Math. Model. Nat. Phenom.
Volume 13, Number 5, 2018
Mathematical Modelling of Physiological Flows
|Number of page(s)||20|
|Published online||06 September 2018|
Personalized mathematical model of endotoxin-induced inflammatory responses in young men and associated changes in heart rate variability
Dept of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Center,
SRB-4, 12902 Magnolia Drive,
2 Dept of Surgery, Duke University School of Medicine, 40 Duke Medicine Circle, DUMC 3805, 3576 White Zone, Durham 27710, NC, USA
3 Dept of Mathematics, NC State University, Box 8205, Raleigh 27695, NC, USA
4 Frederiksberg and Bispebjerg Hospitals, Nordre Fasanvej 57, 2000 Frederiksberg, Denmark
5 Dept of Neuroanaesthesiology, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100 Copenhagen Ø, Denmark
6 Dept of Biotechnology and Biomedicine, Technical University of Denmark, Søltofts Plads, 2800 Kgs. Lyngby, Denmark
7 Dept of Science and Environment, Roskilde University, Universitetsvej 1, 4000 Roskilde, Denmark
* Corresponding author: email@example.com
Accepted: 7 February 2018
The objective of this study was to develop a personalized inflammatory model and estimate subject-specific parameters that could be related to changes in heart rate variability (HRV), a measure that can be obtained non-invasively in real time. An inflammatory model was developed and calibrated to measurements of interleukin-6 (IL-6), tumor necrosis factor (TNF-alpha), interleukin-8 (IL-8) and interleukin-10 (IL-10) over 8 hours in 20 subjects administered a low dose of lipopolysaccharide. For this model, we estimated 11 subject-specific parameters for all 20 subjects. Estimated parameters were correlated with changes in HRV, computed from ECG measurements using a built-in HRV module available in Labchart. Results revealed that patients could be separated into two groups expressing normal and abnormal responses to endotoxin. Abnormal responders exhibited increased HRV, most likely as a result of increased vagal firing. The observed correlation between the inflammatory response and HRV brings us a step further towards understanding if HRV predictions can be used as a marker for inflammation. Analyzing HRV parameters provides an easy, non-invasively obtained measure that can be used to assess the state of the subject, potentially translating to identifying a non-invasive marker that can be used to detect the onset of sepsis.
Mathematics Subject Classification: 92C-08 / 92C30 / 34A55 / 49Q12 / 65L09
Key words: Acute inflammation / mathematical modeling / heart rate variability / parameter estimation
© EDP Sciences, 2018
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