Anup Das

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.

Optimization of Mechanical Ventilator Settings for Pulmonary Disease States

The selection of mechanical ventilator settings that ensure adequate oxygenation and carbon dioxide clearance while minimizing the risk of ventilator-associated lung injury (VALI) is a significant challenge for intensive-care clinicians. Current guidelines are largely based on previous experience combined with recommendations from a limited number of in vivo studies whose data are typically more applicable to populations than to individuals suffering from particular diseases of the lung. By combining validated computational models of pulmonary pathophysiology with global optimization algorithms, we generate in silico experiments to examine current practice and uncover optimal combinations of ventilator settings for individual patient and disease states. Formulating the problem as a multiobjective, multivariable constrained optimization problem, we compute settings of tidal volume, ventilation rate, inspiratory/expiratory ratio, positive end-expiratory pressure and inspired fraction of oxygen that optimally manage the tradeoffs between ensuring adequate oxygenation and carbon dioxide clearance and minimizing the risk of VALI for different pulmonary disease scenarios.

Can computer simulators accurately represent the pathophysiology of individual COPD patients

BackgroundComputer simulation models could play a key role in developing novel therapeutic strategies for patients with chronic obstructive pulmonary disease (COPD) if they can be shown to accurately represent the pathophysiological characteristics of individual patients.MethodsWe evaluated the capability of a computational simulator to reproduce the heterogeneous effects of COPD on alveolar mechanics as captured in a number of different patient datasets.ResultsOur results show that accurately representing the pathophysiology of individual COPD patients necessitates the use of simulation models with large numbers (up to 200) of compartments for gas exchange. The tuning of such complex simulation models ‘by hand’ to match patient data is not feasible, and thus we present an automated approach based on the use of global optimization algorithms and high-performance computing. Using this approach, we are able to achieve extremely close matches between the simulator and a range of patient data including PaO2, PaCO2, pulmonary deadspace fraction, pulmonary shunt fraction, and ventilation/perfusion (V̇/Q) curves. Using the simulator, we computed combinations of ventilator settings that optimally manage the trade-off between ensuring adequate gas exchange and minimizing the risk of ventilator-associated lung injury for an individual COPD patient.ConclusionsOur results significantly strengthen the credibility of computer simulation models as research tools for the development of novel management protocols in COPD and other pulmonary disease states.

High PEEP in acute respiratory distress syndrome: quantitative evaluation between improved arterial oxygenation and decreased oxygen delivery

Background. Positive end-expiratory pressure (PEEP) is widely used to improve oxygenation and prevent alveolar collapse in mechanically ventilated patients with the acute respiratory distress syndrome (ARDS). Although PEEP improves arterial oxygenation predictably, high-PEEP strategies have demonstrated equivocal improvements in ARDS-related mortality. The effect of PEEP on tissue oxygen delivery is poorly understood and is difficult to quantify or investigate in the clinical environment. Methods. We investigated the effects of PEEP on tissue oxygen delivery in ARDS using a new, high-fidelity, computational model with highly integrated respiratory and cardiovascular systems. The model was configured to replicate published clinical trial data on the responses of 12 individual ARDS patients to changes in PEEP. These virtual patients were subjected to increasing PEEP levels during a lung-protective ventilation strategy (0–20 cm H2O). Measured variables included arterial oxygenation, cardiac output, peripheral oxygen delivery, and alveolar strain. Results. As PEEP increased, tissue oxygen delivery decreased in all subjects (mean reduction of 25% at 20 cm H2O PEEP), despite an increase in arterial oxygen tension (mean increase 6.7 kPa at 20 cm H2O PEEP). Changes in arterial oxygenation and tissue oxygen delivery differed between subjects but showed a consistent pattern. Static and dynamic alveolar strain decreased in all patients as PEEP increased. Conclusions. Incremental PEEP in ARDS appears to protect alveoli and improve arterial oxygenation, but also appears to impair tissue oxygen delivery significantly because of reduced cardiac output. We propose that this trade-off may explain the poor improvements in mortality associated with high-PEEP ventilation strategies.

Development of an integrated model of cardiovascular and pulmonary physiology for the evaluation of mechanical ventilation strategies.

We describe the development of an integrated cardiovascular and pulmonary model for use in the investigation of novel mechanical ventilation strategies in the intensive care unit. The cardiac model includes the cardiac chambers, the pulmonary circulation and the systemic circulation. The modeling of complex mechanisms for vascular segments, time varying elastance functions of cardiovascular components and the effect of vascular resistances, in health and disease under the influence of mechanical ventilation is investigated. The resulting biomedical simulator can aid in understanding the underlying pathophysiology of critically-ill patients and facilitate the development of more effective therapeutic strategies for evaluation in clinical trials.

Creating virtual ARDS patients

This paper presents the methodology used in patient-specific calibration of a novel highly integrated model of the cardiovascular and pulmonary pathophysiology associated with Acute Respiratory Distress Syndrome (ARDS). We focus on data from previously published clinical trials on the static and dynamic cardio-pulmonary responses of three ARDS patients to changes in ventilator settings. From this data, the parameters of the integrated model were identified using an optimization-based methodology in multiple stages. Computational simulations confirm that the resulting model outputs accurately reproduce the available clinical data. Our results open up the possibility of creating in silico a biobank of virtual ARDS patients that could be used to evaluate current, and investigate novel, therapeutic strategies.

Optimization of Mechanical Ventilator Settings

Abstract In this paper we investigate the use of global optimization algorithms coupled with a validated pulmonary physiology simulator, to determine optimal solutions to the tradeoffs involved in Mechanical Ventilation (MV) therapy. Formulating the problem as a multiobjective, multivariable constrained optimization problem, we use two dissimilar algorithms, one based on evolutionary computation, the other on a pattern search strategy, to compute optimal settings of tidal volume (Vtidal, ml), ventilation rate (breaths min−1), inspiratory to expiratory duty cycle (IE) and positive end expiratory pressure (PEEP, kPa). The resulting settings minimize the values of a number of risk factors for VALI, while maintaining adequate levels of patient oxygenation, and provide valuable insights into the effects of variations in the different settings.

Primary blast lung injury simulator: a new computerised model

Mathematical modelling and computational simulation are becoming increasingly important tools in many fields of medicine where in vivo studies are expensive, difficult or impractical. This is particularly the case with primary blast lung injury, and in this paper, we give a brief overview of mathematical models before describing how we generated our blast lung injury simulator and describe some early results of its use.

Inhaled sGC Modulator Can Lower PH in Patients With COPD Without Deteriorating Oxygenation

This study uses a highly fidelity computational simulator of pulmonary physiology to evaluate the impact of a soluble guanylate cyclase (sGC) modulator on gas exchange in patients with chronic obstructive pulmonary disease (COPD) and pulmonary hypertension (PH) as a complication. Three virtual patients with COPD were configured in the simulator based on clinical data. In agreement with previous clinical studies, modeling systemic application of an sGC modulator results in reduced partial pressure of oxygen (PaO2) and increased partial pressure of carbon dioxide (PaCO2) in arterial blood, if a drug‐induced reduction of pulmonary vascular resistance (PVR) equal to that observed experimentally is assumed. In contrast, for administration via dry powder inhalation (DPI), our simulations suggest that the treatment results in no deterioration in oxygenation. For patients under exercise, DPI administration lowers PH, whereas oxygenation is improved with respect to baseline values.

Investigating the effect of cardiac oscillations and deadspace gas mixing during apnea using computer simulation

Gaseous mixing in the anatomical deadspace with stimulation of respiratory ventilation through cardiogenic oscillations is an important physiological mechanism at the onset of apnea, which has been credited with various beneficial effects, e.g. reduction of hypercapnia during the use of low flow ventilation techniques. In this paper, a novel method is proposed to investigate the effect of these mechanisms in silico. An existing computational model of cardio-pulmonary physiology is extended to include the apneic state, gas mixing within the anatomical deadspace, insufflation into the trachea and cardiogenic oscillations. The new model is validated against data published in an experimental animal (dog) study that reported an increase in arterial partial pressure of carbon dioxide (PaCO2) during apnea. Computational simulations confirm that the model outputs accurately reproduce the available experimental data. This new model can be used to investigate the physiological mechanisms underlying clearance of carbon dioxide during apnea, and hence to develop more effective ventilation strategies for apneic patients.