Velia Malizia

Objectives: Chest high‐resolution computed tomography (HRCT) is conditionally recommended to rule out conditions that mimic or coexist with severe asthma in children. However, it may provide valuable insights into identifying structural airway changes in pediatric patients. This study aims to develop a machine learning‐based chest HRCT image analysis model to aid pediatric pulmonologists in identifying features of severe asthma. Methods: This retrospective case‐control study compared children with severe asthma (as defined by ERS/ATS guidelines) to age‐ and sex‐matched controls without asthma, using chest HRCT scans for detailed imaging analysis. Statistical analysis included classification trees, random forests, and conventional ROC analysis to identify the most significant imaging features that mark severe asthma from controls. Results: Chest HRCT scans differentiated children with severe asthma from controls. Compared to controls ( n = 21, mean age 11.4 years), children with severe asthma ( n = 20, mean age 10.4 years) showed significantly greater bronchial thickening (BT) scores ( p < 0.001), airway wall thickness percentage (AWT%, p < 0.001), bronchiectasis grading (BG) and bronchiectasis severity (BS) scores ( p = 0.016), mucus plugging, and centrilobular emphysema ( p = 0.009). Using AWT% as the predictor in conventional ROC analysis, an AWT% ≥ 38.6 emerged as the optimal classifier for discriminating severe asthmatics from controls, with 95% sensitivity, specificity, and overall accuracy. Conclusion: Our study demonstrates the potential of machine learning‐based analysis of chest HRCT scans to accurately identify features associated with severe asthma in children, enhancing diagnostic evaluation and contributing to the development of more targeted treatment approaches.

Introduction: The diversity of allergic rhinitis (AR) phenotypes is particularly evident in childhood, suggesting the need to analyze and identify new approaches to capture such clinical heterogeneity. Nasal cytology (NC) is a very useful diagnostic tool for identifying and quantifying nasal inflammation. Data‐driven approaches such as latent class analysis (LCA) assign subjects to classes based on their characteristics. We hypothesized that LCA based on NC, including the assessment of neutrophils, eosinophils, and mast cells, may be helpful for identifying AR endotypes in children.Methods: A total of 168 children were enrolled. Sociodemographic characteristics and detailed medical history were obtained from their parents. All children performed NC and skin prick tests. LCA was applied for identifying AR endotypes based on NC, using the R package poLCA. All the statistical analyses were performed using R 4.0.5 software. Statistical significance was set at p ≤ .05.Results: LCA identified two classes: Class 1 (n = 126, 75%): higher frequency of children with moderate/large number of neutrophils (31.45%); almost all the children in this class had no mast cells (91.27%) and Class 2 (n = 42, 25%): higher frequency of children with moderate/large number of eosinophils (45.24%) and moderate/large number of mast cells (50%).Conclusions: The present study used a machine learning approach for endotyping childhood AR, which may contribute to improve the diagnostic accuracy and to deliver personalized health care in the context of precision medicine.

Nowadays, technology permeates every aspect of human activity, from the professional to the recreational side, and the recent pandemic crisis has only amplified a clearly defined trend.Through portable devices, people play, work, and get information. The immediacy of information and of many aspects of our life is a condition that, once acquired, is difficult to give up. However, immediate information must also be correct, and not all issues can easily be simplified to the point of being understandable in the absence of basic theoretical notions to a large audience. This aspect is particularly relevant when dealing with issues related to human health and the communication problems encountered in recent months concerning the spread of SARS‐CoV‐2, and the vaccination campaign is there to prove it. With the intent of clarifying the advantages and disadvantages of fast and accessible information, in this article, some of the most widespread applications for mobile devices, dedicated to pollen allergens, air quality, parks, gardens, and green areas, were examined.A widespread lack of clarity on the sources of the data and on the criteria used to formulate synthetic judgments—that often confuse the final users—was noted.

Allergic rhinitis (AR) is a global health problem: its prevalence is 23% in Europe, although it is underestimated because as many as 45% of the cases remain undiagnosed. Globally, almost 500 million people suffer from AR, which shows its increasing incidences. The diagnostic course of AR is based on clinical history, supported by anterior rhinoscopy. This inspects the anterior part of the nasal cavity accompanied by allergic sensitivity tests (cutaneous allergic skin tests or specific immunoglobulin E levels). The availability of standardised diagnostic proceduresis able to provide objective evaluations of inflammatory situation, and the degree of nasal obstruction may give an advantage in reducing the risk of underestimating the diagnosis of AR. Diagnostic tests with a high level of accuracy are able to provide immediate results, which can sustain the doctor in diagnostic–therapeutic framework. The development of Point of Care Tests (POCTs) could be a useful tool. Considering that nasal obstruction is the mostcommon symptom in patients with AR, the rhinomanometry (RM) test is the most indicated objective evaluation for nasal obstruction. Several studies have also shown the practicability of such diagnostic techniques applied in children. So far, no study has evaluated whether all the applicable requirements are fulfilled by RM in order to be considered as a POCT. The purpose of this perspective was to assess whether all the POCT requirements are fulfilled by RM by conducting a narrative review of the existing literature in which RM has been used in the diagnosis and management of AR in children. A few but encouraging results of studies on children supported the potential use of RM in the area of POCT. However, costs of instruments and the training of personnel involved remain to be explored. The studies support the potential use of RM in POCTs.

Background: Specific instruments for health‐related quality of life (HRQoL) assessment in adolescents with rhinoconjunctivitis or asthma are available. None of them evaluates rhinitis and asthma together, although they often coexist. Our aim was to validate a HRQoL questionnaire for adolescents with rhinoconjunctivitis, asthma, or both.Methods: A pool of 38 items covering the main symptoms and problems related to respiratory allergy was generated based on literature review, clinical experience, and unstructured interviews to 54 adolescents. The items were randomly listed and presented to 88 consecutive outpatients (44 M; mean age 15.2 ± 3.1). Patients had to indicate which item they had experienced and, for each selected item, its importance on a four‐point scale (1 = not at all; 4 = very much). Twelve items were excluded from the list, because of low importance. In the validation phase, 102 patients (54 M; mean age 15.36 ± 1.12) completed the KINDL, a generic HRQoL tool, and the new questionnaire (RHINASTHMA‐Adolescents).Results: Factor analysis revealed a five‐dimensional structure, which explained up to 71.23% of the total variance. Association between RHINASTHMA‐Adolescents and KINDL scores was all in the expected direction. Internal consistency for the extracted factors was satisfactory: Upper Airways (0.81), Lower Airways (0.89), Emotions (0.85), Social Relationship (0.79), Daily life management (0.74). Reliability was good for all factors with a Pearson coefficient ranged from 0.91 to 0.99.Conclusions: RHINASTHMA‐Adolescents is the first tool for evaluating HRQoL in patients with rhinitis and/or asthma. It provides a simple assessment and met the standards of validity, internal consistency, and reliability.