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

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 composed of two sequential phases: a derivation/training phase (binary classification algorithm development, Aim 1) and validation phase (binary classification algorithm validation and cost-effective analysis Aims 2 and 3) (Graphical Abstract). The study design is based on SPIRIT 2025 Guideline. IRB approval has been obtained by each clinical unit.

Study population:

Derivation phase. Study population will include neonates with known cardiovascular status including newbornswith and without CHD.

Validation phase. Study population will include newborns without previous cardiovascular examination and unknown cardiovascular status. Pre-test probability for CHD will be defined: high-risk newborn for structural cardiovascular abnormalities versus neonates at low-risk (general population risk level). High-risk sub-group will include newborns with existing fetal ultrasound suggesting cardiovascular abnormalities and those with clinical indication for pediatric cardiology evaluation (abnormal neonatal screening, signs/symptoms). Low- risk sub-group (approaching patient level prevalence for structural cardiovascular abnormalities) will include consecutive neonates specifically enrolled for such research aim and without any indication for cardiovascular examination.

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

Study tool:

Cardiac sounds will be recorded using the Littmann Core Stethoscope (Eko Software) (3M Company, Minnesota/USA). Digital recordings will be stored as wav files in a dedicate encrypted platform for data sharing among centers.

Each newborn enrolled in the study will undergo a standardized and complete echocardiogram by experienced operators as reported in Table 1 and in the full Study Protocol submitted as Online Supplementary Material. Echocardiographic evaluation will be performed according to existing guidelines for neonatal echocardiography.28 Exams will be performed with standardized and reproducible approach (Table 1). They will be stored on a digital support. After anonymization, the exams will be transferred to theEchocardiographic Study Core Imaging Laboratory for formal assessment and adjudication.

A web-based, encrypted Case Report Form will be created using the institutional REDCap (Vanderbilt University) license of IRCCS Azienda Ospedaliero-Universitaria di Bologna.

Study procedure:

Patient enrollment For the derivation phase newborns without CHD will be enrolled at the time of discharge from Ob/Gyn program and newborns with CHD will be enrolled at the time of cardiovascular examination. For the validation phase consecutive newborns will be enrolled at the time of discharge from Ob/Gyn unit.

Graphical abstractsummarizes the study pipeline. Derivation phase After screening of eligible patients, written informed consent will be obtained from parents or caregivers.

The first echocardiographic evaluation will take place at a post natal age\< 30days.Age at recording and echocardiographic evaluation will be planned not before 7 days of post-natal life to allow completion of proper cardio-circulatory transition in the healthy newborns (i.e. ductal closure, pulmonary vascular remodeling, foramen ovale physiologic shunt). The echocardiogram will be linked to an anonymous identifier which will be used to link the exam to the patient without breaching patient privacy. The examination will be digitally stored and transferred to the Echocardiography Study Core Lab for formal revision. The cardiovascular neonatal status will be appropriately labeled as being with or without cardiovascular abnormalities.

Heart sound will be digitally recorded using as acquisition device Littmann Core Stethoscope (Eko Software) (3M Company, Minnesota/USA). Cardiac sound tracings will be recorded and stored in wav format and encrypted transferred to the Bioengineer Research Unit for further elaboration. Cardiovascular neonatal status will be un-blinded to the Bioengineer Research Unit to allow for proper handling of classifier training.

The details of the acquisition method are as follows:

1. Acquisition device: Littmann Core Stethoscope (Eko Software) (3M Company, Minnesota/USA) 2. Position of the auscultation: mid precordium. Gentle pressure is applied on chest, spontaneous breathing, patients are kept as calm as possible using maternage without any sedation 3. Average recording duration: 15 seconds Phonocardiographic signals will be stored as wav files in an encrypted cloud system to be transferred for further analysis (Graphical abstract). Tracings will undergo preliminary processing to remove background noise, and will be segmented to generate sound samples of homogeneous suitable tuned duration. As the last step of the preprocessing, the filtered records will be normalized for preventing inhomogeneity in the extracted features. Data analysis will be then implemented based on handcrafted feature extraction. The main objective of feature extraction is to identify a small number of representative features, i.e., characteristic properties of a sound sample, that replace the high- dimensional raw signals still preserving its informative content with respect to the phenomenon under investigation. Relevant features are then fed to the classification algorithm to discriminate between healthy and abnormal subjects. Multiple different techniques are typically used for feature extraction in sound signals, and can be loosely grouped into frequency-domain techniques (e.g., Fast Fourier transform (FFT), Discrete Cosine Transform (DCT), Short Time Fourier transform (STFT)), time-frequency domain techniques (e.g., Linear Frequency Band Cepstral (LFBC), Mel Frequency Cepstrum Coefficients (MFCC) and linear predictive coding (LPC)), and time-domain techniques (e.g., zero-crossing detection and peak finding). Each of these techniques provides descriptors of potentially relevant properties of the sound sample, which can be used as features or further processed to extract higher level characteristics of the signal (such as systole and diastole variability). To maximize the robustness of our results to less-than-ideal acquisition conditions, we will train the classifiers using features from all the above-mentioned domains and will perform a feature selection procedure to select those features which are mostly correlated to the outcome of interest.We will consider as possible classifiers Logistic Regression, AdaBoost, XGBoost, Random Forest, Support Vector Machines and Hidden Markov Models. Neural networks may be implemented if appropriate and feasible.Following the standard best practice, the dataset will be split into training set, validation set and test data set. For evaluating the performance of classifiers, K-Fold cross-validation with different fold numbers will be used: 10-fold, 5-fold, and Leave- One-Out- Cross-Validation (LOOCV). In LOOCV, the number of folds is equal to number of records.Gini importance for Random Forest classifiers will be used as a tool to validate parameters with least predictive contribution.Once a proper classifier has been trained a post training analysis to determine the most relevant features will be conducted. The purpose of this analysis is to investigate to which extent each feature is contributing to the selection of one class with respect to the other. Techniques for feature relevance estimation based on SHAP values, or permutation analysis will be applied also with the aim of providing a proper explanation to the model decision (Graphical Abstract).

Validation phase Clinical software validation (Aim 2) After screening of eligible patients written informed consent will be obtained from parents or caregivers.

Clinical screening will be performed as mandated by the italian law. Pre-ductal (right arm) oxygen saturation and post-ductal (leg) oxygen saturation will be recored. Physical examination will include femoral arterial pulse detection and heart auscultation.

Clinical screening output threshold criteria is summarized in Table 2. For this phase we will enroll patients using a block stratification for high CHD risk versus low (standard) CHD risk status for each newborn.

Heart recordings will be acquired (30 seconds length) before comprehensive echocardiography by a trained research investigator. Age at recording and echocardiographic evaluation will be planned not before 7 days of post-natal life to allow completion of proper cardio-circulatory transition in the healthy newborns (i.e. ductal closure, pulmonary vascular remodeling, foramen ovale physiologic shunt).

The echocardiography will be performed according to current guidelines by expert pediatric cardiovascular imager blinded to clinical and digital screening results. Preliminary inter- and intra-observer variability between dedicated expert cardiovascular imagers among centers will be perf

• Validation of the binary classifier in a consecutive, independent cohort of newborns [Срок оценки: one year]