Ventricular Ejection Fraction
Conditions
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.
Interventions
DEVICEAnumana 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.
OTHERCare-as-Usual
Clinicians will not have access to the Anumana Low EF AI-ECG algorithm and will provide care-as-usual.
Sponsors
Mayo Clinic
Anumana, Inc.
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
SCREENING
Masking
NONE
Intervention model description
Clinicians in primary care practice groups will be consented for enrollment into the study. Practice groups that decide to participate in the study will be randomized to have the software available or to provide care as usual without the software.
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Diagnosis rates of low ejection fraction of less than or equal to 40 percent by echocardiography compared to care-as-usual | 90 days | Diagnosis rates of low ejection fraction of less than or equal to 40 percent by echocardiography compared to care-as-usual |
Countries
United States
Outcome results
None listed