Development and Validation of a Deep-Learning Model to Detect CRP Level from the Electrocardiogram

Background: 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. Objectives: To evaluate the performance of a deep learning model in detection of CRP levels from the ECG in patients with sinus rhythm. Methods: 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). Results: Overweight, smoking, hypertension and diabetes were more common in the High CRP group (p < 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%. Conclusion: 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.