A LOGLOGISTIC MODEL FOR ALTITUDE DECOMPRESSION-SICKNESS

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.