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Automated Phonocardiography Analysis in Adults

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ClinicalTrials.gov Identifier: NCT03600051
Recruitment Status : Completed
First Posted : July 26, 2018
Last Update Posted : July 26, 2018
Sponsor:
Information provided by (Responsible Party):
CSD Labs GmbH

Study Description
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Brief Summary:

Background: Computer aided auscultation in the differentiation of pathologic (AHA class I) from no- or innocent murmurs (AHA class III) via artificial intelligence algorithms could be a useful tool to assist healthcare providers in identifying pathological heart murmurs and may avoid unnecessary referrals to medical specialists.

Objective: Assess the quality of the artificial intelligence (AI) algorithm that autonomously detects and classifies heart murmurs as either pathologic (AHA class I) or as no- or innocent (AHA class III).

Hypothesis: The algorithm used in this study is able to analyze and identify pathologic heart murmurs (AHA class I) in an adult population with valve defects with a similar sensitivity compared to medical specialist.

Methods: Each patient is auscultated and diagnosed independently by a medical specialist by means of standard auscultation. Auscultation findings are verified via gold-standard echocardiogram diagnosis. For each patient, a phonocardiogram (PCG) - a digital recording of the heart sounds - is acquired. The recordings are later analyzed using the AI algorithm. The algorithm results are compared to the findings of the medical professionals as well as to the echocardiogram findings.


Condition or disease Intervention/treatment
Aortic Insufficiency Aortic Stenosis Mitral Insufficiency Mitral Insufficiency and Aortic Stenosis Tricuspid Regurgitation Insufficiency, Pulmonary Insufficiency, Tricuspid Device: Automated Heart Murmur Detection AI

Study Design
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Study Type : Observational
Actual Enrollment : 90 participants
Observational Model: Cohort
Time Perspective: Cross-Sectional
Official Title: Phonokardiographie Bei Erwachsenen
Actual Study Start Date : December 10, 2015
Actual Primary Completion Date : January 18, 2017
Actual Study Completion Date : January 31, 2017

Resource links provided by the National Library of Medicine


Groups and Cohorts
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Intervention Details:
  • Device: Automated Heart Murmur Detection AI
    Automated AI algorithm-based analysis of digital heart sound recordings to detect pathological heart murmurs. Heart sound recordings were fully blinded before undergoing one-time automated analysis. Algorithm results for each recording included: AHA classification (I "pathologic" versus III "innocent/no murmur"), murmur timing, murmur grade, heart rate and S1/S2 identification.

Outcome Measures
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Primary Outcome Measures :
  1. Sensitivity for pathological heart murmur detection [ Time Frame: 2 months ]
    Ability to detect a pathological heart murmur in digital heart sound recordings obtained from an elderly population with heart valve disease.


Eligibility Criteria
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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
Elderly in-patient subjects admitted to the Division of Cardiology, University Hospital Graz, Austria. All patients had known pathological murmurs caused by multiple valve defects, confirmed by gold standard echocardiography. Defects were interpreted by the cardiologist as "low, medium or high" severity. Altogether, 155 valve defects were observed, including insufficiencies of the aortic, mitral, tricuspid, and pulmonary valves; and stenosis of the aortic and mitral valves.
Criteria

Inclusion Criteria:

  • Adults with a heart defect verified by echocardiography
Contacts and Locations
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Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03600051


Locations
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Austria
University Hospital
Graz, Styria, Austria, 8010
Sponsors and Collaborators
CSD Labs GmbH
Investigators
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Principal Investigator: Rita Riedlbauer, MD Medical University of Graz
More Information
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Responsible Party: CSD Labs GmbH
ClinicalTrials.gov Identifier: NCT03600051    
Other Study ID Numbers: GRZ03 (PbE)
First Posted: July 26, 2018    Key Record Dates
Last Update Posted: July 26, 2018
Last Verified: July 2018
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Additional relevant MeSH terms:
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Respiratory Insufficiency
Aortic Valve Stenosis
Tricuspid Valve Insufficiency
Mitral Valve Insufficiency
Aortic Valve Insufficiency
Pulmonary Valve Insufficiency
Constriction, Pathologic
Pathological Conditions, Anatomical
 Aortic Valve Disease
Heart Valve Diseases
Heart Diseases
Cardiovascular Diseases
Ventricular Outflow Obstruction
Respiration Disorders
Respiratory Tract Diseases