Department of Anesthesiology, University of Utah, 30 N 1900 E, Salt Lake City, UT, 84132, USA.
MedVis, 2050 E 1700 S, Salt Lake City, UT, 84108, USA.
J Clin Monit Comput. 2019 Dec;33(6):959-971. doi: 10.1007/s10877-019-00298-9. Epub 2019 Mar 12.
Titrating 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.
将术中麻醉滴定至术后快速苏醒和延长镇痛的目标可能具有挑战性,因为存在患者间变异性和需要提供术中镇静和镇痛。对麻醉给药的药代动力学和药效动力学建模估计药物浓度和预测麻醉期间对刺激的反应。利用这些 PK/PD 模型,我们创建了一种算法来优化术中给药方案。我们假设优化算法会找到一种可以增加术后镇痛持续时间、不增加苏醒时间并满足术中镇静和镇痛要求的给药方案。为了评估这些假设,我们对之前收集的麻醉数据进行了模拟研究。我们开发了一种算法来推荐不同的术中给药方案以获得更好的术后效果。为了测试算法的术后效果,我们将其应用于之前收集的麻醉数据。从犹他大学的麻醉数据库中的一项先前研究中获得了 21 名患者的麻醉数据集。我们使用这些手术的麻醉记录,使用与数据集相同的患者人口统计学和麻醉要求对 21 名患者进行建模。对 21 名患者中的每一名患者进行三种不同给药方案的模拟。三种给药方案是:来自数据集的麻醉师记录(对照组)、临床场景一中的算法(测试组 1)和临床场景二中的算法(测试组 2)。我们为优化算法创建了两个执行临床场景;一个具有正常全身麻醉约束和目标,另一个允许苏醒时间延迟以进一步最大限度地延长镇痛持续时间的条件。通过将术中瑞芬太尼输注率降低、增加芬太尼给药方案和不改变丙泊酚输注率来评估算法。将临床场景 1 给药与实际给药的结果进行比较,发现镇痛持续时间中位数增加了 6 分钟,苏醒时间中位数增加了 0.3 分钟。这是通过降低术中瑞芬太尼输注率、增加芬太尼给药方案和不改变丙泊酚输注率来实现的。将临床场景 2 给药与实际给药的结果进行比较,发现镇痛持续时间中位数增加了 26 分钟,苏醒时间中位数增加了 1.5 分钟。临床场景 2 的给药方案大大增加了芬太尼的给药方案,并大大降低了瑞芬太尼输注率,而丙泊酚输注率没有变化。对优化算法的初步分析结果表明,它似乎可以按预期运行。然而,需要进行临床研究以确定优化算法确定的最佳给药方案在不延迟临床环境中苏醒时间的情况下可以在多大程度上最大限度地延长术后镇痛持续时间。