British journal of anaesthesia
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Review Meta Analysis
Machine learning-augmented interventions in perioperative care: a systematic review and meta-analysis.
We lack evidence on the cumulative effectiveness of machine learning (ML)-driven interventions in perioperative settings. Therefore, we conducted a systematic review to appraise the evidence on the impact of ML-driven interventions on perioperative outcomes. ⋯ CRD42023433163 (PROSPERO).
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Managing postoperative pain in patients with obesity is challenging. Although multimodal analgesia has proved effective for pain relief, the specific impacts of different nonopioid i.v. analgesics and adjuvants on these patients are not well-defined. This study aims to assess the effectiveness of nonsteroidal antiinflammatory drugs, paracetamol, ketamine, α-2 adrenergic receptor agonists, lidocaine, magnesium, and oral gabapentinoids in reducing perioperative opioid consumption and, secondarily, in mitigating the occurrence of general and postoperative pulmonary complications (POPCs), nausea, vomiting, PACU length of stay (LOS), and hospital LOS among surgical patients with obesity. ⋯ CRD42023399373 (PROSPERO).
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Operating theatres consume large amounts of energy and consumables and produce large amounts of waste. There is an increasing evidence base for reducing the climate impacts of healthcare that could be enacted into routine practice; yet, healthcare-associated emissions increase annually. Implementation science aims to improve the systematic uptake of evidence-based care into practice and could, therefore, assist in addressing the environmental impacts of healthcare. ⋯ This review demonstrates a gap between evidence for reducing environmental impacts and uptake of proposed practice changes to deliver low-carbon healthcare. Future research into 'greening' healthcare should use implementation research methods to establish a solid implementation evidence base. SYSTEMATIC REVIEW PROTOCOL: PROSPERO CRD42022342786.