Background: Altitude decompression sickness (DCS) is a potential hazar
d encountered during high altitude flights or during extravehicular ac
tivity in space. In this study, the loglogistic distribution was used
to model DCS risk and symptom onset time. Methods: The Air Force Resea
rch Laboratory, Brooks AFB, TX, has conducted studies on human subject
s exposed to simulated altitudes in hypobaric chambers. The dataset fr
om those studies was used to develop the DCS models and consisted oi 9
75 subject-exposures to various altitudes, preoxygenation times, and e
xercise regimens. Since the risk of DCS is known to increase over time
at altitude, and then decrease because of denitrogenation, the loglog
istic model was fit to the data. The model assumes that the probabilit
y oi DCS depends on several risk factors. Maximum likelihood estimates
oi the parameters were obtained using the statistical software packag
e SAS. Cross validation techniques were provided to examine the goodne
ss of iii of the model. Results: The fitted model indicated that altit
ude, ratio of preoxygenation to exposure time, and exercise were the m
ost significant risk factors. The model was used to predict the risk o
f DCS for a variety oi exposure profiles. The predicted probability oi
DCS agreed very closely with the actual percentages in the database.
Conclusion: The loglogistic distribution was found to be appropriate f
or modeling the risk oi DCS. Based on the cross validation and validat
ion results, we conclude that this model provides good estimates of th
e probability of DCS over time.