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 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. 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: PO2-based alteration of inert gas kinetics (model 1) and PO2 contribution to total inert gas (model 2). Both modifications statistically significantly improved the fit, and each predicts 90% of the observed DCS incidence in O2 dives. The success of models 1 and 2 in improving prediction of DCS occurrence suggests that elevated PO2 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.

Experimental tissue gas kinetics do not follow the prediction for a single stirred perfusion-limited compartment. One hypothesis proposes that the kinetics might be explained by considering the tissue as a collection of parallel compartments, each with its own flow, reflecting the tissue microcirculatory flow heterogeneity. In this study, observed tissue gas kinetics were compared with the kinetics predicted by a model of multiple parallel compartments. Gas exchange curves were generated by recording the time course of tissue radioactivity in the intact calf muscles of anesthetized ventilated dogs exposed to step function changes of 133Xe in the inspired air for 5-h periods. Microcirculatory flow heterogeneity in the same tissue was determined by the radioactive microsphere method. Observed mean tissue transit times were on average longer than predicted by a factor of 6.7. Observed means averaged 52.1 min compared with 8.3 min predicted by the perfusion-limited model. Relative dispersions of tissue transit times were also uniformly larger than predicted. We conclude that Xe gas kinetics in intact canine skeletal muscle are not explained by a model of multiple parallel perfusion-limited compartments. Countercurrent exchange of gas between vessels is a possible explanation.

No abstract

Weathersby. Natural history of severe decompression sickness after rapid ascent from air saturation in a porcine model. J Appl Physiol 89: 791-798, 2000.-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; 2.52-5.55 atmospheres absolute (ATA)]. After 22 h, they returned to the surface (1 ATA) at 30 fsw/min (0.91 ATA/min) without decompression stops and were observed. Depth was the primary predictor of DCS incidence (R ϭ 0.52, P Ͻ 0.0001) and death (R ϭ 0.54, P Ͻ 0.0001). Severe DCS, defined as neurological or cardiopulmonary impairment, occurred in 78 of 128 animals, and 42 of 51 animals with cardiopulmonary DCS died within 1 h after surfacing. Within 24 h, 29 of 30 survivors with neurological DCS completely resolved their deficits without intervention. Pretrial Monte Carlo analysis decreased subject requirement without sacrificing power. This model provides a useful platform for investigating the pathophysiology of severe DCS and testing therapeutic interventions. The results raise important questions about present models of human responses to similar decompressive insults.