Ecg-Derived Age and Survival: Validating the Concept of Physiologic Age Detected by Ecg Using Artificial Intelligence

Ladejobi, Adetola; Medina‐Inojosa, José R.; Shelly, Michal; Attia, Zachi I.; Gersh, Bernard J.; Noseworthy, Peter A.; Friedman, Paul A.; Kapa, Suraj; López-Jiménez, Francisco

doi:10.1016/s0735-1097(20)34096-1

Cited by 3 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, our models could be particularly suitable as benchmark for testing new pharmaceutical drugs or other interventions which may have impact on cardiac health in the near future. Differences between physiologic ECG age and chronological age have been shown to predict all-cause and cardiovascular mortality and reflect physiologic age, cardiovascular health and long term outcomes [27]. It has also been found that a difference in predicted (cardiac) age and chronological age (higher cardiac age) was greater in patients with peripheral microvascular endothelial function [28].…”

Section: Pharmaceutical Drug Testing and Potential Implicationsmentioning

confidence: 99%

Cardiac age detected by machine learning applied to the surface ECG of healthy subjects: Creation of a benchmark

Wall

Hassing

Doll

et al. 2022

Journal of Electrocardiology

View full text Add to dashboard Cite

Section: Pharmaceutical Drug Testing and Potential Implicationsmentioning

confidence: 99%

Cardiac age detected by machine learning applied to the surface ECG of healthy subjects: Creation of a benchmark

Wall

Hassing

Doll

et al. 2022

Journal of Electrocardiology

View full text Add to dashboard Cite

Artificial Intelligence–Derived Electrocardiogram Assessment of Cardiac Age and Molecular Markers of Senescence in Heart Failure

Meenakshi-Siddharthan¹,

Livia²,

Peterson³

et al. 2023

Mayo Clinic Proceedings

View full text Add to dashboard Cite

Capturing population differences in rates of vascular aging using a deep learning electrocardiogram algorithm: a cross-sectional study

Benavente

Lopez-Jimenez²,

Iakunchykova³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Background: Cardiovascular event rates increase with age in all populations. This is thought to be the result of multiple underlying molecular and cellular processes that lead to cumulative vascular damage. Apart from arterial stiffness based on pulse wave velocity there are few other non-invasive measures of this process of vascular aging. We have developed a potential biomarker of vascular aging using deep-learning to predict age from a standard 12-lead electrocardiogram (ECG). The difference between ECG predicted and chronological age (δ-age) can be interpreted as a measure of vascular aging. <br />Methods: We use data collected in two cross-sectional studies of adults aged 40-69 years in Norway and Russia to test the hypothesis that mean levels of δ-age, derived from a deep-learning model trained on a US population, correspond to the known large differences in cardiovascular mortality between the two countries. <br />Findings: Substantial differences were found in mean δ-age between populations: Russia-USA (+5·2 years; 0·7, 10 IQR) and Norway-USA (-2·6 years; -7, 2 IQR). These differences were only marginally explained when accounting for differences in established cardiovascular disease risk factors. <br />Interpretation: δ-age may be an important biomarker of fundamental differences in cardiovascular disease risk between populations as well as between individuals.

show abstract

Ecg-Derived Age and Survival: Validating the Concept of Physiologic Age Detected by Ecg Using Artificial Intelligence

Cited by 3 publications

References 0 publications

Cardiac age detected by machine learning applied to the surface ECG of healthy subjects: Creation of a benchmark

Cardiac age detected by machine learning applied to the surface ECG of healthy subjects: Creation of a benchmark

Artificial Intelligence–Derived Electrocardiogram Assessment of Cardiac Age and Molecular Markers of Senescence in Heart Failure

Capturing population differences in rates of vascular aging using a deep learning electrocardiogram algorithm: a cross-sectional study

Contact Info

Product

Resources

About