Field trials of no-decompression stop limits for diving at 3500 m

Introduction: In 1990, Bogazici University (Istanbul, Turkey) launched an altitude diving program to develop techniques and safe decompression profiles for diving at high terrestrial altitudes. Following pioneering diving expeditions to lakes at high elevations in 1990-1992, it was deemed necessary to calculate new tables. Methods: Bottom time limits for dives requiring no decompression stops (no-d) were calculated for 3500 m using linear extrapolation of U.S. Navy M-values decreased by 4 ft of sea water (M4 limits). These limits were tested for 15, 18, 21, 24, 27, and 30 m of depth by diving in the Great Sea Lake at Mt Kackar (3412 m) with 10 dives per profile. Results. The mean decompression sickness (DCS) risk estimated from precordial bubble scores (Spencer Scale) ranged from 0.3% to 2.8% per profile. After three expeditions, 165 dives had been achieved with a cumulative bottom time of 3199 min. No DCS occurred in dives that adhered to the M4 no-d limits. However, two cases of Type I and one case of Type II DCS were encountered where the divers accidentally exceeded those limits. Discussion: Considering the estimated risk of DCS and the relatively small number of trials, a more conservative approach was used to develop a final set of high altitude dive tables. This conclusive approach used continuous compartment half-lives. It is based on fitting a surface of allowable supersaturation limits using the empirical M-values from existing tables as well as our altitude diving data, together with an added constraint that forces calculated M-values to stay below the available M-value data

Simulation of dynamic bubble spectra in tissues

Decompression sickness (DCS) is the result of bubble formation in the body due to excessive/rapid reduction in the ambient pressure. Existing models relate the decompression stress either to the inert gas load or to the size of a single bubble in a tissue compartment. This paper presents a model that uses the gas exchange equations combined with bubble dissolution physics and population balance equations to produce a new mathematical framework for DCS modeling. This framework, the population balance model for decompression sickness (PBMDS), simulates the number of bubbles with their corresponding size distributions in a compartmental tissue array, The model has a modular structure that enables one to explore different modeling results with respect to key aspects of DCS, such as gas exchange, nucleation, and surface tension. The paper's goal is to present the derivation of PBMDS in detail, however, three simple application case studies are provided. The aim of these case studies is to suggest that PBMDS supplies additional information on bubble distribution while supporting the results from current practice