A systems engineering approach to validation of a pulmonary physiology simulator for clinical applications
A systems engineering approach to validation of a pulmonary physiology simulator for clinical applications
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DOI：
10.1098/rsif.2010.0224
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发表时间：
2011-01-06
影响因子：
3.9
通讯作者：
Bates, D. G.
中科院分区：
文献类型：
--
作者：
Das, A.;Gao, Z.;Bates, D. G.
用于临床环境的生理模拟器面临着医生的苛刻期望；它们必须处理由不可测量的参数、种群异质性和疾病异质性引起的显著水平的不确定性，并且它们的验证必须为它们在临床领域的适用性和可靠性提供无懈可击的证明。本文描述了一个用于验证肺生理学计算机模拟模型的系统工程框架。我们将不确定性/可变性的显式建模与先进的全局优化方法相结合，以证明模型预测永远不会偏离实际参数不确定性水平的生理似是而非的值。这里所考虑的模拟模型被设计成代表动态的体内心肺状态，基于既定的生理学原理迭代通过一组质量守恒的方程，并且已经被开发用于重症监护环境中的直接临床应用。不确定性建模方法借鉴了当前系统和控制工程领域的最佳实践，并采用了一系列先进的优化方法来检验模型的稳健性，包括序列二次规划、网格自适应直接搜索和遗传算法。对这些方法进行了概述，并与蒙特卡罗模拟等统计方法进行了比较，比较了它们的可靠性和计算效率。我们的研究结果表明，该模拟器对所有实际范围的模型参数提供了稳健的动脉气体压力预测，并证明了所提出的方法对生理模拟的模型验证的普遍适用性。
Physiological simulators which are intended for use in clinical environments face harsh expectations from medical practitioners; they must cope with significant levels of uncertainty arising from non-measurable parameters, population heterogeneity and disease heterogeneity, and their validation must provide watertight proof of their applicability and reliability in the clinical arena. This paper describes a systems engineering framework for the validation of an in silico simulation model of pulmonary physiology. We combine explicit modelling of uncertainty/variability with advanced global optimization methods to demonstrate that the model predictions never deviate from physiologically plausible values for realistic levels of parametric uncertainty. The simulation model considered here has been designed to represent a dynamic in vivo cardiopulmonary state iterating through a mass-conserving set of equations based on established physiological principles and has been developed for a direct clinical application in an intensive-care environment. The approach to uncertainty modelling is adapted from the current best practice in the field of systems and control engineering, and a range of advanced optimization methods are employed to check the robustness of the model, including sequential quadratic programming, mesh-adaptive direct search and genetic algorithms. An overview of these methods and a comparison of their reliability and computational efficiency in comparison to statistical approaches such as Monte Carlo simulation are provided. The results of our study indicate that the simulator provides robust predictions of arterial gas pressures for all realistic ranges of model parameters, and also demonstrate the general applicability of the proposed approach to model validation for physiological simulation.