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使用多窗口加权算法监测颅脑损伤患者的最佳脑灌注压。

Monitoring of Optimal Cerebral Perfusion Pressure in Traumatic Brain Injured Patients Using a Multi-Window Weighting Algorithm.

机构信息

1 Brain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, Addenbrooke's Hospital, University of Cambridge , Cambridge, United Kingdom .

2 Department of Neurology, University Medical Center Groningen , Groningen, The Netherlands .

出版信息

J Neurotrauma. 2017 Nov 15;34(22):3081-3088. doi: 10.1089/neu.2017.5003. Epub 2017 Aug 2.

Abstract

Methods to identify an autoregulation guided "optimal" cerebral perfusion pressure (CPPopt) for traumatic brain injury patients (TBI) have been reported through several studies. An important drawback of existing methodology is that CPPopt can be calculated only in ∼50-60% of the monitoring time. In this study, we hypothesized that the CPPopt yield and the continuity can be improved significantly through application of a multi-window and weighting calculation algorithm, without adversely affecting preservation of its prognostic value. Data of 526 severe TBI patients admitted between 2003 and 2015 were studied. The multi-window CPPopt calculation was based on automated curve fitting in pressure reactivity index (PRx)-CPP plots using data from 36 increasing length time windows (2-8 h). The resulting matrix of CPPopts was then averaged in a weighted manner. The yield, continuity, and stability of CPPopt were studied. The difference between patients' actual CPP and CPPopt (ΔCPP) was calculated and the association with outcome was analyzed. Overall, the multi-window method demonstrated more continuous and stable presentation of CPPopt in this cohort. The new method resulted in a mean (±SE) CPPopt yield of 94% ± 2.1%, as opposed to the previous single-window-based CPPopt yield of 51% ± 0.94%. The stability of CPPopt across the whole monitoring period was significantly improved by using the new algorithm (p < 0.001). The relationship between ΔCPP according to the multi-window algorithm and outcome was similar to that for CPPopt calculated on the basis of a single window. In conclusion, this study validates the use of a new multi-window and weighting algorithm for significant improvement of CPPopt yield in TBI patients. This methodological improvement is essential for its clinical application in future CPPopt trials.

摘要

已有多项研究报道了识别创伤性脑损伤(TBI)患者自动调节“最佳”脑灌注压(CPPopt)的方法。现有方法学的一个重要缺点是 CPPopt 只能在监测时间的约 50-60%计算出来。在这项研究中,我们假设通过应用多窗口和加权计算算法,CPPopt 的产量和连续性可以得到显著提高,同时不影响其预后价值的保留。研究纳入了 2003 年至 2015 年间收治的 526 例严重 TBI 患者的数据。多窗口 CPPopt 计算基于使用来自 36 个长度递增时间窗口(2-8 小时)的 PRx-CPP 图中的自动曲线拟合,在压力反应性指数(PRx)-CPP 图上进行。然后,以加权方式对 CPPopt 的结果矩阵进行平均。研究了 CPPopt 的产量、连续性和稳定性。计算了患者实际 CPP 和 CPPopt 之间的差异(ΔCPP),并分析了其与结局的相关性。总体而言,该多窗口方法在该队列中更连续、更稳定地呈现 CPPopt。与之前基于单窗口的 CPPopt 产量 51%±0.94%相比,新方法的 CPPopt 产量平均(±SE)为 94%±2.1%。使用新算法后,CPPopt 在整个监测期间的稳定性得到了显著提高(p<0.001)。根据多窗口算法的ΔCPP 与结局之间的关系与基于单个窗口计算的 CPPopt 相似。总之,这项研究验证了使用新的多窗口和加权算法显著提高 TBI 患者 CPPopt 产量的有效性。这种方法学的改进对于 CPPopt 在未来 CPPopt 试验中的临床应用至关重要。

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