191 adults with severe hypokalemia (Lab-K + ≤2.5 mmol/L; matched ECG-K + <3.5 mmol/L) and ≥1 follow-up paired measurement within 24 hours of K + supplementation at three teaching hospitals between September 2019 and August 2024. Laboratory-measured K + (Lab-K + ) and K + estimated by ECG (ECG-K + ) overall and stratified by the etiology of hypokalemia (acute K + shift vs. chronic K + deficit). Primary: agreement between paired ECG-K + and Lab-K + . Secondary: diagnostic accuracy and K + trajectories. Linear mixed-effects models with patient-level random intercepts; repeated-measures correlation (rmcorr) and Bland-Altman plots; patient-level clustered bootstrapped ROC analysis for diagnostic accuracy. Of 191 patients, 156 (81.7%) had chronic K + deficits (most commonly gastrointestinal disorders [n=47] or diuretic use [n=35]), and 35 (18.3%) had acute K + shifts (most commonly thyrotoxic periodic paralysis [n=25]). The chronic K + deficits group had more comorbidities and use of medications affecting K + . ECG-K + correlated strongly with Lab-K + (rmcorr 0.847; 95% CI, 0.81-0.88;...