A Multicenter Pragmatic Implementation Study of AI-ECG-Based Clinical Decision Support Software to Identify Low LVEF
Study Details
Study Description
Brief Summary
A prospective, cluster-randomized, care-as-usual controlled trial to evaluate the impact of an ECG-based artificial intelligence (AI-ECG) 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 AI-ECG algorithm to detect low LVEF and an associated clinical decision support software when used during routine outpatient care. The study will be conducted in 2 phases: feasibility assessment phase and clinical impact phase.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
|
N/A |
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 6 week feasibility phase followed by a 3-month initial observation period and up to 2x 3 month follow-up periods. Data will be collected after the completion of each 3 month follow-up period.
At the completion of the 6 week feasibility period, we will evaluate quantitative and qualitative outcomes to inform the following observational period(s).
Primary endpoints and exploratory endpoints will be assessed at each timepoint.
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Experimental: Anumana Low EF AI-ECG Algorithm
|
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
|
Other: Care-as-Usual
Clinicians will not have access to the Anumana Low EF AI-ECG algorithm and will provide care-as-usual.
|
Outcome Measures
Primary Outcome Measures
- Diagnosis rates of low ejection fraction of less than or equal to 40 percent by echocardiography compared to care-as-usual [90 days]
Eligibility Criteria
Criteria
Inclusion Criteria:
-
Adults 18 years or older
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Digital 10 second, 12 Lead ECG captured or available in EHR or ECG data store for AI-ECG analysis at point-of-care
Exclusion Criteria:
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Known history of LVEF ≤ 40%
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Known history of heart failure
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Opted out of electronic health record-based research
Contacts and Locations
Locations
No locations specified.Sponsors and Collaborators
- Anumana, Inc.
- Mayo Clinic
Investigators
- Principal Investigator: Francisco Lopez-Jimenez, MD, MSc, MBA, Mayo Clinic
Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- DOC-244