Aortic Valve Stenosis Clinical Trial
— PEA-ValveOfficial title:
Phono- and Electrocardiogram Assisted Detection of Valvular Disease
Verified date | June 2021 |
Source | University of California, San Francisco |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Observational |
The diagnosis of valvular heart disease (VHD), or its absence, invariably requires cardiac imaging. A familiar and inexpensive tool to assist in the diagnosis or exclusion of significant VHD could both expedite access to life-saving therapies and reduce the need for costly testing. The FDA-approved Eko Duo device consists of a digital stethoscope and a single-lead electrocardiogram (ECG), which wirelessly pairs with the Eko Mobile application to allow for simultaneous recording and visualization of phono- and electrocardiograms. These features uniquely situate this device to accumulate large sets of auscultatory data on patients both with and without VHD. In this study, the investigators seek to develop an automated system to identify VHD by phono- and electrocardiogram. Specifically, the investigators will attempt to develop machine learning algorithms to learn the phonocardiograms of patients with clinically important aortic stenosis (AS) or mitral regurgitation (MR), and then task the algorithms to identify subjects with clinically important VHD, as identified by a gold standard, from naïve phonocardiograms. The investigators anticipate that the study has the potential to revolutionize the diagnosis of VHD by providing a more accurate substitute to traditional auscultation.
Status | Completed |
Enrollment | 156 |
Est. completion date | November 11, 2019 |
Est. primary completion date | November 11, 2019 |
Accepts healthy volunteers | Accepts Healthy Volunteers |
Gender | All |
Age group | 18 Years and older |
Eligibility | Inclusion Criteria: - Able to provide consent - Undergoing a complete echocardiogram Exclusion Criteria: - Refusal to participate |
Country | Name | City | State |
---|---|---|---|
United States | University of California San Francisco | San Francisco | California |
Lead Sponsor | Collaborator |
---|---|
University of California, San Francisco | Eko Devices, Inc. |
United States,
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Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Differentiation of clinically significant aortic stenosis from structurally normal hearts | Identification by the trained machine learning algorithm of clinically important aortic stenosis (defined as moderate-to-severe or greater) from control subjects with structurally normal hearts and no greater than mild valvular heart disease, with comparison to the gold standard echocardiogram interpretation. As our algorithm will provide a continuous "score" to determine the likelihood of disease, the data will primarily come in the form of a receiver operating characteristic curve, for which we will calculate accuracy, specificity, and likelihood ratios at sensitivity cutoffs of 0.9, 0.95, and 0.99. | Close of study (after final enrollment of the aortic stenosis validation set), within 1 year. | |
Primary | Differentiation of clinically significant mitral stenosis from structurally normal hearts | Identification by the trained machine learning algorithm of clinically important mitral regurgitation (defined as moderate-to-severe or greater) from control subjects with structurally normal hearts and no greater than mild valvular heart disease, with comparison to the gold standard echocardiogram interpretation. As our algorithm will provide a continuous "score" to determine the likelihood of disease, the data will primarily come in the form of a receiver operating characteristic curve, for which we will calculate accuracy, specificity, and likelihood ratios at sensitivity cutoffs of 0.9, 0.95, and 0.99.. | Close of study (after final enrollment of the mitral regurgitation validation set), within 1 year. | |
Secondary | Differentiation of clinically significant aortic stenosis from the absence of clinically significant aortic stenosis | Identification by the trained machine learning algorithm of clinically important aortic stenosis (defined as moderate-to-severe or greater) from controls with less than moderate-to-severe aortic stenosis, with comparison to the gold standard echocardiogram interpretation. As our algorithm will provide a continuous "score" to determine the likelihood of disease, the data will primarily come in the form of a receiver operating characteristic curve, for which we will calculate accuracy, specificity, and likelihood ratios at sensitivity cutoffs of 0.9, 0.95, and 0.99. | Close of study (after final enrollment of the aortic stenosis validation set), within 1 year. | |
Secondary | Differentiation of clinically significant mitral regurgitation from the absence of clinically significant mitral regurgitation | Identification by the trained machine learning algorithm of clinically important mitral regurgitation (defined as moderate-to-severe or greater) from controls with less than moderate-to-severe mitral regurgitation, with comparison to the gold standard echocardiogram interpretation. As our algorithm will provide a continuous "score" to determine the likelihood of disease, the data will primarily come in the form of a receiver operating characteristic curve, for which we will calculate accuracy, specificity, and likelihood ratios at sensitivity cutoffs of 0.9, 0.95, and 0.99. | Close of study (after final enrollment of the mitral regurgitation validation set), within 1 year. |
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