ArticleJournal of personalized medicine2014
Predicting "heart age" using electrocardiography.
Article in Journal of personalized medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
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The trial behind it
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Who cites it
27 citing papers in PubMed, 52 citations in OpenAlex.
- Data-driven prioritization of mouse strains for improved preclinical modeling of rare and common disease.bioRxiv : the preprint server for biology · 2026Article
- A foundation transformer model with self-supervised learning for ECG-based assessment of cardiac and coronary function.NEJM AI · 2025Article
- Determinants of artificial intelligence electrocardiogram-derived age and its association with cardiovascular events and mortality: a systematic review and meta-analysis.NPJ digital medicine · 2025Article
- Biological heart and brain ageing in subjects with cardiovascular diseases.Frontiers in cardiovascular medicine · 2025Article
- Advanced electrocardiography heart age: a prognostic, explainable machine learning approach applicable to sinus and non-sinus rhythms.European heart journal. Digital health · 2025Article
- Enabling end-to-end secure federated learning in biomedical research on heterogeneous computing environments with APPFLx.Computational and structural biotechnology journal · 2025Article
- From bench to bedside: translational insights into aging research.Frontiers in aging · 2025Review
- Artificial intelligence estimated electrocardiographic age as a recurrence predictor after atrial fibrillation catheter ablation.NPJ digital medicine · 2024Article
- DNA methylation in cardiovascular disease and heart failure: novel prediction models?Clinical epigenetics · 2024Review
- Impact of Aging on Cardiovascular Diseases: From Chronological Observation to Biological Insights:JACC. Asia · 2024Review
- Accelerated DNA methylation age plays a role in the impact of cardiovascular risk factors on the human heart.Clinical epigenetics · 2023Article
- A biomarker framework for cardiac aging: the Aging Biomarker Consortium consensus statement.Life medicine · 2023Article
- Heart age gap estimated by explainable advanced electrocardiography is associated with cardiovascular risk factors and survival.European heart journal. Digital health · 2023Article
- Cardiovascular events and artificial intelligence-predicted age using 12-lead electrocardiograms.International journal of cardiology. Heart & vasculature · 2023Article
- Artificial intelligence-estimated biological heart age using a 12-lead electrocardiogram predicts mortality and cardiovascular outcomes.Frontiers in cardiovascular medicine · 2023Article
- Association of lifestyle with deep learning predicted electrocardiographic age.Frontiers in cardiovascular medicine · 2023Article
- Deep learning-derived cardiovascular age shares a genetic basis with other cardiac phenotypes.Scientific reports · 2022Article
- Combined Effects of Age and Comorbidities on Electrocardiographic Parameters in a Large Non-Selected Population.Journal of clinical medicine · 2022Article
- Heart age estimated using explainable advanced electrocardiography.Scientific reports · 2022Article
- Electrocardiogram-Based Heart Age Estimation by a Deep Learning Model Provides More Information on the Incidence of Cardiovascular Disorders.Frontiers in cardiovascular medicine · 2022Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Knowledge of a patient's cardiac age, or "heart age", could prove useful to both patients and physicians for better encouraging lifestyle changes potentially beneficial for cardiovascular health. This may be particularly true for patients who exhibit symptoms but who test negative for cardiac pathology. We developed a statistical model, using a Bayesian approach, that predicts an individual's heart age based on his/her electrocardiogram (ECG). The model is tailored to healthy individuals, with no known risk factors, who are at least 20 years old and for whom a resting ~5 min 12-lead ECG has been obtained. We evaluated the model using a database of ECGs from 776 such individuals. Secondarily, we also applied the model to other groups of individuals who had received 5-min ECGs, including 221 with risk factors for cardiac disease, 441 with overt cardiac disease diagnosed by clinical imaging tests, and a smaller group of highly endurance-trained athletes. Model-related heart age predictions in healthy non-athletes tended to center around body age, whereas about three-fourths of the subjects with risk factors and nearly all patients with proven heart diseases had higher predicted heart ages than true body ages. The model also predicted somewhat higher heart ages than body ages in a majority of highly endurance-trained athletes, potentially consistent with possible fibrotic or other anomalies recently noted in such individuals.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.