Xiaoxi Yao1, Rozalina G McCoy2, Paul A Friedman3, Nilay D Shah4, Barbara A Barry5, Emma M Behnken6, Jonathan W Inselman7, Zachi I Attia3, Peter A Noseworthy3. 1. Division of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic, 55905 Rochester, MN; Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN. Electronic address: yao.xiaoxi@mayo.edu. 2. Division of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic, 55905 Rochester, MN; Division of Community Internal Medicine, Department of Medicine, Mayo Clinic, Rochester, MN. 3. Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN. 4. Division of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic, 55905 Rochester, MN; Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN. 5. Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN. 6. Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, MN. 7. Division of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic, 55905 Rochester, MN.
Abstract
BACKGROUND: A 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. OBJECTIVES: To 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 and a post-implementation survey will be conducted among select clinicians to identify facilitators and barriers to using the new screening report. SUMMARY: This trial will examine the effectiveness of the AI-enabled ECG for detection of asymptomatic low EF in routine primary care practices and will be among the first to prospectively evaluate the value of AI in real-world practice. Its findings will inform future implementation strategies for the translation of other AI-enabled algorithms.
RCT Entities:
BACKGROUND: A 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. OBJECTIVES: To 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 and a post-implementation survey will be conducted among select clinicians to identify facilitators and barriers to using the new screening report. SUMMARY: This trial will examine the effectiveness of the AI-enabled ECG for detection of asymptomatic low EF in routine primary care practices and will be among the first to prospectively evaluate the value of AI in real-world practice. Its findings will inform future implementation strategies for the translation of other AI-enabled algorithms.
Authors: Albert K Feeny; Mina K Chung; Anant Madabhushi; Zachi I Attia; Maja Cikes; Marjan Firouznia; Paul A Friedman; Matthew M Kalscheur; Suraj Kapa; Sanjiv M Narayan; Peter A Noseworthy; Rod S Passman; Marco V Perez; Nicholas S Peters; Jonathan P Piccini; Khaldoun G Tarakji; Suma A Thomas; Natalia A Trayanova; Mintu P Turakhia; Paul J Wang Journal: Circ Arrhythm Electrophysiol Date: 2020-07-06
Authors: David Duncker; Wern Yew Ding; Susan Etheridge; Peter A Noseworthy; Christian Veltmann; Xiaoxi Yao; T Jared Bunch; Dhiraj Gupta Journal: Sensors (Basel) Date: 2021-04-05 Impact factor: 3.576
Authors: 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 Noseworthy Journal: Data Brief Date: 2019-11-27
Authors: Adetola O Ladejobi; Jessica Cruz; Zachi I Attia; Martin van Zyl; Jason Tri; Francisco Lopez-Jimenez; Peter A Noseworthy; Paul A Friedman; Suraj Kapa; Samuel J Asirvatham Journal: Cardiovasc Digit Health J Date: 2020-08-28