Attia, Itzhak Zachi; Tseng, Andrew S; Benavente, Ernest Diez; Medina-Inojosa, Jose R; Clark, Taane G; Malyutina, Sofia; Kapa, Suraj; Schirmer, Henrik; Kudryavtsev, Alexander V; Noseworthy, Peter A; +6 more... Carter, Rickey E; Ryabikov, Andrew; Perel, Pablo; Friedman, Paul A; Leon, David A; Lopez-Jimenez, Francisco; (2021) External validation of a deep learning electrocardiogram algorithm to detect ventricular dysfunction. International journal of cardiology, 329. pp. 130-135. ISSN 0167-5273 DOI: https://doi.org/10.1016/j.ijcard.2020.12.065
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Abstract
OBJECTIVE: To validate a novel artificial-intelligence electrocardiogram algorithm (AI-ECG) to detect left ventricular systolic dysfunction (LVSD) in an external population. BACKGROUND: LVSD, even when asymptomatic, confers increased morbidity and mortality. We recently derived AI-ECG to detect LVSD using ECGs based on a large sample of patients treated at the Mayo Clinic. METHODS: We performed an external validation study with subjects from the Know Your Heart Study, a cross-sectional study of adults aged 35-69 years residing in two cities in Russia, who had undergone both ECG and transthoracic echocardiography. LVSD was defined as left ventricular ejection fraction ≤ 35%. We assessed the performance of the AI-ECG to identify LVSD in this distinct patient population. RESULTS: Among 4277 subjects in this external population-based validation study, 0.6% had LVSD (compared to 7.8% of the original clinical derivation study). The overall performance of the AI-ECG to detect LVSD was robust with an area under the receiver operating curve of 0.82. When using the LVSD probability cut-off of 0.256 from the original derivation study, the sensitivity, specificity, and accuracy in this population were 26.9%, 97.4%, 97.0%, respectively. Other probability cut-offs were analysed for different sensitivity values. CONCLUSIONS: The AI-ECG detected LVSD with robust test performance in a population that was very different from that used to develop the algorithm. Population-specific cut-offs may be necessary for clinical implementation. Differences in population characteristics, ECG and echocardiographic data quality may affect test performance.
Item Type | Article |
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Faculty and Department |
Faculty of Infectious and Tropical Diseases > Department of Infection Biology Faculty of Epidemiology and Population Health > Dept of Non-Communicable Disease Epidemiology |
Research Centre | Centre for the Mathematical Modelling of Infectious Diseases |
PubMed ID | 33400971 |
Elements ID | 155265 |
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