Jiang Junrong, Deng Hai, Liao Hongtao, Fang Xianhong, Zhan Xianzhang, Wu Shulin, Xue Yumei
Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
Guangdong Provincial Key Laboratory of Clinical Pharmacology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
Front Physiol. 2022 May 30;13:864747. doi: 10.3389/fphys.2022.864747. eCollection 2022.
C-reactive protein (CRP), as a non-specific inflammatory marker, is a predictor of the occurrence and prognosis of various arrhythmias. It is still unknown whether electrocardiographic features are altered in patients with inflammation. To evaluate the performance of a deep learning model in detection of CRP levels from the ECG in patients with sinus rhythm. The study population came from an epidemiological survey of heart disease in Guangzhou. 12,315 ECGs of 11,480 patients with sinus rhythm were included. CRP > 5mg/L was defined as high CRP level. A convolutional neural network was trained and validated to detect CRP levels from 12 leads ECGs. The performance of the model was evaluated by calculating the area under the curve (AUC), accuracy, sensitivity, specificity, and balanced F Score (F1 score). Overweight, smoking, hypertension and diabetes were more common in the High CRP group ( < 0.05). Although the ECG features were within the normal ranges in both groups, the high CRP group had faster heart rate, longer QTc interval and narrower QRS width. After training and validating the deep learning model, the AUC of the validation set was 0.86 (95% CI: 0.85-0.88) with sensitivity, specificity of 89.7 and 69.6%, while the AUC of the testing set was 0.85 (95% CI: 0.84-0.87) with sensitivity, specificity of 90.7 and 67.6%. An AI-enabled ECG algorithm was developed to detect CRP levels in patients with sinus rhythm. This study proved the existence of inflammation-related changes in cardiac electrophysiological signals and provided a noninvasive approach to screen patients with inflammatory status by detecting CRP levels.
C反应蛋白（CRP）作为一种非特异性炎症标志物，是各种心律失常发生和预后的预测指标。炎症患者的心电图特征是否改变仍不清楚。为了评估深度学习模型从窦性心律患者的心电图中检测CRP水平的性能。研究人群来自广州的一项心脏病流行病学调查。纳入了11480例窦性心律患者的12315份心电图。CRP>5mg/L被定义为高CRP水平。训练并验证了一个卷积神经网络，以从12导联心电图中检测CRP水平。通过计算曲线下面积（AUC）、准确性、敏感性、特异性和平衡F分数（F1分数）来评估模型的性能。高CRP组超重、吸烟、高血压和糖尿病更为常见（<0.05）。虽然两组的心电图特征均在正常范围内，但高CRP组心率更快，QTc间期更长，QRS宽度更窄。在对深度学习模型进行训练和验证后，验证集的AUC为0.86（95%CI：0.85-0.88），敏感性和特异性分别为89.7%和69.6%，而测试集的AUC为0.85（95%CI：0.84-0.87），敏感性和特异性分别为90.7%和67.6%。开发了一种基于人工智能的心电图算法来检测窦性心律患者的CRP水平。本研究证明了心脏电生理信号中存在炎症相关变化，并提供了一种通过检测CRP水平来筛查炎症状态患者的非侵入性方法。
