External validation of a deep learning electrocardiogram algorithm to detect ventricular dysfunction.

Attia, IZ; Tseng, AS; Benavente, ED; Medina-Inojosa, JR; Clark, TGORCID logo; Malyutina, S; Kapa, S; Schirmer, H; Kudryavtsev, AV; Noseworthy, PA; +6 more...Carter, RE; Ryabikov, A; Perel, PORCID logo; Friedman, PA; Leon, DAORCID logo; Lopez-Jimenez, F and (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: 10.1016/j.ijcard.2020.12.065
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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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