한빛사논문
- 조회932
Michal Cohen-Shelly1, Zachi I. Attia1, Paul A. Friedman1, Saki Ito1, Benjamin A. Essayagh1, Wei-Yin Ko1, Dennis H. Murphree1, Hector I. Michelena1, Maurice Enriquez-Sarano1, Rickey E. Carter2, Patrick W. Johnson2, Peter A. Noseworthy1, Francisco Lopez-Jimenez1, and Jae K. Oh1,*
1Department of Cardiovascular Medicine, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA; and 2Health Sciences Research, Mayo Clinic, 4500 San Pablo Rd S, Jacksonville, FL 32224, USA
*Corresponding author
Abstract
Aims
Early detection of aortic stenosis (AS) is becoming increasingly important with a better outcome after aortic valve replacement in asymptomatic severe AS patients and a poor outcome in moderate AS. We aimed to develop artificial intelligence-enabled electrocardiogram (AI-ECG) using a convolutional neural network to identify patients with moderate to severe AS.
Methods and results
Between 1989 and 2019, 258 607 adults [mean age 63 ± 16.3 years; women 122 790 (48%)] with an echocardiography and an ECG performed within 180 days were identified from the Mayo Clinic database. Moderate to severe AS by echocardiography was present in 9723 (3.7%) patients. Artificial intelligence training was performed in 129 788 (50%), validation in 25 893 (10%), and testing in 102 926 (40%) randomly selected subjects. In the test group, the AI-ECG labelled 3833 (3.7%) patients as positive with the area under the curve (AUC) of 0.85. The sensitivity, specificity, and accuracy were 78%, 74%, and 74%, respectively. The sensitivity increased and the specificity decreased as age increased. Women had lower sensitivity but higher specificity compared with men at any age groups. The model performance increased when age and sex were added to the model (AUC 0.87), which further increased to 0.90 in patients without hypertension. Patients with false-positive AI-ECGs had twice the risk for developing moderate or severe AS in 15 years compared with true negative AI-ECGs (hazard ratio 2.18, 95% confidence interval 1.90–2.50).
Conclusion
An AI-ECG can identify patients with moderate or severe AS and may serve as a powerful screening tool for AS in the community.
논문정보
- Research article
- 2021년 03월 (BRIC 등록일 2021-03-26)
- 국외 연구진
댓글 작성 로그인
팝업 닫기관련 링크
연구자 키워드
관련분야 연구자보기
소속기관 논문보기
관련분야 논문보기
해당논문 저자보기
