Ecg-derived global longitudinal strain using artificial intelligence: a comparative study with transthoracic echocardiography

Background: Despite 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. Objectives: We 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). Methods: A 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). Results: In the validation cohort, the ECG-GLS score could identify patients with impaired LVGLS (≤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. Conclusions: The 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.