AUTHOR=Chang Chiao-Hsiang , Lin Chin-Sheng , Luo Yu-Sheng , Lee Yung-Tsai , Lin Chin 

TITLE=Electrocardiogram-Based Heart Age Estimation by a Deep Learning Model Provides More Information on the Incidence of Cardiovascular Disorders

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2022.754909

DOI=10.3389/fcvm.2022.754909

ISSN=2297-055X

ABSTRACT=Objective 
The biological age progression of the heart varies from person to person. We developed a deep learning model (DLM) to predict biological age via electrocardiography (ECG) to explore its contribution on future cardiovascular diseases (CVDs).
Methods
There were 71,741 cases ranging from 20 to 80 years old recruited from the health examination center. The development set used 32,707 cases to train the DLM for estimating ECG-age, and 8,295 cases were used as the tuning set. The validation set included 30,469 ECGs to follow the outcomes, including all-cause mortality, cardiovascular-cause mortality, heart failure (HF), diabetes mellitus (DM), chronic kidney disease (CKD), acute myocardial infarction (AMI), stroke (STK), coronary artery disease (CAD), atrial fibrillation (AF), and hypertension (HTN). Two independent external validation sets (SaMi-Trop and CODE15) were also used to validate our DLM.
Results
The mean absolute errors of chronologic age and ECG-age was 6.899 years (r=0.822). The higher difference between ECG-age and chronological age was related with more comorbidities and abnormal ECG rhythm. The cases with the difference more than 7 years had higher risk on all-cause mortality [hazard ratio (HR): 1.61, 95% confidence interval (CI): 1.23-2.12], CV-cause mortality (HR: 3.49, 95% CI: 1.74-7.01), HF (HR: 2.79, 95% CI: 2.25-3.45), DM (HR: 1.70, 95% CI: 1.53-1.89), CKD (HR: 1.67, 95% CI: 1.41-1.97), AMI (HR: 1.76, 95% CI: 1.20-2.57), STK (HR: 1.65, 95% CI: 1.42-1.92), CAD (HR: 1.24, 95% CI: 1.12-1.37), AF (HR: 2.38, 95% CI: 1.86-3.04), and HTN (HR: 1.67, 95% CI: 1.51-1.85). The external validation sets also validated that ECG-age older >7 years compare to chronologic age had 3.16-fold risk (95% CI: 1.72-5.78) and 1.59-fold risk (95% CI: 1.45-1.74) on all-cause mortality in SaMi-Trop and CODE15 cohorts. The ECG-age significantly contributed additional information on heart failure, stroke, coronary artery disease, and atrial fibrillation predictions after considering all known risk factors. 
Conclusions
The ECG-age estimated via DLM provides additional information for CVDs incidence. Older ECG-age is correlated with not only on mortality but also on other CVDs comparing with chronological age.