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Probabilistic model of decompression sickness based on stochastic models of bubbling in tissues.

BACKGROUND: Decompression sickness (DCS) is caused by gas bubbles formed from pre-existing and new microscopic gas nuclei in blood and tissues. Assuming a random pattern of bubbling processes in living tissues, we developed a probabilistic model of DCS. We hypothesized that symptoms of DCS in an individual exposed to decompression appear when the total volume of bubbles in a unit volume of any tissue, w(t), exceeds the critical specific volume of a free gas phase, wcr. Therefore, one may consider the expectation of w(t)/wcr as a measure of the dynamic risk of gas bubble lesion of a given tissue segment. METHODS: Using the standard approach to estimation of various risks and the sum rule of probabilities of joint events, we defined the cumulative probability of DCS onset by the equation Pcum(t) = 1 - exp[Fcum(t)], where Fcum(t) = sigmaVnQnMnc(t), Qn = 1/wncr, where Vn is the volume of a tissue n. The function Mnc(t) coincides with the function Mn(t), defining a time history of the expectation of wn(t) until it achieves its maximum and then becomes a constant. Evaluating Pcum(t) for particular altitude decompressions, we identified the additive cumulative risk function of development of any DCS symptoms, Fcum-tot(t), with the function defining the cumulative risk of any bubble lesion of the "worst" virtual tissue (WVT) of Type A. On the other hand, we identified the additive cumulative risk function of development of intolerable DCS symptoms, Fcum-int(t), with the function defining the cumulative risk of acute bubble lesion of the WVT of Type phi. RESULTS: We found parameters of the curves Pcum-tot(t) and Pcum-int(t) that fit the known empirical curves for the cumulative probability of DCS onset. For men performing mild exercise at 30 kPa after preoxygenation, our estimated parameters for curves Pcum-tot(t) indicate that the WVTs of Type A have nitrogen washout half-times of 260 and 290 min for preoxygenation times of 75 and 135 min, respectively. On the other hand, the parameters of curves Pcum-int(t) show that the WVTs of Type phi in men performing mild exercise at 20-40 kPa after preoxygenation during 0-6 h are virtual tissues with nitrogen washout half-times of 400 to 615 min. CONCLUSION: Our model provides a new approach to predicting DCS risk for various decompression profiles. By demonstrating the dependence of DCS risk on body tissue parameters, the model explains why resistance to DCS in mammals increases with a lower body mass and greater specific blood flow in tissues.

Decompression Sickness↗

[Growth dynamics and the largest size of gas bubbles emerging in body tissues due to decompression].

Symptoms of decompression sickness (DCS) develop when the total volume of gas bubbles due to decompression reaches the magnitude critical for a body tissue. Number of the bubbles is a function of random nucleation intensity before, during or after decompression and tissue superaeration dynamics, whereas their size is unambiguously dependent on a tissue, decompression phase and bubbling time. A mathematical model of bubble tissue dynamics has been proposed for calculating the dynamics of mathematical expectation of the total gas in tissues and mounting a method for comparative analysis of the maximal DCS probability as a result of implementation of different decompression tables. Unequal intensity of nucleation during spaceflight EVA and its ground simulation w/o spacesuit is the course of inequality of decompression safety of these operations.

Decompression Sickness↗

A probabilistic model of hypobaric decompression sickness based on 66 chamber tests.

One consequence of the NASA tissue ratio (TR) model is that calculated probability of decompression sickness [P(DCS)] is constant in tests at different ambient pressures so long as the ratio of P1N2 to P2 is the same in each test; P1N2 is N2 pressure in the 360 minute half-time compartment, and P2 is ambient pressure after decompression. We test the hypothesis that constant P(DCS) is better described by TRs that decrease as P2 decreases. Data were from 66 NASA and USAF hypobaric chamber tests resulting in 211 cases of DCS in 1075 exposures. The response variable was presence or absence of DCS while at P2. Explanatory variables were P1N2, P2, exercise at P2, (yes or no), time to DCS (failure time), and time to end of test in those without DCS (censored time). Probability models were fitted using techniques from survival analysis. The log likelihood for the two parameter log logistic survival model was -846 with only failure and censored times, -801 when TR [P1N2/P2] plus exercise were added, and -663 when modified TR [(((P1N2+cl)/P2)-1)c2] plus exercise were added, where c1 and c2 are fitted parameters in the five parameter model. Constant P(DCS) was better described by TRs that decrease as P2 decreases; a conclusion supported by additional empirical observations, and bubble growth models that are independent of DCS data. Exercise increased the P(DCS) at P2. As a description of decompression "dose", the modified TR was superior to TR over a wider range of experimental conditions.

Adult↗