Anaesthesia
-
Review Meta Analysis
The impact of pre-operative depression on pain outcomes after major surgery: a systematic review and meta-analysis.
Symptoms of depression are common among patients before surgery. Depression may be associated with worse postoperative pain and other pain-related outcomes. This review aimed to characterise the impact of pre-operative depression on postoperative pain outcomes. ⋯ The change in pain scores from pre-operative baseline to 1-2 years after surgery was similar between patients with and without pre-operative depression (standardised mean difference 0.13 (95%CI -0.06-0.32), p = 0.15, I2 = 54%; very low certainty). Overall, pre-existing depression before surgery was associated with worse pain severity postoperatively. Our findings highlight the importance of incorporating psychological care into current postoperative pain management approaches in patients with depression.
-
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.