A Multi-Center Study of Detection of Low Ventricular Ejection Fraction

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

16000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2021-08-30

Study Completion Date

2022-04-13

Brief Summary

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This is a multi-site, retrospective study to evaluate the performance of a locked AI-based algorithm for detection of left ventricular systolic dysfunction. A prerequisite for inclusion of subjects from each institution will be the availability of at least one digital 12-lead ECG paired with an echocardiogram with LVEF information within 30 days of the date of the ECG. The AI-ECG LVSD algorithm will be applied on all ECGs and diagnostic performance features for the detection of LVSD will be estimated using the provided paired LVEF value (Low LVEF as the reference label). Performance will also be assessed in subgroups of subjects determined by demographic and clinical factors.

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Detailed Description

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Following institutional review board approval, 12,000 12-lead ECG's paired with an echocardiogram with LVEF information within 30 days of the date of the ECG will be collected across three enrolled sites. Each site will provide data from up to 4000 enrolled subjects that meet the inclusion criteria. No other demographic characteristics or enrichment will be considered in the selection of subjects in order to best represent the general population for that site. Sites will securely transfer the data to a centralized repository for processing.

Once data is collected, the device will be used to analyze the ECG data for all enrolled subjects without reference or access to the echocardiogram data. The device will display a binary 36 prediction of the likelihood of LVEF less than or equal to 40%. Results will be compared to the echocardiogram reference standard in accordance with the statistical analysis plan.

Conditions

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Cardiac Disease

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Interventions

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AI Algorithm to detect LVEF in ECG

A clinical decision support software as a medical device that detects whether a patient has LVEF less than or equal to 40% based upon the input of one or more ECG vectors at the point-of-care.

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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Inclusion Criteria

\- Adult subjects with or without known cardiac disease who are either inpatients or outpatients with ECGs and an echocardiogram within 30-days of the ECG date.

Exclusion Criteria

* No research authorization provided
* An ECG signal shorter than 10 seconds or that is not interpretable
* An echocardiogram is considered technically challenging
* Only qualitative interpretation of left ventricular systolic function available (i.e., "decreased EF") without a numerical value.
* A paced rhythm

Minimum Eligible Age

18 Years

Maximum Eligible Age

99 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No