Automated Algorithm Based Analysis of Phonocardiograms of Newborns
Automated Algorithm Based Analysis of Phonocardiograms of Newborns
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.
The following registry procedures and quality factors have been implemented:
Quality assurance plan, including
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.
Inclusion Criteria:
Exclusion Criteria: