Department of Cardiovascular Medicine, The Second Affiliated Hospital of Nanchang University, Nanchang University School of Medicine, Nanchang, Jiangxi 330006, China.
Department of Anesthesiology, The Second Affiliated Hospital of Nanchang University, Nanchang University School of Medicine, Nanchang, Jiangxi 330006, China.
Chin Med J (Engl). 2021 Sep 2;134(19):2333-2339. doi: 10.1097/CM9.0000000000001650.
A deep learning model (DLM) that enables non-invasive hypokalemia screening from an electrocardiogram (ECG) may improve the detection of this life-threatening condition. This study aimed to develop and evaluate the performance of a DLM for the detection of hypokalemia from the ECGs of emergency patients.
We used a total of 9908 ECG data from emergency patients who were admitted at the Second Affiliated Hospital of Nanchang University, Jiangxi, China, from September 2017 to October 2020. The DLM was trained using 12 ECG leads (lead I, II, III, aVR, aVL, aVF, and V1-6) to detect patients with serum potassium concentrations <3.5 mmol/L and was validated using retrospective data from the Jiangling branch of the Second Affiliated Hospital of Nanchang University. The blood draw was completed within 10 min before and after the ECG examination, and there was no new or ongoing infusion during this period.
We used 6904 ECGs and 1726 ECGs as development and internal validation data sets, respectively. In addition, 1278 ECGs from the Jiangling branch of the Second Affiliated Hospital of Nanchang University were used as external validation data sets. Using 12 ECG leads (leads I, II, III, aVR, aVL, aVF, and V1-6), the area under the receiver operating characteristic curve (AUC) of the DLM was 0.80 (95% confidence interval [CI]: 0.77-0.82) for the internal validation data set. Using an optimal operating point yielded a sensitivity of 71.4% and a specificity of 77.1%. Using the same 12 ECG leads, the external validation data set resulted in an AUC for the DLM of 0.77 (95% CI: 0.75-0.79). Using an optimal operating point yielded a sensitivity of 70.0% and a specificity of 69.1%.
In this study, using 12 ECG leads, a DLM detected hypokalemia in emergency patients with an AUC of 0.77 to 0.80. Artificial intelligence could be used to analyze an ECG to quickly screen for hypokalemia.
一种能够从心电图（ECG）中进行非侵入性低钾血症筛查的深度学习模型（DLM）可能会提高对这种危及生命的疾病的检测能力。本研究旨在开发并评估一种用于从急诊患者心电图中检测低钾血症的 DLM 的性能。
我们使用了 2017 年 9 月至 2020 年 10 月期间来自中国南昌大学第二附属医院的 9908 份急诊患者的心电图数据。该 DLM 使用 12 个心电图导联（导联 I、II、III、aVR、aVL、aVF 和 V1-6）进行训练，以检测血清钾浓度<3.5mmol/L 的患者，并用南昌大学第二附属医院江陵分院的回顾性数据进行验证。采血是在心电图检查前 10 分钟内和检查后进行的，在此期间没有新的或正在进行的输液。
我们分别使用 6904 份心电图和 1726 份心电图作为开发和内部验证数据集。此外，我们还使用了南昌大学第二附属医院江陵分院的 1278 份心电图作为外部验证数据集。使用 12 个心电图导联（导联 I、II、III、aVR、aVL、aVF 和 V1-6），DLM 在内部验证数据集中的受试者工作特征曲线（ROC）下面积（AUC）为 0.80（95%置信区间[CI]：0.77-0.82）。使用最佳工作点，灵敏度为 71.4%，特异性为 77.1%。使用相同的 12 个心电图导联，外部验证数据集的 DLM 的 AUC 为 0.77（95%CI：0.75-0.79）。使用最佳工作点，灵敏度为 70.0%，特异性为 69.1%。
在这项研究中，使用 12 个心电图导联，DLM 对急诊患者的低钾血症检测的 AUC 为 0.77 至 0.80。人工智能可以用于分析心电图以快速筛查低钾血症。
