ECG AI-guided screening for low ejection fraction (EAGLE): Rationale and design of a pragmatic cluster randomized trial

Publication date: Available online 25 October 2019Source: American Heart JournalAuthor(s): Xiaoxi Yao, Rozalina G. McCoy, Paul A. Friedman, Nilay D. Shah, Barbara A. Barry, Emma M. Behnken, Jonathan W. Inselman, Zachi I. Attia, Peter A. NoseworthyAbstractBackgroundA deep learning algorithm to detect low ejection fraction (EF) using routine 12-lead electrocardiogram (ECG) has recently been developed and validated. The algorithm was incorporated into the electronic health record (EHR) to automatically screen for low EF, encouraging clinicians to obtain a confirmatory transthoracic echocardiogram (TTE) for previously undiagnosed patients, thereby facilitating early diagnosis and treatment.ObjectivesTo prospectively evaluate a novel artificial intelligence (AI) screening tool for detecting low EF in primary care practices.Design.The EAGLE trial is a pragmatic two-arm cluster randomized trial (NCT04000087) that will randomize>100 clinical teams (i.e., clusters) to either intervention (access to the new AI screening tool) or control (usual care) at 48 primary care practices across Minnesota and Wisconsin. The trial is expected to involve approximately 400 clinicians and 20,000 patients. The primary endpoint is newly discovered EF ≤ 50%. Eligible patients will include adults who undergo ECG for any reason and have not been previously diagnosed with low EF. Data will be pulled from the EHR, and no contact will be made with patients. A positive deviance qualitative study a...
Source: American Heart Journal - Category: Cardiology Source Type: research