J Korean Med Sci.  2023 Nov;38(45):e322. 10.3346/jkms.2023.38.e322.

Hyperkalemia Detection in Emergency Departments Using Initial ECGs: A Smartphone AI ECG Analyzer vs. Board-Certified Physicians

• Kim D
• Jeong J
• Kim J
• Cho Y
• Park I
• Lee SM
• Oh YT
• Baek S
• Kang D
• Lee E
• Jeong B

• KMID: 2547979
• DOI: http://doi.org/10.3346/jkms.2023.38.e322

Abstract

Background
Hyperkalemia is a potentially fatal condition that mandates rapid identification in emergency departments (EDs). Although a 12-lead electrocardiogram (ECG) can indicate hyperkalemia, subtle changes in the ECG often pose detection challenges. An artificial intelligence application that accurately assesses hyperkalemia risk from ECGs could revolutionize patient screening and treatment. We aimed to evaluate the efficacy and reliability of a smartphone application, which utilizes camera-captured ECG images, in quantifying hyperkalemia risk compared to human experts.
Methods
We performed a retrospective analysis of ED hyperkalemic patients (serum potassium ≥ 6 mmol/L) and their age- and sex-matched non-hyperkalemic controls. The application was tested by five users and its performance was compared to five board-certified emergency physicians (EPs).
Results
Our study included 125 patients. The area under the curve (AUC)-receiver operating characteristic of the application’s output was nearly identical among the users, ranging from 0.898 to 0.904 (median: 0.902), indicating almost perfect interrater agreement (Fleiss’ kappa 0.948). The application demonstrated high sensitivity (0.797), specificity (0.934), negative predictive value (NPV) (0.815), and positive predictive value (PPV) (0.927). In contrast, the EPs showed moderate interrater agreement (Fleiss’ kappa 0.551), and their consensus score had a significantly lower AUC of 0.662. The physicians’ consensus demonstrated a sensitivity of 0.203, specificity of 0.934, NPV of 0.527, and PPV of 0.765. Notably, this performance difference remained significant regardless of patients’ sex and age (P < 0.001 for both).
Conclusion
Our findings suggest that a smartphone application can accurately and reliably quantify hyperkalemia risk using initial ECGs in the ED.

Keyword

Hyperkalemia; Emergency Departments; Artificial Intelligence; Smartphone Application; Electrocardiography

Figure

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  Fig. 1 The operating screen of the evaluated artificial intelligence software. (A) ECG image input. (B) ECG image analysis result. It reports rhythm classification and the risk scores of 10 cardiac function abnormalities and emergencies including hyperkalemia.ECG = electrocardiogram.

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  Fig. 2 Scatter plots of artificial intelligence scores by the five application users.PH1 = physician #1, PH2 = physician #2, RN1= registered nurse #1, RN2 = registered nurse #2, PM = paramedic.

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