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Processes 2016, 4(4), 38; doi:10.3390/pr4040038

Modeling and Hemofiltration Treatment of Acute Inflammation

Department of Chemical and Petroleum Engineering; Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA
Department of Critical Care Medicine, University of Pittsburgh Medical Center, 3550 Terrace St, Pittsburgh, PA 15213, USA
Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA
McGowan Institute for Regenerative Medicine, University of Pittsburgh Medical Center, 450 Technology Dr, Suite 300, Pittsburgh, PA 15219, USA
Carnegie Mellon–University of Pittsburgh Ph.D. Program in Computational Biology, 3501 Fifth Ave, 3064 BST3, Pittsburgh, PA 15260, USA
Department of Surgery, University of Pittsburgh Medical Center, W944 Biomedical Sciences Tower, Pittsburgh, PA 15213, USA
Department of Mathematics, University of Pittsburgh, 301 Thackeray Hall, Pittsburgh, PA 15261, USA
This paper is an extended version of our paper published in Hogg, J.S.; Clermont, G.; Parker, R.S. Acute Inflammation Treatment via Particle Filter State Estimation and MPC. In Proceedings of the 9th International Symposium on the Dynamics and Control of Process Systems (DYCOPS), Leuven, Belgium, 5–7 July 2010; pp. 678–683.
Current address: Medtronic, 18000 Devonshire St, Northridge, CA 91325, USA
Current address: Service d’Anesthésie-Réanimation; Hôpital Edouard Herriot; 5 Place d’Arsonval; 69437 LYON Cedex 03, France
Current address: Research Center Juelich and Department of Zoology; University of Cologne; Biowissenschaftliches Zentrum, Zi. 1.104; Z ulpicher-Straße 47b; 50674 Koln, Germany
Author to whom correspondence should be addressed.
Academic Editor: B. Wayne Bequette
Received: 1 August 2016 / Revised: 21 September 2016 / Accepted: 29 September 2016 / Published: 18 October 2016
(This article belongs to the Special Issue Biomedical Systems Control)
View Full-Text   |   Download PDF [609 KB, uploaded 18 October 2016]   |  


The body responds to endotoxins by triggering the acute inflammatory response system to eliminate the threat posed by gram-negative bacteria (endotoxin) and restore health. However, an uncontrolled inflammatory response can lead to tissue damage, organ failure, and ultimately death; this is clinically known as sepsis. Mathematical models of acute inflammatory disease have the potential to guide treatment decisions in critically ill patients. In this work, an 8-state (8-D) differential equation model of the acute inflammatory response system to endotoxin challenge was developed. Endotoxin challenges at 3 and 12 mg/kg were administered to rats, and dynamic cytokine data for interleukin (IL)-6, tumor necrosis factor (TNF), and IL-10 were obtained and used to calibrate the model. Evaluation of competing model structures was performed by analyzing model predictions at 3, 6, and 12 mg/kg endotoxin challenges with respect to experimental data from rats. Subsequently, a model predictive control (MPC) algorithm was synthesized to control a hemoadsorption (HA) device, a blood purification treatment for acute inflammation. A particle filter (PF) algorithm was implemented to estimate the full state vector of the endotoxemic rat based on time series cytokine measurements. Treatment simulations show that: (i) the apparent primary mechanism of HA efficacy is white blood cell (WBC) capture, with cytokine capture a secondary benefit; and (ii) differential filtering of cytokines and WBC does not provide substantial improvement in treatment outcomes vs. existing HA devices. View Full-Text
Keywords: mathematical model; inflammation; cytokines; sepsis; endotoxemia; hemoadsorption; nonlinear MPC; particle filter; state estimation mathematical model; inflammation; cytokines; sepsis; endotoxemia; hemoadsorption; nonlinear MPC; particle filter; state estimation

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Parker, R.S.; Hogg, J.S.; Roy, A.; Kellum, J.A.; Rimmelé, T.; Daun-Gruhn, S.; Fedorchak, M.V.; Valenti, I.E.; Federspiel, W.J.; Rubin, J.; Vodovotz, Y.; Lagoa, C.; Clermont, G. Modeling and Hemofiltration Treatment of Acute Inflammation. Processes 2016, 4, 38.

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