A Multicenter Pragmatic Implementation Study of ECG-AI-Based Clinical Decision Support Software to Identify Low LVEF
NCT05867407 · Status: TERMINATED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 11610
Last updated 2025-09-04
Summary
A prospective, cluster-randomized, care-as-usual controlled trial to evaluate the impact of an ECG-based artificial intelligence (ECG-AI) algorithm to detect low left ventricular ejection fraction (LVEF) on diagnosis rates of LVEF ≤ 40% in the outpatient setting.
The objective of this study is to evaluate the impacts of an ECG-AI algorithm to detect low LVEF and an associated Medical Device Data System when used during routine outpatient care. The study will be conducted in 2 phases: feasibility assessment phase and clinical impact phase.
Conditions
- Ventricular Ejection Fraction
Interventions
- DEVICE
-
Anumana Low EF AI-ECG Algorithm
Clinician will have access to the Anumana Low EF AI-ECG algorithm via a link in the patient's electronic health record which will display results applied to patients' ECGs, as well as supporting information. Using the results of the algorithm, combined with the clinician's knowledge of patient-specific risk factors, the clinician will determine whether further evaluation is warranted.
- OTHER
-
Care-as-Usual
Clinicians will not have access to the Anumana Low EF AI-ECG algorithm and will provide care-as-usual.
Sponsors & Collaborators
- collaborator OTHER
-
Anumana, Inc.
lead INDUSTRY
Principal Investigators
-
Francisco Lopez-Jimenez, MD, MSc, MBA · Mayo Clinic
Study Design
- Allocation
- RANDOMIZED
- Purpose
- SCREENING
- Masking
- NONE
- Model
- PARALLEL
Eligibility
- Min Age
- 18 Years
- Sex
- ALL
- Healthy Volunteers
- No
Timeline & Regulatory
- Start
- 2024-06-13
- Primary Completion
- 2025-05-30
- Completion
- 2025-05-30
- FDA Device
- Yes
Countries
- United States
Study Locations
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