Lung Ultrasound in Pediatric CAP

Pneumonia

Community-acquired Pneumonia (CAP) represents the single largest cause of death and morbidity in children worldwide (1). Respiratory viruses are the most common cause of CAP in preschool children, followed by bacteria. The atypical bacteria Mycoplasma pneumoniae and Chlamydia pneumoniae are common causes of pneumonia in children >5 years. The identification of the causal agent is pivotal, especially in children who require hospital admission, as it guides the choice of appropriate treatment. However, the microbial diagnosis of CAP in children is not easy to establish without invasive procedures, and chest X-Ray has failed to identify the aetiology of CAP. Clinical features of bacterial pneumonia, atypical bacterial pneumonia or viral pneumonia frequently overlap and cannot be used reliably to distinguish between the various aetiologies, as well as blood tests like white blood cell, C-reactive protein, including the more recently introduced serum procalcitonin (85% sensitivity and 45% specificity in identifying children without typical bacterial CAP. As a consequence, children with CAP usually receive unnecessary empirical antibiotics, contributing to the spread of antibiotic resistance or to side effects. Therefore, new methods, possibly fast, non- invasive and easily accessible in the outpatient settings (point-of-care) to optimize and personalize the management of children with suspected CAP are urgently needed Specific aim 1 To perform a clinical prospective study aimed to evaluate clinical, laboratory, microbiolical and outcomes data and to define LUS patterns (ultrasonomic) in children with CAP of different aetiologies: (viral, bacterial and atypical CAP) in different italian regions (Lazio, Puglia). Specific Aim 2 Development and validation of multi-factorial prediction models for the personalized diagnosis and management of paediatric CAP and building of a Decision Support System (DSS) based on validated prediction models that will be build based on the collection of "ultrasonomic", clinical, laboratory, treatments, outcomes and microbiological data collected from all partners. In particular, we will: i) develop, validate, and improve prediction models for the prediction of aetiology, outcome and treatment response; ii) take advantage of prediction models to better inform patients/caregivers on the risks and benefits of the proposed treatments; iii) use the outcome of the prediction models to individualize the management

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