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Biomedical subjects

J G Chase

Publications and source records attributed to J G Chase.

14 recordsLinked to original sources
PubMed2006

Fast normalized cross correlation for motion tracking using basis functions.

Digital image-based elasto-tomography (DIET) is an emerging method for non-invasive breast cancer screening. Effective clinical application of the DIET system requires highly accurate motion tracking of the surface of an actuated breast with minimal computation. Normalized cross correlation (NCC) is the most robust correlation measure for determining similarity between points in two or more images providing an accurate foundation for motion tracking. However, even using fast Fourier transform (FFT) methods, it is too computationally intense for rapidly managing several large images. A significantly faster method of calculating the NCC is presented that uses rectangular approximations in place of randomly placed landmark points or the natural marks on the breast. These approximations serve as an optimal set of basis functions that are automatically detected, dramatically reducing computational requirements. To prove the concept, the method is shown to be 37-150 times faster than the FFT-based NCC with the same accuracy for simulated data, a visco-elastic breast phantom experiment and human skin. Clinically, this approach enables thousands of randomly placed points to be rapidly and accurately tracked providing high resolution for the DIET system.

Algorithms↗
PubMed2006

Integral-based 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). However, identifying patient specific parameters with the limited measurements often available, hinders the clinical application of the model for diagnosis and therapy selection. This paper presents an integral-based parameter identification method for fast, accurate identification of patient specific parameters using limited measured data. The integral method turns a previously non-linear and non-convex optimization problem into a linear and convex identification problem. The model includes ventricular interaction and physiological valve dynamics. A healthy human state and four disease states, valvular stenosis, pulmonary embolism, cardiogenic shock and septic shock are used to test the method. Parameters for the healthy and disease states are accurately identified using only discretized flows into and out of the two cardiac chambers, the minimum and maximum volumes of the left and right ventricles, and the pressure waveforms through the aorta and pulmonary artery. These input values can be readily obtained non-invasively using echo-cardiography and ultra-sound, or invasively via catheters that are often used in Intensive Care. The method enables rapid identification of model parameters to match a particular patient condition in clinical real time (3-5 min) to within a mean value of 4-10% in the presence of 5-15% uniformly distributed measurement noise. The specific changes made to simulate each disease state are correctly identified in each case to within 10% without false identification of any other patient specific parameters. Clinically, the resulting patient specific model can then be used to assist medical staff in understanding, diagnosis and treatment selection.

Algorithms↗
PubMed2006

Physiological modelling of agitation-sedation dynamics.

Agitation-sedation cycling in critically ill patients, characterized by oscillations between states of agitation and over-sedation, damages patient health and increases length of stay and cost. A model that captures the essential dynamics of the agitation-sedation system and is physiologically representative is developed, and validated using data from 37 critical care patients. It is more physiologically representative than a previously published agitation-sedation model, and captures more realistic and complex dynamics. The median time in the 90% probability band is 90%, and the total drug dose, relative to recorded drug dose data, is a near ideal 101%. These statistical model validation metrics are 5-13% better than a previously validated model. Hence, this research provides a platform to develop and test semi-automated sedation management controllers that offer the significant clinical potential of improved agitation management and reduced length of stay in critical care.

Procedural Sedation↗
PubMed2006

A novel, model-based insulin and nutrition delivery controller for glycemic regulation in critically ill patients.

BACKGROUND: Critically ill patients are often hyperglycemic and insulin resistant, as well as highly dynamic. Tight glucose control has been shown to significantly reduce mortality in critical care. A physiological model of the glucose-insulin regulatory system is improved and used to develop an adaptive control protocol utilizing both nutritional and insulin inputs to control hyperglycemia. The approach is clinically verified in a critical care patient cohort. METHODS: A simple two-compartment model for glucose rate of appearance in plasma due to stepwise enteral glucose fluxes is developed and incorporated into a previously validated system model. A control protocol modulating intravenous insulin infusion and bolus, with an enteral feed rate, is developed, enabling tight and predictive glycemic regulation to preset targets. The control protocol is adaptive to patient time-variant effective insulin resistance. The model and protocol are verified in seven 10-h and one 24-h proof-of-concept clinical trials. Ethics approval was granted by the Canterbury Ethics Committee. RESULTS: Insulin requirements varied widely following acute changes in patient physiology. The algorithm developed successfully adapted to patient metabolic status and insulin sensitivity, achieving an average target acquisition error of 9.3% with 90.7% of all targets achieved within +/-20%. Prediction errors may not be distinguishable from sensor measurement errors. Large errors (>20%) are attributable to highly dynamic and unpredictable changes in patient condition. CONCLUSIONS: Tight, targeted stepwise regulation was exhibited in all trials. Overall, tight glycemic regulation is achieved in a broad critical care cohort with optimized insulin and nutrition delivery, effectively managing glycemia even with high effective insulin resistance.

Aged↗
PubMed2006

Metabolic model of autoregulation in the Circle of Willis.

The Circle of Willis (CoW) is a ringlike structure of blood vessels found at the base of the brain. Its main function is to distribute oxygen-rich arterial blood to the cerebral mass. In a previous study, a one-dimensional (1D) model of the CoW was created to simulate a series of possible clinical scenarios such as occlusions in afferent arteries, absent or stringlike circulus vessels, or arterial infarctions (Moorhead et al., 2004, Comput. Methods Biomech. Biomed. Eng., 7(3), pp. 121-130). The model captured cerebral haemodynamic autoregulation by using a proportional-integral-derivative (PID) controller to modify efferent artery resistances. Although some good results and correlations were achieved, the model was too simple to capture all the transient dynamics of autoregulation. Hence a more physiologically accurate model has been created that additionally includes the oxygen dynamics that drive the autoregulatory response. Results very closely match accepted physiological response and limited clinical data. In addition, a set of boundary conditions and geometry is presented for which the autoregulated system cannot provide sufficient perfusion, representing a condition with increased risk of stroke and highlighting the importance of modeling the haemodynamics of the CoW. The system model created is computationally simple so it can be used to identify at-risk cerebral arterial geometries and conditions prior to surgery or other clinical procedures.

Animals↗
PubMed2005

Model predictive glycaemic regulation in critical illness using insulin and nutrition input: a pilot study.

Stress-induced hyperglycaemia is prevalent in intensive care, impairing the immune response. Nutritional support regimes with high glucose content further exacerbate the problem. Tight glucose control has been shown to reduce mortality by up to 43% if levels are kept below 6.1 mmol/L. This research develops a control algorithm with insulin and nutritional inputs for targeted glucose control in the critically ill. Ethics approval for this research was granted by the Canterbury Ethics Committee. Proof-of-concept clinical pilot trials were conducted on intubated, insulin-dependent Christchurch ICU patients (n=7) on constant nutritional support. A target 10-15% reduction in glucose level per hour for a desired glucose level of 4-6 mmol/L was set. 43% and 91% of glucose targets were achieved within +/-5 and +/-20%, respectively. The mean error was 8.9% (0.5 mmol/L), with an absolute range [0, 2.9] mmol/L. End glucose levels were 40% lower compared to initial values. All large target errors are attributable to sudden changes in patient physiology at low glucose values, rather than systemic deficiencies. Target errors are consistent with and explainable by published sensor error distributions. The results show that intensive model-based glucose management with nutrition control reduced absolute glucose levels progressively while reducing the severity of glycaemic fluctuation even with significant inter-patient variability and time-varying physiological condition. Trials spanning longer periods of time are in development to verify the short-term pilot studies performed and to test the adaptability of the controller. Clinically, these results indicate potential in clinical use to reduce ICU mortality as well as reduce risk of severe complications.

Aged↗
PubMed2005

Physiological modelling of agitation-sedation dynamics including endogenous agitation reduction.

Sedation administration and agitation management are fundamental activities in any intensive care unit. A lack of objective measures of agitation and sedation, as well as poor understanding of the underlying dynamics, contribute to inefficient outcomes and expensive healthcare. Recent models of agitation-sedation pharmacodynamics have enhanced understanding of the underlying dynamics and enable development of advanced protocols for semi-automated sedation administration. However, these initial models do not capture all observed dynamics, particularly periods of low sedative infusion. A physiologically representative model that incorporates endogenous agitation reduction (EAR) dynamics is presented and validated using data from 37 critical care patients. High median relative average normalised density (RAND) values of 0.77 and 0.78 support and minimum RAND values of 0.51 and 0.55 for models without and with EAR dynamics respectively show that both models are valid representations of the fundamental agitation-sedation dynamics present in a broad spectrum of intensive care unit (ICU) patients. While the addition of the EAR dynamic increases the ability of the model to capture the observed dynamics of the agitation-sedation system, the improvement is relatively small and the sensitivity of the model to the EAR dynamic is low. Although this may represent a limitation of the model, the inclusion of EAR is shown to be important for accurately capturing periods of low, or no, sedative infusion, such as during weaning prior to extubation.

Biomedical Engineering↗
PubMed2005

3D models of blood flow in the cerebral vasculature.

The circle of Willis (CoW) is a ring-like arterial structure located in the base of the brain and is responsible for the distribution of oxygenated blood throughout the cerebral mass. To investigate the effects of the complex 3D geometry and anatomical variability of the CoW on the cerebral hemodynamics, a technique for generating physiologically accurate models of the CoW has been created using a combination of magnetic resonance data and computer-aided design software. A mathematical model of the body's cerebral autoregulation mechanism has been developed and numerous computational fluid dynamics simulations performed to model the hemodynamics in response to changes in afferent blood pressure. Three pathological conditions were explored, namely a complete CoW, a fetal P1 and a missing A1. The methodology of the cerebral hemodynamic modelling is proposed with the potential for future clinical application in mind, as a diagnostic tool.

Blood Flow Velocity↗
PubMed2005

Efficient implementation of non-linear valve law and ventricular interaction dynamics in the minimal cardiac model.

A minimal model of the cardio-vascular system (CVS) with ventricular interaction and inertial effects that accurately captures the physiological trends of a variety of disease states has been developed. However, the physiologically accurate open on pressure, close on flow valve law is computationally heavy to implement, reducing the model's potential clinical benefit. A significantly simpler representation of the valve law using Heaviside functions is derived and the ventricular interaction equations are reformulated to obtain a unique closed form analytical solution. The new formulation is tested and compared with the previous formulation for a healthy human and four clinically significant disease states: mitral and aortic stenosis, pulmonary embolism and septic shock. The new model formulation matches the previous model definition, differing by a mean model response error of no more than 0.2%. Computationally, it is 24 x faster than the previous method. More specifically, a short 20-beat simulation that took 102 s now requires 4.3 s, significantly improving the model's potential for practical use in a diagnostic and/or decision support role in the intensive care unit.

Cardiovascular System↗
PubMed2005

One-dimensional and three-dimensional models of cerebrovascular flow.

The Circle of Willis is a ring-like structure of blood vessels found beneath the hypothalamus at the base of the brain. Its main function is to distribute oxygen-rich arterial blood to the cerebral mass. One-dimensional (1D) and three-dimensional (3D) computational fluid dynamics (CFD) models of the Circle of Willis have been created to provide a simulation tool which can potentially be used to identify at-risk cerebral arterial geometries and conditions and replicate clinical scenarios, such as occlusions in afferent arteries and absent circulus vessels. Both models capture cerebral haemodynamic autoregulation using a proportional-integral (PI) controller to modify efferent artery resistances to maintain optimal efferent flow rates for a given circle geometry and afferent blood pressure. The models can be used to identify at-risk cerebral arterial geometries and conditions prior to surgery or other clinical procedures. The 1D model is particularly relevant in this instance, with its fast solution time suitable for real-time clinical decisions. Results show the excellent correlation between models for the transient efferent flux profile. The assumption of strictly Poiseuille flow in the 1D model allows more flow through the geometrically extreme communicating arteries than the 3D model. This discrepancy was overcome by increasing the resistance to flow in the anterior communicating artery in the 1D model to better match the resistance seen in the 3D results.

Blood Flow Velocity↗
PubMed2004

Lumped parameter and feedback control models of the auto-regulatory response in the Circle of Willis.

The Circle of Willis (CoW) is a ring-like structure of blood vessels found beneath the hypothalamus at the base of the brain. Its main function is to distribute oxygen-rich arterial blood to the cerebral mass. A 1-dimensional model of the CoW has been created to simulate a series of possible clinical scenarios such as occlusions in afferent arteries, absent or string-like circulus vessels, or arterial infarctions. The model captures cerebral haemodynamic auto-regulation by using a proportional-integral-derivative (PID) controller to modify efferent resistances and maintain optimal efferent flowrates for a given circle geometry and afferent blood pressure. Results match limited clinical data and results obtained in prior studies to within 6%. In addition, a set of boundary conditions and geometry is presented for which the auto-regulated system cannot provide the necessary efferent flowrates and perfusion, representing a condition with increased risk of stroke and highlighting the importance of modelling the haemodynamics of the CoW. The system model created is computationally simple so it can be used to identify at-risk cerebral arterial geometries and conditions prior to surgery or other clinical procedures.

Algorithms↗
PubMed2003

Rethinking sedation and agitation management in critical illness.

OBJECTIVE: To examine difficulties in sedation management in the critically ill patient and explore how a semi automated sedation controller can improve agitation control. To present recent work on measurements of agitation, dynamic systems modelling and control of patient agitation response. DATA SOURCES: Articles and peer-reviewed studies identified through a PUBMED search and selected original works from the biomedical engineering literature of relevance to agitation control and management. SUMMARY OF REVIEW: Over-sedation has an adverse impact on intensive care resources. Interventions to constrain sedation delivery through development of protocols or regular cessation of infusions result in reduction in resource utilisation, but have not significantly addressed existing difficulties in agitation control. We develop a paradigm in which control of agitation in critically ill patients becomes the primary objective of sedation management. This principle is central to the function of a nurse-managed semi-automated sedation delivery device. The clinical application of this device using subjective assessments of agitation is presented. A framework for the development of improved automated sedation delivery systems using objective measurements of agitation and control, based on agitation feedback, is described. Using dynamic systems modelling and a simulated nurse, a bolus-driven approach significantly reduced agitation and minimised drug utilisation. This result challenges the current practice of sedating patients using continuous infusions. CONCLUSIONS: A simple computerised interface with an algorithm that continually reduces the infusion rate in the absence of agitation has successfully been introduced into clinical practice. Nursing staff reported high levels of satisfaction with this device and it has enabled detailed data on patterns of sedation administration to be extracted for analysis. This data has been used to validate a model of the fundamental agitation-sedation dynamics.

Journal Article↗
PubMed2002

Active insulin infusion using optimal and derivative-weighted control.

Close control of blood glucose levels significantly reduces vascular complications in Type I diabetes. A control method for the automation of insulin infusion that utilizes emerging technologies in blood glucose biosensors is presented. The controller developed provides tighter, more optimal control of blood glucose levels, while accounting for variation in patient response, insulin employed and sensor bandwidth. Particular emphasis is placed on controller simplicity and robustness necessary for medical devices and implants.A PD controller with heavy emphasis on the derivative term is found to outperform the typically used proportional-weighted controllers in glucose tolerance and multi-meal tests. Simulation results show reductions of over 50% in the magnitude and duration of blood glucose excursions from basal levels. A closed-form steady state optimal solution is also developed as a benchmark, and results in a flat glucose response. The impact and trade-offs associated with sensor bandwidth, sensor lag and proportional versus derivative-based control methods are illustrated. Overall, emerging blood glucose sensor technologies that enable frequent measurement are shown to enable more effective, automated control of blood glucose levels within a tight, acceptable range for Type I and II diabetic individuals.

Blood Glucose↗