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Research

Patient and Process Outcomes among Pediatric Patients Undergoing Appendectomy during the COVID-19 Pandemic: An International Retrospective Cohort Study

COVID-19 forced healthcare systems to make unprecedented changes in clinical care processes. The authors hypothesized that the COVID-19 pandemic adversely impacted timely access to care, perioperative processes, and clinical outcomes for pediatric patients undergoing primary appendectomy.

Research

The effect of the COVID-19 pandemic on paediatric anaesthesia research as evidenced by the contrasting recruitment experiences of centres in Australia and Scotland

At two hospitals in Western Australia, we conducted a prospective, open-label, randomised, controlled trial of 240 patients undergoing tonsillectomy to investigate the effect of chewing a confectionery jelly snake on postoperative nausea and vomiting. The results were published in Anaesthesia Critical Care & Pain Medicine. Recruitment for this study was completed uneventfully between July 2018 and August 2019.

Research

Clinical utility of preoperative pulmonary function testing in pediatrics

Perioperative respiratory adverse events pose a significant risk in pediatric anesthesia, and identifying these risks is vital. Traditionally, this is assessed using history and examination. However, the perioperative risk is multifactorial, and children with complex medical backgrounds such as chronic lung disease or obesity may benefit from additional objective preoperative pulmonary function tests.

Research

Kids voices: Exploring children's perspective of tonsillectomy surgery

Britta Regli-von Ungern-Sternberg AM FAHMS MD, PhD, DEAA, FANZA Chair of Paediatric anaesthesia, University of Western Australia; Consultant

Research

Quantitative electroencephalogram and machine learning to predict expired sevoflurane concentration in infants

Processed electroencephalography (EEG) indices used to guide anesthetic dosing in adults are not validated in young infants. Raw EEG can be processed mathematically, yielding quantitative EEG parameters (qEEG). We hypothesized that machine learning combined with qEEG can accurately classify expired sevoflurane concentrations in young infants. Knowledge from this may contribute to development of future infant-specific EEG algorithms.