Development and validation of a deep learning model to screen hypokalemia from electrocardiogram in emergency patients

作     者：Chen-Xi Wang Yi-Chu Zhang Qi-Lin Kong Zu-Xiang Wu Ping-Ping Yang Cai-Hua Zhu Shou-Lin Chen Tao Wu Qing-Hua Wu Qi Chen 

作者机构：Department of Cardiovascular MedicineThe Second Affiliated Hospital of Nanchang UniversityNanchang University School of MedicineNanchangJiangxi 330006China Department of AnesthesiologyThe Second Affiliated Hospital of Nanchang UniversityNanchang University School of MedicineNanchangJiangxi 330006China 

出 版 物：《Chinese Medical Journal》 (中华医学杂志（英文版）)

年 卷 期：2021年第134卷第19期

页      面：2333-2339页

核心收录：

学科分类：1002[医学-临床医学] 100201[医学-内科学(含：心血管病、血液病、呼吸系病、消化系病、内分泌与代谢病、肾病、风湿病、传染病)] 10[医学] 

基　　金：supported by the National Natural Science Foundation of China(No.81360025) 

主　　题：Deep learning Hypokalemia Electrocardiogram Artificial intelligence 

摘      要：Background:A deep learning model(DLM)that enables non-invasive hypokalemia screening from an electrocardiogram(ECG)may improve the detection of this life-threatening *** study aimed to develop and evaluate the performance of a DLM for the detection of hypokalemia from the ECGs of emergency ***: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 *** DLM was trained using 12 ECG leads(lead Ⅰ,Ⅱ,Ⅲ,aVR,aVL,aVF,and V1–6)to detect patients with serum potassium concentrations3.5 mmol/L and was validated using retrospective data from the Jiangling branch of the Second Affiliated Hospital of Nanchang *** blood draw was completed within 10 min before and after the ECG examination,and there was no new or ongoing infusion during this ***:We used 6904 ECGs and 1726 ECGs as development and internal validation data sets,*** addition,1278 ECGs from the Jiangling branch of the Second Affiliated Hospital of Nanchang University were used as external validation data *** 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 *** 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%.Conclusions:In this study,using 12 ECG leads,a DLM detected hypokalemia in emergency patients with an AUC of 0.77 to *** intelligence could be used to analyze an ECG to quickly screen for hypokalemia.