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High altitude exposure in aircraft, hypobaric chambers and with extravehicular activity (EVA) in space results in an inherent risk of altitude decompression sickness (DCS). In the past, general guidelines for safer altitude exposure have been developed through costly, time-consuming studies, each specific to unique scenarios of altitude exposure. Rapidly changing technology in aircraft design and mission requirements demand improved capabilities in predicting DCS risk during mission planning and execution. In 1990, a new bubble growth algorithm and a statistical model based on the existing USAF DCS Database were initiated at Brooks AFB. The first version of this combined model was completed in 1996. A model validation study using human subjects was completed in 1999. An updated version of this model based on the validation results has been produced and the software developed. A portable hand-held model is being developed for use in situations requiring more flexible operations (e.g.; high altitude parachuting). Application of this technology would specifically aid aviators, special operations personnel, and civilian aviators in determining altitude DCS risk.
In response to the need for a standardized methodology for altitude decompression sickness risk assessment across the wide range of exposures encountered in USAF flight operations, the Armstrong Laboratory's Crew Technology Division initiated a research program in this area in late 1989. The focus of work has been on determining whether development of an operational altitude decompression computer for both predictive and real-time DCS risk assessment is feasible, given the current level of understanding about altitude decompression sickness, the amount of available experimental data, and the inherent variability in individual susceptibility to altitude DCS. The results of this feasibility study indicate that although some technical risk is involved, development of the proposed altitude decompression computer is feasible. This was demonstrated through the implementation of a simplified, preliminary model for altitude DCS risk assessment. This technical report documents the work accomplished during this research effort and provides a road map for development of the desired operational altitude decompression computers.
To predict altitude decompression sickness (DCS) risk with any degree of accuracy, one must weigh variables such as prebreathe time, rate of ascent/ descent, time at altitude, altitude, mixed breathing gas (dependent upon altitude), and profiles with multiple ascents and descents. The length of research chamber exposures is fixed. Therefore, risk assessment is based on DCS incidence after this fixed period at simulated altitude. From an operational standpoint, variable time at altitude complicates any predictive capability, although a computer model to handle all of these variables is in development. In the interim, a retrospective study from the Armstrong Laboratory Decompression Sickness Research Database has produced risk curves which can be used to predict DCS or venous gas emboli (VGE) incidence as a function of time at various altitudes. We limited the data to: (1) zero-prebreathe exposures to less than 20,000 ft breathing 50% O2, 50% N2; (2) zero-prebreathe exposures to less than 20,000 ft breathing 100% O2; and (3) 1-h prebreathe exposures to greater than 20,000 ft breathing 100% 02. Using the curves, one can select a time/altitude of exposure and estimate the DCS and VGE percentage. Decompression sickness, Venous gas emboli, Prebreathe, Latency.
Through the cooperation of AFRL's Biomechanics Branch and Air Crew Performance and Protection Branch, a new web database application, the AFRL Air Crew Performance and Protection (Altitude) Data Bank, has been developed. The Altitude Data Bank consists of the altitude decompression sickness (DCS) research database, the DCS bibliographic database, and the Altitude Decompression Sickness Risk Assessment Computer (ADRAC) model. It represents over 17 years of AFRL research and experiments in high altitude decompression sickness areas. This developmental work brings web-based multi-parameter search and multi-database access capabilities to the DCS research data. The ADRAC web toolkit is based on the combined Bubble Growth model and statistical model of hypobaric chamber simulations. It offers a quick and readily accessible online DCS risk assessment tool for flight mission planners, operators, pilots, and commanders.
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.
High altitude exposure in aircraft hypobaric chambers and with extravehicular- activity (EVA) in space results in an inherent risk of altitude decompression sickness (DCS). In the past general guidelines for safer altitude exposures have been developed through costly time-consuming studies each specific to unique scenarios of altitude exposure. Rapidly changing technology in aircraft design and mission requirements demand improved capabilities in predicting DCS risk during mission planning and execution.
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.
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.
Now in its Fourth Edition with a new editorial team, this comprehensive text addresses all medical and public health issues involved in the care of crews, passengers, and support personnel of aircraft and space vehicles. Coverage includes human physiology under flight conditions, clinical medicine in the aerospace environment, and the impact of the aviation industry on global public health. This edition features new chapters on radiation, toxicology and microbiology, dental considerations in aerospace medicine, women's health issues, commercial human space flight, space exploration, and unique aircraft including parachuting. Other highlights include significant new information on respiratory diseases, cardiovascular medicine, infectious disease transmission, and human response to acceleration.
These proceedings include the Technical Evaluation Report, a tribute to Dr. Henning E. von Gierke, Director Emeritus, Wright-Patterson Air Force Base (WPAFB), OH, three Keynote Addresses and 32 invited papers of a Specialists' Meeting sponsored by the NATO/RTO Human Factors and Medicine Panel. It was held at WPAFB from 26-28 October 1998. Significant advances have been made in modelling human physical and physiological responses to extreme environments. Technological advances in computer speed and power have made modelling a feasible research and design tool. Computer simulations are being used extensively for predicting human physical and physiological responses, for reducing testing requirements, for rapidly designing improved protective systems, and for performing human safety-systems analyses. A variety of models were reviewed at this Specialists' Meeting including lumped-parameter, rigid-body, finite-element, statistical, physiologic, and empirical models. Topics covered included modelling human-body responses to environmental stressors, and the systems with which the body interacts to: impact, emergency escape, sustained acceleration, vibration, mechanical shock, motion sickness, high altitude, blast, extreme thermal conditions, directed energy and live firing. These proceedings will be of interest to military and civilian scientists and engineers interested in exploiting data bases, tolerance criteria, and new models and methods in the research of physiological systems and in simulating the design, test set up and evaluation of safety systems.