Knowledge of a patient’s cardiac age, or “heart age,” could prove useful to both patients and physicians for encouraging lifestyle changes that are potentially beneficial for cardiovascular health. This may be particularly true for patients who exhibit symptoms, but who test negative for cardiac pathology.
This innovation covers 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-minute, 12-lead ECG has been obtained. The model has been evaluated using a database of 1,438 subjects of which 776 were classified as healthy, 221 had risk factors for cardiac disease, and 441 were diagnosed with cardiac disease using clinical imaging tests. 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.
This work was done by Todd T. Schlegel and Alan H. Feiveson of Johnson Space Center, and Robyn L. Ball and Alan R. Dabney of Texas A&M University. MSC-25780-1