Deep learned representations of the resting 12-lead electrocardiogram to predict at peak exercise.
Eur J Prev Cardiol
; 31(2): 252-262, 2024 Jan 25.
Article
in En
| MEDLINE
| ID: mdl-37798122
ABSTRACT
AIMS:
To leverage deep learning on the resting 12-lead electrocardiogram (ECG) to estimate peak oxygen consumption (VËO2peak) without cardiopulmonary exercise testing (CPET). METHODS ANDRESULTS:
V Ë O 2 peak estimation models were developed in 1891 individuals undergoing CPET at Massachusetts General Hospital (age 45 ± 19 years, 38% female) and validated in a separate test set (MGH Test, n = 448) and external sample (BWH Test, n = 1076). Three penalized linear models were compared (i) age, sex, and body mass index ('Basic'), (ii) Basic plus standard ECG measurements ('Basic + ECG Parameters'), and (iii) basic plus 320 deep learning-derived ECG variables instead of ECG measurements ('Deep ECG-VËO2'). Associations between estimated VËO2peak and incident disease were assessed using proportional hazards models within 84 718 primary care patients without CPET. Inference ECGs preceded CPET by 7 days (median, interquartile range 27-0 days). Among models, Deep ECG-VËO2 was most accurate in MGH Test [r = 0.845, 95% confidence interval (CI) 0.817-0.870; mean absolute error (MAE) 5.84, 95% CI 5.39-6.29] and BWH Test (r = 0.552, 95% CI 0.509-0.592, MAE 6.49, 95% CI 6.21-6.67). Deep ECG-VËO2 also outperformed the Wasserman, Jones, and FRIEND reference equations (P < 0.01 for comparisons of correlation). Performance was higher in BWH Test when individuals with heart failure (HF) were excluded (r = 0.628, 95% CI 0.567-0.682; MAE 5.97, 95% CI 5.57-6.37). Deep ECG-VËO2 estimated VËO2peak <14 mL/kg/min was associated with increased risks of incident atrial fibrillation [hazard ratio 1.36 (95% CI 1.21-1.54)], myocardial infarction [1.21 (1.02-1.45)], HF [1.67 (1.49-1.88)], and death [1.84 (1.68-2.03)].CONCLUSION:
Deep learning-enabled analysis of the resting 12-lead ECG can estimate exercise capacity (VËO2peak) at scale to enable efficient cardiovascular risk stratification.
Researchers here present data describing a method of estimating exercise capacity from the resting electrocardiogram. Electrocardiogram estimation of exercise capacity was accurate and was found to predict the onset of the wide range of cardiovascular diseases including heart attacks, heart failure, arrhythmia, and death.This approach offers the ability to estimate exercise capacity without dedicated exercise testing and may enable efficient risk stratification of cardiac patients at scale.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Electrocardiography
/
Heart Failure
Type of study:
Prognostic_studies
/
Risk_factors_studies
Limits:
Adult
/
Female
/
Humans
/
Male
/
Middle aged
Language:
En
Journal:
Eur J Prev Cardiol
Year:
2024
Document type:
Article
Affiliation country:
Estados Unidos