External validation of a deep learning electrocardiogram algorithm to detect ventricular dysfunction. (15th April 2021)

• Record Type: Journal Article
• Authors: 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.; Carter, Rickey E.; Ryabikov, Andrew; Perel, Pablo; Friedman, Paul A.; Leon, David A.; Lopez-Jimenez, Francisco
• Is Part Of: International journal of cardiology. Volume 329(2021)
• Page Start: 130
• Page End: 135
• Publication Date: 2021-04-15
• Subjects: AI-ECG artificial-intelligence electrocardiogram algorithm -- AUC area under the curve -- CNN convoluted neural network -- LVEF left ventricular ejection fraction -- LVSD left ventricular systolic dysfunction -- NPV negative predictive value -- PPV positive predictive value -- TTE transthoracic echocardiography; Artificial intelligence -- Machine learning -- Electrocardiogram -- Left ventricular systolic dysfunction; Cardiology -- Periodicals; Electronic journals; 616.12
• DOI: 10.1016/j.ijcard.2020.12.065
• Languages: English
• ISSNs: 0167-5273

Abstract

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.