Remote ischaemic preconditioning with RIPostC by transient upper limb ischaemia did not improve clinical outcome in patients who underwent cardiac surgery.

During spinal block, there seems to be a safety margin of 2-4 vertebral bodies and intervertebral spaces between the conus medullaris and Tuffier's line, which is consistent regardless of sex or presence of transitional vertebra. However, because the conus medullaris and Tuffier's line become closer with age and the clinical use of Tuffier's line requires palpation through subcutaneous fat, caution must be exercised regarding selection of the intervertebral space, especially in the aged and obese population.

Machine learning approaches were introduced for better or comparable predictive ability than statistical analysis to predict postoperative outcomes. We sought to compare the performance of machine learning approaches with that of logistic regression analysis to predict acute kidney injury after cardiac surgery. We retrospectively reviewed 2010 patients who underwent open heart surgery and thoracic aortic surgery. Baseline medical condition, intraoperative anesthesia, and surgery-related data were obtained. The primary outcome was postoperative acute kidney injury (AKI) defined according to the Kidney Disease Improving Global Outcomes criteria. The following machine learning techniques were used: decision tree, random forest, extreme gradient boosting, support vector machine, neural network classifier, and deep learning. The performance of these techniques was compared with that of logistic regression analysis regarding the area under the receiver-operating characteristic curve (AUC). During the first postoperative week, AKI occurred in 770 patients (38.3%). The best performance regarding AUC was achieved by the gradient boosting machine to predict the AKI of all stages (0.78, 95% confidence interval (CI) 0.75–0.80) or stage 2 or 3 AKI. The AUC of logistic regression analysis was 0.69 (95% CI 0.66–0.72). Decision tree, random forest, and support vector machine showed similar performance to logistic regression. In our comprehensive comparison of machine learning approaches with logistic regression analysis, gradient boosting technique showed the best performance with the highest AUC and lower error rate. We developed an Internet–based risk estimator which could be used for real-time processing of patient data to estimate the risk of AKI at the end of surgery.

Use of the LMA in smaller children results in more airway obstruction, higher ventilatory pressures, larger inspiratory leak, and more complications than in older children.

Purpose The purpose of this study was to evaluate the effect of head rotation in adults and children on endotracheal tube (ETT) position and to confirm previous results regarding the influence of head flexion and extension on ETT position. Methods After inducing anesthesia in 24 young adults and 22 children (aged 1-9 yr), ETTs were secured on the right corner of each of their mouths. Using a fiberoptic bronchoscope, the distance from the carina to the tip of the ETT was measured with each patient's head and neck placed in a neutral position, flexed, extended, rotated to the right, and rotated to the left.