Automated Phonocardiography Analysis in Adults

NCT03600051 | Completed

• 1 other identifier: GRZ03 (PbE)
• Study Type: observational
• Target: 90
• Locations: 1 country
• Sites: 1

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.

Conditions

Aortic Insufficiency; Aortic Stenosis; Mitral Insufficiency; Mitral Insufficiency and Aortic Stenosis; Tricuspid Regurgitation; Insufficiency, Pulmonary; Insufficiency, Tricuspid

Outcome Measures

Primary Outcomes (1)

• Sensitivity for pathological heart murmur detection

  Ability to detect a pathological heart murmur in digital heart sound recordings obtained from an elderly population with heart valve disease.

  2 months

Interventions

Automated Heart Murmur Detection AI DEVICE

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.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• Adults with a heart defect verified by echocardiography

Sponsors & Collaborators

• CSD Labs GmbH: lead

Study Sites (1)

University Hospital

Graz, Styria, 8010, Austria

Location

MeSH Terms

Conditions

Aortic Valve Insufficiency; Aortic Valve Stenosis; Mitral Valve Insufficiency; Tricuspid Valve Insufficiency

Condition Hierarchy (Ancestors)

Aortic Valve Disease; Heart Valve Diseases; Heart Diseases; Cardiovascular Diseases; Ventricular Outflow Obstruction

Study Officials

• Rita Riedlbauer, MD

  Medical University of Graz

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: July 16, 2018
• First Posted: July 26, 2018
• Study Start: December 10, 2015
• Primary Completion: January 18, 2017
• Study Completion: January 31, 2017
• Last Updated: July 26, 2018

Record last verified: 2018-07

Data Sharing

• IPD Sharing: Will not share

Locations

AU (1)