In International journal of cardiology ; h5-index 68.0
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.
Attia Itzhak Zachi, Tseng Andrew S, Benavente Ernest Diez, Inojosa Jose Medina, Clark Taane G, Malyutina Sofia, Kapa Suraj, Schirmer Henrik, Kudryavtsev Alexander V, Noseworthy Peter A, Carter Rickey E, Ryabikov Audrey, Perel Pablo, Friedman Paul A, Leon David A, Lopez-Jimenez Francisco
Artificial intelligence, Electrocardiogram, Left ventricular systolic dysfunction, Machine learning