Anesthesia and analgesia
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Anesthesia and analgesia · May 2020
Observational StudyUsing the Knowledge to Action Framework to Describe a Nationwide Implementation of the WHO Surgical Safety Checklist in Cameroon.
Surgical safety has advanced rapidly with evidence of improved patient outcomes through structural and process interventions. However, knowledge of how to apply these interventions successfully and sustainably at scale is often lacking. The 2019 Global Ministerial Patient Safety Summit called for a focus on implementation strategies to maintain momentum in patient safety improvements, especially in low- and middle-income settings. This study uses an implementation framework, knowledge to action, to examine a model of nationwide World Health Organization (WHO) Surgical Safety Checklist implementation in Cameroon. Cameroon is a lower-middle-income country, and based on data from high- and low-income countries, we hypothesized that more than 50% of participants would be using the checklist (penetration) in the correct manner (fidelity) 4 months postintervention. ⋯ This study shows that a multifaceted implementation strategy is associated with successful checklist implementation in a lower-middle-income country such as Cameroon, and suggests that a theoretical framework can be used to practically drive nationwide scale-up of checklist use.
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Anesthesia and analgesia · May 2020
Observational StudyAssociation Between Postoperative Body Temperature and All-Cause Mortality After Off-Pump Coronary Artery Bypass Graft Surgery: A Retrospective Observational Study.
Inadvertent perioperative hypothermia is common in patients undergoing off-pump coronary artery bypass grafting (OPCAB). We investigated the association between early postoperative body temperature and all-cause mortality in patients undergoing OPCAB. ⋯ Even mild early postoperative hypothermia was associated with all-cause mortality after OPCAB. Patients who regained normothermia postoperatively were at lower risk of all-cause mortality compared to those who did not.
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Anesthesia and analgesia · May 2020
Predicting Deep Hypnotic State From Sleep Brain Rhythms Using Deep Learning: A Data-Repurposing Approach.
Brain monitors tracking quantitative brain activities from electroencephalogram (EEG) to predict hypnotic levels have been proposed as a labor-saving alternative to behavioral assessments. Expensive clinical trials are required to validate any newly developed processed EEG monitor for every drug and combinations of drugs due to drug-specific EEG patterns. There is a need for an alternative, efficient, and economical method. ⋯ We propose a novel method to develop hypnotic level monitors using large sleep EEG data, deep learning, and a data-repurposing approach, and for optimizing such a system for monitoring any given individual. We provide a novel data-repurposing framework to predict hypnosis levels using sleep EEG, eliminating the need for new clinical trials to develop hypnosis level monitors.