A systems engineering approach to validation of a pulmonary physiology simulator for clinical applications

• Published in: Journal of the Royal Society interface 2011-01, Vol.8 (54), p.44-55
• Main Authors: Das, A., Gao, Z., Menon, P. P., Hardman, J. G., Bates, D. G.
• Format: Article
• Language: English
• Subjects: Algorithms; Computer Simulation; Model Validation; Models, Biological; Monte Carlo Method; Physiological Simulation; Respiratory Physiological Phenomena; Software; Systems Biology; Systems Engineering; Uncertainty
• ISSN: 1742-5689; 1742-5662
• DOI: 10.1098/rsif.2010.0224

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.