Natural history of severe decompression sickness after rapid ascent from air saturation in a porcine model.

We developed a swine model to describe the untreated natural history of severe decompression sickness (DCS) after direct ascent from saturation conditions. In a recompression chamber, neutered male Yorkshire swine were pressurized to a predetermined depth from 50-150 feet of seawater [fsw; 2.52-5.

Probabilistic models of the role of oxygen in human decompression sickness.

Probabilistic models of human decompression sickness (DCS) have been successful in describing DCS risk observed across a wide variety of N2-O2 dives but have failed to account for the observed DCS incidence in dives with high PO2 during decompression. Our most successful previous model, calibrated with 3,322 N2-O2 dives, predicts only 40% of the observed incidence in dives with 100% O2 breathing during decompression. We added 1,013 O2 decompression dives to the calibration data.

Improved probabilistic decompression model risk predictions using linear-exponential kinetics.

Using a data base of 2,383 air and nitrogen-oxygen dives resulting in 131 cases of decompression sickness (DCS), risk functions were developed for a set of probabilistic decompression models according to survival analysis techniques. Parameters were optimized using the method of maximum likelihood Gas kinetics were either traditional exponential uptake and elimination, or an exponential uptake followed by linear elimination (LE kinetics) when calculated supersaturation was excessive. Risk functions either used the calculated relative gas supersaturation directly, or a delayed risk using a time integral of prior supersaturation.

A model for predicting central nervous system oxygen toxicity from hyperbaric oxygen exposures in humans.

Under certain circumstances, Navy divers breathe 100% O2 when working underwater. Serious symptoms of central nervous system (CNS) O2 toxicity can develop from hyperbaric O2 exposure; immersion and exercise are also known to exacerbate toxicity. We developed risk models for quantitative prediction of the probability of developing symptoms using a large set of human data in which occupational exposure conditions were simulated.