Dataset Information

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

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.

SUBMITTER: Attia IZ

PROVIDER: S-EPMC7955278 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Publications

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

Attia Itzhak Zachi IZ Tseng Andrew S AS Benavente Ernest Diez ED Medina-Inojosa Jose R JR Clark Taane G TG Malyutina Sofia S Kapa Suraj S Schirmer Henrik H Kudryavtsev Alexander V AV Noseworthy Peter A PA Carter Rickey E RE Ryabikov Andrew A Perel Pablo P Friedman Paul A PA Leon David A DA Lopez-Jimenez Francisco F

International journal of cardiology 20210102

<h4>Objective</h4>To validate a novel artificial-intelligence electrocardiogram algorithm (AI-ECG) to detect left ventricular systolic dysfunction (LVSD) in an external population.<h4>Background</h4>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.<h4>Methods</h4>We performed an external validation study with subjects from the Know Your Heart Study, a cross- ...[more]

PMID: 33400971

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Dataset Information

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

Objective

Background

Methods

Results

Conclusions

Publications

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

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