Serum Potassium Monitoring Using AI-Enabled Smartwatch Electrocardiograms
Open accessAbstract
Background Hyperkalemia, characterized by elevated serum potassium levels, heightens the risk of sudden cardiac death, particularly increasing risk for individuals with chronic kidney disease and end-stage renal disease (ESRD). Traditional laboratory test monitoring is resource-heavy, invasive, and unable to provide continuous tracking. Wearable technologies like smartwatches with electrocardiogram (ECG) capabilities are emerging as valuable tools for remote monitoring, potentially allowing for personalized monitoring with artificial intelligence (AI)-ECG interpretation.Objectives The purpose of this study was to develop an AI-ECG algorithm to predict serum potassium level in ESRD patients with smartwatch-generated ECG waveforms.Methods A cohort of 152,508 patients with 293,557 ECGs paired serum potassium levels obtained within 1 hour at Cedars Sinai Medical Center 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- and single-lead ECGs. Kardio-Net was evaluated in held-out test cohorts from Cedars Sinai Medical Center and Stanford Healthcare (SHC) as well as a prospective international cohort of 40 ESRD patients with smartwatch ECGs at Chang Gung Memorial Hospital.Results The Kardio-Net, when applied to 12-lead ECGs, identified severe hyperkalemia (>6.5 mEq/L) with an AUC of 0.852 (95% CI: 0.745-0.956) and a mean absolute error (MAE) of 0.527 mEq/L. In external validation at SHC, the model achieved an AUC of 0.849 (95% CI: 0.823-0.875) and an MAE of 0.599 mEq/L. For single-lead ECGs, Kardio-Net detected severe hyperkalemia with an AUC of 0.876 (95% CI: 0.765-0.987) in the primary cohort and had an MAE of 0.575 mEq/L. In the external SHC validation, the AUC was 0.807 (95% CI: 0.778-0.835) with an MAE of 0.740 mEq/L. Using prospectively obtained smartwatch data, the AUC was 0.831 (95% CI: 0.693-0.975), 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.
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Bioelectric sensors capture bioelectrical signals (e.g., EMG) and bioelectrical impedance (BI) variations via electrodes, and are used for heart rate detection, muscle fatigue analysis, emotion monitoring, respiratory and body composition monitoring.
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- Authors
- Chiu, I.-M., Wu, P.-J., Zhang, H., Hughes, J. W., Rogers, A. J., Jalilian, L., Perez, M., Lin, C.-H. R., Lee, C.-T., Zou, J., & Ouyang, D.
- Affiliations
- Cedars-Sinai Medical Center (US)
- Kaohsiung Chang Gung Memorial Hospital (TW)
- National Sun Yat-sen University (TW)
- Stanford University (US)
- Stanford Medicine (US)
- University of California, Los Angeles (US)
- ORCID
- Venue
- JACC: Clinical Electrophysiology
- Publisher
- Elsevier BV
- Published
- 2024-12
- Volume
- 10
- Issue
- 12
- Pages
- 2644-2654
- ISSN
- 2405-500X (ISSN-L)
- PMC
- PMC13549449
- OpenAlex
- W4403271828
- Cited by
- 31
- References
- 33
- Citations by year
- 2026: 12 · 2025: 18 · 2024: 1
- FWCI
- 9.29
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- JACC. Clinical electrophysiology (publishedVersion)
- medRxiv (acceptedVersion, cc-by-nd)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- NIH
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Potassium and Related Disorders
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- hyperkalemia, wearable ECG, end-stage renal disease, chronic kidney disease, remote patient monitoring, single-lead ECG, sudden cardiac death risk, external validation, hospital readmission prediction
- MeSH terms
- Wearable Electronic Devices, Adult, Aged, Algorithms, Artificial Intelligence, Electrocardiography, Female, Humans, Hyperkalemia, Kidney Failure, Chronic, Male, Middle Aged, Potassium
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