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首页> 外文期刊>Medical engineering & physics. >Between-centre variability in transfer function analysis, a widely used method for linear quantification of the dynamic pressure-flow relation: The CARNet study
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Between-centre variability in transfer function analysis, a widely used method for linear quantification of the dynamic pressure-flow relation: The CARNet study

机译：传递函数分析中的中心间变异性，一种广泛用于动态定量分析动态压力-流量关系的方法：CARNet研究

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Transfer function analysis (TFA) is a frequently used method to assess dynamic cerebral autoregulation (CA) using spontaneous oscillations in blood pressure (BP) and cerebral blood flow velocity (CBFV). However, controversies and variations exist in how research groups utilise TFA, causing high variability in interpretation. The objective of this study was to evaluate between-centre variability in TFA outcome metrics. 15 centres analysed the same 70 BP and CBFV datasets from healthy subjects (n= 50 rest; n= 20 during hypercapnia); 10 additional datasets were computer-generated. Each centre used their in-house TFA methods; however, certain parameters were specified to reduce a priori between-centre variability. Hypercapnia was used to assess discriminatory performance and synthetic data to evaluate effects of parameter settings. Results were analysed using the Mann-Whitney test and logistic regression. A large non-homogeneous variation was found in TFA outcome metrics between the centres. Logistic regression demonstrated that 11 centres were able to distinguish between normal and impaired CA with an AUC. >. 0.85. Further analysis identified TFA settings that are associated with large variation in outcome measures.These results indicate the need for standardisation of TFA settings in order to reduce between-centre variability and to allow accurate comparison between studies. Suggestions on optimal signal processing methods are proposed.
机译：传递函数分析（TFA）是使用血压（BP）和脑血流速度（CBFV）的自发振荡评估动态脑自动调节（CA）的常用方法。但是，研究小组利用TFA的方式存在争议和差异，导致解释的差异很大。这项研究的目的是评估TFA结果指标的中心间差异。 15个中心分析了来自健康受试者的相同的70个BP和CBFV数据集（n = 50个休息;高碳酸血症期间n = 20）;计算机生成了10个其他数据集。每个中心都使用自己的内部TFA方法;但是，指定了某些参数以减少先验中心间的可变性。高碳酸血症用于评估歧视性表现和综合数据以评估参数设置的影响。使用Mann-Whitney检验和逻辑回归分析结果。在各中心之间的TFA结果指标中发现了很大的不均匀变化。 Logistic回归表明，有11个中心能够通过AUC区分正常和受损的CA。 >。 0.85。进一步的分析确定了与结果测量值的大差异相关的TFA设置。这些结果表明，需要对TFA设置进行标准化，以减少中心之间的差异并允许在研究之间进行准确的比较。提出了关于最佳信号处理方法的建议。

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