Simulation of cumulative risk of developing altitude decompression sickness

Background: Recently we proposed the probabilistic model of decompression sickness (DCS) based on stochastic simulation of bubbling processes in body tissues under decompression, and on the concept of a critical volume of a free gas phase in tissues. The model defines the cumulative probability of developing all DCS symptoms by an exponential equation whose index is the integral cumulative risk function of all body tissue lesions by bubbles, F-cum(t). Methods: In this study, we modified this model by considering differences in the blood flow rates and the nucleation intensities in rested and exercised subjects. Using the new model, we analyzed the dependence of the function F-cum(t) on decompression magnitude, preoxygenation duration, and physical activity of subjects during sojourn at an altitude. Results: Simulation of the integral function F-cum(t) for various decompression profiles with the use of the hypothetic values of tissue parameters showed that body tissues which experience the largest risk of bubble lesions are different for various decompression profiles. In a finite period, F-cum(t) for any profile is identical to the function F-w(t), defining the time history of the risk of bubble lesions of some virtual tissues having a comparatively small value of nitrogen washout half-time. The virtual rates of new bubble generation in such tissues are significantly smaller than the tentative values of the rates of new bubble generation in real tissues. Good agreement of predictions of the model with the known empirical data for DCS incidence justifies the specific features of the functions F-cum(t) and F-w(t). Conclusion: Our model provides a new approach to evaluating DCS risk for various decompression profiles.