Modelling: a core technique in anaesthesia and critical care research

Cardiopulmonary responses to maximal aerobic exercise in patients with cystic fibrosis

Cystic fibrosis (CF) is a debilitating chronic condition, which requires complex and expensive disease management. Exercise has now been recognised as a critical factor in improving health and quality of life in patients with CF. Hence, cardiopulmonary exercise testing (CPET) is used to determine aerobic fitness of young patients as part of the clinical management of CF. However, at present there is a lack of conclusive evidence for one limiting system of aerobic fitness for CF patients at individual patient level. Here, we perform detailed data analysis that allows us to identify important systems-level factors that affect aerobic fitness. We use patients’ data and principal component analysis to confirm the dependence of CPET performance on variables associated with ventilation and metabolic rates of oxygen consumption. We find that the time at which participants cross the gas exchange threshold (GET) is well correlated with their overall performance. Furthermore, we propose a predictive modelling framework that captures the relationship between ventilatory dynamics, lung capacity and function and performance in CPET within a group of children and adolescents with CF. Specifically, we show that using Gaussian processes (GP) we can predict GET at the individual patient level with reasonable accuracy given the small sample size of the available group of patients. We conclude by presenting an example and future perspectives for improving and extending the proposed framework. The modelling and analysis have the potential to pave the way to designing personalised exercise programmes that are tailored to specific individual needs relative to patient’s treatment therapies.

Modelling primary blast lung injury: current capability and future direction

Primary blast lung injury frequently complicates military conflict and terrorist attacks on civilian populations. The fact that it occurs in areas of conflict or unpredictable mass casualty events makes clinical study in human casualties implausible. Research in this field is therefore reliant on the use of some form of biological or non-biological surrogate model. This article briefly reviews the modelling work undertaken in this field until now and describes the rationale behind the generation of an in silico physiological model.

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

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.

Pre-oxygenation in pregnancy: an investigation using physiological modelling

Hypoxaemia during anaesthetic induction in obstetrics is hazardous for mother and baby, but the onset of desaturation can be delayed by pre-oxygenation. This study investigated pre-oxygenation during pregnancy using computer simulation. The Nottingham Physiology Simulator was configured to replicate normal pregnant physiology. Three pregnant and three non-pregnant subjects were created, representing population variation according to published physiological values. They underwent pre-oxygenation by tidal and vital capacity breathing of 100% oxygen. Pre-oxygenation during tidal breathing proceeded more rapidly in pregnancy, the median [range] time to achieve 95% of the maximum change in P(E)O2 being 1 min 37 s [1:23-1:52] in pregnant subjects, compared to 2 min 51 s [2:28-3:15] in non-pregnant subjects. Vital capacity pre-oxygenation required seven breaths [5-10] in pregnant subjects, compared to six breaths [4-9] in non-pregnant subjects, to achieve the same P(E)O2 as after 95% complete tidal pre-oxygenation. We recommend 2 min of tidal breathing for pre-oxygenation in pregnancy.

Apnoea in pregnancy: an investigation using physiological modelling

Oxygen desaturation during apnoea following anaesthetic induction in obstetrics is hazardous for mother and baby. This study investigated apnoea during pregnancy using computer simulation. The Nottingham Physiology Simulator was configured to replicate normal pregnant physiology. Three pregnant and three non-pregnant subjects were created, representing population variation according to published physiological values. They each commenced apnoea from four levels of pre-oxygenation from none to 99% complete denitrogenation. During apnoea, the physiological changes of rapid sequence induction were simulated. We found reduced apnoea tolerance in pregnancy, the median [range] time taken to fall to S(a)O2 < 90% after 99% complete denitrogenation being 4 min 52 s [3:43-6:17] in the pregnant subjects, vs 7 min 25 s [5:49-9:42] in the non-pregnant subjects. The time taken to fall from S(a)O2 90% to 40% was 35 s [32-45] in the pregnant vs 45 s [38-56] in the non-pregnant subjects. Two minutes of pre-oxygenation by tidal breathing provides approximately 3.5-6 min before desaturation to < 90%.