Probabilistic models of the role of oxygen in human decompression sickness

Abstract

Probabilistic models of human decompression sickness (DCS) have been successful in describing DCS risk observed across a wide variety of N2-O2dives but have failed to account for the observed DCS incidence in dives with high [Formula: see text] during decompression. Our most successful previous model, calibrated with 3,322 N2-O2dives, 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. Fitting the prior model to this expanded data set resulted in only a modest improvement in DCS prediction of O2 data. Therefore, two O2-specific modifications were proposed: [Formula: see text]-based alteration of inert gas kinetics ( model 1) and[Formula: see text] contribution to total inert gas ( model 2). Both modifications statistically significantly improved the fit, and each predicts 90% of the observed DCS incidence in O2dives. The success of models 1 and 2 in improving prediction of DCS occurrence suggests that elevated [Formula: see text]levels contribute to DCS risk, although less than the equivalent amount of N2. Both models allow rational optimization of O2 use in accelerating decompression procedures.