A Prospective Pragmatic Cluster-Randomized Care-as-Usual Controlled Study to Evaluate the Impact of an ECG-Based AI Algorithm to Detect Low Left Ventricular Ejection Fraction on Diagnosis Rates of LVEF ≤40% in the Outpatient Setting
Trial Snapshot
- Phase
- Not Applicable
- Status
- Terminated
- Sponsor
- Anumana, Inc.
- Enrollment
- 11,610
- Locations
- 5
- Primary Endpoint
- Diagnosis rates of low ejection fraction of less than or equal to 40 percent by echocardiography compared to care-as-usual
Study Overview
Brief 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.
Detailed Description
The study is a prospective, cluster randomized, care-as-usual controlled trial that will be conducted at 6 sites in the USA.
Primary care clinicians and general cardiologists will be invited and consented to participate in the study. For clinicians that accept, practice groups will be randomized to receive access to and education about the Low EF AI-ECG software and encompassing software or to provide care-as-usual in the control group. The study will be conducted in two phases: a feasibility pilot to evaluate integration and usability followed by observational period(s) to evaluate clinical outcomes.
Analyses of the primary and secondary endpoints will be conducted on data from patients that meet the inclusion and exclusion criteria. The expected duration of the study is 12 months, including a feasibility phase (estimated 6 weeks) followed by a 3-month initial observation period with rolling observation count monitoring until the target number of patient encounters is reached, followed by a 90-day follow up period.
At the completion of the feasibility period, we will evaluate quantitative and qualitative outcomes to inform the following observational period(s).
Primary endpoints and exploratory endpoints will be assessed the end of the study.
Study Design
- Study Type
- Interventional
- Allocation
- Randomized
- Intervention Model
- Parallel
- Primary Purpose
- Screening
- Masking
- None
Eligibility Criteria
- Ages
- 18 Years to — (Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- No
Inclusion Criteria
- •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
Exclusion Criteria
- •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
Arms & Interventions
Anumana Low EF AI-ECG Algorithm
Anumana Low EF AI-ECG Algorithm
Intervention: Anumana Low EF AI-ECG Algorithm (Device)
Care-as-Usual
Care-as-Usual
Intervention: Care-as-Usual (Other)
Outcomes
Primary Outcomes
Diagnosis rates of low ejection fraction of less than or equal to 40 percent by echocardiography compared to care-as-usual
Time Frame: 90 days
Diagnosis rates of low ejection fraction of less than or equal to 40 percent by echocardiography compared to care-as-usual
Secondary Outcomes
No secondary outcomes reported