A physiologically realistic virtual patient database for the study of arterial haemodynamics

• Published in: International journal for numerical methods in biomedical engineering Vol. 37; no. 10; pp. e3497 - n/a
• Main Authors: Jones, Gareth, Parr, Jim, Nithiarasu, Perumal, Pant, Sanjay
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.10.2021; Wiley Subscription Services, Inc
• Subjects: aneurysm; Average flow; Bayesian analysis; Hemodynamics; Markov chains; MCMC; Monte Carlo simulation; Parameters; Physiology; Pressure; Pulse propagation; pulse wave haemodynamics; screening; stenosis; virtual patient database; virtual patients; Wave propagation; Waveforms; aneurysm; pulse wave haemodynamics; screening; MCMC; virtual patient database; virtual patients; stenosis
• ISSN: 2040-7939; 2040-7947; 2040-7947
• DOI: 10.1002/cnm.3497

This study creates a physiologically realistic virtual patient database (VPD), representing the human arterial system, for the primary purpose of studying the effects of arterial disease on haemodynamics. A low dimensional representation of an anatomically detailed arterial network is outlined, and a physiologically realistic posterior distribution for its parameters constructed through the use of a Bayesian approach. This approach combines both physiological/geometrical constraints and the available measurements reported in the literature. A key contribution of this work is to present a framework for including all such available information for the creation of virtual patients (VPs). The Markov Chain Monte Carlo (MCMC) method is used to sample random VPs from this posterior distribution, and the pressure and flow‐rate profiles associated with each VP computed through a physics based model of pulse wave propagation. This combination of the arterial network parameters (representing a virtual patient) and the haemodynamics waveforms of pressure and flow‐rates at various locations (representing functional response and potential measurements that can be acquired in the virtual patient) makes up the VPD. While 75,000 VPs are sampled from the posterior distribution, 10,000 are discarded as the initial burn‐in period of the MCMC sampler. A further 12,857 VPs are subsequently removed due to the presence of negative average flow‐rate, reducing the VPD to 52,143. Due to undesirable behaviour observed in some VPs—asymmetric under‐ and over‐damped pressure and flow‐rate profiles in left and right sides of the arterial system—a filter is proposed to remove VPs showing such behaviour. Post application of the filter, the VPD has 28,868 subjects. It is shown that the methodology is appropriate by comparing the VPD statistics to those reported in literature across real populations. Generally, a good agreement between the two is found while respecting physiological/geometrical constraints. This is a first‐of‐its kind study presenting a Bayesian approach to create virtual patients, which consist of the arterial network and associated pressure and flow‐rate waveforms. The virtual patients account for both the geometrical/anatomical constraints and the measurements reported in the literature across various populations. The virtual patient database consisting of 75,000 subjects is made publicly available to enable testing of data‐mining, machine learning, and deep learning methods for arterial disease detection.