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. 2021 Apr 15:329:130-135.
doi: 10.1016/j.ijcard.2020.12.065. Epub 2021 Jan 2.

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

Itzhak Zachi Attia  1 , Andrew S Tseng  1 , Ernest Diez Benavente  2 , Jose R Medina-Inojosa  1 , Taane G Clark  3 , Sofia Malyutina  4 , Suraj Kapa  1 , Henrik Schirmer  5 , Alexander V Kudryavtsev  6 , Peter A Noseworthy  1 , Rickey E Carter  7 , Andrew Ryabikov  4 , Pablo Perel  8 , Paul A Friedman  1 , David A Leon  9 , Francisco Lopez-Jimenez  10
Affiliations

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

Itzhak Zachi Attia et al. Int J Cardiol. .

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.

Keywords: Artificial intelligence; Electrocardiogram; Left ventricular systolic dysfunction; Machine learning.

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Conflict of interest statement

Declaration of Competing Interest Mayo Clinic has licensed the underlying technology to EKO, a maker of digital stethoscopes with embedded ECG electrodes. Mayo Clinic may receive financial benefit from the use of this technology, but at no point will Mayo Clinic benefit financially from its use for the care of subjects at Mayo Clinic. P.A.F., F.L.-J., S.K., and Z.I.A. may also receive financial benefit from this agreement.

Figures

Fig. 1
Fig. 1
Derivation of included study participants.
Fig. 2
Fig. 2
Test performance of AI-ECG, overall and subgroups by age and sex. (A) ROC of AI-ECG with test performance characteristics using an LVSD probability cut-off of 0.256. (B) Test performance in subgroups by age and sex using an LVSD probability cut-off of 0.256.
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