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Modeling the hematocrit distribution in microcirculatory networks: A quantitative evaluation of a phase separation model
Author(s) -
Rasmussen Peter M.,
Secomb Timothy W.,
Pries Axel R.
Publication year - 2018
Publication title -
microcirculation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.793
H-Index - 83
eISSN - 1549-8719
pISSN - 1073-9688
DOI - 10.1111/micc.12445
Subject(s) - separation (statistics) , hematocrit , experimental data , phase (matter) , bayesian probability , computer science , biological system , statistics , statistical physics , mathematics , artificial intelligence , chemistry , physics , medicine , organic chemistry , biology , endocrinology
Objective Theoretical models are essential tools for studying microcirculatory function. Recently, the validity of a well‐established phase separation model was questioned and it was claimed that it produces problematically low hematocrit predictions and lack of red cells in small diameter vessels. We conducted a quantitative evaluation of this phase separation model to establish common ground for future research. Methods Model predictions were validated against a comprehensive database with measurements from 4 mesenteric networks. A Bayesian data analysis framework was used to integrate measurements and network model simulations into a combined analysis and to model uncertainties related to network boundary conditions as well as phase separation model parameters. The model evaluation was conducted within a cross‐validation scheme. Results Unlike the recently reported results, our analysis demonstrated good correspondence in global characteristics between measurements and predictions. In particular, predicted hematocrits for vessels with small diameters were consistent with measurements. Incorporating phase separation model parameter uncertainties further reduced the hematocrit validation error by 17% and led to the absence of red‐cell‐free segments. Corresponding model parameters are presented as alternatives to standard parameters. Conclusions Consistent with earlier studies, our quantitative model evaluation supports the continued use of the established phase separation model.