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Survival Analysis methods have been used to model the onset of Decompression Sickness (DCS) which occurs routinely as a result of high altitude exposure. Both parametric and nonparametric models were developed. These models were used to predict the risk of DCS for different flight profiles. The risk factors that have a significant effect on the risk of DCS were also identified. Cross validation techniques are provided to examine the goodness of fit of the model. The loglogistic model was modified to incorporate data on bubble grades and times.
Decompression sickness (DCS) is a complex, multivariable problem. A mathematical description or model of the likelihood of DCS requires a large amount of quality research data, ideas on how to define a decompression dose using physical and physiological variables, and an appropriate analytical approach. It also requires a high-performance computer with specialized software. I have used published DCS data to develop my decompression doses, which are variants of equilibrium expressions for evolved gas.
Decompression sickness (DCS) is a complex, multivariable problem. A mathematical description or model of the likelihood of DCS requires a large amount of quality research data, ideas on how to define a decompression dose using physical and physiological variables, and an appropriate analytical approach. It also requires a high-performance computer with specialized software. I have used published DCS data to develop my decompression doses, which are variants of equilibrium expressions for evolved gas plus other explanatory variables. My analytical approach is survival analysis, where the time of DCS occurrence is modeled. My conclusions can be applied to simple hypobaric decompressions - ascents lasting from 5 to 30 minutes - and, after minutes to hours, to denitrogenation (prebreathing). They are also applicable to long or short exposures, and can be used whether the sufferer of DCS is at rest or exercising at altitude. Ultimately I would like my models to be applied to astronauts to reduce the risk of DCS during spacewalks, as well as to future spaceflight crews on the Moon and Mars. Conkin, Johnny Johnson Space Center NASA/TP-2001-210775, S-885, NAS 1.60:210775, JSC-CN-7170
Air Force personnel are routinely exposed to atmospheric decompressions that often incur significant risk of decompression sickness (DCS). Management of these risks requires analytic methods able to: (a) define risk/hazard envelopes for all routine and emergency decompressions, (b) assess the DCS risks included or introduced in the contemplation or design of new operational procedures and equipment, and; (c) support real-time monitoring of DCS risk incurred by personnel during various chamber and aircraft operations. Present work contributed to meeting these requirements through development and application of methods by which DCS risks during decompression profiles are determined from statistical/biophysical models of in vivo gas exchange and bubble growth and resolution using maximum likelihood, both logistic and survival models were fit to DCS incidence data from the USAF Armstrong Laboratory (USAFAL) for a wide variety of decompression profiles. The models were incorporated into software that operates on personal computers. System software, including a data transcription routine to serve as a software interface between the USAFAL Hypobaric Decompression Sickness Database and the present modeling system, was delivered for use and evaluation of USAFAL personnel.
Decompression sickness (DCS) is a complex, multivariable problem. A mathematical description or model of the likelihood of DCS requires a large amount of quality research data, ideas on how to define a decompression dose using physical and physiological variables, and an appropriate analytical approach. It also requires a high-performance computer with specialized software. I have used published DCS data to develop my decompression doses, which are variants of equilibrium expressions for evolved gas plus other explanatory variables. My analytical approach is survival analysis, where the time of DCS occurrence is modeled. My conclusions can be applied to simple hypobaric decompressions - ascents lasting from 5 to 30 minutes - and, after minutes to hours, to denitrogenation (prebreathing). They are also applicable to long or short exposures, and can be used whether the sufferer of DCS is at rest or exercising at altitude. Ultimately I would like my models to be applied to astronauts to reduce the risk of DCS during spacewalks, as well as to future spaceflight crews on the Moon and Mars.
Estimating the risk of decompression sickness (DCS) in aircraft operations remains a challenge, making the reduction of this risk through the development of operationally acceptable denitrogenation schedules difficult. In addition, the medical recommendations which are promulgated are often not supported by rigorous evaluation of the available data, but are instead arrived at by negotiation with the aircraft operations community, are adapted from other similar aircraft operations, or are based upon the opinion of the local medical community. We present a systematic approach for defining DCS risk in aircraft operations by analyzing the data available for a specific aircraft, flight profile, and aviator population. Once the risk of DCS in a particular aircraft operation is known, appropriate steps can be taken to reduce this risk to a level acceptable to the applicable aviation community. Using this technique will allow any aviation medical community to arrive at the best estimate of DCS risk for its specific mission and aviator population and will allow systematic reevaluation of the decisions regarding DCS risk reduction when additional data are available.
In this paper we fit Cox proportional hazards models to a subset of data from the Hypobaric Decompression Sickness Databank. The data bank contains records on the time to decompression sickness (DCS) and venous gas emboli (VGE) for over 130,000 person-exposures to high altitude in chamber tests. The subset we use contains 1,321 records, with 87% censoring, and has the most recent experimental tests on DCS made available from Johnson Space Center. We build on previous analyses of this data set by considering more expanded models and more detailed model assessments specific to the Cox model. Our model - which is stratified on the quartiles of the final ambient pressure at altitude - includes the final ambient pressure at altitude as a nonlinear continuous predictor, the computed tissue partial pressure of nitrogen at altitude, and whether exercise was done at altitude. We conduct various assessments of our model, many of which are recently developed in the statistical literature, and conclude where the model needs improvement. We consider the addition of frailties to the stratified Cox model, but found that no significant gain was attained above a model that does not include frailties. Finally, we validate some of the models that we fit.Thompson, Laura A. and Chhikara, Raj S. and Conkin, JohnnyJohnson Space CenterDECOMPRESSION SICKNESS; EXTRAVEHICULAR ACTIVITY; DATA BASES; PARTIAL PRESSURE; HIGH ALTITUDE TESTS; HAZARDS; QUARTILES; PRESSURE; PHYSICAL EXERCISE; NONLINEARITY; NITROGEN; EXPOSURE; ALTITUDE SIMULATION; AEROEMBOLISM