College of Engineering, Mathematics and Physical Sciences, University of Exeter, UK.
J R Soc Interface. 2011 Jan 6;8(54):44-55. doi: 10.1098/rsif.2010.0224. Epub 2010 Jun 10.
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.
生理模拟器旨在用于临床环境，因此医疗从业者对其抱有很高的期望；它们必须能够应对因不可测量参数、人群异质性和疾病异质性而产生的重大不确定性，其验证必须提供确凿的证据，证明其在临床领域的适用性和可靠性。本文描述了一种用于验证肺生理计算仿真模型的系统工程框架。我们结合不确定性/可变性的显式建模和先进的全局优化方法，证明模型预测在现实的参数不确定性水平下，从不偏离生理上合理的数值。本文考虑的仿真模型旨在代表一个动态的体内心肺状态，通过基于既定生理原理的质量守恒方程组进行迭代，并为重症监护环境中的直接临床应用而设计。不确定性建模方法改编自系统和控制工程领域当前的最佳实践，采用了一系列先进的优化方法来检查模型的稳健性，包括序列二次规划、网格自适应直接搜索和遗传算法。本文提供了这些方法的概述，并比较了它们的可靠性和计算效率与统计方法（如蒙特卡罗模拟）的比较。我们的研究结果表明，模拟器为所有现实的模型参数范围内的动脉气体压力提供了稳健的预测，也证明了所提出的方法在生理仿真模型验证中的普遍适用性。
