Automatic Differentiation of Innocent and Pathologic Murmurs in Pediatrics

Background:

Computer aided auscultation in the differentiation of pathologic (AHA class I) from no- or innocent murmurs (AHA class III) would be of great value to the general practitioner and potentially also to the specialist. This would allow objective screening for structural heart disease, standardized documentation of auscultation findings, and may avoid unnecessary referrals to pediatric cardiologists. Our goal was to further develop and assess the quality of a novel computational algorithm that automatically classifies murmurs of phonocardiograms (PCGs) acquired in a pediatric population.

Methods:

Patients with no-, innocent-, and pathologic murmurs were recruited from the general pediatric cardiology clinic. Using an electronic stethoscope, PCGs were acquired by the pediatric cardiologist from each patient. The PCGs were analyzed by the algorithm and diagnoses were compared to findings by the pediatric specialist and echocardiography as the gold standard.

The following registry procedures and quality factors have been implemented in this preliminary clinical study:

* Quality assurance plan, including

* data validation * proper registration procedures * 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, including patient recruitment, data collection, data management, data analysis.