Statistically Based Decompression Tables IX: Probabilistic Models of the Role ofOxygen in Human Decompression Sickness

摘要

While probabilistic models of human decompression sickness (DCS) have beensuccessful in describing both the level and timing of DCS risk in a wide variety of N2-O2 data, they have failed to account for the observed DCS risk in the currently available collection of dives with significant periods of 100% oxygen breathing. The best model to date, calibrated with over 2300 air and N2-O2 dives, under-predicts the DCS risk of these O2 dives by 60%, whether O2 is breathed during in-water decompression or during urface decompression procedures. This overestimation of the benefit of 0 is due to an exaggerated acceleration of N2 wash-out during 02 breathing. Seven-hundred and twenty-nine 02 decompression and surface decompression dives were added to the calibration data set. Fitting the existing base model to the new combined data set resulted in some improvement in OCS prediction n 02 data, but DCS predictions remained about 30% below the observed level. Three classes of 02-specific modifications to the base model were proposed: (1) modification of inert gas kinetics as a function of 02 pressure, (2) direct contribution of DCS risk as a function of either O pressure or fraction, (3) a separate O risk compartment with independent 02 wash-in and wash-out kinetics. each of these modifications attempts to include O2 as an explicit contributor to DCS risk, where the base' model considers only N2 S risk producing. Six of the resulting seven models slightly improved the fit to the data, with only the separate O compartment model resulting in a significant improvement in fit. The estimated 02 time constant for this model is very short at 0.4+0.3 minutes. his model is a good predictor of DCS incidence in both the original N2-02 and the new 02 data. In contrast to the base' model, his O model is able to distinguish between 02 dives of different risk level.