Clinical refinement of the automatic lung parameter estimator (ALPE).

The automatic lung parameter estimator (ALPE) method was developed in 2002 for bedside estimation of pulmonary gas exchange using step changes in inspired oxygen fraction (FIO2). Since then a number of studies have been conducted indicating the potential for clinical application and necessitating systems evolution to match clinical application. This paper describes and evaluates the evolution of the ALPE method from a research implementation (ALPE1) to two commercial implementations (ALPE2 and ALPE3). A need for dedicated implementations of the ALPE method was identified: one for spontaneously breathing (non-mechanically ventilated) patients (ALPE2) and one for mechanically ventilated patients (ALPE3). For these two implementations, design issues relating to usability and automation are described including the mixing of gasses to achieve FIO2 levels, and the automatic selection of FIO2. For ALPE2, these improvements are evaluated against patients studied using the system. The major result is the evolution of the ALPE method into two dedicated implementations, namely ALPE2 and ALPE3. For ALPE2, the usability and automation of FIO2 selection has been evaluated in spontaneously breathing patients showing that variability of gas delivery is 0.3 % (standard deviation) in 1,332 breaths from 20 patients. Also for ALPE2, the automated FIO2 selection method was successfully applied in 287 patient cases, taking 7.2 ± 2.4 min and was shown to be safe with only one patient having SpO2 < 86 % when the clinician disabled the alarms. The ALPE method has evolved into two practical, usable systems targeted at clinical application, namely ALPE2 for spontaneously breathing patients and ALPE3 for mechanically ventilated patients. These systems may promote the exploration of the use of more detailed descriptions of pulmonary gas exchange in clinical practice.

Variation in the PaO2/FiO2 ratio with FiO2: mathematical and experimental description, and clinical relevance.

INTRODUCTION: Previous studies have shown through theoretical analyses that the ratio of the partial pressure of oxygen in arterial blood (PaO2) to the inspired oxygen fraction (FiO2) varies with the FiO2 level. The aim of the present study was to evaluate the relevance of this variation both theoretically and experimentally using mathematical model simulations, comparing these ratio simulations with PaO2/FiO2 ratios measured in a range of different patients. METHODS: The study was designed as a retrospective study using data from 36 mechanically ventilated patients and 57 spontaneously breathing patients studied on one or more occasions. Patients were classified into four disease groups (normal, mild hypoxemia, acute lung injury and acute respiratory distress syndrome) according to their PaO2/FiO2 ratio. On each occasion the patients were studied using four to eight different FiO2 values, achieving arterial oxygen saturations in the range 85-100%. At each FiO2 level, measurements were taken of ventilation, of arterial acid-base and of oxygenation status. Two mathematical models were fitted to the data: a one-parameter 'effective shunt' model, and a two-parameter shunt and ventilation/perfusion model. These models and patient data were used to investigate the variation in the PaO2/FiO2 ratio with FiO2, and to quantify how many patients changed disease classification due to variation in the PaO2/FiO2 ratio. An F test was used to assess the statistical difference between the two models' fit to the data. A confusion matrix was used to quantify the number of patients changing disease classification. RESULTS: The two-parameter model gave a statistically better fit to patient data (P < 0.005). When using this model to simulate variation in the PaO2/FiO2 ratio, disease classification changed in 30% of the patients when changing the FiO2 level. CONCLUSION: The PaO2/FiO2 ratio depends on both the FiO2 level and the arterial oxygen saturation level. As a minimum, the FiO2 level at which the PaO2/FiO2 ratio is measured should be defined when quantifying the effects of therapeutic interventions or when specifying diagnostic criteria for acute lung injury and acute respiratory distress syndrome. Alternatively, oxygenation problems could be described using parameters describing shunt and ventilation/perfusion mismatch.

Quantitative assessment of pulmonary shunt and ventilation-perfusion mismatch without a blood sample.

The automated lung parameter estimator (ALPE) system for quantitatively assessing pulmonary gas exchange in clinical practice has been shown to be useful for diagnosing lung dysfunction and monitoring treatment. However, the method requires at least one blood sample, which is routine in intensive care, but not readily available in many other hospital departments. This study investigates the feasibility of using default blood gas data and pulse oximetry to determine gas exchange parameters non-invasively. It was found that values of shunt and V/Q mismatch estimated using only non-invasively measured data, correlated well with the same values found using more accurate, multiple invasive, methods. This method greatly improves the feasibility of using the ALPE method for diagnosing and monitoring patients outside the intensive care department.

Modelling the cardiovascular system.

Cardiovascular disease claims more lives than any other disease in westernised countries, affecting millions. Pinpointing cardiovascular system dysfunction is often difficult because the clinical signs, or the availability and interpretation of physiological measurements, are unreliable. Often patient-specific information is incomplete or confusing, as it comes from a diverse range of sources, such as invasive and non-invasive pressure measurements, flow rates and electrocardiogram signals. Health professionals therefore rely on intuition and experience to make a "clinical" diagnosis and decide treatment. Sometimes this approach results in multiple therapies being applied until a suitable treatment is found. Poor outcomes result from failure to quickly and correctly diagnose and treat the underlying condition. We introduce the concept of using full circulatory and cardiovascular models to aggregate the large number of diverse signals facing clinicians into a clear physiological picture of haemodynamic status. We briefly review the field, still in its infancy, of such models, focusing primarily on the basic approaches taken in the literature. Finally, we present one of the more advanced and best validated models, including initial results of animal validation studies. The overall approach is shown to have significant potential to provide clear, measured insight to replace often misled intuition in the monitoring, diagnosis and treatment of circulatory dysfunction in critical care. In the future, models and modern sensors will increasingly "invade" the critical care environment, and will provide the opportunity for better, more consistent care at the bedside in real time.

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.

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.

Minimal haemodynamic system model including ventricular interaction and valve dynamics.

Characterising circulatory dysfunction and choosing a suitable treatment is often difficult and time consuming, and can result in a deterioration in patient condition, or unsuitable therapy choices. A stable minimal model of the human cardiovascular system (CVS) is developed with the ultimate specific aim of assisting medical staff for rapid, on site modelling to assist in diagnosis and treatment. Models found in the literature simulate specific areas of the CVS with limited direct usefulness to medical staff. Others model the full CVS as a closed loop system, but they were found to be very complex, difficult to solve, or unstable. This paper develops a model that uses a minimal number of governing equations with the primary goal of accurately capturing trends in the CVS dynamics in a simple, easily solved, robust model. The model is shown to have long term stability and consistency with non-specific initial conditions as a result. An "open on pressure close on flow" valve law is created to capture the effects of inertia and the resulting dynamics of blood flow through the cardiac valves. An accurate, stable solution is performed using a method that varies the number of states in the model depending on the specific phase of the cardiac cycle, better matching the real physiological conditions. Examples of results include a 9% drop in cardiac output when increasing the thoracic pressure from -4 to 0 mmHg, and an increase in blood pressure from 120/80 to 165/130 mmHg when the systemic resistance is doubled. These results show that the model adequately provides appropriate magnitudes and trends that are in agreement with existing data for a variety of physiologically verified test cases simulating human CVS function.

Velocity profile method for time varying resistance in minimal cardiovascular system models.

This paper investigates the fluid dynamics governing arterial flow used in lumped parameter cardiovascular system (CVS) models, particularly near the heart where arteries are large. Assumptions made in applying equations conventionally used in lumped parameter models are investigated, specifically that of constant resistance to flow. The Womersley number is used to show that the effects of time varying resistance must be modelled in the pulsatile flow through the large arteries near the heart. It is shown that the equation commonly used to include inertial effects in fluid flow calculations is inappropriate for including time varying resistance. A method of incorporating time varying resistance into a lumped parameter model is developed that uses the Navier-Stokes equations to track the velocity profile. Tests on a single-chamber model show a 17.5% difference in cardiac output for a single-chamber ventricle model when comparing constant resistance models with the velocity profile tracking method modelling time varying resistance. This increase in precision can be achieved using 20 nodes with only twice the computational time required. The method offers a fluid dynamically and physiologically accurate method of calculating large Womersley number pulsatile fluid flows in large arteries around the heart and valves. The proposed velocity profile tracking method can be easily incorporated into existing lumped parameter CVS models, improving their clinical application by increasing their accuracy.