Abstract

Decompression sickness (DCS) in humans is associated with reductions in ambient pressure that occur during diving, aviation, or certain manned spaceflight operations. Its signs and symptoms can include, but are not limited to, joint pain, radiating abdominal pain, paresthesia, dyspnea, general malaise, cognitive dysfunction, cardiopulmonary dysfunction, and death. Probabilistic models of DCS allow the probability of DCS incidence and time of occurrence during or after a given hyperbaric or hypobaric exposure to be predicted based on how the gas contents or gas bubble volumes vary in hypothetical tissue compartments during the exposure. These models are calibrated using data containing the pressure and respired gas histories of actual exposures, some of which resulted in DCS, some of which did not, and others in which the diagnosis of DCS was not clear. The latter are referred to as marginal DCS cases. In earlier works, a marginal DCS event was typically weighted as 0.1, with a full DCS event being weighted as 1.0, and a non-event being weighted as 0.0. Recent work has shown that marginal DCS events should be weighted as 0.0 when calibrating gas content models. We confirm this indication in the present work by showing that such models have improved performance when calibrated to data with marginal DCS events coded as non-events. Further, we investigate the ramifications of derating marginal events on model-prescribed air diving no-stop limits.

Full Text
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