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A Zero-Dimensional Model and Protocol for Simulating Patient-Specific Pulmonary Hemodynamics From Limited Clinical
Vitaly O Kheyfets1, Jamie Dunning1, Uyen Truong1
1University of Colorado Anschutz Medical Campus, Children's Hospital Colorado, Aurora, CO 80045
Journal of Biomechanical Engineering
|September 30, 2016
Summary
A new computational model of the right ventricle and pulmonary artery (RV-PA) axis shows promise for pulmonary hypertension (PH) research. This model accurately predicts hemodynamic variability, offering a potential solution for limited clinical data in PH studies.
Area of Science:
- Cardiovascular Physiology
- Computational Modeling
- Pediatric Cardiology
Background:
- Clinical markers for pulmonary hypertension (PH) diagnosis and management are often difficult to implement.
- Research is limited by scarce retrospective data, necessitating simulations for hemodynamic extrapolation.
- Accurate modeling of the right ventricle-pulmonary artery (RV-PA) axis is crucial for understanding PH.
Purpose of the Study:
- To develop and validate a 0D computational model of the RV-PA axis.
- To create a numerical implementation protocol for the RV-PA model.
- To assess the model's ability to predict hemodynamic parameters in pediatric PH patients.
Main Methods:
- A 0D computational model representing the RV-PA circuit was developed.
- The RV was modeled using a general elastance function; the PA used a three-element Windkessel model.
- The model was validated against right heart catheterization (RHC) data from 115 pediatric PH patients.
Main Results:
- The model predicted 96-98% of pressure variability and 98-99% of volumetric (cardiac output, stroke volume) variability.
- Qualitative comparison with published clinical data was performed.
- Bland Altman analysis revealed consistent bias and considerable error for most parameters, despite high variability prediction.
Conclusions:
- The RV-PA computational model shows promising proof of concept for simulating PH hemodynamics.
- The model's ability to predict variability is strong, but bias requires further investigation.
- Future work will focus on refining the model and comparing specific waveforms for improved accuracy.

