Aortic InsufficiencyNCT03600051
Automated Phonocardiography Analysis in Adults
An observational study of Aortic Insufficiency and Aortic Stenosis, run by CSD Labs GmbH at 1 site in Austria.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Aortic Insufficiency, Aortic Stenosis, Mitral Insufficiency
- What is being tested?
- Automated Heart Murmur Detection AI
- Type of treatment
- Medical device
- How many people
- 90 participants
- Where
- 1 siteAustria
- Study dates
- 10 Dec 2015to 31 Jan 2017
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- Adults with a heart defect verified by echocardiography
Who cannot join
None listed.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Phonokardiographie Bei Erwachsenen
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).
Read more
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
- Being tested
- Automated Heart Murmur Detection AI
- Medical subject headings
- Aortic Valve Insufficiency, Aortic Valve Stenosis, Mitral Valve Insufficiency, Tricuspid Valve Insufficiency
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Automated Heart Murmur Detection AIDevice
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.
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 2 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Jul 2018
- Study start10 Dec 2015
- Primary completion18 Jan 2017
- Study completion31 Jan 2017
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Aortic InsufficiencyStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Enrolment
- 90 participants (actual)
Sponsor and investigators
- Lead sponsor
- CSD Labs GmbH
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Rita Riedlbauer, MD, Principal investigator, Medical University of Graz
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03600051
- Sponsor's protocol ID
- GRZ03 (PbE)
- Status last verified
- Jul 2018
- First submitted
- 16 Jul 2018
- Last updated
- 26 Jul 2018
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 26 Jul 2018. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Phonokardiographie Bei Erwachsenen
- ClinicalTrials.gov ID
- NCT03600051
- Acronym / sponsor ID
- GRZ03 (PbE)
- Conditions
- Aortic Insufficiency, Aortic Stenosis, Mitral Insufficiency, Mitral Insufficiency and Aortic Stenosis, Tricuspid Regurgitation, Insufficiency, Pulmonary
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 90 participants (actual)
- Lead sponsor
- CSD Labs GmbHUniversity, hospital or other
- Study start
- 10 Dec 2015 (actual)
- Primary completion
- 18 Jan 2017 (actual)
- Study completion
- 31 Jan 2017 (actual)
- First posted
- 26 Jul 2018 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Cross-sectional
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (1 listed)
- Adults with a heart defect verified by echocardiography
Key exclusion criteria (0 listed)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Automated Heart Murmur Detection AIAutomated 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. | Device | – |
Primary outcome
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.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 Jul 2018.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Rita Riedlbauer, MD principal investigator
Medical University of Graz
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Aortic Insufficiency and Aortic Stenosis, run by CSD Labs GmbH at 1 site in Austria.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 0 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 26 Jul 2018. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Austria. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Sensitivity for pathological heart murmur detection (time frame: 2 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 26 Jul 2018; AzTrials last refreshed its copy on 4 Nov 2026.