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Published on: December 11, 2019
Predicting "heart age" using electrocardiography
Robyn L Ball1, Alan H Feiveson2, Todd T Schlegel3
1The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA. robyn.ball@jax.org.
Insights
A new statistical model predicts heart age from electrocardiograms (ECGs). This tool helps identify cardiovascular risks, even in seemingly healthy individuals, guiding lifestyle changes for better heart health.
Area of Science:
- Cardiology
- Biostatistics
- Preventive Medicine
Background:
- Assessing cardiovascular health is crucial, especially for individuals with symptoms but no diagnosed cardiac pathology.
- Patient understanding of cardiovascular risk can motivate lifestyle modifications.
Purpose of the Study:
- To develop and evaluate a Bayesian statistical model for predicting an individual's "heart age" using electrocardiogram (ECG) data.
- To assess the model's utility across diverse populations, including healthy individuals, those with risk factors, cardiac disease patients, and athletes.
Main Methods:
- A Bayesian statistical model was created to predict heart age based on resting 12-lead ECGs.
- The model was trained and validated on a dataset of 776 healthy individuals aged 20+ with no known risk factors.
- The model was secondarily applied to groups with cardiac risk factors, diagnosed cardiac disease, and highly endurance-trained athletes.
Main Results:
- In healthy non-athletes, predicted heart age generally aligned with chronological age.
- Approximately 75% of subjects with cardiac risk factors and nearly all patients with diagnosed heart disease showed higher predicted heart ages than their body ages.
- A majority of highly endurance-trained athletes also exhibited higher predicted heart ages, potentially indicating subclinical cardiac changes.
Conclusions:
- The developed heart age prediction model, based on ECG, shows promise in identifying individuals at higher cardiovascular risk.
- The model's ability to differentiate heart age from body age may aid in targeted interventions and lifestyle change encouragement.
- Further research may explore the model's clinical implications, particularly in athletes and those with subclinical cardiac conditions.
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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