eMurmur ID - Clinical Performance Evaluation

NCT ID: NCT03227848

Last Updated: 2018-07-18

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

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

COMPLETED

Total Enrollment

120 participants

Study Classification

OBSERVATIONAL

Study Start Date

2017-01-04

Study Completion Date

2018-04-30

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

The differentiation between innocent and pathologic murmurs through traditional auscultation can often be challenging, which in the end makes the diagnosis strongly dependent on the clinitians experience and clinical expertise. With the development of technology it is now possible to help diagnose heart murmurs using computer aided auscultation systems (CAA). eMurmur ID is an investigational CAA system (not FDA cleared) and the investigators hypothesize that it can distinguish between AHA class I (pathologic murmurs) and AHA class III heart sounds (innocent murmurs and/or no murmurs) with a sensitivity and specificity not worse compared to a similar FDA cleared CAA system on market.

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

Heart Murmurs Pathologic Murmurs Innocent Murmurs Congenital Heart Defect Systolic Murmurs Diastolic Murmurs

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Observational Model Type

COHORT

Study Time Perspective

CROSS_SECTIONAL

Interventions

Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.

Automated Heart Murmur Detection AI

Automated AI algorithm-based analysis of digital heart sound recordings to detect and classify heart murmurs. Heart sound recordings were fully blinded before undergoing one-time automated analysis. AI algorithm results for each recording include: AHA classification (Class I (pathologic heart murmur) versus class III (innocent heart murmur or no heart murmur), murmur timing, murmur grade, heart rate and S1/S2 identification.

Intervention Type DEVICE

Other Intervention Names

Discover alternative or legacy names that may be used to describe the listed interventions across different sources.

eMurmur ID

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

Inclusion Criteria

* All age groups of patients will be included from 1day old
* Patients who are being followed for known congenital heart disease and are returning for follow up
* Patients referred for a suspected heart murmur

Exclusion Criteria

* Mismatch between the expert physician's diagnosis (auscultation based) and the diagnosis resulting from echocardiography (independently read by a cardiologist blinded to the auscultation results). Note: both, the expert physician and echocardiography results must independently reach the same diagnosis, which is then accepted as the gold standard reference diagnosis to which both devices are compared to. This is necessary because not every pathology visible on an echocardiogram causes an audible murmur, and not every murmur heard by a medical expert might correlate to pathology.
* Patient whose behaviour does not allow for a standard auscultation by the physician (e.g. a screaming fit).
Minimum Eligible Age

1 Day

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

Meet the organizations funding or collaborating on the study and learn about their roles.

CSD Labs GmbH

OTHER

Sponsor Role lead

Responsible Party

Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.

Responsibility Role SPONSOR

Principal Investigators

Learn about the lead researchers overseeing the trial and their institutional affiliations.

Lillian Lai, MD

Role: PRINCIPAL_INVESTIGATOR

Children's Hopsital of Eastern Ontario, Canada

Locations

Explore where the study is taking place and check the recruitment status at each participating site.

Children's Hospital of Eastern Ontario

Ottawa, , Canada

Site Status

Countries

Review the countries where the study has at least one active or historical site.

Canada

References

Explore related publications, articles, or registry entries linked to this study.

Lai LS, Redington AN, Reinisch AJ, Unterberger MJ, Schriefl AJ. Computerized Automatic Diagnosis of Innocent and Pathologic Murmurs in Pediatrics: A Pilot Study. Congenit Heart Dis. 2016 Sep;11(5):386-395. doi: 10.1111/chd.12328. Epub 2016 Mar 15.

Reference Type BACKGROUND
PMID: 26990211 (View on PubMed)

Related Links

Access external resources that provide additional context or updates about the study.

https://www.ncbi.nlm.nih.gov/pubmed/26990211

Computerized Automatic Diagnosis of Innocent and Pathologic Murmurs in Pediatrics: A Pilot Study.

Other Identifiers

Review additional registry numbers or institutional identifiers associated with this trial.

OTT03

Identifier Type: -

Identifier Source: org_study_id

More Related Trials

Additional clinical trials that may be relevant based on similarity analysis.