Predicting the time of occurrence of decompression sickness

Abstract

Probabilistic models and maximum likelihood estimation have been used to predict the occurrence of decompression sickness (DCS). We indicate a means of extending the maximum likelihood parameter estimation procedure to make use of knowledge of the time at which DCS occurs. Two models were compared in fitting a data set of nearly 1,000 exposures, in which greater than 50 cases of DCS have known times of symptom onset. The additional information provided by the time at which DCS occurred gave us better estimates of model parameters. It was also possible to discriminate between good models, which predict both the occurrence of DCS and the time at which symptoms occur, and poorer models, which may predict only the overall occurrence. The refined models may be useful in new applications for customizing decompression strategies during complex dives involving various times at several different depths. Conditional probabilities of DCS for such dives may be reckoned as the dive is taking place and the decompression strategy adjusted to circumstance. Some of the mechanistic implications and the assumptions needed for safe application of decompression strategies on the basis of conditional probabilities are discussed.

Probabilistic Modelling for Estimating Gas Kinetics and Decompression Sickness Risk in Pigs During H2 Biochemical Decompression

“…The probabilistic models used previously were constructed to be well behaved in the sense that the [P(DCS)] was predicted to be 0 when no pressure reduction occurred and increased with increasing pressure reduction (Weathersby et al, 1992).…”

“…is defined as the normalized difference between the net tissue tension (P tis , atm) and the absolute ambient pressure (P amb , atm) as described earlier (Weathersby et al, 1985(Weathersby et al, , 1992Thalmann et al, 1997).…”

“…In this model, r is constrained to be 0 or any positive number, and accordingly, r will be set to zero at any time P amb > P tis (Weathersby et al, 1985(Weathersby et al, , 1992Fahlman et al, 2001).…”

“…Gas fluxes in tissues have been described commonly in hyperbaric research by a time constant, τ (Tikuisis et al, 1991;Weathersby et al, 1992). The classic models computed the probability of decompression sickness [P(DCS)] as a single exponential time function, which therefore assumes that gas fluxes can be described adequately by one value for τ for the entire body, with symmetrical kinetics for gas uptake and elimination (Boycott et al, 1908;Weathersby et al, 1984).…”

On the likelihood of decompression sickness during H2 biochemical decompression in pigs

“…The only physiological variable that has been undisputedly correlated with DCS risk in rats is body weight (20). Because reliable physiological correlates are lacking, researchers have used a variety of models based solely on the physical history of the compression and decompression sequence to find variables that can predict the probability of DCS (26, 31-34).The DCS risk assessment used in this study builds on previously published models used in DCS research (26,31,33,34). The goal is to estimate the beneficial effects on DCS risk of the active removal of tissue H 2 by injecting H 2 -metabolizing microbes into the intestines of pigs during simulated H 2 dives and to suggest a physiological mechanism for the process called H 2 biochemical decompression (19).…”