Clinical practice guidelines: Approach to cough in children: The official statement endorsed by the Saudi Pediatric Pulmonology Association (SPPA)

Cough in Children and Adults: Diagnosis, Assessment and Management (CICADA). Summary of an updated position statement on chronic cough in Australia

INTRODUCTION

Cough is the most common symptom leading to medical consultation. Chronic cough results in significant health care costs, impairs quality of life, and may indicate the presence of a serious underlying condition. Here, we present a summary of an updated position statement on cough management in the clinical consultation.

MAIN RECOMMENDATIONS

Assessment of children and adults requires a focused history of chronic cough to identify any red flag cough pointers that may indicate an underlying disease. Further assessment with examination should include a chest x-ray and spirometry (when age > 6 years). Separate paediatric and adult diagnostic management algorithms should be followed. Management of the underlying condition(s) should follow specific disease guidelines, as well as address adverse environmental exposures and patient/carer concerns. First Nations adults and children should be considered a high risk group. The full statement from the Thoracic Society of Australia and New Zealand and Lung Foundation Australia for managing chronic cough is available at https://lungfoundation.com.au/resources/cicada-full-position-statement.

CHANGES IN MANAGEMENT AS A RESULT OF THIS STATEMENT

Algorithms for assessment and diagnosis of adult and paediatric chronic cough are recommended. High quality evidence supports the use of child-specific chronic cough management algorithms to improve clinical outcomes, but none exist in adults. Red flags that indicate serious underlying conditions requiring investigation or referral should be identified. Early and effective treatment of chronic wet/productive cough in children is critical. Culturally specific strategies for facilitating the management of chronic cough in First Nations populations should be adopted. If the chronic cough does not resolve or is unexplained, the patient should be referred to a respiratory specialist or cough clinic.

Pediatric Allergy: An Overview

Allergy is a broad topic encompassing common clinical allergic diseases, asthma, and complex immunodeficiencies. In this article, the authors discuss the most common allergic diseases and anaphylaxis and briefly review the current knowledge and management of food allergies, allergic rhinitis, otitis media, sinusitis, chronic cough, atopic dermatitis, urticarial and angioedema, contact dermatitis, allergic ophthalmopathy, drug allergy, latex allergy, and insect sting. Because the prevalence of allergic disorders continues to increase, it is increasingly important for physicians to stay up to date on most recent evidence-based diagnosis and management of allergic disorders.

Development of Machine Learning for Asthmatic and Healthy Voluntary Cough Sounds: A Proof of Concept Study

(1) Background: Cough is a major presentation in childhood asthma. Here, we aim to develop a machine-learning based cough sound classifier for asthmatic and healthy children. (2) Methods: Children less than 16 years old were randomly recruited in a Children’s Hospital, from February 2017 to April 2018, and were divided into 2 cohorts—healthy children and children with acute asthma presenting with cough. Children with other concurrent respiratory conditions were excluded in the asthmatic cohort. Demographic data, duration of cough, and history of respiratory status were obtained. Children were instructed to produce voluntary cough sounds. These clinically labeled cough sounds were randomly divided into training and testing sets. Audio features such as Mel-Frequency Cepstral Coefficients and Constant-Q Cepstral Coefficients were extracted. Using a training set, a classification model was developed with Gaussian Mixture Model–Universal Background Model (GMM-UBM). Its predictive performance was tested using the test set against the physicians’ labels. (3) Results: Asthmatic cough sounds from 89 children (totaling 1192 cough sounds) and healthy coughs from 89 children (totaling 1140 cough sounds) were analyzed. The sensitivity and specificity of the audio-based classification model was 82.81% and 84.76%, respectively, when differentiating coughs from asthmatic children versus coughs from ‘healthy’ children. (4) Conclusion: Audio-based classification using machine learning is a potentially useful technique in assisting the differentiation of asthmatic cough sounds from healthy voluntary cough sounds in children.