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Korean Circ J. 2019 Jul;49(7):629-639. English. Original Article. https://doi.org/10.4070/kcj.2018.0446
Kwon JM , Kim KH , Jeon KH , Kim HM , Kim MJ , Lim SM , Song PS , Park J , Choi RK , Oh BH .
Department of Emergency Medicine, Mediplex Sejong Hospital, Incheon, Korea.
Division of Cardiology, Department of Internal Medicine, Cardiovascular Center, Mediplex Sejong Hospital, Incheon, Korea. learnbyliving9@gmail.com
Abstract

Background and Objectives

Screening and early diagnosis for heart failure (HF) are critical. However, conventional screening diagnostic methods have limitations, and electrocardiography (ECG)-based HF identification may be helpful. This study aimed to develop and validate a deep-learning algorithm for ECG-based HF identification (DEHF).

Methods

The study involved 2 hospitals and 55,163 ECGs of 22,765 patients who performed echocardiography within 4 weeks were study subjects. ECGs were divided into derivation and validation data. Demographic and ECG features were used as predictive variables. The primary endpoint was detection of HF with reduced ejection fraction (HFrEF; ejection fraction [EF]≤40%), and the secondary endpoint was HF with mid-range to reduced EF (≤50%). We developed the DEHF using derivation data and the algorithm representing the risk of HF between 0 and 1. We confirmed accuracy and compared logistic regression (LR) and random forest (RF) analyses using validation data.

Results

The area under the receiver operating characteristic curves (AUROCs) of DEHF for identification of HFrEF were 0.843 (95% confidence interval, 0.840–0.845) and 0.889 (0.887–0.891) for internal and external validation, respectively, and these results significantly outperformed those of LR (0.800 [0.797–0.803], 0.847 [0.844–0.850]) and RF (0.807 [0.804–0.810], 0.853 [0.850–0.855]) analyses. The AUROCs of deep learning for identification of the secondary endpoint was 0.821 (0.819–0.823) and 0.850 (0.848–0.852) for internal and external validation, respectively, and these results significantly outperformed those of LR and RF.

Conclusions

The deep-learning algorithm accurately identified HF using ECG features and outperformed other machine-learning methods.

Copyright © 2019. Korean Association of Medical Journal Editors.