โก Quick Summary
This study developed an AI-ECG algorithm named Kardio-Net to predict serum potassium levels in patients with end-stage renal disease (ESRD) using smartwatch-generated ECG waveforms. The model demonstrated impressive performance, achieving an AUC of 0.876 for detecting severe hyperkalemia, showcasing the potential of wearable technology in remote health monitoring.
๐ Key Details
- ๐ Dataset: 152,508 patients, 293,557 ECGs
- ๐งฉ Features used: 12-lead and single-lead ECG waveforms
- โ๏ธ Technology: AI-ECG model “Kardio-Net”
- ๐ Performance: AUC of 0.876 for single-lead ECGs in primary cohort
๐ Key Takeaways
- ๐ AI-ECG technology offers a non-invasive method for monitoring serum potassium levels.
- ๐ก Kardio-Net was trained on a large dataset, enhancing its predictive accuracy.
- ๐ฉโ๐ฌ The model achieved an AUC of 0.852 for severe hyperkalemia detection using 12-lead ECGs.
- ๐ฅ External validation confirmed the model’s robustness across different healthcare settings.
- ๐ Potential applications include personalized monitoring for patients with chronic kidney disease.
- ๐ Mean absolute error (MAE) was low, indicating precise predictions of potassium levels.
- ๐ฌ Study conducted at Cedars Sinai Medical Center and Chang Gung Memorial Hospital.
๐ Background
Hyperkalemia, or elevated serum potassium levels, poses a significant risk of sudden cardiac death, especially in individuals with chronic kidney disease and ESRD. Traditional monitoring methods are often invasive and resource-intensive, highlighting the need for innovative solutions. The rise of wearable technologies, particularly smartwatches equipped with ECG capabilities, presents an exciting opportunity for continuous, non-invasive health monitoring.
๐๏ธ Study
The study aimed to develop and validate an AI-ECG algorithm, Kardio-Net, to predict serum potassium levels in ESRD patients. Researchers utilized a vast dataset from Cedars Sinai Medical Center, comprising over 293,000 ECGs paired with serum potassium measurements. The model was fine-tuned using additional ECG data from ESRD patients, ensuring its applicability in real-world settings.
๐ Results
The Kardio-Net model demonstrated remarkable performance, achieving an AUC of 0.876 for detecting severe hyperkalemia using single-lead ECGs. In external validation, the model maintained a strong AUC of 0.807, indicating its reliability across different cohorts. The mean absolute error (MAE) was consistently low, with values around 0.575 mEq/L, underscoring the model’s precision in predicting potassium levels.
๐ Impact and Implications
The findings from this study could significantly impact the management of patients with ESRD. By integrating AI with wearable technology, healthcare providers can offer real-time monitoring of potassium levels, potentially preventing life-threatening complications. This approach not only enhances patient safety but also promotes a more personalized healthcare experience, paving the way for broader applications in chronic disease management.
๐ฎ Conclusion
This study highlights the transformative potential of AI in healthcare, particularly in the realm of remote monitoring. The successful validation of the Kardio-Net model for predicting serum potassium levels from ECG data signifies a promising advancement in patient care. As wearable technologies continue to evolve, we anticipate further innovations that will enhance the quality of life for patients with chronic conditions.
๐ฌ Your comments
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Serum Potassium Monitoring Usingย AI-Enabled Smartwatch Electrocardiograms.
Abstract
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%ย 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%ย 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%ย 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%ย CI: 0.778-0.835) with an MAE of 0.740 mEq/L. Using prospectively obtained smartwatch data, the AUC was 0.831 (95%ย 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.
Author: [‘Chiu IM’, ‘Wu PJ’, ‘Zhang H’, ‘Hughes JW’, ‘Rogers AJ’, ‘Jalilian L’, ‘Perez M’, ‘Lin CR’, ‘Lee CT’, ‘Zou J’, ‘Ouyang D’]
Journal: JACC Clin Electrophysiol
Citation: Chiu IM, et al. Serum Potassium Monitoring Usingย AI-Enabled Smartwatch Electrocardiograms. Serum Potassium Monitoring Usingย AI-Enabled Smartwatch Electrocardiograms. 2024; (unknown volume):(unknown pages). doi: 10.1016/j.jacep.2024.07.023