Anaesthesia
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Randomized Controlled Trial Multicenter Study
Anaesthetic efficacy and postinduction hypotension with remimazolam compared with propofol: a multicentre randomised controlled trial.
Remimazolam, a short-acting benzodiazepine, may be used for induction and maintenance of total intravenous anaesthesia, but its role in the management of patients with multiple comorbidities remains unclear. In this phase 3 randomised controlled trial, we compared the anaesthetic efficacy and the incidence of postinduction hypotension during total intravenous anaesthesia with remimazolam vs. propofol. A total of 365 patients (ASA physical status 3 or 4) scheduled for elective surgery were assigned randomly to receive total intravenous anaesthesia with remimazolam (n = 270) or propofol (n = 95). ⋯ Mean (SD) number of postinduction hypotension events was 62 (38.1) and 71 (41.1) for patients allocated to the remimazolam and propofol groups, respectively; p = 0.015. Noradrenaline administration events (requirement for a bolus and/or infusion) were also lower in patients allocated to remimazolam compared with propofol (14 (13.5) vs. 20 (14.6), respectively; p < 0.001). In conclusion, in patients who were ASA physical status 3 or 4, the anaesthetic effect of remimazolam was non-inferior to propofol.
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Deep learning-based facial analysis for predicting difficult videolaryngoscopy: a feasibility study.
While videolaryngoscopy has resulted in better overall success rates of tracheal intubation, airway assessment is still an important prerequisite for safe airway management. This study aimed to create an artificial intelligence model to identify difficult videolaryngoscopy using a neural network. Baseline characteristics, medical history, bedside examination and seven facial images were included as predictor variables. ⋯ The facial model (only including facial images) using the Light Gradient Boosting Machine algorithm showed the highest area under the curve (95%CI) of 0.779 (0.733-0.825) with a sensitivity (95%CI) of 0.757 (0.650-0.845) and specificity (95%CI) of 0.721 (0.626-0.794) in the test set. Compared with bedside examination and multivariate scores (El-Ganzouri and Wilson), the facial model had significantly higher predictive performance (p < 0.001). Artificial intelligence-based facial analysis is a feasible technique for predicting difficulty during videolaryngoscopy, and the model developed using neural networks has higher predictive performance than traditional methods.