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空气和氮氧潜水减压病发生的概率性气体与气泡动力学模型

Probabilistic gas and bubble dynamics models of decompression sickness occurrence in air and nitrogen-oxygen diving.

作者信息

Gerth W A, Vann R D

机构信息

F.G. Hall Hypo-Hyperbaric Center, Department of Anesthesiology, Duke University Medical Center, Durham, NC 27710, USA.

出版信息

Undersea Hyperb Med. 1997 Winter;24(4):275-92.

PMID:9444059
Abstract

Probabilistic models of the occurrence of decompression sickness (DCS) with instantaneous risk defined as the weighted sum of bubble volumes in each of three parallel-perfused gas exchange compartments were fit using likelihood maximization to the subset of the USN Primary Air and N2-O2 database [n = 2,383, mean P(DCS) = 5.8%] used in development of the USN LE1 probabilistic models. Bubble dynamics with one diffusible gas in each compartment were modeled using the Van Liew equations with the nucleonic bubble radius, compartmental volume, compartmental bulk N2 diffusivity, compartmental N2 solubility, and the N2 solubility in blood x compartmental blood flow as adjustable parameters. Models were also tested that included the effects of linear elastic resistance to bubble growth in one, two, or all three of the modeled compartments. Model performance about the training data and separate validation data was compared to results obtained about the same data using the LE1 probabilistic model, which was independently implemented from published descriptions. In the most successful bubble volume model, BVM(3), diffusion significantly slows bubble growth in one of the modeled compartments, whereas mechanical resistance to bubble growth substantially accelerates bubble resolution in all compartments. BVM(3) performed generally on a par with LE1, despite inclusion of 12 more adjustable parameters, and tended to provide more accurate incidence-only estimates of DCS probability than LE1, particularly for profiles in which high fractional O2 gas mixes are breathed. Values of many estimated BVM(3) parameters were outside of the physiologic range, indicating that the model emerged from optimization as a mathematical descriptor of processes beyond bubble formation and growth that also contribute to DCS outcomes. Although incomplete as a mechanistic description of DCS etiology, BVM(3) remains applicable to a wider variety of decompressions than LE1 and affords a conceptual framework for further refinements motivated by mechanistic principles.

摘要

减压病(DCS)发生的概率模型,其瞬时风险定义为三个平行灌注气体交换腔室中每个腔室气泡体积的加权和,使用似然最大化方法拟合到美国海军初级空气和N2 - O2数据库的子集中[n = 2,383,平均P(DCS)= 5.8%],该子集用于美国海军LE1概率模型的开发。每个腔室中有一种可扩散气体的气泡动力学使用范·利夫方程进行建模,其中核子气泡半径、腔室体积、腔室总体N2扩散率、腔室N2溶解度以及血液中N2溶解度x腔室血流作为可调参数。还测试了包括在一个、两个或所有三个建模腔室中对气泡生长的线性弹性阻力影响的模型。将关于训练数据和单独验证数据的模型性能与使用LE1概率模型对相同数据获得的结果进行比较,LE1概率模型是根据已发表的描述独立实现的。在最成功的气泡体积模型BVM(3)中,扩散显著减缓了一个建模腔室中的气泡生长,而对气泡生长的机械阻力在所有腔室中大大加速了气泡消散。尽管BVM(3)比LE1多包含12个可调参数,但总体表现与LE1相当,并且在估计DCS概率时,特别是对于呼吸高分数O2气体混合物的情况,往往比LE1提供更准确的仅发病率估计。许多估计的BVM(3)参数值超出了生理范围,这表明该模型是从优化中产生的,作为对除气泡形成和生长之外也对DCS结果有贡献的过程的数学描述。尽管作为DCS病因的机制描述不完整,但BVM(3)仍然比LE1适用于更广泛的减压情况,并为基于机制原理的进一步改进提供了概念框架。

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