Preventing unnecessary echocardiograms in de novo patients referred to the cardiology outpatient clinic using electrocardiogram-based deep learning

Abstract Introduction The growing demand on European echocardiography labs, driven by the rise in structural heart disease (SHD), is further complicated by a shortage of skilled sonographers, necessitating resource optimisation strategies. Electrocardiogram (ECG)-based deep learning models may assist in streamlining the echocardiography workflow. Purpose This study sought to develop a deep learning model capable of identifying patients at very low risk of SHD in a population of de novo patients referred to the cardiology outpatient clinic. Methods We identified all adult patients who underwent a 12-lead ECG and echocardiogram at a Dutch academic teaching hospital between 2011 and August 2023. Echocardiogram outcomes were extracted from reports, including left ventricular systolic dysfunction (LVSD), left ventricular dilatation (LVD), aortic stenosis (AS), aortic regurgitation (AR), mitral regurgitation (MR), and tricuspid regurgitation (TR), from which a composite outcome label was derived. A cohort of patients newly referred to our cardiology outpatient clinic with a primary complaint of chest pain, dyspnea, palpitations, dizziness, a murmur, or an abnormal ECG was held out as a test set, while the remaining dataset was split patient-wise into training (90%) and validation (10%) sets. A convolutional neural network was trained using only the ECG tracing as input and the composite label as outcome. Results We included 92,210 ECG-echo pairs from 44,785 patients (19,717 female) with 25,543 (27.7%) abnormal ECG-echo pairs. Among 2724 newly referred patients in the test set, 210/1389 males (15.1%) and 201/1335 females (15.1%) were diagnosed with SHD. In the test set, the model for predicting composite outcome demonstrated an area-under-the-curve, negative predictive value, and specificity of 0.74 (95% confidence interval [CI] 0.71-0.79), 0.94 (95% CI 0.92-0.95), and 0.47 (95% CI 0.45-0.49), respectively. Conclusion This study demonstrates the feasibility of employing deep learning models based solely on electrocardiogram (ECG) data to identify patients at very low risk of structural heart disease (SHD). Further validation and prospective studies are warranted to confirm the generalizability and clinical utility of our findings in diverse patient populations and healthcare settings.