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首页> 外文期刊>The Internet Journal of Neuromonitoring >Transcranial Cerebral Oximetry During Surgical Procedures: A New Matrix Model For Minimization Of Problems Of Interpretation And Intervention In Desaturation Events
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Transcranial Cerebral Oximetry During Surgical Procedures: A New Matrix Model For Minimization Of Problems Of Interpretation And Intervention In Desaturation Events

机译：手术过程中经颅脑血氧饱和度测定：一种新的矩阵模型，用于最小化去饱和事件中的解释和干预问题

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Transcranial cerebral oximetry is a method to detect cerebral hypoxia and to avoid cerebral dysfunctions. Regional cerebral oxygen saturation was measured in a 35-year-old female during cerebral aneurysm surgery. For the effective clinical use of near infrared spectroscopy (NIRS) during surgical or endovascular procedures the intrinsic and extrinsic factors which can influence the NIRS data have to be considered. Therefore a NIRS matrix as a short information for the specific management to correct changes of cerebral oxygen saturation (COS) was developed.The aim of development of the NIRS matrix is to support the patient management specifically adapted to the surgical, neurointerventional and anesthesiological context. Introduction Transcranial near infrared spectroscopy (NIRS) can detect changes in cerebral oxygen metabolism even following minimal physiologic, pathophysiologic, and therapeutic events [1,2,3]. This method has potential applications for monitoring patients at risk of cerebral oxygen desaturation during certain types of surgery [4] such as cardiac surgery, major vascular surgery and carotid endarterectomy, for patients with surgical positioning issues, for patients older than 60 years [5] and neuroendovascular procedures [6]. There are prerequisites and limitations associated with NIRS which have to be appreciated for the use of this technique [7]. However, reports about the beneficial aspects of intraoperative NIRS monitoring for early identification of vulnerable episodes [8] and the improved postoperative neurological outcome [9] are encouraging.Using NIRS effectively requires accurate interpretation of the measurement values and this in turn requires correcting for intrinsic and extrinsic factors that can affect the results. We developed a matrix - i.e., the shortest unit of information - for interpreting NIRS data with the aim of integrating NIRS results into procedural care. Methods Our NIRS matrix for intraoperative applications contains five descriptors which are causally related to one another. The horizontal left-right link at one level contains the changes of cerebral oxygen saturation (COS) at one end. At the other end - as the result of conclusions - there are specific anesthesiologic interventions to consolidate the altered COS-values.NIRS matrix descriptors The major determinant is the “change of COS” as an indicator of changes in cerebral oxygen balance. COS changes are described as decrease (in special cases fluctuations of couplets of COS decreases and increases) from baseline. The next determinant is the “key variable”, which as the value that undergoes marked changes is related to the changes in COS. This determinant results from a group of values obtained with conventional intraoperative monitoring named the basic data (Tab. 1): mean arterial blood pressure (MAP), hemoglobin (Hb), peripheral oxygen saturation (SaO2), partial carbon dioxide pressure (pCO2), core temperature (t). In the logistics chain group A (explained below) one parameter of the basic data (= vital data) becomes the key variable once it shows a marked change (shift into an abnormal range or change from baseline). It is thus necessary to obtain a series of values to detect changes.For the logistics chain groups B and C (explained below) the key variables are not vital parameters obtained from the patient but rather physical (anatomical) (B) or (neuro-) pharmacologic (C) variables that have an effect on the COS. The key variable is compared to the “associated parameters” which within the groups of basic data described above showed no marked changes (Fig. 1). Each of the basic data can also assume the position of key variable that cause decrease of the COS. For the sake of didactic order the matrix does not use complex representations of synergistic constellations or antagonistic combinations. The “clinical interpretation” of the most probable underlying event is deduced from the data constellation COS - key variable - associa
机译：经颅脑血氧测定法是一种检测脑缺氧并避免脑功能障碍的方法。在脑动脉瘤手术期间测量了一名35岁女性的区域性脑氧饱和度。为了在手术或血管内手术中有效地使用近红外光谱（NIRS），必须考虑可能影响NIRS数据的内在因素和外在因素。因此，开发了NIRS矩阵作为用于纠正脑血氧饱和度（COS）变化的特定管理的简短信息。开发NIRS矩阵的目的是支持专门适应于外科，神经介入和麻醉学背景的患者管理。引言经颅近红外光谱（NIRS）甚至可以在发生最小程度的生理，病理生理和治疗事件后检测出脑氧代谢的变化[1,2,3]。这种方法在监测某些类型的手术[4]（例如心脏手术，大血管手术和颈动脉内膜切除术）中存在脑氧饱和度降低风险的患者，有手术定位问题的患者以及60岁以上的患者中具有潜在的应用[5]。和神经内血管手术[6]。使用该技术必须了解与NIRS相关的先决条件和限制[7]。然而，有关术中NIRS监测有利于早期发现易感发作的有益方面[8]和术后神经系统结局改善[9]的报道令人鼓舞。有效使用NIRS需要对测量值进行准确的解释，而这反过来又需要校正内在因素。以及可能影响结果的外部因素。我们开发了一个矩阵-即最短的信息单位-来解释NIRS数据，目的是将NIRS结果整合到程序护理中。方法我们用于手术中应用的NIRS矩阵包含五个因果关系。一级水平的左右链接包含一端的大脑氧饱和度（COS）的变化。在另一端（作为结论的结果），有专门的麻醉干预措施来巩固改变的COS值。NIRS矩阵描述符主要决定因素是“ COS的变化”作为大脑氧平衡变化的指标。 COS变化被描述为相对于基线的减少（在特殊情况下，COS的耦合波动减少和增加）。下一个决定因素是“关键变量”，它的值发生显着变化与COS的变化有关，该决定因素来自一组通过常规术中监测获得的称为基础数据的值（表1）：动脉血压（MAP），血红蛋白（Hb），外周血氧饱和度（SaO2），二氧化碳分压（pCO2），核心温度（t）。在物流链组A（如下所述）中，基本数据（=重要数据）的一个参数一旦显示出明显的变化（转变为异常范围或从基线变化），便成为关键变量。因此，有必要获取一系列值来检测变化。对于物流链组B和C（如下所述），关键变量不是从患者那里获得的重要参数，而是物理（解剖）（B）或（神经- ）对COS有影响的药理学（C）变量，将关键变量与“相关参数”进行比较，在上述基本数据组中，“相关参数”没有明显变化（图1）。每个基本数据还可以假设导致COS降低的关键变量的位置，为了使教学顺序更简单，矩阵不使用协同星座或拮抗组合的复杂表示。最可能发生的潜在事件的“临床解释”是根据数据星座COS得出的-关键变量-关联

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