Paediatric Respiratory Reviews最新文献

Non-invasive ventilation (NIV) has become an essential component of pediatric respiratory support, offering effective alternatives to invasive mechanical ventilation in both acute and chronic conditions. Advances in technology and a growing understanding of pediatric respiratory physiology have expanded the application of NIV across a range of clinical scenarios, from acute respiratory failure in the intensive care unit to long-term management of neuromuscular and sleep-related breathing disorders in outpatient settings. As part of a review series, in this part 2 we provide an overview of new and emerging modes of NIV, their recommended clinical applications and we will briefly discuss the available evidence in pediatrics.

Large language models (LLMs) such as ChatGPT, Claude, and Gemini have become widely accessible since 2022. As childhood asthma remains the most common chronic paediatric condition with persistent gaps in optimal management, these tools present both opportunities and challenges for families and healthcare professionals. This narrative review examines the role of commercially available LLMs in childhood asthma care, exploring their fundamental principles, current evidence, and potential applications. Studies show that LLMs can generate medically accurate and comprehensible responses to asthma-related queries. Healthcare professionals may also benefit from rapid summarisation and tailored educational content. However, risks include hallucinations, bias, and data privacy concerns. Further research is required to evaluate the safety, clinical utility, and real-world acceptability of LLMs - particularly in acute asthma management by families and in supporting clinical decisions by healthcare professionals - and to guide the development of reliable, inclusive tools tailored to paediatric respiratory care.

Despite a high burden of respiratory disease among infants globally, limited options exist for lung function testing in this age group. Tidal breathing techniques such as oscillometry allow for understanding the pathophysiology of diseases that originate early in life, thus providing the opportunity to develop timely prevention and treatment strategies. This review summarises the principles of infant oscillometry, the primary oscillometry measures, physiological differences compared to older age groups, commonly used testing platforms, laboratory benchmarking, and future directions for research. Expanding the use of infant oscillometry will require further standardisation of equipment, calibration, protocols, and technical standards, followed by well-designed studies to outline clinical utility. While infant oscillometry is currently limited to the research setting, this technique has the potential for clinical applications in the future, ultimately providing opportunities for improving respiratory outcomes in infants globally.

Proponents of artificial intelligence (AI) believe that it will revolutionise the modern world, affecting how healthcare is delivered and improve both the clinical care we provide and the ease with which we perform our work. In this paper we explain what is meant by 'artificial intelligence' and explore how this technology has been implemented, or might be implemented, with respect to paediatric respiratory medicine. We review the current literature on how AI has been used to improve diagnostics - including examples in radiology, primary ciliary dyskinesia (PCD) diagnostics, sleep medicine, and pulmonary function tests. We also review how AI has been applied to therapeutics and drug discovery, how it will impact evidence-based medicine and literature review, and how clinician support tools will assist us in our work.

Asthma is an umbrella term for several phenotypes and endotypes. It most frequently begins before the age of 6, with significant morbidity and decline in lung function occurring among all pediatric age groups. A delay in the diagnosis of asthma in preschoolers is associated with more severe exacerbations. One problem clinicians face is how to diagnose asthma early in its course; epidemiological phenotypes (i.e., transient, persistent, late-onset, and mid-childhood remitting wheeze) can only be ascertained retrospectively, and clinical phenotypes (e.g., episodic viral and multi-trigger wheeze) suffer from high variability and no relation with underlying pathological airway markers. International guidelines recommend that lung function tests and biomarkers be performed before diagnosing asthma in children under 5 years old. However, spirometry and airway resistance measures are typically normal in most preschoolers with asthma, and blood eosinophil counts, the most reliable biomarker for inhaled corticosteroid therapy, vary widely over time. Clinical predictive indices can help in predicting and diagnosing asthma in preschoolers. At least eight clinical predictive indices have been published, and four have been validated (API, PIAMA, APT, and PARS). Here, we will review the challenges of diagnosing asthma in the preschool age, the utility of several clinical indices, and the usefulness of incorporating biomarkers such as volatile organic components, exhaled breath condensate, and gene expression. Finally, we will discuss existing gaps and future directions for research in the field.

Smart inhaler systems represent a major opportunity to transform paediatric asthma care by potentially addressing three fundamental problems affecting paediatric asthma outcomes: medication adherence, inhalation technique and reliever overuse. The data generated across these domains opens up multiple applications - from supporting patient self-management to enabling early detection of asthma deterioration. In this review, we outline the main categories of smart inhalers - including digital dose counters, smart spacers, and smart nebulisers - and summarise the current evidence surrounding their use in children. We discuss key obstacles to implementation, including technical limitations, behavioural factors, and health system-level challenges. Establishing clear smart inhaler systems quality standards, defining appropriate indications resulting in better asthma outcomes, achieving better integration of smart inhaler data into electronic health care records, and generating robust cost-effectiveness data will be essential to support the widespread clinical adoption of these technologies.