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Maximum likelihood analysis of bubble incidence for mixed gas diving
P Tikuisis1, K Gault, G Carrod
1Defence and Civil Institute of Environmental Medicine, Downsview, Ontario, Canada.
Undersea Biomedical Research
|March 1, 1990
Summary
Predicting diver decompression sickness (DCS) bubbles requires advanced models. A two-compartment model distinguishing inert gas properties offers superior prediction accuracy for both air and helium dives.
Area of Science:
- Hyperbaric Medicine
- Diving Physiology
- Biophysics
Background:
- Decompression sickness (DCS) is a risk in diving.
- Predicting bubble formation is crucial for diver safety.
- Current models may not fully capture inert gas behavior.
Purpose of the Study:
- To apply the method of maximum likelihood for predicting diver bubbling.
- To compare one- and two-compartment models for air and helium diving.
- To evaluate the impact of distinguishing gas kinetics and potency on prediction accuracy.
Main Methods:
- Utilized data from 108 air and 622 helium man-dives in a hyperbaric chamber.
- Employed Doppler ultrasonics to monitor bubbles post-dive (up to 2 hours).
- Applied maximum likelihood analysis with monoexponential gas kinetics in one- and two-compartment models.
Main Results:
- The two-compartment model significantly improved prediction when distinguishing nitrogen and helium kinetics.
- Further significant improvement was achieved by distinguishing the potency of the two gases.
- The potential bubble volume criterion proved superior to the gas pressure criterion.
Conclusions:
- Multi-compartment models are recommended for predicting DCS bubbling using maximum likelihood.
- Distinguishing the potencies of inert gases enhances prediction accuracy.
- The two-compartment model with differentiated gas potency offers the best predictive performance.