Predicting “Heart Age” Using Electrocardiography
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 pathol...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2014-03-01
|
| Series: | Journal of Personalized Medicine |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2075-4426/4/1/65 |
| _version_ | 1797758782201135104 |
|---|---|
| author | Robyn L. Ball Alan H. Feiveson Todd T. Schlegel Vito Starc Alan R. Dabney |
| author_facet | Robyn L. Ball Alan H. Feiveson Todd T. Schlegel Vito Starc Alan R. Dabney |
| author_sort | Robyn L. Ball |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T18:35:05Z |
| format | Article |
| id | doaj.art-8c8592a5030e4c4c87c0a132b9ca2a70 |
| institution | Directory Open Access Journal |
| issn | 2075-4426 |
| language | English |
| last_indexed | 2024-03-12T18:35:05Z |
| publishDate | 2014-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Personalized Medicine |
| spelling | doaj.art-8c8592a5030e4c4c87c0a132b9ca2a702023-08-02T08:07:21ZengMDPI AGJournal of Personalized Medicine2075-44262014-03-0141657810.3390/jpm4010065jpm4010065Predicting “Heart Age” Using ElectrocardiographyRobyn L. Ball0Alan H. Feiveson1Todd T. Schlegel2Vito Starc3Alan R. Dabney4The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USAHuman Adaptation and Countermeasures Division, NASA Johnson Space Center, Houston, TX 77058, USAHuman Adaptation and Countermeasures Division, NASA Johnson Space Center, Houston, TX 77058, USAInstitute of Physiology, School of Medicine, University of Ljubljana, 1000 Ljubljana, SloveniaDepartment of Statistics, Texas A&M University, 3143 TAMU, College Station, TX 77843, USAKnowledge 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.http://www.mdpi.com/2075-4426/4/1/65cardiologypersonalized medicineelectrocardiogramheart ageBayesian statistics |
| spellingShingle | Robyn L. Ball Alan H. Feiveson Todd T. Schlegel Vito Starc Alan R. Dabney Predicting “Heart Age” Using Electrocardiography Journal of Personalized Medicine cardiology personalized medicine electrocardiogram heart age Bayesian statistics |
| title | Predicting “Heart Age” Using Electrocardiography |
| title_full | Predicting “Heart Age” Using Electrocardiography |
| title_fullStr | Predicting “Heart Age” Using Electrocardiography |
| title_full_unstemmed | Predicting “Heart Age” Using Electrocardiography |
| title_short | Predicting “Heart Age” Using Electrocardiography |
| title_sort | predicting heart age using electrocardiography |
| topic | cardiology personalized medicine electrocardiogram heart age Bayesian statistics |
| url | http://www.mdpi.com/2075-4426/4/1/65 |
| work_keys_str_mv | AT robynlball predictingheartageusingelectrocardiography AT alanhfeiveson predictingheartageusingelectrocardiography AT toddtschlegel predictingheartageusingelectrocardiography AT vitostarc predictingheartageusingelectrocardiography AT alanrdabney predictingheartageusingelectrocardiography |