• Carl Tams, Noah Syroid, Terrie Vasilopoulos, and Ken Johnson.
• Department of Anesthesiology, University of Utah, 30 N 1900 E, Salt Lake City, UT, 84132, USA. carl.tams@utah.edu.
• J Clin Monit Comput. 2019 Dec 1; 33 (6): 959-971.

AbstractTitrating an intraoperative anesthetic to achieve the postoperative goals of rapid emergence and prolonged analgesia can be difficult because of inter-patient variability and the need to provide intraoperative sedation and analgesia. Modeling pharmacokinetics and pharmacodynamics of anesthetic administrations estimates drug concentrations and predicted responses to stimuli during anesthesia. With utility of these PK/PD models we created an algorithm to optimize the intraoperative dosing regimen. We hypothesized the optimization algorithm would find a dosing regimen that would increase the postoperative duration of analgesia, not increase the time to emergence, and meet the intraoperative requirements of sedation and analgesia. To evaluate these hypotheses we performed a simulation study on previously collected anesthesia data. We developed an algorithm to recommend different intraoperative dosing regimens for improved post-operative results. To test the post-operative results of the algorithm we tested it on previously collected anesthesia data. An anesthetic dataset of 21 patients was obtained from a previous study from an anesthetic database at the University of Utah. Using the anesthetic records from these surgeries we modeled 21 patients using the same patient demographics and anesthetic requirements as the dataset. The anesthetic was simulated for each of the 21 patients with three different dosing regimens. The three dosing regimens are: from the anesthesiologist as recorded in the dataset (control group), from the algorithm in the clinical scenario one (test group), and from the algorithm in the clinical scenario two (test group). We created two clinical scenarios for the optimization algorithm to perform; one with normal general anesthesia constraints and goals, and a second condition where a delayed time to emergence is allowed to further maximize the duration of analgesia. The algorithm was evaluated by comparing the post-operative results of the control group to each of the test groups. Comparing results between the clinical scenario 1 dosing to the actual dosing showed a median increase in the duration of analgesia by 6 min and the time to emergence by 0.3 min. This was achieved by decreasing the intraoperative remifentanil infusion rate, increased the fentanyl dosing regimen, and not changing the propofol infusion rate. Comparing results between the clinical scenario 2 dosing to the actual dosing showed a median increase in the duration of analgesia by 26 min and emergence by 1.5 min. To dosing regimen from clinical scenario 2 greatly increased the fentanyl dosing regimen and greatly decreased the remifentanil infusion rate with no change to the propofol infusion rate. The results from this preliminary analysis of the optimization algorithm appear to imply that it can operate as intended. However a clinical study is warranted to determine to what extent the optimization algorithm determined optimal dosing regimens can maximize the postoperative duration of analgesia without delaying the time to emergence in a clinical setting.

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