Itzhak Zachi Attia1, Andrew S Tseng1, Ernest Diez Benavente2, Jose R Medina-Inojosa1, Taane G Clark3, Sofia Malyutina4, Suraj Kapa1, Henrik Schirmer5, Alexander V Kudryavtsev6, Peter A Noseworthy1, Rickey E Carter7, Andrew Ryabikov4, Pablo Perel8, Paul A Friedman1, David A Leon9, Francisco Lopez-Jimenez10. 1. Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA. 2. Department of Infection Biology, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK; Department of Experimental Cardiology, University Medical Center Utrecht, Netherlands. 3. Department of Infection Biology, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK. 4. Novosibirsk State Medical University, Russian Ministry of Health, Novosibirsk 630091, Russia; Research Institute of Internal and Preventive Medicine, Branch of Institute of Cytology and Genetics, Siberian Branch of the Russion Academy of Sciences, Novosibirsk 630090, Russia. 5. Department of Community Medicine, UiT The Arctic University of Norway, Tromsø 9037, Norway; Institute for Clinical Medicine, University of Oslo, Campus Ahus, Lørenskog PB 1000 1478, Norway; Department of Cardiology, Akershus University Hospital, 1478 Nordbyhagen, Oslo, Norway. 6. Northern State Medical University, Arkhangelsk 163000, Russia; Department of Community Medicine, UiT The Arctic University of Norway, Tromsø 9037, Norway. 7. Department of Health Sciences Research, Mayo Clinic, Jacksonville, FL, USA. 8. Department of Non-communicable Diseases Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK. 9. Department of Community Medicine, UiT The Arctic University of Norway, Tromsø 9037, Norway; Department of Non-communicable Diseases Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK; International Laboratory for Population and Health, National Research University, Higher School of Economics, Moscow, Russia. 10. Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA. Electronic address: lopez@mayo.edu.
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.
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.
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