ECG-derived global longitudinal strain using artificial intelligence: A comparative study with transthoracic echocardiography

BackgroundDespite the versatility of the left ventricular (LV) global longitudinal strain (LVGLS), its complex measurement and interpretation make it difficult to use. An artificial intelligence (AI)-generated electrocardiography (ECG) score for LVGLS estimation (ECG-GLS score) may offer a cost-effective alternative. ObjectivesWe evaluated the potential of an AI-generated ECG-GLS score to diagnose LV systolic dysfunction and predict the prognosis of patients with heart failure (HF). MethodsA convolutional neural network-based deep-learning algorithm was trained to estimate the echocardiography-derived GLS (LVGLS) using retrospective ECG data from a tertiary hospital (n=2,882). ECG-GLS score performance was evaluated using data from an acute HF registry at another tertiary hospital (n=1,186). ResultsIn the validation cohort, the ECG-GLS score could identify patients with impaired LVGLS ([&le;]12%) (area under the receiver-operating characteristic curve [AUROC], 0.82; sensitivity, 85%; specificity, 59%). ECG-GLS performance in identifying patients with an LV ejection fraction (LVEF) of <40% (AUROC, 0.85) was comparable to that for LVGLS (AUROC, 0.83) (p=0.08). Five-year outcomes (all-cause death; composite of all-cause death and hospitalization for HF) occurred significantly more frequently in patients with low ECG-GLS scores. Low ECG-GLS score was a significant risk factor for these outcomes after adjustment for other clinical risk factors and LVEF. The prognostic performance of the ECG-GLS score was comparable to that of the LVGLS. ConclusionsThe ECG-GLS score demonstrates a strong correlation with the LVGLS and is effective in risk stratification for the long-term prognosis after acute HF, suggesting its potential role as a practical alternative to the LVGLS. Condensed abstractThis study is the first to attempt to estimate the left ventricular global longitudinal strain (LVGLS) from electrocardiography (ECG) data using an artificial intelligence-based algorithm (ECG-GLS score). The ECG-GLS score was correlated with the LVGLS and performed as well as the LVGLS in predicting the long-term prognosis of patients with heart failure. Thus, the ECG-GLS score has potential as practical alternative to the LVGLS on echocardiography, with reductions in time and effort. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/24306468v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@1db335borg.highwire.dtl.DTLVardef@8afceorg.highwire.dtl.DTLVardef@4e11eaorg.highwire.dtl.DTLVardef@17a43dd_HPS_FORMAT_FIGEXP M_FIG C_FIG