Bram Wallace Smith

Using physiological models and decision theory for selecting appropriate ventilator settings.

ObjectiveTo present a decision support system for optimising mechanical ventilation in patients residing in the intensive care unit.MethodsMathematical models of oxygen transport, carbon dioxide transport and lung mechanics are combined with penalty functions describing clinical preference toward the goals and side-effects of mechanical ventilation in a decision theoretic approach. Penalties are quantified for risk of lung barotrauma, acidosis or alkalosis, oxygen toxicity or absorption atelectasis, and hypoxaemia.ResultsThe system is presented with an example of its use in a post-surgical patient. The mathematical models describe the patient’s data, and the system suggests an optimal ventilator strategy in line with clinical practice.ConclusionsThe system illustrates how mathematical models combined with decision theory can aid in the difficult compromises necessary when deciding on ventilator settings.

Simulation of cardiovascular system diseases by including the autonomic nervous system into a minimal model

Diagnosing cardiovascular system (CVS) diseases from clinically measured data is difficult, due to the complexity of the hemodynamic and autonomic nervous system (ANS) interactions. Physiological models could describe these interactions to enable simulation of a variety of diseases, and could be combined with parameter estimation algorithms to help clinicians diagnose CVS dysfunctions. This paper presents modifications to an existing CVS model to include a minimal physiological model of ANS activation. A minimal model is used so as to minimise the number of parameters required to specify ANS activation, enabling the effects of each parameter on hemodynamics to be easily understood. The combined CVS and ANS model is verified by simulating a variety of CVS diseases, and comparing simulation results with common physiological understanding of ANS function and the characteristic hemodynamics seen in these diseases. The model of ANS activation is required to simulate hemodynamic effects such as increased cardiac output in septic shock, elevated pulmonary artery pressure in left ventricular infarction, and elevated filling pressures in pericardial tamponade. This is the first known example of a minimal CVS model that includes a generic model of ANS activation and is shown to simulate diseases from throughout the CVS.

Does a positive end-expiratory pressure-induced reduction in stroke volume indicate preload responsiveness? An experimental study

Background:  Increases in positive end‐expiratory pressure (PEEP) are often associated with cardiovascular depression, responding to fluid loading. Therefore, we hypothesized that if stroke volume (SV) is reduced by an increase in PEEP this reduction is an indicator of hypovolemia or preload responsiveness, i.e. that SV would increase by fluid administration at zero end‐expiratory pressure (ZEEP). The relationship between the cardiovascular response to different PEEP levels and fluid load as well as the relation between change in SV as a result of change in preload (Frank–Starling relationship) were evaluated in a porcine model. In addition, other measures of fluid status were assessed.

Prediction of hemodynamic changes towards PEEP titrations at different volemic levels using a minimal cardiovascular model

A cardiovascular system model and parameter identification method have previously been validated for porcine experiments of induced pulmonary embolism and positive end-expiratory pressure (PEEP) titrations, accurately tracking all the main hemodynamic trends. In this research, the model and parameter identification process are further validated by predicting the effect of intervention. An overall population-specific rule linking specific model parameters to increases in PEEP is formulated to predict the hemodynamic effects on arterial pressure, pulmonary artery pressure and stroke volume. Hemodynamic changes are predicted for an increase from 0 to 10 cm H(2)O with median absolute percentage errors less than 7% (systolic pressures) and 13% (stroke volume). For an increase from 10 to 20 cm H(2)O median absolute percentage errors are less than 11% (systolic pressures) and 17% (stroke volume). These results validate the general applicability of such a rule, which is not pig-specific, but holds over for all analyzed pigs. This rule enables physiological simulation and prediction of patient response. Overall, the prediction accuracy achieved represents a further clinical validation of these models, methods and overall approach to cardiovascular diagnosis and therapy guidance.

Model-based identification of PEEP titrations during different volemic levels

A cardiovascular system (CVS) model has previously been validated in simulated cardiac and circulatory disease states. It has also been shown to accurately capture all main hemodynamic trends in a porcine model of pulmonary embolism. In this research, a slightly extended CVS model and parameter identification process are presented and validated in a porcine experiment of positive end-expiratory pressure (PEEP) titrations at different volemic levels. The model is extended to more physiologically represent the separation of venous and arterial circulation. Errors for the identified model are within 5% when re-simulated and compared to clinical data. All identified parameter trends match clinically expected changes. This work represents another clinical validation of the underlying fundamental CVS model, and the methods and approach of using them for cardiovascular diagnosis in critical care.

Simulating transient ventricular interaction using a minimal cardiovascular system model.

A minimal closed-loop cardiovascular system (CVS) model has been developed that can simulate ventricular interaction due to both direct interaction through the septum and series interaction through the circulation system. The model is used to simulate canine experiments carried out to study the transient response of the left ventricle due to changes in right ventricle pressures and volumes. The model-simulated trends in left and right ventricle pressures and volumes, septum deflection and arterial flow rates are compared with the experimental results. In spite of the limited physiological data available describing the animals, the model is shown to capture all the transient trends in the experimental data. This is the first known example of a physiological model that can capture all these trends. The model is then used to illustrate the separate effects of direct and series interactions independently. This study proves the value of this modelling method to be used in conjunction with experimental data for delineating and understanding the factors that contribute to ventricular dynamics.

Identification of patient specific parameters for a minimal cardiac model

A minimal cardiac model has been developed which accurately captures the essential dynamics of the cardiovascular system (CVS). This paper develops an integral based parameter identification method for fast and accurate identification of patient specific parameters for this minimal model. The integral method is implemented using a single chamber model to prove the concept, and turns a previously nonlinear and nonconvex optimization problem into a linear and convex problem. The method can be readily extended to the full minimal cardiac model and enables rapid identification of model parameters to match a particular patient condition in clinical real time (3-5 minutes). This information can then be used to assist medical staff in understanding, diagnosis and treatment selection.

Modeling the influence of the pulmonary pressure-volume curve on gas exchange

Current models of lung mechanics and gas exchange act independently to simulate variations in pressure-volume (PV) and ventilation-perfusion (V/Q) properties in the lungs respectively. However, changes in ventilator pressures can cause alveoli recruitment, collapse or over-distension causing V/Q changes in the lungs that are unaccounted for in these models. A compartmental model of the lungs is presented that is based on a physiological interpretation of lung function and simulates each alveolus individually. By combining this model with currently available lung mechanics and gas exchange models, the effect of changing ventilator settings on gas exchange could be simulated. The model is shown to simulate experimentally measured static PV data from an ARDS patient with an accuracy equivalent to that achieved by the sigmoid function. It could enable quantification of variations in V/Q in the lungs and also gives estimates of other physiological lung properties such as lung density and alveoli compliance. The alveoli model offers a physiologically relevant method of simulating the PV relationship in the lungs and its influence of gas exchange

A mathematical physiological model of the pulmonary ventilation

Abstract This paper presents a model of the lung mechanics and simulates the pulmonary alveolar ventilation. The model includes the alveolar geometry and distribution and pressures exerted by the chest wall, due to surface tension affected by surfactant activity, due to lung tissue elasticity and due to the hydrostatic effects of the lung tissue and blood utilizing a stratified subdivision of the lungs. The model simulates a heterogenous ventilation distribution down the lungs in agreement with experimental studies. Furthermore the model is in agreement with experimentally measured hysteresis, static lung compliance, lung volumes and density distribution at different lung volumes. The presented model is the first to simulate alveolar ventilation including all of the above mentioned components of the respiratory system.

Decision support of inspired oxygen fraction using a model of oxygen transport

Abstract Setting inspired oxygen fraction (FiO 2 ) is a complicated balance between ensuring adequate oxygenation and minimizing the risk of lung damage. This paper presents a retrospective test of a model-based decision support system (INVENT) for advising on FiO 2 levels in intensive care patients. Clinically determined FiO 2 levels and the resulting blood oxygenation are compared with INVENT determined FiO levels and model simulated blood oxygenation. The results indicate that INVENT can maintain an acceptable level of oxygenation using similar or more appropriate levels of FiO compared to clinical practice.