IMPROVED PROBABILISTIC DECOMPRESSION MODEL RISK PREDICTIONS USING LINEAR-EXPONENTIAL KINETICS

Using a data base of 2,383 air and nitrogen-oxygen dives resulting in 131 cases of decompression sickness (DCS), risk functions were develop ed for a set of probabilistic decompression models according to surviv al analysis techniques. Parameters were optimized using the method of maximum likelihood. Gas kinetics were either traditional exponential u ptake and elimination, or an exponential uptake followed by linear eli mination (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 supersatur ation. 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 incorporat ing metabolic gases in physiologically plausible functions were usuall y found in lumped threshold terms and did not explicitly affect the ov erall data fit. The role of physiologic fidelity vs. empirical data fi tting ability in accounting for model success is discussed.