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

W. Huberts*, C. de Jonge, W. P. M. van der Linden, M. A. Inda, K. Passera, J. H. M. Tordoir, F. N. van de Vosse, E. M. H. Bosboom

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


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.
Original languageEnglish
Pages (from-to)827-837
JournalMedical Engineering & Physics
Issue number6
Publication statusPublished - Jun 2013


  • Patient-specific modeling
  • Sensitivity analysis
  • Uncertainty analysis
  • Vascular access
  • Predictive surgery

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