Serum Potassium Monitoring using AI-enabled Smart Watch Electrocardiograms

Abstract

AbstractBackgroundHyperkalemia 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.ObjectivesTo develop an AI-ECG algorithm to predict serum potassium level in ESRD patient with smartwatch generated ECG waveforms.MethodsA 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).ResultsThe 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.ConclusionsWe 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.Condensed AbstractHyperkalemia significantly increases the risk of sudden cardiac death in end-stage renal disease (ESRD) patients. We developed ‘Kardio-Net,’ an AI-driven ECG model, using data from 152,508 patients at Cedars Sinai Medical Center, and refined it with ECGs from 1,463 ESRD patients using inputs from 12-lead and single-lead ECGs. This model facilitates continuous and non-invasive potassium monitoring, leveraging both traditional and smartwatch-generated ECGs. Tested across various cohorts, including a prospective smartwatch group, Kardio-Net achieved an AUC range of 0.807 to 0.876, demonstrating its effectiveness for real-time hyperkalemia monitoring.