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Probabilistic pharmacokinetic models of decompression sickness in humans: Part 2, coupled perfusion-diffusion models.

Authors :
Murphy FG
Hada EA
Doolette DJ
Howle LE
Source :
Computers in biology and medicine [Comput Biol Med] 2018 Jan 01; Vol. 92, pp. 90-97. Date of Electronic Publication: 2017 Nov 15.
Publication Year :
2018

Abstract

Decompression sickness (DCS) can be experienced following a reduction in ambient pressure; such as that associated with diving or ascent to high altitudes. DCS is believed to result when supersaturated inert gas dissolved in biological tissues exits solution and forms bubbles. Models to predict the probability of DCS are typically based on nitrogen and/or helium gas uptake and washout in several theoretical tissues, each represented by a single perfusion-limited compartment. It has been previously shown that coupled perfusion-diffusion compartments are better descriptors than solely perfusion-based models of nitrogen and helium uptake and elimination kinetics observed in the brain and skeletal muscle of sheep. In this work, we examine the application of these coupled pharmacokinetic structures with at least one diffusion compartment to the prediction of the incidence of decompression sickness in humans. We compare these models to LEM-NMRI98, a well-described U.S. Navy gas content model, consisting of three uncoupled perfusion-limited compartments incorporating oxygen and linear-exponential kinetics. Pharmacokinetic gas content models with a diffusion component describe the probability of DCS in human bounce dives made with air, single non-air bounce dives, and oxygen decompression dives better than LEM-NMRI98. However, for the full data set, LEM-NMRI98 remains the best descriptor of the data.<br /> (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Details

Language :
English
ISSN :
1879-0534
Volume :
92
Database :
MEDLINE
Journal :
Computers in biology and medicine
Publication Type :
Academic Journal
Accession number :
29161578
Full Text :
https://doi.org/10.1016/j.compbiomed.2017.11.011