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Open AccessFeature PaperArticle

Mathematical Modeling of RBC Count Dynamics after Blood Loss

1
Institute for Mathematical Optimization, Otto-von-Guericke-University Magdeburg, Universitätsplatz 2, 39106 Magdeburg, Germany
2
Department of Cardiology and Angiology I, Heart Center Freiburg University, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
3
Department of Hematology and Oncology, Medical Center, Otto-von-Guericke-University Magdeburg, 39120 Magdeburg, Germany
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Processes 2018, 6(9), 157; https://doi.org/10.3390/pr6090157
Received: 13 June 2018 / Revised: 23 August 2018 / Accepted: 30 August 2018 / Published: 5 September 2018
(This article belongs to the Special Issue Modeling & Control of Disease States)
The regeneration of red blood cells (RBCs) after blood loss is an individual complex process. We present a novel simple compartment model which is able to capture the most important features and can be personalized using parameter estimation. We compare predictions of the proposed and personalized model to a more sophisticated state-of-the-art model for erythropoiesis, and to clinical data from healthy subjects. We discuss the choice of model parameters with respect to identifiability. We give an outlook on how extensions of this novel mathematical model could have an important impact for personalized clinical decision support in the case of polycythemia vera (PV). PV is a slow-growing type of blood cancer, where especially the production of RBCs is increased. The principal treatment targeting the symptoms of PV is bloodletting (phlebotomy), at regular intervals that are based on personal experiences of the physicians. Model-based decision support might help to identify optimal and individualized phlebotomy schedules. View Full-Text
Keywords: erythropoiesis; phlebotomy; modeling; parameter estimation; numerical simulation erythropoiesis; phlebotomy; modeling; parameter estimation; numerical simulation
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MDPI and ACS Style

Tetschke, M.; Lilienthal, P.; Pottgiesser, T.; Fischer, T.; Schalk, E.; Sager, S. Mathematical Modeling of RBC Count Dynamics after Blood Loss. Processes 2018, 6, 157.

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