A Survey of Ethics in Anesthesia around the World / 대한마취과학회지

BACKGROUND: In recent years, an unprecedented expansion of high-tech medical knowledge has forced anesthesiologists to confront a moral climate never experienced before, thereby escalating the need to refer to ethical resources for the appropriate responses. This study was prepared to identify the issues and to determine the extent of activities in making proper ethical decisions regarding anesthesia around the world. METHODS: Eighteen questions were asked via electronic mail to anestheisa-associated doctors recognized by the Internet between April and June 1998. Information about issues, guidelines/standards/policy, consulting system/committee, education and training were requested. RESULTS: One hundred and twenty-two practitioners completed the questionnaire. Informed consent (38, 31.1%) was the most frequently raised issue, followed by medical economics (13.1%) and DNR (11.5%). The most serious broad issue was medical economics (13.1%), but specific or unique issues were not prominent. Among respondents, 73.8% had no guideline/standards/policy in their department, 56.6% felt the need to have one, 38.5% had one in the hospital and 51.6% indicated they needed one in their hospital. Most departments had no consulting system (61.5%) or committee (79.5%), while 48.4% had no consulting system and 63.9% had no committee in the hospital. A few (28, 23.1%) had a program/ curriculum, and lectures were the most common format. The most common duration of ethical education was more than 3 hours and the main obstacle was time constraints. Informed consent (41.8%) was indicated as the most important issue to be taught. CONCLUSIONS: A limited number favored systemic support and the educational benefit of lectures on ethics. Most respondents were willing to have such a lecture program in the department. Establishing a systemic structure for ethics was recommended by most respondents and restricted resources should be distributed for this purpose according to the survey results.

Prediction on lengths of stay in the postanesthesia care unit following general anesthesia: preliminary study of the neural network and logistic regression delling

The length of stay in the postanesthesia care unit (PACU) following general anesthesia in adults is an important issue. A model, which can predict the results of PACU stays, could improve the utilization of PACU and operating room resources through a more efficient arrangement. The purpose of study was to compare the performance of neural network to logistic regression analysis using clinical sets of data from adult patients undergoing general anesthesia. An artificial neural network was trained with 409 clinical sets using backward error propagation and validated through independent testing of 183 records. Twenty-two inputs were used to find determinants and to predict categorical values. Logistic regression analysis was performed to provide a comparison. The neural network correctly predicted in 81.4% of situations and identified discriminating variables (intubated state, sex, neuromuscular blocker and intraoperative use of opioid), whereas the figure was 65.0% in logistic regression analysis. We concluded that the neural network could provide a useful predictive model for the optimization of limited resources. The neural network is a new alternative classifying method for developing a predictive paradigm, and it has a higher classifying performance compared to the logistic regression model.