BACKGROUND: Altitude decompression sickness (DCS) is a potential hazard encountered during high altitude flights or during extravehicular activity in space. In this study, the loglogistic distribution was used to model DCS risk and symptom onset time. METHODS: The Air Force Research Laboratory, Brooks AFB, TX, has conducted studies on human subjects exposed to simulated altitudes in hypobaric chambers. The dataset from those studies was used to develop the DCS models and consisted of 975 subject-exposures to various altitudes, preoxygenation times, and exercise regimens. Since the risk of DCS is known to increase over time at altitude, and then decrease because of denitrogenation, the loglogistic model was fit to the data. The model assumes that the probability of DCS depends on several risk factors. Maximum likelihood estimates of the parameters were obtained using the statistical software package SAS. Cross validation techniques were provided to examine the goodness of fit of the model. RESULTS: The fitted model indicated that altitude, ratio of preoxygenation to exposure time, and exercise were the most significant risk factors. The model was used to predict the risk of DCS for a variety of exposure profiles. The predicted probability of DCS agreed very closely with the actual percentages in the database. CONCLUSION: The loglogistic distribution was found to be appropriate for modeling the risk of DCS. Based on the cross validation and validation results, we conclude that this model provides good estimates of the probability of DCS over time.
BACKGROUND: Altitude decompression sickness (DCS) is a potential hazard encountered during high altitude flights or during extravehicular activity in space. In this study, the loglogistic distribution was used to model DCS risk and symptom onset time. METHODS: The Air Force Research Laboratory, Brooks AFB, TX, has conducted studies on human subjects exposed to simulated altitudes in hypobaric chambers. The dataset from those studies was used to develop the DCS models and consisted of 975 subject-exposures to various altitudes, preoxygenation times, and exercise regimens. Since the risk of DCS is known to increase over time at altitude, and then decrease because of denitrogenation, the loglogistic model was fit to the data. The model assumes that the probability of DCS depends on several risk factors. Maximum likelihood estimates of the parameters were obtained using the statistical software package SAS. Cross validation techniques were provided to examine the goodness of fit of the model. RESULTS: The fitted model indicated that altitude, ratio of preoxygenation to exposure time, and exercise were the most significant risk factors. The model was used to predict the risk of DCS for a variety of exposure profiles. The predicted probability of DCS agreed very closely with the actual percentages in the database. CONCLUSION: The loglogistic distribution was found to be appropriate for modeling the risk of DCS. Based on the cross validation and validation results, we conclude that this model provides good estimates of the probability of DCS over time.
Authors: Tara L Sharma; Julia Morrow Kerrigan; David L McArthur; Kevin Bickart; Steven P Broglio; Thomas W McAllister; Michael McCrea; Christopher C Giza Journal: JAMA Netw Open Date: 2020-11-02