Adaptive Inert Gas Exchange Model for Improved Hypobaric Decompression Sickness Risk Estimation.

Abstract

Introduction: Future high-altitude military operations and spaceflight will require new procedures to protect crews from decompression sickness while limiting the operational impact. It is hypothesized that the current prediction models do not accurately reflect actual inert gas dynamics, making them unsuitable for the risk estimation of new hypobaric exposure profiles.

Methods: A biophysical gas exchange model was created, allowing modification of various physiological parameters. Predicted nitrogen (N2) volume flows were compared with an experimental study by the Swedish Aerospace Physiology Centre. Bubble growth predictions, made using the Tissue Bubble Dynamics Model, were compared with measured venous gas emboli (VGE).

Results: While the simulated washout curves captured the general trends, some important discrepancies were observed when using the nominal model parameters. The new biophysical gas exchange model, incorporating changes in cardiac output and individual anthropometric variations, improved the predictions and approximated the experimentally observed N2 washout. The standard bubble growth predictions did not match measured VGE. Using weighing factors based on the N2 gas flow components predicted by the new biophysical model, the bubble growth pattern agrees much better with the measured VGE scores.

Discussion: Traditional decompression models do not account for variations in physiological and environmental factors, leading to incorrect estimates of N2 washout and bubble growth predictions. Using an adaptive biophysical gas exchange model significantly improves the predictions for various altitude exposure profiles. We therefore strongly recommend incorporating adaptive physiological parameters in any model to be used for estimating decompression sickness risk and designing mitigation procedures. De Ridder S, Neyt X, Germonpré P. Adaptive inert gas exchange model for improved hypobaric decompression sickness risk estimation. Aerosp Med Hum Perform. 2025; 96(2):85-92.