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| title | chunk | source | category | tags | date_saved | instance |
|---|---|---|---|---|---|---|
| Decompression theory | 14/17 | https://en.wikipedia.org/wiki/Decompression_theory | reference | science, encyclopedia | 2026-05-05T10:06:49.112339+00:00 | kb-cron |
=== Probabilistic models === Probabilistic decompression models, also referred to as evidence-based decompression models, are designed to calculate the risk (or probability) of decompression sickness (DCS) occurring on a given decompression profile, based on actual outcomes of a statistically robust set of data. Statistical analysis is well suited to compressed air work in tunneling operations due to the large number of subjects undergoing similar exposures at the same ambient pressure and temperature, with similar workloads and exposure times, with the same decompression schedule. Large numbers of decompressions under similar circumstances have shown that it is not reasonably practicable to eliminate all risk of DCS, so it is necessary to set an acceptable risk, based on the other factors relevant to the application. For example, easy access to effective treatment in the form of hyperbaric oxygen treatment on site, or greater advantage to getting the diver out of the water sooner, may make a higher incidence acceptable, while interfering with work schedule, adverse effects on worker morale or a high expectation of litigation would shift acceptable incidence rate downward. Efficiency is also a factor, as decompression of employees occurs during working hours. These methods can vary the decompression stop depths and times to arrive at a decompression schedule that assumes a specified probability of DCS occurring, while minimizing the total decompression time. This process can also work in reverse allowing one to calculate the probability of DCS for any decompression schedule, given sufficient reliable data. In 1936 an incidence rate of 2% was considered acceptable for compressed air workers in the UK. The US Navy in 2000 accepted a 2% incidence of mild symptoms, but only 0.1% serious symptoms. Commercial diving in the North Sea in the 1990s accepted 0.5% mild symptoms, but almost no serious symptoms, and commercial diving in the Gulf of Mexico also during the 1990s, accepted 0.1% mild cases and 0.025% serious cases. Health and Safety authorities tend to specify the acceptable risk as as low as reasonably practicable taking into account all relevant factors, including economic factors. To analyse probability of mild and severe symptoms it is first necessary to define these classes of manifestation, as applicable to the analysis. The necessary tools for probability estimation for decompression sickness are a biophysical model which describes the inert gas exchange and bubble formation during decompression, exposure data in the form of pressure/time profiles for the breathing gas mixtures, and the DCS outcomes for these exposures, statistical methods, such as survival analysis or Bayesian analysis to find a best fit between model and experimental data, after which the models can be quantitatively compared and the best fitting model used to predict DCS probability for the model. This process is complicated by the influence of environmental conditions on DCS probability. Factors that affect perfusion of the tissues during ingassing and outgassing, which affect rates of inert gas uptake and elimination respectively, include immersion, temperature and exercise. Exercise is also known to promote bubble formation during decompression. The distribution of decompression stops is also known to affect DCS risk. A USN experiment using symptomatic decompression sickness as the endpoint, compared two models for dive working exposures on air using the same bottom time, water temperature and workload, with the same total decompression time, for two different depth distributions of decompression stops, also on air, and found the shallower stops to carry a statistically very significantly lower risk. The model did not attempt to optimise depth distribution of decompression time, or the use of gas switching, it just compared the effectiveness of two specific models, but for those models the results were convincing. Another set of experiments was conducted for a series of increasing bottom time exposures at a constant depth, with varying ambient temperature. Four temperature conditions were compared: warm during the bottom sector and decompression, cold during bottom sector and decompression, warm at the bottom and cold during decompression, and cold at the bottom and warm during decompression. The effects were very clear that DCS incidence was much lower for divers that were colder during the ingassing phase and warmer during decompression than the reverse, which has been interpreted as indicating the effects of temperature on perfusion on gas uptake and elimination. A retrospective statistical analysis of a large data set of case reports of air and nitrox dives published in 2017 indicated that for an acceptable risk of 2% for mild symptoms, and 0.1% for severe symptoms, using a linear-exponential degassing model, the severe symptom risk was the limiting factor. One of the factors complicating this analysis was the variability in methods for distinguishing between mild and severe cases.
=== Saturation decompression ===