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Introduction The aim of the article is to determine the appropriate concentration of desflurane to effectively counteract the increase in blood pressure resulting from surgical stress. In medical practice, this increase is often limited by using additional doses of opioid drugs. Additional medications or higher doses of those already used may adversely affect your health. During anesthesia, physician must note the use of drugs and remember them, especially those that he has recently administered, which affect his concentration. For this purpose, the authors decided to propose support for the selection of desflurane concentration so that frequent use of opioid drugs is not necessary. The authors used a system based on AI issues to accomplish this task. The learned system supports the anesthesiologist’s work by imitating him. Patients and Methods The proposed method for selecting the desflurane concentration is based on a fuzzy controller. This system includes a learning mechanism that allows for minimizing the operating error. The main advantage of this system is the ability to build a function allowing the selection of anesthesia parameters without knowledge of the mathematical description of the process. To accomplish this task, you need an expert who will provide information in the construction of logical if-then sentences (points in space). The fuzzy controller connects the points in the consideration space appropriately, generating a hypersurface. The algorithm test was performed only by computer without the participation of patients. Results The operation of the proposed algorithm was verified by computer simulation. The authors of the article analyzed the compliance of the obtained results with the table provided by the expert. The desflurane concentration values obtained by computer simulation are similar to those given in the table Minimal driver error does not affect the patient’s clinical response. This error results from the functions used in the fuzzy system and its settings. The results of the performance test of the proposed algorithm are presented in a time course, and it has the shape of a step function. The work proposes a function that allows you to enter the time needed for the body’s reaction to reach the desired E tdes level. Conclusion In this study, a controller was created to support the selection of the concentration of desflurane allowing for a reduction in blood pressure (resulting from surgical stress). The results obtained by computer simulation provide valuable insights for optimizing anesthesia. This system can also be used as an important simulation program for teaching purposes.
Introduction The aim of the article is to determine the appropriate concentration of desflurane to effectively counteract the increase in blood pressure resulting from surgical stress. In medical practice, this increase is often limited by using additional doses of opioid drugs. Additional medications or higher doses of those already used may adversely affect your health. During anesthesia, physician must note the use of drugs and remember them, especially those that he has recently administered, which affect his concentration. For this purpose, the authors decided to propose support for the selection of desflurane concentration so that frequent use of opioid drugs is not necessary. The authors used a system based on AI issues to accomplish this task. The learned system supports the anesthesiologist’s work by imitating him. Patients and Methods The proposed method for selecting the desflurane concentration is based on a fuzzy controller. This system includes a learning mechanism that allows for minimizing the operating error. The main advantage of this system is the ability to build a function allowing the selection of anesthesia parameters without knowledge of the mathematical description of the process. To accomplish this task, you need an expert who will provide information in the construction of logical if-then sentences (points in space). The fuzzy controller connects the points in the consideration space appropriately, generating a hypersurface. The algorithm test was performed only by computer without the participation of patients. Results The operation of the proposed algorithm was verified by computer simulation. The authors of the article analyzed the compliance of the obtained results with the table provided by the expert. The desflurane concentration values obtained by computer simulation are similar to those given in the table Minimal driver error does not affect the patient’s clinical response. This error results from the functions used in the fuzzy system and its settings. The results of the performance test of the proposed algorithm are presented in a time course, and it has the shape of a step function. The work proposes a function that allows you to enter the time needed for the body’s reaction to reach the desired E tdes level. Conclusion In this study, a controller was created to support the selection of the concentration of desflurane allowing for a reduction in blood pressure (resulting from surgical stress). The results obtained by computer simulation provide valuable insights for optimizing anesthesia. This system can also be used as an important simulation program for teaching purposes.
Background: Prolonged times to tracheal extubation (≥15 minutes from dressing on the patient) are consequential based on their clinical and economic effect. We evaluated the variability among anesthesia practitioners in their goals for the age-adjusted end-tidal minimum alveolar concentration of sevoflurane (MAC) at surgery end and achievement of their goals. Methods: We prospectively studied a cohort of 56 adult patients undergoing general anesthesia with sevoflurane as the sole anesthetic agent, scheduled operating room time of at least 3 hours, and non-prone positioning. At the start of surgical closure, an observer asked the anesthesia practitioner their goal for MAC when the surgical drapes are lowered (i.e., the functional end of surgery for the studied procedures). When the drapes were lowered, the MAC achieved was recorded, and the values were compared. Results: The standard deviation of the practitioners’ MAC goal was large, 0.199 (N = 56 cases, 95% confidence interval 0.17-0.24), not significantly different from the standard deviation of the MAC achieved of 0.253, P = 0.071. The MAC goal and MAC achieved were correlated pairwise, Pearson r =0.65, P < 0.0001. There was no incremental effect of operating room conversation(s) related to case progress on the association (partial correlation ‑0.01, P = 0.96). Differences among practitioners in the MAC achieved at surgery end were consequential. Specifically, for the N = 12 cases with prolonged extubation, the mean MAC was 0.60 (standard deviation 0.10) versus 0.48 (0.21) among the N = 44 cases without prolonged extubation (P = 0.0070). Conclusions: The standard deviation of the MAC goal among practitioners was sufficiently large to contribute significantly to the variability in the MAC achieved at the end of surgery. We confirmed prospectively that the age-adjusted end-tidal MAC at the end of surgery matters clinically and economically because differences of 0.60 versus 0.48 were associated with more prolonged extubations. Our novel finding is that the MAC achieved ≥0.60 were caused in part by the anesthesia practitioners’ stated MAC goals when surgical closures started.
Introduction: Prolonged times to tracheal extubation are intervals from the end of surgery to extubation ≥15 minutes. We examined why there are associations with the end-tidal inhalational agent concentration as a proportion of the age‑adjusted minimum alveolar concentration (MAC fraction) at the end of surgery. Methods: The retrospective cohort study used 11.7 years of data from one hospital. All p‑values were adjusted for multiple comparisons. Results: There was a greater odds of prolonged time to extubation if the anesthesia practitioner was a trainee (odds ratio 1.68) or had finished fewer than five cases with the surgeon during the preceding three years (odds ratio 1.12) (both P<0.0001). There was a greater risk of prolonged time to extubation if the MAC fraction was >0.4 at the end of surgery (odds ratio 2.66, P<0.0001). Anesthesia practitioners who were trainees and all practitioners who had finished fewer than five cases with the surgeon had greater mean MAC fractions at the end of surgery and had greater relative risks of the MAC fraction >0.4 at the end of surgery (all P<0.0001). The source for greater MAC fractions at the end of surgery was not greater MAC fractions throughout the anesthetic because the means during the case did not differ among groups. Rather, there was substantial variability of MAC fractions at the end of surgery among cases of the same anesthesia practitioner, with the mean (standard deviation) among practitioners of each practitioner's standard deviation being 0.35 (0.05) and the coefficient of variation being 71% (13%). Conclusion: More prolonged extubations were associated with greater MAC fractions at the end of surgery. The cause of the large MAC fractions was the substantial variability of MAC fractions among cases of each practitioner at the end of surgery. That variability matches what was expected from earlier studies, both from variability among practitioners in their goals for the MAC fraction given at the start of surgical closure and from inadequate dynamic forecasting of the timing of when surgery would end. Future studies should examine how best to reduce prolonged extubations by using anesthesia machines' display of MAC fraction and feedback control of end-tidal agent concentration.
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