Artificial Intelligence-Enabled Electrocardiography for Predicting Dyskalemia Using Home-Based Electrocardiography Devices

Background: Dyskalemia is the common electrolytes abnormality associated with an increased morbidity and mortality. We have developed a bloodless artificial Intelligence-enabled electrocardiogram (AI-ECG) system capable of predicting hyperkalemia and hypokalemia using Philips ECG devices and providing quantitative serum potassium (K+) detection and monitoring. This study aims to validate the application of a newly-developed home-based ECG device for dyskalemia detection. Methods: Home-based ECG device is a portable 12-lead ECG machine developed by QT Medical (PCA500). We conducted a preliminary study with 107 hospitalized patients for various causes including acute kidney injury and advanced CKD on hemodialysis at a single academic medical center, resulting in 1033 ECG recordings. Among them, eighty-eight ECGs had the corresponding serum K+ concentration measured within the previous three hours. The dataset included 12 patients with hyperkalemia (Lab-K+ ≥ 5.5 mmol/L) and 11 hypokalemia (Lab-K+ ≤ 3.5 mmol/L). Results: The AI-ECG system demonstrated an AUC of >0.95 for predicting hyperkalemia and an AUC of 0.809 for hypokalemia. The correlation coefficient between ECG-derived K+ levels (ECG-K+) and laboratory-measured K+ levels (Lab-K+) was 0.796, with a mean absolute error (MAE) of 0.456 Conclusion: Using this home-based ECG device, these results show that AI-ECG predict hyperkalemia better than hypokalemia and may be applied to future remote detection or monitoring of hyperkalemia in high-risk patients. A further validation with larger sample sizes is still required to confirm these findings.