Developing slow wave activity saturation as a marker of depth of anaesthesia

开发慢波活动饱和度作为麻醉深度的标志

基本信息

  • 批准号:
    MR/R006423/1
  • 负责人:
  • 金额:
    $ 75.98万
  • 依托单位:
  • 依托单位国家:
    英国
  • 项目类别:
    Research Grant
  • 财政年份:
    2018
  • 资助国家:
    英国
  • 起止时间:
    2018 至 无数据
  • 项目状态:
    未结题

项目摘要

General anaesthesia is delivered to individuals during an operation to stop them being aware of what is going around them, prevent any pain and immobilise the body so surgery can be carried out safely. Unfortunately the anaesthetists, who are the specialist doctors responsible for delivering the anaesthesia, do not currently have a reliable way of measuring the exact point when an individual's brain becomes unconscious during the surgery. They tend to judge the amount of anaesthetic they give depending on when the average person would lose consciousness. They then increase or decrease the dose for that person depending on how their heart or the lungs react during the operation. We believe that we have discovered an interesting change in the brain's electrical activity that indicates the point when an individual person having surgery loses perception of what is happening in the outside world. This potentially means that anaesthetists could give just the right amount of drug for that particular person. This is important because, whilst anaesthesia is very safe, some patients who are older or particularly sick may suffer from long-term side effects if they are given too much anaesthesia. It will also prevent the very rare event that someone is aware during the operation. Our interesting observation is that when the anaesthetic dose is increased, slow waves in the brain reach a maximum level, and then do not increase any further even though much more anaesthetic drug is given. These slow waves are low frequency oscillations in the brain (at around 1 Hz or 1 cycle per second) and are also an important feature of deep sleep. We have called this observation slow wave activity saturation (or SWAS) and it can be measured by applying electrical sensors to the scalp - a technique called electroencephalography (or EEG for short). When we discovered SWAS, we also performed simultaneous brain imaging with a technique called functional magnetic resonance imaging (FMRI), and found that brain's response to pain and words altered dramatically when the individual's electrical activity reached this SWAS level. The brain network activated in response to these stimuli at SWAS was very different to the one that was activated when they were awake, or even the brain network that was activated at lower anaesthetic concentrations. It was this change in how stimuli are processed in the brain that makes us believe that the person is no longer aware of the outside world.We have recently developed a mathematical model that, when applied to an EEG system, can dynamically track the changes in slow wave activity in real-time. The model will allow us to predict when an individual has entered this SWAS state. We hope that, by delivering the anaesthesia to achieve this state, we can make sure everyone who has surgery is unaware of what is going but also doesn't receive too much medication so that it takes them longer to recover. To test this, we plan to use our system in 200 patients having surgery and deliver just enough anaesthesia so that their brain's activity reaches the saturation state. We will then check whether SWAS is a good measure to assess how deeply someone is anaesthetised in two ways. Firstly, we will use a technique called the isolated forearm test before the surgery to confirm that the patient is not aware of what is going on around them. Secondly, we will check how they recover from the operation by measuring how sick they feel afterwards and how much pain they are in. We hope that we can show that patients who receive anaesthesia delivered to the SWAS state have an improved recovery after surgery than patients who have anaesthesia delivered in the usual way. If we can show that this study is a success, we hope that in the long-term we can create a depth of anaesthesia monitor that will enable patients all over the world to be given just the right amount of anaesthetic for their operations.
全身麻醉是在手术过程中提供给个人的,以阻止他们意识到周围发生了什么,防止任何疼痛和固定身体,以便手术可以安全地进行。不幸的是,麻醉师,谁是负责提供麻醉的专业医生,目前没有一个可靠的方法来测量一个人的大脑在手术过程中变得无意识的确切点。他们倾向于根据一般人失去意识的时间来判断他们给予的麻醉剂的量。然后,他们根据手术期间心脏或肺部的反应增加或减少该人的剂量。我们相信,我们已经发现了大脑电活动的一个有趣的变化,这表明当一个人接受手术时,会失去对外部世界发生的事情的感知。这可能意味着麻醉师可以为特定的人提供适量的药物。这一点很重要,因为虽然麻醉是非常安全的,但如果给予过多的麻醉,一些年龄较大或病情特别严重的患者可能会遭受长期的副作用。这也将防止非常罕见的事件,有人知道在操作过程中。我们有趣的观察是,当麻醉剂量增加时,大脑中的慢波达到最大水平,然后即使给予更多的麻醉药物也不再增加。这些慢波是大脑中的低频振荡(大约每秒1 Hz或1个周期),也是深度睡眠的重要特征。我们称这种观察为慢波活动饱和(SWAS),它可以通过将电传感器应用于头皮来测量-一种称为脑电图(EEG)的技术。当我们发现SWAS时,我们还使用一种称为功能性磁共振成像(FMRI)的技术进行了同步脑成像,发现当个体的电活动达到SWAS水平时,大脑对疼痛和言语的反应发生了显着变化。在SWAS中,对这些刺激做出反应而激活的大脑网络与清醒时激活的大脑网络非常不同,甚至与在较低麻醉剂浓度下激活的大脑网络也非常不同。正是大脑处理刺激方式的这种变化让我们相信这个人不再意识到外部世界。我们最近开发了一种数学模型,当应用于脑电系统时,可以动态跟踪慢波活动的变化实时。该模型将使我们能够预测一个人何时进入这种SWAS状态。我们希望,通过提供麻醉来实现这种状态,我们可以确保每个接受手术的人都不知道发生了什么,但也不会接受太多的药物治疗,这样他们就需要更长的时间才能恢复。为了测试这一点,我们计划在200名接受手术的患者中使用我们的系统,并提供足够的麻醉,使他们的大脑活动达到饱和状态。然后,我们将检查SWAS是否是一个很好的措施,以评估有多深的人是麻醉在两个方面。首先,我们将在手术前使用一种称为孤立前臂测试的技术,以确认患者不知道周围发生了什么。第二,我们将通过测量他们术后的不适程度和疼痛程度来检查他们从手术中恢复的情况。我们希望我们能够证明,接受SWAS状态麻醉的患者比以通常方式接受麻醉的患者术后恢复更好。如果我们能证明这项研究是成功的,我们希望从长远来看,我们可以创造一种麻醉深度监测器,使世界各地的病人能够在手术中获得适量的麻醉剂。

项目成果

期刊论文数量(10)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Brainwave viscosity in propofol anaesthesia
  • DOI:
    10.1016/j.bja.2021.11.005
  • 发表时间:
    2022-03
  • 期刊:
  • 影响因子:
    9.8
  • 作者:
    Fabus MS;Woolrich MW;Warnaby CE
  • 通讯作者:
    Warnaby CE
Adding objectivity to submaximal exercise testing by assessment of heart rate recovery at home-a healthy volunteer study iv (search-iv)
通过在家中评估心率恢复来增加次最大运动测试的客观性——一项健康志愿者研究 iv (search-iv)
  • DOI:
    10.1016/j.bja.2023.06.007
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    9.8
  • 作者:
    Luckhurst J
  • 通讯作者:
    Luckhurst J
Understanding Harmonic Structures Through Instantaneous Frequency.
Regional anaesthetic brain susceptibility to propofol is linked with local GABA
区域麻醉大脑对异丙酚的敏感性与局部 GABA 有关
  • DOI:
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    10.7
  • 作者:
    Fabus M.
  • 通讯作者:
    Fabus M.
Automatic decomposition of electrophysiological data into distinct nonsinusoidal oscillatory modes.
  • DOI:
    10.1152/jn.00315.2021
  • 发表时间:
    2021-11-01
  • 期刊:
  • 影响因子:
    2.5
  • 作者:
    Fabus MS;Quinn AJ;Warnaby CE;Woolrich MW
  • 通讯作者:
    Woolrich MW
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