External validation of a deep learning electrocardiogram algorithm to detect ventricular dysfunction
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 crosssectional
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 LVSDwith robust test performance in a population thatwas 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.