Serum Potassium Monitoring using AI-enabled Smart Watch Electrocardiograms

Abstract

Background Hyperkalemia poses a significant risk of sudden cardiac death, especially for those with end-stage renal diseases (ESRD). Smartwatches with ECG capabilities offer a promising solution for continuous, non-invasive monitoring using AI. Objectives To develop an AI-ECG algorithm to predict serum potassium level in ESRD patient with smartwatch generated ECG waveforms. Methods A cohort of 152,508 patients with 293,557 ECGs paired serum potassium levels obtained within one hour at Cedars Sinai Medical Center (CSMC) was used to train an AI-ECG model (Kardio-Net) to predict serum potassium level. The model was further fine-tuned on 4,337 ECGs from 1,463 patients with ESRD using inputs from 12-lead and single-lead ECGs. Kardio-Net was evaluated in held-out test cohorts from CSMC and Stanford Healthcare (SHC) as well as a prospective international cohort of 40 ESRD patients with smartwatch ECGs at Chang Gung Memorial Hospital (CGMH). Results The Kardio-Net, when applied to 12-lead ECGs, identified severe hyperkalemia with an AUC of 0.852 and a mean absolute error (MAE) of 0.527 mEq/L. In external validation at SHC, the model achieved an AUC of 0.849 and an MAE of 0.599 mEq/L. For single-lead ECGs, Kardio-Net detected hyperkalemia with an AUC of 0.876 and had an MAE of 0.575 mEq/L in the CSMC test cohort. Using prospectively obtained smartwatch data, the AUC was 0.831, with an MAE of 0.580 mEq/L. Conclusions We validate a deep learning model to predict serum potassium levels from both 12-lead ECGs and single-lead smartwatch data, demonstrating its utility for remote monitoring of hyperkalemia.