Description

Structural heart disease (SHD) is a major cause of illness and death, especially in older adults. SHD includes conditions such as valvular heart disease (e.g., aortic stenosis, mitral regurgitation), left or right ventricular dysfunction, left ventricular hypertrophy, pulmonary artery hypertension, and pericardial effusions. These conditions are often underdiagnosed, and delayed recognition can lead to complications such as heart failure, stroke, and cardiomyopathy. Early detection and treatment can improve outcomes, but current approaches often miss patients in the early stages of disease.

EchoNext is an artificial intelligence model trained on over 400,000 ECG-echocardiogram pairs. It analyzes the standard 12-lead ECG to predict the presence of structural heart disease, as defined by echocardiographic findings. The model has demonstrated strong diagnostic performance, with an area under the receiver operating characteristic (AUROC) curve of 0.86 across diverse populations.

The purpose of this trial is to evaluate whether deploying EchoNext into the Emergency Department (ED) electronic health record (EHR) as a clinical decision support alert can increase the detection of undiagnosed SHD. The ED setting is ideal because: (1) ECGs are widely obtained for many presenting complaints, (2) abnormal ECG findings related to SHD are often overlooked in the acute setting, and (3) EDs provide access to disadvantaged populations and a unique opportunity to deliver population-level interventions.

In this study, ECGs obtained in the ED will be analyzed in real time by the EchoNext model. If the model predicts moderate or severe SHD, an EHR alert will be displayed to the treating provider (attending physician, fellow, resident, physician assistant, or nurse practitioner). The alert will inform providers of the potential for underlying SHD and recommend consideration of follow-up echocardiography or specialty referral. Outcomes will include rates of new SHD diagnoses confirmed by echocardiography, follow-up testing and referrals, and downstream patient outcomes.

The Community Tele-Paramedicine (CTP) program, an established post-discharge care initiative at NewYork-Presbyterian, will also serve as a follow-up resource for patients identified in the ED. CTP combines home visits by community paramedics, telehealth visits by emergency physicians, and nurse care management to coordinate outpatient testing and follow-up care. Integration with CTP will facilitate timely echocardiography and referral for patients flagged by EchoNext, particularly for high-risk or underserved populations.

This trial will determine whether AI-driven ECG interpretation can improve early detection of SHD and lead to better patient outcomes compared to standard care.