Tele-electrocardiography and bigdata: The CODE (Clinical Outcomes in Digital Electrocardiography) study

• Published in: Journal of electrocardiology Vol. 57; pp. S75 - S78
• Main Authors: Ribeiro, Antonio Luiz P., Paixão, Gabriela M.M., Gomes, Paulo R., Ribeiro, Manoel Horta, Ribeiro, Antônio H., Canazart, Jéssica A., Oliveira, Derick M., Ferreira, Milton P., Lima, Emilly M., Moraes, Jermana Lopes de, Castro, Nathalia, Ribeiro, Leonardo B., Macfarlane, Peter W.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.11.2019; Elsevier Science Ltd
• Subjects: Artificial intelligence; Big Data; Cardiovascular; Cardiovascular disease; Clinical outcomes; Electrocardiography; Epidemiology; Telehealth; Telemedicine; Electrocardiography; Telehealth; Artificial intelligence; Big-data
• ISSN: 0022-0736; 1532-8430; 1532-8430
• DOI: 10.1016/j.jelectrocard.2019.09.008

Digital electrocardiographs are now widely available and a large number of digital electrocardiograms (ECGs) have been recorded and stored. The present study describes the development and clinical applications of a large database of such digital ECGs, namely the CODE (Clinical Outcomes in Digital Electrocardiology) study. ECGs obtained by the Telehealth Network of Minas Gerais, Brazil, from 2010 to 17, were organized in a structured database. A hierarchical free-text machine learning algorithm recognized specific ECG diagnoses from cardiologist reports. The Glasgow ECG Analysis Program provided Minnesota Codes and automatic diagnostic statements. The presence of a specific ECG abnormality was considered when both automatic and medical diagnosis were concordant; cases of discordance were decided using heuristisc rules and manual review. The ECG database was linked to the national mortality information system using probabilistic linkage methods. From 2,470,424 ECGs, 1,773,689 patients were identified. After excluding the ECGs with technical problems and patients <16 years-old, 1,558,415 patients were studied. High performance measures were obtained using an end-to-end deep neural network trained to detect 6 types of ECG abnormalities, with F1 scores >80% and specificity >99% in an independent test dataset. We also evaluated the risk of mortality associated with the presence of atrial fibrillation (AF), which showed that AF was a strong predictor of cardiovascular mortality and mortality for all causes, with increased risk in women. In conclusion, a large database that comprises all ECGs performed by a large telehealth network can be useful for further developments in the field of digital electrocardiography, clinical cardiology and cardiovascular epidemiology.