A Multicenter Pragmatic Implementation Study of ECG-AI-Based Clinical Decision Support Software to Identify Low LVEF

Stopped early · Not applicable

Conditions studied: Ventricular Ejection Fraction

In brief

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.

Key facts

Study ID

NCT05867407

Run by

Anumana, Inc.

People needed

11610

Starts

2024-06-13

Expected to finish

2025-05-30

Last updated by the study team

2025-09-04

Who can join

Age: 18 and older. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

• Males and females 18 years or older (including females who are pregnant, breastfeeding and/or lactating)
• Digital ECG captured or available within site for ECG-AI analysis at point-of-care

You may not qualify if…

• Known history of LVEF ≤ 40%
• Known history of systolic heart failure
• Known history of heart failure with reduced ejection fraction
• Opted out of electronic health record-based research

Where it is running

• Mayo Clinic Arizona — Phoenix, Arizona, United States
• Mayo Clinic Florida — Jacksonville, Florida, United States
• Mayo Clinic Rochester — Rochester, Minnesota, United States
• Duke Health — Durham, North Carolina, United States
• University of Texas Southwestern — Dallas, Texas, United States

Full record on ClinicalTrials.gov

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