13 results for Chase, J.G., Conference poster

  • A 2 parameter model of lung mechanics to predict volume response and optimise ventilator therapy in ARDS

    Sundaresan, A.; Hann, C.E.; Chase, J.G.; Yuta, T.; Shaw, G.M. (2009)

    Conference poster
    University of Canterbury Library

    A majority of patients admitted to the Intensive Care Unit (ICU) require some form of respiratory support. In the case of Acute Respiratory Distress Syndrome (ARDS), the patient often requires full intervention from a mechanical ventilator. ARDS is also associated with mortality rates as high as 70%. Despite many recent studies on ventilator treatment of the disease, there are no well established methods to determine the optimal Positive End expiratory Pressure (PEEP) ventilator setting for individual patients [1]. A model of fundamental lung mechanics is developed based on capturing the recruitment status of lung units. The model produces good correlation with clinical data, and is clinically applicable due to the minimal number of patient specific parameters to identify. The ability to use this identified patient specific model to optimize ventilator management and lung volume recruitment is demonstrated. It thus provides a platform for continuous monitoring of lung unit recruitment and capability for a patient.

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  • Breath Ammonia Reduction Ratio (ARR) Measures Dialysis Efficacy

    Endre, Z.; Moorhead, K.; Storer, M.; Hu, W-P.; Dean, J.; Logan, K.; Allardyce, R.; Ledingham, K.; McGregor, D.; Senthilmohan, S.; Lee, D.; Scotter, J.; Chase, J.G. (2007)

    Conference poster
    University of Canterbury Library

    Contemporary evidence supports the centuries old notion that expired breath and the headspaces above body fluids and products can serve as biomarkers of organ function. Clinical responsiveness to alterations in clinical status or therapy is dependent upon timely, accurate, relevant physiological data. Current measures of urea and creatinine to assess renal urea reduction are invasive and cannot be repeated frequently or reported quickly enough to define individual response to treatment in real time. In contrast, breath analysis is minimally invasive and can provide real time information about low molecular weight volatile organic compounds (VOCs) such as ammonia1,2.

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  • Clinical Data Validation of an improved, physiologically relevant Critical Care Glycaemic Control Model

    Pretty, C.G.; Parente, J.; Razak, N.; Lin, J.; LeCompte, A.J.; Shaw, G.M.; Hann, C.E.; Chase, J.G. (2008)

    Conference poster
    University of Canterbury Library

    Introduction: Stress induced hyperglycaemia is prevalent in critical care. Tight glycaemic control is associated with significantly improved clinical outcomes. Providing tight control is difficult due to evolving patient condition and drug therapies. Model-based/derived methods (e.g. SPRINT) have shown significant mortality reductions. This research validates an improved metabolic control model and its parameters based on predictive capability for use in real-time glycaemic control.

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  • Diagnosis using a minimal cardiac model including reflex actions

    Hann, C.E.; Chase, J.G.; Andreassen, S.; Smith, B.W.; Shaw, G.M. (2005)

    Conference poster
    University of Canterbury Library

    Heart disease is difficult to diagnose due to often confusing clinical a data. A minimal cardiac model has been developed that captures the major dynamics of the cardiovascular system (CVS). To assist medical staff in diagnosis and treatment, a fast accurate patient-specific parameter identification method, which can account for time varying disease state and the body’s natural reflex response, is required.

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  • Model-based therapeutics for the Cardiovascular System - a Clinical Focus

    Hann, C.E.; Chase, J.G.; Desaive, T.; Lambermont, B.; Ghuysen, A.; Kolh, P.; Shaw, G.M. (2009)

    Conference poster
    University of Canterbury Library

    6-pages (invited)

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  • Highly correlated model-based testing of insulin sensitivity – initial results for a proposed low-intensity test

    Lotz, T.; Chase, J.G.; McAuley, K.A.; Shaw, G.M.; Wong, X-W.; Lin, J.; LeCompte, A.; Hann, C.E.; Mann, J.I. (2006)

    Conference poster
    University of Canterbury Library

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  • An eigen-analysis of the relationships between model structure, discrete data, measurement error and resulting parameter identification distributions

    Mansell, E.J.; Docherty, P.D.; Chase, J.G.; Benyo, B. (2015)

    Conference poster
    University of Canterbury Library

    Practical rather than structural identifiability is often the determining factor whether effective parameter identification is possible in a physiological model. This paper presents analysis into relationships between the population outcomes, and the original model and data properties as part of ongoing research into a deterministic approach to evaluate a-priori identifiability. Data size, output noise variance and true parameter values were varied for a simple 2-parameter model with a linear regression equation Ax = b for discrete data points. Principal Component Analysis of a Monte Carlo simulation was compared to these varied properties and the eigendecomposition of ATA. Principal component vectors were found to be parallel with ATA eigenvectors and the eigenvalues were inversely related. Principal component eigenvalues decreased in inverse proportion to data size, were scaled by the sum of squared parameter values and noise variance. ATA eigenvalues on the other hand were unchanged by output noise and parameter value, but increased in linear, rather than inverse proportion, to data size. The ratio of principal component eigenvalues to each other was affected by data size and some parameter values, while the ATA eigenvalue ratio was affected by data size only. Deterministic relationships have been found between population parameter identification outcomes, model properties and data. If all of the factors determining principle components can be calculated then population variance can be estimated from a single set of data, facilitating confidence of individual outcomes and evaluation of practical identifiability.

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  • The precision of identified variables with respect to multivariable set size in glycaemic data from a virtual type 1 diabetic patient

    Mansell, E.J.; Docherty, P.D.; Chase, J.G. (2015)

    Conference poster
    University of Canterbury Library

    Prior research had been carried out to identify a large number of glycaemic variables in sparse, noisy data from a virtual diabetic patient. This paper investigates the precision of variables as an identification scheme introduces progressively more parameters into the variable set and as the quantity of data increases. Virtual data was simulated with a diabetic glycaemic meal model that contained six variable parameters. Data was sampled 6 times daily with noise. Increasing variable sets were identified for data subsets of increasing size. Norm-error of equivalent variable groups was compared before and after new parameter introductions. A Monte Carlo analysis was carried out to evaluate a population of results. Identifying new variables improved parameter estimates in all equivalent variable groups by 34 days in the mean population case. However, variability from data noise resulted in some cases never yielding sixparameter identification that improved upon results that relied on a-priori information. When parameters were introduced as variables too soon for the given data quality/quantity, reduced practical identifiability caused interference between these and other variables, diminishing their precision. However, when introduced too late the precision in the variable set was hindered by effects not fully described by the apriori guesses. Introducing the 3rd and 4th variables early in the data produced significant benefit in most cases. In contrast, the 5th and 6th parameters could not be introduced as early, improved precision by a lesser degree on average and in many cases never improved precision. The influence of noise on practical identifiability highlighted the need for similar analyses in-vivo so as to strategise parameter identification to gain the most information at the highest precision.

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  • Identifying pressure dependent elastance in lung mechanics with reduced influence of unmodelled effects

    Laufer, B.; Docherty, P.D.; Chiew, Y.S.; Moeller, K.; Chase, J.G. (2015)

    Conference poster
    University of Canterbury Library

    The selection of optimal positive end expiratory pressure (PEEP) levels during ventilation therapy of patients with ARDS (acute respiratory distress syndrome) remains a problem for clinicians. One particular mooted strategy states that minimizing the energy transferred to the lung by mechanical ventilation could potentially be used to determine the optimal PEEP level. This minimization could potentially be undertaken by finding the minimum range of dynamic elastance. In this study, we compare an adapted Gauss-Newton method with the typical gauss newton method in terms of the level of agreement obtained in elastance-pressure curves across different PEEP levels in 10 patients. The Gauss-Newton adaptation effectively ignored characteristics in the data that are un-modelled. The adapted method successfully determined regions of the data that were un-modelled, as expected. In ignoring this un-modelled behavior, the adapted method captured the desired elastance-pressure curves with more consistency than the typical least-squares Gauss Newton method.

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  • Implementation of a Non-Linear Autoregressive Model with Modified Gauss-Newton Parameter Identification to Determine Pulmonary Mechanics of Respiratory Patients that are Intermittently Resisting Ventilator Flow Patterns

    Langdon, R.; Docherty, P.D.; Chiew, Y.S.; Damanhuri, N.S.; Chase, J.G. (2015)

    Conference poster
    University of Canterbury Library

    Modelling the respiratory system of intensive care patients can enable individualized mechanical ventilation therapy and reduce ventilator induced lung injuries. However, spontaneous breathing (SB) efforts result in asynchronous pressure waveforms that mask underlying respiratory mechanics. In this study, a nonlinear auto-regressive (NARX) model was identified using a modified Gauss-Newton (GN) approach, and demonstrated on data from one SB patient. The NARX model uses three pressure dependent basis functions to capture respiratory system elastance, and contains a single resistance coefficient and positive end expiratory pressure (PEEP) coefficient. The modified GN method exponentially reduces the contribution of large residuals on the step in the coefficients at each GN iteration. This approach allows the model to effectively ignore the anomaly in the pressure waveform due to SB efforts, while successfully describing the shape of normal breathing cycles. This method has the potential to be used in the ICU to more robustly capture patient-specific behaviour, and thus enable clinicians to select optimal ventilator settings and improve patient care

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  • A Polynomial Model of Patient-specific Breathing Effort During Controlled Mechanical Ventilation

    Redmond, D.P.; Docherty, P.D.; Chiew, Y.S.; Chase, J.G. (2015)

    Conference poster
    University of Canterbury Library

    Patient breathing efforts occurring during controlled ventilation causes perturbations in pressure data, which cause erroneous parameter estimation in conventional models of respiratory mechanics. A polynomial model of patient effort can be used to capture breath-specific effort and underlying lung condition. An iterative multiple linear regression is used to identify the model in clinical volume controlled data. The polynomial model has lower fitting error and more stable estimates of respiratory elastance and resistance in the presence of patient effort than the conventional single compartment model. However, the polynomial model can converge to poor parameter estimation when patient efforts occur very early in the breath, or for long duration. The model of patient effort can provide clinical benefits by providing accurate respiratory mechanics estimation and monitoring of breath-to-breath patient effort, which can be used by clinicians to guide treatment.

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  • Brain magnetic resonance elastography based on Rayleigh damping material model

    Petrov, A.; Chase, J.G.; Sellier, M.; Latta, P.; Gruwell, M.; McGarry, M.; Van Houten, E.E.W. (2012)

    Conference poster
    University of Canterbury Library

    1-page

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  • Testing of Lead Extrusion Damping Devices Undergoing Representative Earthquake Velocities

    Rodgers, G.W.; Chase, J.G.; Heaton, D.; Cleeve, L. (2013)

    Conference poster
    University of Canterbury Library

    In recent years, significant research has been undertaken into the development of lead-extrusion damping technology. The high force-to-volume (HF2V) devices developed at the University of Canterbury have been the subject of much of this research. However, while these devices have undergone a limited range of velocity testing, limitations in test equipment has meant that they have never been tested at representative earthquake velocities. Such testing is important as the peak resistive force provided by the dampers under large velocity spikes is an important design input that must be known for structural applications. This manuscript presents the high-speed testing of HF2V devices with quasi-static force capacities of 250-300kN. These devices have been subjected to peak input velocities of approximately 200mm/s, producing peak resistive forces of approximately 350kN. The devices show stable hysteretic performance, with slight force reduction during high-speed testing due to heat build-up and softening of the lead working material. This force reduction is recovered following cyclic loading as heat is dissipated and the lead hardens again. The devices are shown to be only weakly velocity dependent, an advantage in that they do not deliver large forces to the connecting elements and surrounding structure if larger than expected response velocities occur. This high-speed testing is an important step towards uptake as it provides important information to designers.

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