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Probability of hypobaric decompression sickness including extreme exposures
Johnny Conkin1, Michael L Gernhardt, Andrew F Abercromby
1Universities Space Research Association, Houston, TX 77058-2769, USA. johnny.conkin-1@nasa.gov
Aviation, Space, and Environmental Medicine
|July 17, 2013
Summary
This study developed a model to predict decompression sickness (DCS) probability using decompression dose, gender, and exercise intensity. Higher values in these factors increase DCS risk, with tissue ratio (TR) proving adequate for altitude exposures.
Area of Science:
- Aerospace Medicine
- Physiology
- Risk Assessment
Background:
- Probabilistic decompression sickness (DCS) models benefit from diverse DCS incidence data.
- The Air Force Research Laboratory Altitude Decompression Sickness Research Database provided data from 708 human altitude chamber exposures.
- Data included 340 DCS outcomes with incidence ranging from 0% to 88%.
Purpose of the Study:
- To develop a probabilistic model for DCS.
- To identify key predictors of DCS probability.
- To evaluate different measures of decompression dose.
Main Methods:
- Logistic regression and the Hill equation were employed.
- Decompression dose was defined using tissue ratio (TR) or bubble growth index (BGI).
- Gender and peak exercise intensity at altitude were included as predictors.
Main Results:
- Decompression dose, gender, and exercise intensity significantly contributed to the DCS probability model.
- Higher decompression dose (TR or BGI), male gender, and increased exercise intensity elevated the modeled DCS risk.
- TR and BGI yielded comparable results for predicting DCS.
Conclusions:
- A simple tissue ratio (TR) is sufficient for modeling DCS in straightforward altitude exposures.
- The developed model is heuristic and applicable to 4-hour exposures.
- Factors like gender and exercise intensity are crucial in DCS risk assessment.
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