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.
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Binary classifier for normal versus abnormal cardiac sounds in newborns
Timeframe: one year