No abstract

SummaryThe inability to maintain oxygenation by non‐invasive means is one of the most pressing emergencies in anaesthesia and emergency care. To prevent hypoxic brain damage and death in a ‘cannot intubate, cannot oxygenate’ situation, emergency percutaneous airway access must be performed immediately. Even though this emergency is rare, every anaesthetist should be capable of performing an emergency percutaneous airway as the situation may arise unexpectedly. Clear knowledge of the anatomy and the insertion technique, and repeated skill training are essential to ensure completion of this procedure rapidly and successfully. Various techniques have been described for emergency oxygenation and several commercial emergency cricothyroidotomy sets are available. There is, however, no consensus on the best technique or device. As each has its limitations, it is recommended that all anaesthetists are skilled in more than one technique of emergency percutaneous airway. Avoiding delay in initiating rescue techniques is at least as important as choice of device in determining outcome.

Hoarseness and vocal cord injuries are clinically relevant complications related to short-term general anesthesia using an endotracheal tube or laryngeal mask. However, more well-designed prospective studies are necessary to generate reliable data as well as to investigate techniques to reduce adverse laryngeal effects. For future research, a proposal to categorize the vocal cord lesions due to general anesthesia is presented. Furthermore, use of a preoperative and postoperative standardized measurement protocol using acoustic analysis and the Voice Handicap Index is advised.

For efficient utilization of operating rooms (ORs), accurate schedules of assigned block time and sequences of patient cases need to be made. The quality of these planning tools is dependent on the accurate prediction of total procedure time (TPT) per case. In this paper, we attempt to improve the accuracy of TPT predictions by using linear regression models based on estimated surgeon-controlled time (eSCT) and other variables relevant to TPT. We extracted data from a Dutch benchmarking database of all surgeries performed in six academic hospitals in The Netherlands from 2012 till 2016. The final dataset consisted of 79,983 records, describing 199,772 h of total OR time. Potential predictors of TPT that were included in the subsequent analysis were eSCT, patient age, type of operation, American Society of Anesthesiologists (ASA) physical status classification, and type of anesthesia used. First, we computed the predicted TPT based on a previously described fixed ratio model for each record, multiplying eSCT by 1.33. This number is based on the research performed by van Veen-Berkx et al., which showed that 33% of SCT is generally a good approximation of anesthesia-controlled time (ACT). We then systematically tested all possible linear regression models to predict TPT using eSCT in combination with the other available independent variables. In addition, all regression models were again tested without eSCT as a predictor to predict ACT separately (which leads to TPT by adding SCT). TPT was most accurately predicted using a linear regression model based on the independent variables eSCT, type of operation, ASA classification, and type of anesthesia. This model performed significantly better than the fixed ratio model and the method of predicting ACT separately. Making use of these more accurate predictions in planning and sequencing algorithms may enable an increase in utilization of ORs, leading to significant financial and productivity related benefits.

The principle of ejector-based EVA seems promising and deserves further evaluation.