Anaesthesia
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Randomized Controlled Trial
The association between epidural labour analgesia and postpartum depression: a randomised controlled trial.
There is conflicting evidence regarding the association between epidural labour analgesia and risk of postpartum depression. Most previous studies were observational trials with limited ability to account for confounders. We aimed to determine if epidural analgesia was associated with a significant change in the incidence of postpartum depression in this randomised controlled trial. ⋯ There were no significant differences in the incidence of postpartum depression between the two groups (adjusted risk difference (95%CI) 1.6 (-3.0-6.3%), p = 0.49). Similar results were obtained with per-protocol analysis (adjusted risk difference (95%CI) -1.0 (-8.3-6.3%), p = 0.79). We found no significant difference in the risk of postpartum depression between patients who received epidural labour analgesia and those who utilised non-epidural analgesic modalities.
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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.