Dataset Information

Development and Validation of Deep-Learning Algorithm for Electrocardiography-Based Heart Failure Identification.

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.

SUBMITTER: Kwon JM

PROVIDER: S-EPMC6597456 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development and Validation of Deep-Learning Algorithm for Electrocardiography-Based Heart Failure Identification.

Kwon Joon Myoung JM Kim Kyung Hee KH Jeon Ki Hyun KH Kim Hyue Mee HM Kim Min Jeong MJ Lim Sung Min SM Song Pil Sang PS Park Jinsik J Choi Rak Kyeong RK Oh Byung Hee BH

Korean circulation journal 20190321 7

<h4>Background and objectives</h4>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).<h4>Methods</h4>The study involved 2 hospitals and 55,163 ECGs of 22,765 patients who performed echocardiography within 4 weeks were study subjects. ECGs ...[more]

PMID: 31074221

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Dataset Information

Development and Validation of Deep-Learning Algorithm for Electrocardiography-Based Heart Failure Identification.

Background and objectives

Methods

Results

Conclusions

Publications

Development and Validation of Deep-Learning Algorithm for Electrocardiography-Based Heart Failure Identification.

Similar Datasets

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