RESUMEN
AIM: Systematically review the management of infants with severe bronchiolitis in a paediatric intensive care unit (PICU) setting with a focus on high-risk infants to identify gaps in evidence-based knowledge. METHODS: This systematic review utilised Preferred Reporting Items for Systematic Review and Meta-analysis Protocols (PRISMA-P) to examine the literature on the PICU management of bronchiolitis in infants <24 months old. Three databases, Embase, PubMed and Medline, were searched and higher levels of evidence I, II and III were included. RESULTS: There were 455 papers reviewed and 26 met the inclusion criteria. Furthermore, 19 of these studied respiratory interventions such as positive airway pressure and oxygen delivery. The remaining 7 examined: erythropoietin, caffeine, dexamethasone, protein supplementation, ribavirin, respiratory syncytial virus immune globulin, or diuretic therapy. Of the 26 studies, 20 excluded infants with high-risk conditions. Therapies showing favourable outcomes included Heliox, prophylactic dexamethasone pre-extubation, protein supplementation, and diuretic use. CONCLUSIONS: Clinical trials for bronchiolitis management frequently exclude high-risk children. Innovative study design in the future may improve access to clinical trials for the management of bronchiolitis in high-risk infants in a PICU setting. IMPACT: Clinical trials for bronchiolitis management frequently exclude high-risk children. We review the evidence base for the management of an under-investigated patient demographic in the setting of acute bronchiolitis. Randomised controlled trials are needed to determine the efficacy of management strategies for bronchiolitis in high-risk infants in a paediatric intensive care setting.
RESUMEN
Aim: This systematic review aims to evaluate the current body of research surrounding the efficacy of artificial intelligence (AI) in cardiac rehabilitation. Presently, AI can be incorporated into personal devices such as smart watches and smartphones, in diagnostic and home monitoring devices, as well as in certain inpatient care settings. Materials & methods: The PRISMA guidelines were followed in this review. Inclusion and exclusion criteria were set using the Population, Intervention, Comparison and Outcomes (PICO) tool. Results: Eight studies meeting the inclusion criteria were found. Conclusion: Incorporation of AI into healthcare, cardiac rehabilitation delivery, and monitoring holds great potential for early detection of cardiac events, allowing for home-based monitoring, and improved clinician decision making.
Lay abstract Artificial intelligence (AI) involves the use of technologies capable of making decisions based on data provided. AI can be used in healthcare to provide actionable data for a clinician by analyzing patterns in patient data to predict outcomes and guide treatment. Cardiovascular disease is the leading cause of death worldwide. Cardiac rehabilitation is a therapy proven to reduce mortality and morbidity from cardiovascular disease. This study outlines three cases of AI based healthcare tools in cardiac rehabilitation. This includes the provision of personalized, home-based cardiac rehabilitation, the early detection of cardiac events through smart watch monitoring and by providing clinician decision making support in cardiac failure rehabilitation.