Rational and design of the digital diagnosis of cardiac sounds in pediatric patients (DI_SOUND) study

Abstract Aims Congenital heart diseases (CHD) are the most common birth defects in humans. Current clinical newborn screening for CHD has limitations. Digital elaboration of cardiac sounds with diagnostic purposes has been explored in the recent past in adults and older children. We propose to develop a dedicated software for automatic dichotomous clinical classification of heart sounds in newborns. Methods and results The Digital Diagnosis of cardiac SOUNDs in pediatric patients (DI_SOUND) study is a multicenter study with two sequential phases: 1) derivation/training; 2) validation. Each newborn will undergo a digital recording of cardiac sounds (stored as wav file using the Littmann Core Stethoscope) and comprehensive echocardiogram. In the development phase, sound tracings will undergo processing to remove background noise. They will be segmented to generate sound samples of homogeneous suitable tuned duration, and filtered records will be normalized for preventing inhomogeneity in the extracted features. Data analysis will be then implemented based on handcrafted feature extraction. A DI_SOUND classifier algorithm will be developed (Aim 1). In the validation phase, consecutive newborns will undergo clinical (standard) screening and digital screening using the DI_SOUND algorithm. Comprehensive echocardiogram will serve as the reference to assess diagnostic performance of the DI_SOUND algorithm (Aim 2). A cost-effective analysis will be used to model marginal cost-effectiveness of digital screening using clinical screening as comparison (Aim 3). The study will prove if development and validation of a digital classifier of neonatal cardiac sounds is feasible and how this will impact clinical outcomes, patient care, hospital admissions and screening efficiency.