期刊
出版社
WILEY
DOI: 10.1002/cnm.2737
关键词
uncertainty quantification; Fontan palliation surgery; single ventricle congenital heart disease; sparse grids; inverse Bayesian parameter estimation; virtual surgery
类别
资金
- Leducq Foundation as part of a Transatlantic Network of Excellence for Cardiovascular Research
- Burroughs Wellcome Fund Career award at the Scientific Interface [NSF CAREER OCI-1150184]
- British Heart Foundation Clinical Research Fellowship [FS/12/35/29566]
- National Science Foundation [ACI-1053575]
- British Heart Foundation [FS/12/35/29566] Funding Source: researchfish
- Div Of Chem, Bioeng, Env, & Transp Sys
- Directorate For Engineering [1508794] Funding Source: National Science Foundation
The adoption of simulation tools to predict surgical outcomes is increasingly leading to questions about the variability of these predictions in the presence of uncertainty associated with the input clinical data. In the present study, we propose a methodology for full propagation of uncertainty from clinical data to model results that, unlike deterministic simulation, enables estimation of the confidence associated with model predictions. We illustrate this problem in a virtual stage II single ventricle palliation surgery example. First, probability density functions (PDFs) of right pulmonary artery (PA) flow split ratio and average pulmonary pressure are determined from clinical measurements, complemented by literature data. Starting from a zero-dimensional semi-empirical approximation, Bayesian parameter estimation is used to find the distributions of boundary conditions that produce the expected PA flow split and average pressure PDFs as pre-operative model results. To reduce computational cost, this inverse problem is solved using a Kriging approximant. Second, uncertainties in the boundary conditions are propagated to simulation predictions. Sparse grid stochastic collocation is employed to statistically characterize model predictions of post-operative hemodynamics in models with and without PA stenosis. The results quantify the statistical variability in virtual surgery predictions, allowing for placement of confidence intervals on simulation outputs. Copyright (c) 2015 John Wiley & Sons, Ltd.
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