Ball R, Himm J, Homer L D, Thalmann E D
Naval Medical Research Institute, Bethesda, Maryland 20889-5607, USA.
Undersea Hyperb Med. 1995 Sep;22(3):263-80.
A probabilistic model of decompression sickness (DCS) risk based on linear-exponential (LE) kinetics has given the best fit of the human air and nitrox DCS database. To test the hypothesis that its success may be due to the formation of a gas phase during decompression, we developed a physiologically based bubble evolution model using a numerical solution of a partial differential equation system. Because of the computational intensity of this method, it could not be used to fully explore our hypothesis. Consequently, we compared the solution with that of a computationally simpler approximation that was previously published by Van Liew and found the two approaches gave similar results. Using the simpler model, assuming bubble densities of 1 and 1,000 bubbles/cm3, we found a tissue time constant of at least 80 min (equivalent to perfusion of 1/80 ml.g-1.min-1) was required to achieve a delay in bubble dissolution comparable to the prolonged risk of DCS predicted by the LE model. We suggest that the persistence of single bubbles in a uniformly perfused homogeneous tissue alone is unlikely to explain persistent DCS risk.
基于线性指数(LE)动力学的减压病(DCS)风险概率模型对人类空气和氮氧混合气DCS数据库的拟合效果最佳。为了检验其成功可能归因于减压过程中气相形成这一假设,我们使用偏微分方程组的数值解开发了一个基于生理学的气泡演化模型。由于该方法的计算强度较大,无法用于全面探究我们的假设。因此,我们将该解与Van Liew之前发表的计算更简单的近似解进行了比较,发现两种方法得出的结果相似。使用更简单的模型,假设气泡密度为1和1000个气泡/立方厘米,我们发现要实现与LE模型预测的DCS延长风险相当的气泡溶解延迟,需要至少80分钟的组织时间常数(相当于1/80毫升·克⁻¹·分钟⁻¹的灌注量)。我们认为,仅均匀灌注的均质组织中单个气泡的持续存在不太可能解释DCS的持续风险。