Probability of Decompression Sickness in No-Stop Air Diving

We produce statistics-based (probabilistic) and intuition-based (deterministic) models using dive-outcome data from the U.S. Navy Decompression Database to gain an understanding of the no-stop diving instructions used by the U.S. Navy and various other navies. The models allow estimation of probability of decompression sickness (DCS) for various bottom times for air no-stop diving. Our calibration data set contains 2.037 experimental no-stop dives with 104 cases of decompression sickness (DCS) and covers a large range of depths and bottom times; unfortunately the data are not well distributed with regard to depth, bottom time, and DCS incidence. Our probabilistic model shows good agreement between predictions and observations. We augment the same calibration data set with a few dives that have short decompression stops to produce the deterministic model. According to our models, probability of decompression sickness (Pdcs) is 2% or less for current U.S. Navy schedules for most no-stop air dives and near 1% for no-stop schedules of the navies of Great Britain, Canada, and France. Our probabilistic model serves well to provide Pdcs estimates and time limits that are similar to those in current use by navies for most of the range of standard air diving and for subsaturation diving, but it fails for short, deep dives in two ways: it fails to avoid observed DCS cases in our calibration data set. and it indicates that bottom time can be longer than bottom times in current use by the navies we examined. For short, deep dives, we recommend depth/bottom-time combinations yielded by our deterministic model.