Electrocardiogram-based deep learning and decision analysis to improve atrial fibrillation risk estimation

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Project Summary/Abstract Atrial fibrillation (AF) is a major public health problem resulting in preventable strokes and increased incidence of heart failure and early cognitive decline. AF is expected to affect nearly 12 million people in the United States by 2030. Oral anticoagulation (OAC) is highly effective in reducing risk of AF-related stroke, and other preventive interventions such as weight loss, exercise, and alcohol cessation may reduce risk of AF and associated complications. However, AF is commonly asymptomatic and is frequently episodic, and therefore may be difficult to diagnose. Although screening can detect undiagnosed AF, mass screening approaches have not resulted in meaningful improvements in clinical outcomes. A major inefficiency inherent within current screening approaches is the screening of many individuals at relatively low risk for AF, leading to an inefficient and low-yield screening intervention. Therefore, there is a critical unmet need to identify individuals at elevated risk of developing AF upfront, in order to optimize the efficiency of AF screening and preventive interventions. In Aim 1 of this proposal, we will develop and compare novel deep learning-based methods to estimate AF risk in an automated fashion using mobile single-lead electrocardiograms. In Aim 2, we will conduct an individual-level simulation to quantify the comparative and cost-effectiveness of a risk-based approach to AF screening, as compared to the current clinical standard of AF screening based on the simple age cutoff of ³65 years. In Aim 3, we will perform a pilot study to quantify the user acceptability of prospective AF risk estimation and quantify associations between estimated AF risk and true AF incidence at 18 months. The overall goal of this proposal is to establish the feasibility and potential clinical value of automated AF risk estimation to guide preventive interventions designed to reduce the morbidity resulting from AF and its associated complications. The aims will be executed in the setting of a comprehensive career development program designed to provide Dr. Khurshid, an early career investigator, with the skills and experience required to become an independent clinician investigator focused on the improvement of outcomes in cardiac arrhythmias through the use of disease risk prediction. This proposal impanels a multi-disciplinary team comprising experts in machine learning, decision science, and prospective clinical studies, who will guide Dr. Khurshid in his transition to scientific independence.