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Research Papers

A Zero-Dimensional Model and Protocol for Simulating Patient-Specific Pulmonary Hemodynamics From Limited Clinical Data

[+] Author and Article Information
Vitaly O. Kheyfets

University of Colorado Anschutz Medical Campus,
Children's Hospital Colorado,
Aurora, CO 80045
e-mail: vitaly.kheyfets@ucdenver.edu

Jamie Dunning

University of Colorado Anschutz Medical Campus,
Children's Hospital Colorado,
Aurora, CO 80045
e-mail: jamie.dunning@ucdenver.edu

Uyen Truong

University of Colorado Anschutz Medical Campus,
Children's Hospital Colorado,
Aurora, CO 80045
e-mail: Uyen.Truong@childrenscolorado.org

Dunbar Ivy

University of Colorado Anschutz Medical Campus,
Children's Hospital Colorado,
Aurora, CO 80045
e-mail: Dunbar.Ivy@childrenscolorado.org

Kendall Hunter

University of Colorado Anschutz Medical Campus,
Children's Hospital Colorado,
Aurora, CO 80045
e-mail: Kendall.hunter@ucdenver.edu

Robin Shandas

University of Colorado Anschutz Medical Campus,
Children's Hospital Colorado,
Aurora, CO 80045
e-mail: robin.shandas@ucdenver.edu

Manuscript received December 5, 2014; final manuscript received September 16, 2016; published online November 3, 2016. Assoc. Editor: Naomi Chesler.

J Biomech Eng 138(12), 121001 (Nov 03, 2016) (8 pages) Paper No: BIO-14-1610; doi: 10.1115/1.4034830 History: Received December 05, 2014; Revised September 16, 2016

In pulmonary hypertension (PH) diagnosis and management, many useful functional markers have been proposed that are unfeasible for clinical implementation. For example, assessing right ventricular (RV) contractile response to a gradual increase in pulmonary arterial (PA) impedance requires simultaneously recording RV pressure and volume, and under different afterload/preload conditions. In addition to clinical applications, many research projects are hampered by limited retrospective clinical data and could greatly benefit from simulations that extrapolate unavailable hemodynamics. The objective of this study was to develop and validate a 0D computational model, along with a numerical implementation protocol, of the RV–PA axis. Model results are qualitatively compared with published clinical data and quantitatively validated against right heart catheterization (RHC) for 115 pediatric PH patients. The RV–PA circuit is represented using a general elastance function for the RV and a three-element Windkessel initial value problem for the PA. The circuit mathematically sits between two reservoirs of constant pressure, which represent the right and left atriums. We compared Pmax, Pmin, mPAP, cardiac output (CO), and stroke volume (SV) between the model and RHC. The model predicted between 96% and 98% of the variability in pressure and 98–99% in volumetric characteristics (CO and SV). However, Bland Altman plots showed the model to have a consistent bias for most pressure and volumetric parameters, and differences between model and RHC to have considerable error. Future studies will address this issue and compare specific waveforms, but these initial results are extremely promising as preliminary proof of concept of the modeling approach.

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Figures

Grahic Jump Location
Fig. 1

Physical model with circuit diagram of the simulated RV–PA axis. In parts of this manuscript, R = PVR − Zc.

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Fig. 2

Numerical protocol for simulating RV–PA hemodynamics. EDV = end diastolic volume; EDPVR = end diastolic pressure–volume relationship.

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Fig. 3

(a) Typical RV and PA pressure waveforms computed using RV–PA axis model. (b) and (c) Ventricular volume and ventricular pressure–volume loop, respectively. (d) Pulmonary vascular impedance in the frequency domain, computed using simulated PA pressure and flow waveforms as outlined in Ref. [2]. Note: PPAand PRVare pulmonary and RV pressure, respectively.

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Fig. 4

Max (left column), min (middle column), and mean (right column) PA pressure comparison between RV and PA axis model and measured RHC hemodynamics. In each column, the top row shows a correlation between measured and simulated values. The slope (m) and y-intercept (b) are coefficients for the fitted line: CVSIM=m⋅RHC+b. The bottom rows show Bland Altman plots, where the middle and outer lines represent the consistent bias and 1.96SD, respectively.

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Fig. 5

(Left) cardiac output (CO) computed by integrating the flow waveform simulated with the RV–PA model, compared with CO measured using a thermodilution catheter. (Right) stroke volume (SV) computed according the difference between the maximum and minimum volume measured by implementing the elastance function, compared with SV = CO/HR. The slope (m) and y-intercept (b) are coefficients for the fitted line: CVSIM=m⋅RHC+b. The bottom rows show Bland Altman plots, where the middle and outer lines represent the consistent bias and 1.96SD, respectively.

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