Digital diagnoSis of Cardiac sOUNd in peDiatric Patients [DI-SOUND Study]

Neonatal screening procedures for potentially life-threatening congenital cardiovascular diseases (i.e., duct-dependent systemic or pulmonary circulation), currently implemented at the national level, rely primarily on cardiovascular physical examination performed by a neonatologist. More recently, this approach has been complemented by the assessment of hemoglobin oxygen saturation at both the upper and lower extremities (pre- and post-ductal saturation) in order to improve diagnostic sensitivity, although this practice has not yet been uniformly adopted nationwide. Converging evidence indicates that these screening strategies are affected by significant limitations in both sensitivity (failure to identify affected individuals) and specificity (false-positive findings in healthy subjects). These limitations are associated with substantial overall costs for the healthcare system. Failure to correctly identify affected neonates may result in increased morbidity and mortality, whereas overdiagnosis leads to unnecessary second-level diagnostic investigations and imposes a considerable psychological burden on families, who remain understandably anxious until diagnostic confirmation is achieved. The aim of the present research project (proof-of-concept study) is to develop a digital classifier capable to categorize heart sounds with commercially available digital stethoscopes into a binary classification system distinguishing physiological from pathological sounds. The derivation phase will be followed by a prospective validation phase, in which the classifier will be applied to assess its diagnostic performance. This phase will also evaluate the economic impact of the digital screening approach compared with standard practice. During the derivation phase, neonates with known cardiovascular status, as determined by prior echocardiographic assessment (including both healthy subjects and those with congenital heart disease), will be enrolled. Heart sounds will be recorded in a quiet environment under standard clinical conditions, without sedation. Digital recordings will be stored in WAV format and analyzed to develop a binary classification algorithm capable of distinguishing healthy from pathological cases. Following development, the classifier will be prospectively applied to a validation cohort of neonates undergoing conventional cardiovascular screening (clinical examination and pre- and post-ductal pulse oximetry), followed by classification using the digital tool under investigation. All participants will subsequently undergo confirmatory echocardiography. Diagnostic performance metrics, including sensitivity, specificity, positive and negative predictive values, and likelihood ratios, will be calculated for both the digital and conventional screening modalities. Furthermore, the number of missed pathological cases and the number of unnecessary second-level investigations resulting from false-positive findings will be used to define the economic benefit profile of the proposed screening strategy. Monte Carlo simulation techniques will be employed to extrapolate these findings at the national level, using ISTAT data on birth rates and disease prevalence. It is anticipated that the development of a digital classifier for the binary classification of neonatal heart sounds will be feasible. Moreover, it is expected that this tool will demonstrate superior diagnostic performance compared with current neonatal screening strategies, with beneficial implications not only for the accurate identification of affected and healthy neonates but also for reducing overall healthcare costs associated with missed diagnoses and inappropriate overdiagnosis.

Introduction: Congenital heart diseases (CHD) are the most common birth defects in humans. Timely diagnosis of cardiac structural abnormalities in newborns and children is associated with improved outcomes in the general pediatric population. Within CHD, ductal-dependent CHD are a rare group of cardiovascular malformation with heterogeneous anatomical features, sharing the inability to sustain either the pulmonary (ductal-dependent pulmonary circulation) or the systemic (ductal-dependent systemic circulation) circulation at the time of ductal closure. Examples of such condition are hypoplastic left heart syndrome (prevalence of 2/10.000), severe coarctation of the aorta (prevalence of 3/10.000), pulmonary atresia with intact ventricular septum (\<1/10.000), critical neonatal aortic valve stenosis (prevalence \~5/10.000), critical pulmonary valve stenosis (1-5/10.000) and other rare more complex congenital lesions. In this cases, it is imperative to timely establish the correct diagnosis to ensure ductal patency through prostaglandin infusion and refer patient for care to tertiary pediatric cardiovascular centers. Current newborn screening for CHD predominantly relies on brachial and lower extremity pulse oximetry screening (POS) and cardiac auscultation. Diagnostic performance of such practice is limited. POS is plagued by moderate sensitivity, in particular if performed during the first 24 hours of life. Cardiac auscultation is probably even more limited with sensitivity ranging between 75-85%. Although prenatal and neonatal screening of CHD has been associated with increased recognition of disease in newborns a significant number of patients is not correctly identified and delayed diagnosis is still present in western and even more so in developing countries. 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 (normal versus abnormal) in newborns to improve neonatal recognition of structural heart disease in this population. The Digital dIagnosis of cardiac SOUND in pediatric patients (DI\_SOUND) study aims to develop and validate a tool with the overall goal of improving neonatal recognition of CHD. Study Aims Aim 1: Develop a binary classifier for normal versus abnormal cardiac sounds in newborns Aim 2: Validate the binary classifier in a consecutive, independent cohort of newborns Aim 3: Cost-effective analysis of digital versus standard screening modality for CHD in newborns Methods and study design Thisis a multicenter study and itwill be conducted in fourpediatriccardiologyprograms in Italy (IRCCS Azienda Ospedaliero-Universitaria di Bologna, IRCCS Ospedale Pediatrico Bambin Gesù in Roma, Azienda Ospedaliero-Universitaria Policlinico Umberto I in Roma and Ospedale Monaldi in Napoli) along with a Engineeringunit (Politecnico di Milano). The study is

Inclusion criteria: * Age \< 30 days * Signed informed consent obtained from parent(s) or representative(s) Exclusion criteria: * Inability to acquire a diagnostic echocardiogram * Weight less than 1.5Kg