British journal of anaesthesia
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To coincide with the annual scientific meeting of Regional Anaesthesia UK in London 2024, where there is a joint scientific session with the British Journal of Anaesthesia, a special regional anaesthesia edition of the journal has been produced. This editorial offers some highlights from the manuscripts contained within the special edition.
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Observational Study
Assessment of sedation by automated pupillometry in critically ill patients: a prospective observational study.
Quantitative measurement of pupil change has not been assessed against the Richmond Agitation and Sedation Scale (RASS) and spectral edge frequency (SEF) during sedation. The aim of this study was to evaluate pupillometry against these measures in sedated critically ill adult patients. ⋯ Quantitative assessment of %PLR correlates with other indicators of sedation depth in critically ill patients.
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Regional anaesthesia use is growing worldwide, and there is an increasing emphasis on research in regional anaesthesia to improve patient outcomes. However, priorities for future study remain unclear. We therefore conducted an international research prioritisation exercise, setting the agenda for future investigators and funding bodies. ⋯ We prioritised unanswered research questions in regional anaesthesia. These will inform a coordinated global research strategy for regional anaesthesia and direct investigators to address high-priority areas.
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Patients who undergo laparotomy for major trauma are amongst the most critically unwell patients, and they have high morbidity and mortality rates. Despite 20 yr of improvements in resuscitation practices, those who present with hypotension continue to have mortality rates of up to 50%. Currently there is no mechanism for capturing national audit data on these patients, leading to their exclusion from potential quality improvement initiatives. We argue that there is an unmet need for quality assurance in this patient cohort and outline possible mechanisms to address this.
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A recent study by Suissa and colleagues explored the clinical relevance of a medical image segmentation metric (Dice metric) commonly used in the field of artificial intelligence (AI). They showed that pixel-wise agreement for physician identification of structures on ultrasound images is variable, and a relatively low Dice metric (0.34) correlated to a substantial agreement on subjective clinical assessment. We highlight the need to bring structure and clinical perspective to the evaluation of medical AI, which clinicians are best placed to direct.