* Research Fellow, Department of Anaesthesia, Harvard Medical School, Boston, Massachusetts; Research Fellow, Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts; Research Affiliate, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts. † Research Assistant, Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital. ‡ Instructor, Department of Neurology, Harvard Medical School; Assistant in Neurology, Department of Neurology, Massachusetts General Hospital. § Assistant Professor, Department of Anaesthesia, Harvard Medical School; Assistant Anesthetist, Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital; Research Affiliate, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology. ‖ Instructor, Department of Anaesthesia, Harvard Medical School; Instructor, Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital; Research Affiliate, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology. # Warren M. Zapol Professor of Anaesthesia, Department of Anaesthesia, Harvard Medical School; Anesthetist, Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital; Professor of Computational Neuroscience, Edward Hood Taplin Professor of Medical Engineering, Institute for Medical Engineering and Sciences, Department of Brain and Cognitive Sciences, Harvard-MIT Health Sciences and Technology Program, Massachusetts Institute of Technology.
Anesthesiology. 2013 Oct;119(4):848-60. doi: 10.1097/ALN.0b013e31829d4ab4.
A medically induced coma is an anesthetic state of profound brain inactivation created to treat status epilepticus and to provide cerebral protection after traumatic brain injuries. The authors hypothesized that a closed-loop anesthetic delivery system could automatically and precisely control the electroencephalogram state of burst suppression and efficiently maintain a medically induced coma.
In six rats, the authors implemented a closed-loop anesthetic delivery system for propofol consisting of: a computer-controlled pump infusion, a two-compartment pharmacokinetics model defining propofol's electroencephalogram effects, the burst-suppression probability algorithm to compute in real time from the electroencephalogram the brain's burst-suppression state, an online parameter-estimation procedure and a proportional-integral controller. In the control experiment each rat was randomly assigned to one of the six burst-suppression probability target trajectories constructed by permuting the burst-suppression probability levels of 0.4, 0.65, and 0.9 with linear transitions between levels.
In each animal the controller maintained approximately 60 min of tight, real-time control of burst suppression by tracking each burst-suppression probability target level for 15 min and two between-level transitions for 5-10 min. The posterior probability that the closed-loop anesthetic delivery system was reliable across all levels was 0.94 (95% CI, 0.77-1.00; n = 18) and that the system was accurate across all levels was 1.00 (95% CI, 0.84-1.00; n = 18).
The findings of this study establish the feasibility of using a closed-loop anesthetic delivery systems to achieve in real time reliable and accurate control of burst suppression in rodents and suggest a paradigm to precisely control medically induced coma in patients.
医学诱导昏迷是一种深度脑失活的麻醉状态,用于治疗癫痫持续状态,并在创伤性脑损伤后提供脑保护。作者假设闭环麻醉输送系统可以自动精确地控制脑电状态的爆发抑制,并有效地维持医学诱导昏迷。
在六只大鼠中,作者实现了一个由计算机控制的输液泵、一个双室药代动力学模型来定义丙泊酚对脑电图的影响、实时计算脑电图爆发抑制状态的爆发抑制概率算法、在线参数估计程序和比例积分控制器组成的闭环麻醉输送系统。在对照实验中,每只大鼠随机分配到六个爆发抑制概率目标轨迹之一,这些轨迹是通过对 0.4、0.65 和 0.9 的爆发抑制概率水平进行线性转换来构建的。
在每只动物中,控制器通过跟踪每个爆发抑制概率目标水平 15 分钟,在 5-10 分钟内进行两次水平之间的转换,实现了大约 60 分钟的紧密实时爆发抑制控制。闭环麻醉输送系统在所有水平上的可靠性后验概率为 0.94(95%CI,0.77-1.00;n=18),在所有水平上的准确性为 1.00(95%CI,0.84-1.00;n=18)。
本研究的结果证实了使用闭环麻醉输送系统实时可靠和准确地控制啮齿动物爆发抑制的可行性,并提出了一种精确控制患者医学诱导昏迷的范例。