Department of Emergency Medicine, Seoul National University Bundang Hospital, Seongnam, Korea.
Division of Data Science, Department of Emergency Medicine, Seoul National University College of Medicine, Seoul, Korea.
J Korean Med Sci. 2023 Nov 20;38(45):e322. doi: 10.3346/jkms.2023.38.e322.
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.
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).
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 ( < 0.001 for both).
Our findings suggest that a smartphone application can accurately and reliably quantify hyperkalemia risk using initial ECGs in the ED.
高钾血症是一种潜在的致命病症，在急诊科（ED）必须迅速识别。虽然 12 导联心电图（ECG）可以提示高钾血症，但 ECG 中的细微变化常常给检测带来挑战。一种能够准确评估心电图高钾血症风险的人工智能应用程序可能会彻底改变患者的筛查和治疗方式。我们旨在评估一种智能手机应用程序的功效和可靠性，该应用程序利用相机拍摄的 ECG 图像，与人类专家相比，评估高钾血症风险。
我们对 ED 高钾血症患者（血清钾≥6mmol/L）及其年龄和性别匹配的非高钾血症对照患者进行了回顾性分析。该应用程序由五名用户进行了测试，并将其性能与五名经过委员会认证的急诊医师（EP）进行了比较。
我们的研究纳入了 125 名患者。应用程序输出的曲线下面积（AUC）-接受者操作特征在用户之间几乎相同，范围为 0.898 至 0.904（中位数：0.902），表明几乎完美的评分者间一致性（Fleiss'kappa 0.948）。该应用程序表现出较高的灵敏度（0.797）、特异性（0.934）、阴性预测值（NPV）（0.815）和阳性预测值（PPV）（0.927）。相比之下，EP 表现出中度的评分者间一致性（Fleiss'kappa 0.551），他们的共识评分 AUC 显著较低，为 0.662。医师的共识表现出灵敏度为 0.203、特异性为 0.934、NPV 为 0.527 和 PPV 为 0.765。值得注意的是，无论患者的性别和年龄如何（两者均 < 0.001），这种性能差异仍然显著。
我们的研究结果表明，智能手机应用程序可以使用 ED 中的初始 ECG 准确可靠地量化高钾血症风险。
