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

Permanent link
https://hdl.handle.net/10037/23479
DOI
https://doi.org/10.1016/j.ijcard.2020.12.065
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Date
2021-01-02
Type
Journal article
Tidsskriftartikkel
Peer reviewed

Author
Attia, Itzhak Zachi; Tseng, Andrew S.; Benavente, Ernest Diez; Medina-Inojosa, Jose R.; Clark, Taane; Malyutina, Sofia; Kapa, Suraj; Schirmer, Henrik; Kudryavtsev, Alexander V; Noseworthy, Peter A.; Carter, Rickey E.; Ryabikov, Andrey; Perel, Pablo; Friedman, Paul A.; Leon, David A.; Lopez-Jimenez, Francisco
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.

Publisher
Elsevier
Citation
Attia, Tseng, Benavente, Medina-Inojosa, Clark, Malyutina, Kapa, Schirmer, Kudryavtsev, Noseworthy, Carter, Ryabikov, Perel, Friedman, Leon, Lopez-Jimenez. External validation of a deep learning electrocardiogram algorithm to detect ventricular dysfunction. International Journal of Cardiology. 2021;329:130-135
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