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

Ribeiro, A. L. P. et al. (2019) Tele-electrocardiography and bigdata: the CODE (Clinical Outcomes in Digital Electrocardiography) study. Journal of Electrocardiology, 57(Suppl), S75-S78. (doi: 10.1016/j.jelectrocard.2019.09.008) (PMID:31526573)

Abstract

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.

• Item Type: Articles
• Additional Information: This research was partly supported by the Brazilian Agencies CNPq, CAPES, and FAPEMIG, is part of by projects IATS (Instituto de Avaliação de Tecnologias em Saúde) and ATMOSPHERE (Adaptive, Trustworthy, Manageable, Orchestrated, Secure, Privacy-assuring Hybrid, Ecosystem for Resilient Cloud Computing). We also thank NVIDIA for awarding our project with a Titan V GPU. ALR is recipient of an unrestricted research scholarships from CNPq; AHR receives a Split-PhD scholarship from CNPq; MHR receives a Google Latin America Research Award scholarship; MPP receives a PhD scholarship from CAPES; and DMO and EML receive scholarships from CNPq.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Macfarlane, Professor Peter
• Authors: Ribeiro, A. L. P., Paixão, G. M.M., Gomes, P. R., Ribeiro, M. H., Ribeiro, A. H., Canazart, J. A., Oliveira, D. M., Ferreira, M. P., Lima, E. M., de Moraes, J. L., Castro, N., Ribeiro, L. B., and Macfarlane, P. W.
• College/School: College of Medical Veterinary and Life Sciences > School of Health & Wellbeing > Robertson Centre
• Journal Name: Journal of Electrocardiology
• Publisher: Elsevier
• ISSN: 0022-0736
• ISSN (Online): 1532-8430
• Published Online: 07 September 2019
• Copyright Holders: Copyright © 2019 Elsevier Inc.
• First Published: First published in Journal of Electrocardiology 57 Supplement: S75-S78
• Publisher Policy: Reproduced in accordance with the publisher copyright policy