Automated Algorithm Based Analysis of Phonocardiograms of Newborns
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|ClinicalTrials.gov Identifier: NCT02105480|
Recruitment Status : Completed
First Posted : April 7, 2014
Last Update Posted : July 17, 2018
The purpose of this double-blind pivotal clinical utility study is to determine on a large patient population whether heart murmurs can be reliably detected with high sensitivity and specificity using a locked, automated algorithm-based phonocardiogram analysis (also referred to as computer aided auscultation (CAA)).
Each patient is auscultated and diagnosed independently by a medical specialist. Additionally, for each patient, an echocardiogram is performed as the gold-standard for determining heart pathologies. The CAA results are compared to the findings of the medical professionals as well as to the echocardiogram findings.
Hypothesis: The specific (locked) CAA algorithms used in this study are able to automatically diagnose pathological heart murmurs in premature babies and newborns with at least the same accuracy as experienced medical specialists.
|Condition or disease||Intervention/treatment|
|Heart Murmurs Mitral Valve Prolapse Systolic Murmurs||Device: Computer aided auscultation (CAA)|
The following registry procedures and quality factors have been implemented:
Quality assurance plan, including
- data validation
- proper registration procedures
- regular site monitoring
- regular auditing
- Data checks to compare data entered into the registry against predefined rules for range or consistency with other data fields in the registry.
- Source data verification to assess the accuracy, completeness, or representativeness of registry data by comparing the data to external data sources (medical records and paper case report forms).
- Standard Operating Procedures to address registry operations and analysis activities, such as patient recruitment, data collection, data management, data analysis, reporting for adverse events, and change management.
- Sample size assessment to specify the number of participants or participant years necessary to demonstrate an effect.
- Statistical analysis plan describing the analytical principles and statistical techniques to be employed in order to address the primary and secondary objectives, as specified in the study protocol or plan.
- Plan for missing data to address situations where variables are reported as missing, unavailable, "non-reported," uninterpretable, or considered missing because of data inconsistency or out-of-range results.
|Study Type :||Observational [Patient Registry]|
|Actual Enrollment :||220 participants|
|Target Follow-Up Duration:||1 Day|
|Official Title:||Automated Algorithm Based Analysis of Phonocardiograms of Newborns|
|Actual Study Start Date :||November 2013|
|Actual Primary Completion Date :||January 25, 2016|
|Actual Study Completion Date :||January 18, 2017|
- Device: Computer aided auscultation (CAA)
- Phonocardiogram analysis
- Automated heart murmur classification
- Murmur screening
- Number of correctly diagnosed heart murmurs through locked, independent algorithm based auscultation and traditional stethoscope based auscultation by medical experts [ Time Frame: 2 years (expected) ]Each patient is screened and diagnosed regarding a potential heart murmur by a medical expert through standard stethoscope based auscultation. Heart sounds are recorded using an electronic stethoscope and analyzed and diagnosed independently (with no external input) by a locked algorithm. A final diagnoses for each patient is made by a medical expert by performing an echocardiogram, the gold standard method for heart murmur detection. The diagnoses of both the medical expert and the algorithm are finally compared to the echocardiogram based diagnosis (after completion of patient recruitment).
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): NCT02105480
|Graz, Styria, Austria, 8010|
|Principal Investigator:||Gerhard Pichler, MD||Medical University of Graz|