Study Protocol for the Pilot Evaluation for SMartphone-adaptable Artificial Intelligence for PRediction and DeTection of Left Ventricular Systolic Dysfunction (The SMART-LV Pilot Study Protocol)

Abstract

ABSTRACTIntroductionDespite a prevalence of 3-5% among adults, asymptomatic left ventricular systolic dysfunction (LVSD) remains underdiagnosed. There is a critical need for an accurate and widely accessible screening strategy for LVSD, given its association with preventable morbidity and premature mortality. A novel deep learning approach has demonstrated the ability to detect LVSD directly from ECG images, with retrospective validation across multiple institutions. There is a lack of prospective validation. In this pilot study, we evaluate the feasibility of screening and recruiting individuals for prospective echocardiography based on an image-based artificial intelligence (AI)-ECG algorithm applied to the ECG repository at a large academic medical center.Research Methods and AnalysisThis is the protocol for a prospective cohort study in outpatient primary care clinics of the Yale New Haven Hospital (YNHH). Adult patients who have undergone a 12-lead ECG without subsequent echocardiogram as a part of routine clinical care within 90 days of the ECG will be identified in the electronic health record (EHR). The AI-ECG model for LVSD will be deployed to YNHH ECG repository to define the probability of LVSD, identifying 5 patients each with high and low probability of LVSD. After discussion with primary care physicians, and subsequent contact by the study team, screened participants will be invited for and undergo an echocardiogram. The study participants and the cardiologists conducting the echocardiograms will be blinded to the results of the AI-ECG screen. The analysis will focus on feasibility metrics: the proportion (i) of all patients undergoing ECGs who have high probability of LVSD without subsequent echocardiogram, (ii) of patients who agree to participate in the study, and (iii) that undergo an echocardiogram. A descriptive exploration of the comparison of the AI-ECG and echocardiogram results will also be reported.Ethics and DisseminationAll patient EHR data required for assessing eligibility and conducting the AI-ECG screening will be accessed through secure servers approved for protected health information. Potential participants will only be contacted after they have discussed the study information with their primary care physician. All participants will be required to provide written informed consent before participation and data will be deidentified prior to analysis. This study protocol has been approved by the Yale Institutional Review Board (Protocol Number: 2000034006) and has been registered atClinicalTrials.gov(Identifier:NCT05630170). The results of the future validation study will be published in peer-reviewed journals and summaries will be provided to the study participants.