ABSTRACT
Objective:To investigate the Chinese anesthesiologist′s proficiency, training experience and needs of flexible bronchoscope-guided awake flexible bronchoscopy intubation (AFBI) using a questionnaire method.Methods:The cluster sampling was used, and self-designed questionnaires that addressed 54 questions in 5 categories were distributed through WeChat and online platforms. The survey took one month, and the answers were automatically recorded by the WeChat server.Results:A total of 1 250 anesthesiologists participated in the survey in 30 provinces of China, 9 of them were not anesthesiologists, and 1 241 (99.28%) questionnaires were validated. In the valid questionnaires, 52.70% (654) of the anesthesiologists were from tertiary hospitals, and 74.78% (928) of the anesthesiologists were attending physicians or above, only 7.57% (94) of the anesthesiologists had sufficient confidence in AFBI. Twenty-five point two two percent (313) of the anesthesiologists preferred fiberoptic intubation as the first tool when dealing with the anticipated difficult airway. Forty-eight point one one percent (597) of the anesthesiologists had implemented AFBI. Among them, 80.74% (482) had experienced unsuccessful AFBI practices. Eight hundred and ninety-four anesthesiologists had received AFBI training, and the most common AFBI training strategy was theoretical lectures. In addition, the degree of satisfaction regarding the theoretical lectures quality, technical training, clinical practice relativity and non-technical skills training was 21.47% (192), 14.32% (128), 12.3% (110) and 17.90% (160), respectively. The degree of satisfaction with all the 4 training elements mentioned above was 7.27% (65).Conclusions:The awareness and practice of Chinese anesthesiologists in terms of clinical application of AFBI to treat difficult airways need to be strengthened at present, and the lack of high-quality AFBI training may be the key.
ABSTRACT
Improper control of depth of anesthesia is not only detrimental to the rapid and stable recovery of anesthesia, but also affects the postoperative outcome of patients. Therefore, accurate control of anesthesia depth is an urgent clinical and scientific problem in the field of anesthesiology. At present, different algorithm models derived from electroencephalogram (EEG) signals are used to monitor the depth of anesthesia, but they cannot meet the requirements of anesthesiologists to accurately evaluate the depth of anesthesia. In recent years, the research on the mechanism and modulation of anesthesia-related neural network suggests that it has potential value as a method to monitor depth of anesthesia. Anesthesia-related neural networks mainly include sleep-wake circuit, thalamic-cortical circuit and corticocortical network. A thorough understanding of the neural network involved in the loss of consciousness caused by anesthesia will guide the depth of anesthesia monitoring more accurately and provide possibility for improving the quality of clinical anesthesia resuscitation.