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 dev

SUBMITTER: Kwon JM 

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

REPOSITORIES: biostudies-literature