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

Abstract

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. The most successful model (considering both incidence and time of onset of DCS) used supersaturation risk, and LE kinetics (in only 1 of 3 parallel compartments). Several methods of explicitly incorporating metabolic gases in physiologically plausible functions were usually found in lumped threshold terms and did not explicitly affect the overall data fit. The role of physiologic fidelity vs. empirical data fitting ability in accounting for model success is discussed.