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用于动静脉瘘手术的个性化脉搏波传播模型的灵敏度分析。第 B 部分:可能的通用模型参数的识别。

A sensitivity analysis of a personalized pulse wave propagation model for arteriovenous fistula surgery. Part B: Identification of possible generic model parameters.

机构信息

Eindhoven University of Technology, Department of Biomedical Engineering, The Netherlands.

出版信息

Med Eng Phys. 2013 Jun;35(6):827-37. doi: 10.1016/j.medengphy.2012.08.012. Epub 2012 Sep 8.

Abstract

Decision-making in vascular access surgery for hemodialysis can be supported by a pulse wave propagation model that is able to simulate pressure and flow changes induced by the creation of a vascular access. To personalize such a model, patient-specific input parameters should be chosen. However, the number of input parameters that can be measured in clinical routine is limited. Besides, patient data are compromised with uncertainty. Incomplete and uncertain input data will result in uncertainties in model predictions. In part A, we analyzed how the measurement uncertainty in the input propagates to the model output by means of a sensitivity analysis. Of all 73 input parameters, 16 parameters were identified to be worthwhile to measure more accurately and 51 could be fixed within their measurement uncertainty range, but these latter parameters still needed to be measured. Here, we present a methodology for assessing the model input parameters that can be taken constant and therefore do not need to be measured. In addition, a method to determine the value of this parameter is presented. For the pulse wave propagation model applied to vascular access surgery, six patient-specific datasets were analyzed and it was found that 47 out of 73 parameters can be fixed on a generic value. These model parameters are not important for personalization of the wave propagation model. Furthermore, we were able to determine a generic value for 37 of the 47 fixable model parameters.

摘要

在血管通路手术中,决策可以通过脉搏波传播模型得到支持,该模型能够模拟血管通路创建过程中引起的压力和流量变化。为了使模型个性化,应该选择特定于患者的输入参数。然而,在临床常规中可以测量的输入参数数量是有限的。此外,患者数据存在不确定性。不完整和不确定的输入数据将导致模型预测的不确定性。在 A 部分中,我们通过敏感性分析分析了输入中的测量不确定性如何传播到模型输出。在所有 73 个输入参数中,确定了 16 个参数值得更准确地测量,51 个参数可以在其测量不确定度范围内固定,但这些参数仍需要测量。在这里,我们提出了一种评估模型输入参数的方法,这些参数可以被视为常数,因此不需要测量。此外,还提出了一种确定该参数值的方法。对于应用于血管通路手术的脉搏波传播模型,分析了六个患者特定数据集,发现 73 个参数中有 47 个可以固定在通用值上。这些模型参数对于波传播模型的个性化并不重要。此外,我们还能够确定 47 个可固定模型参数中的 37 个的通用值。

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