References of "Chase, J. G"
     in
Bookmark and Share    
Full Text
Peer Reviewed
See detailValidation of subject-specific cardiovascular system models from porcine measurements.
Revie, J. A.; Stevenson, D. J.; Chase, J. G. et al

in Computer Methods & Programs in Biomedicine (2013), 109(2),

A previously validated mathematical model of the cardiovascular system (CVS) is made subject-specific using an iterative, proportional gain-based identification method. Prior works utilised a complete set ... [more ▼]

A previously validated mathematical model of the cardiovascular system (CVS) is made subject-specific using an iterative, proportional gain-based identification method. Prior works utilised a complete set of experimentally measured data that is not clinically typical or applicable. In this paper, parameters are identified using proportional gain-based control and a minimal, clinically available set of measurements. The new method makes use of several intermediary steps through identification of smaller compartmental models of CVS to reduce the number of parameters identified simultaneously and increase the convergence stability of the method. This new, clinically relevant, minimal measurement approach is validated using a porcine model of acute pulmonary embolism (APE). Trials were performed on five pigs, each inserted with three autologous blood clots of decreasing size over a period of four to five hours. All experiments were reviewed and approved by the Ethics Committee of the Medical Faculty at the University of Liege, Belgium. Continuous aortic and pulmonary artery pressures (P(ao), P(pa)) were measured along with left and right ventricle pressure and volume waveforms. Subject-specific CVS models were identified from global end diastolic volume (GEDV), stroke volume (SV), P(ao), and P(pa) measurements, with the mean volumes and maximum pressures of the left and right ventricles used to verify the accuracy of the fitted models. The inputs (GEDV, SV, P(ao), P(pa)) used in the identification process were matched by the CVS model to errors <0.5%. Prediction of the mean ventricular volumes and maximum ventricular pressures not used to fit the model compared experimental measurements to median absolute errors of 4.3% and 4.4%, which are equivalent to the measurement errors of currently used monitoring devices in the ICU ( approximately 5-10%). These results validate the potential for implementing this approach in the intensive care unit. [less ▲]

Detailed reference viewed: 22 (2 ULg)
Full Text
Peer Reviewed
See detailA simplified model for mitral valve dynamics.
Moorhead, K. T.; Paeme, Sabine ULg; Chase, J. G. et al

in Computer Methods & Programs in Biomedicine (2013), 109(2),

Located between the left atrium and the left ventricle, the mitral valve controls flow between these two cardiac chambers. Mitral valve dysfunction is a major cause of cardiac dysfunction and its dynamics ... [more ▼]

Located between the left atrium and the left ventricle, the mitral valve controls flow between these two cardiac chambers. Mitral valve dysfunction is a major cause of cardiac dysfunction and its dynamics are little known. A simple non-linear rotational spring model is developed and implemented to capture the dynamics of the mitral valve. A measured pressure difference curve was used as the input into the model, which represents an applied torque to the anatomical valve chords. A range of mechanical model hysteresis states were investigated to find a model that best matches reported animal data of chord movement during a heartbeat. The study is limited by the use of one dataset found in the literature due to the highly invasive nature of getting this data. However, results clearly highlight fundamental physiological issues, such as the damping and chord stiffness changing within one cardiac cycle, that would be directly represented in any mitral valve model and affect behaviour in dysfunction. Very good correlation was achieved between modeled and experimental valve angle with 1-10% absolute error in the best case, indicating good promise for future simulation of cardiac valvular dysfunction, such as mitral regurgitation or stenosis. In particular, the model provides a pathway to capturing these dysfunctions in terms of modeled stiffness or elastance that can be directly related to anatomical, structural defects and dysfunction. [less ▲]

Detailed reference viewed: 27 (9 ULg)
Full Text
Peer Reviewed
See detailVisualisation of Time-Variant Respiratory System Elastance in ARDS Models.
Van Drunen, E. J.; Chiew, Y. S.; Zhao, Z. et al

in Biomedizinische Technik. Biomedical engineering (2013), 58(suppl 1)

Detailed reference viewed: 8 (0 ULg)
Full Text
Peer Reviewed
See detailDevelopment and Identification of a Closed-Loop Model of the Cardiovascular System Including the Atria
Pironet, Antoine ULg; Revie, James A.; Paeme, Sabine ULg et al

Conference (2012, August 31)

Detailed reference viewed: 52 (6 ULg)
Full Text
Peer Reviewed
See detailDevelopment and Identification of a Closed-Loop Model of the Cardiovascular System Including the Atria
Pironet, Antoine ULg; Revie, James A.; Paeme, Sabine ULg et al

in Proceedings of the 8th IFAC Symposium on Biological and Medical Systems (2012, August)

Detailed reference viewed: 44 (10 ULg)
Full Text
Peer Reviewed
See detailPhysiological relevance and performance of a minimal lung model -- an experimental study in healthy and acute respiratory distress syndrome model piglets
Chiew, Y. S.; Chase, J. G.; LAMBERMONT, Bernard ULg et al

in BMC Pulmonary Medicine (2012), 12:59

Background: Mechanical ventilation (MV) is the primary form of support for acute respiratory distress syndrome (ARDS) patients. However, intra- and inter- patient-variability reduce the efficacy of ... [more ▼]

Background: Mechanical ventilation (MV) is the primary form of support for acute respiratory distress syndrome (ARDS) patients. However, intra- and inter- patient-variability reduce the efficacy of general protocols. Model-based approaches to guide MV can be patient-specific. A physiological relevant minimal model and its patient-specific performance are tested to see if it meets this objective above. Methods: Healthy anesthetized piglets weighing 24.0 kg [IQR: 21.0-29.6] underwent a step-wise PEEP increase manoeuvre from 5cmH2O to 20cmH2O. They were ventilated under volume control using Engstrom Care Station (Datex, General Electric, Finland), with pressure, flow and volume profiles recorded. ARDS was then induced using oleic acid. The data were analyzed with a Minimal Model that identifies patient-specific mean threshold opening and closing pressure (TOP and TCP), and standard deviation (SD) of these TOP and TCP distributions. The trial and use of data were approved by the Ethics Committee of the Medical Faculty of the University of Liege, Belgium.Results and discussions3 of the 9 healthy piglets developed ARDS, and these data sets were included in this study. Model fitting error during inflation and deflation, in healthy or ARDS state is less than 5.0% across all subjects, indicating that the model captures the fundamental lung mechanics during PEEP increase. Mean TOP was 42.4cmH2O [IQR: 38.2-44.6] at PEEP = 5cmH2O and decreased with PEEP to 25.0cmH2O [IQR: 21.5-27.1] at PEEP = 20cmH2O. In contrast, TCP sees a reverse trend, increasing from 10.2cmH2O [IQR: 9.0-10.4] to 19.5cmH2O [IQR: 19.0-19.7]. Mean TOP increased from average 21.2-37.4cmH2O to 30.4-55.2cmH2O between healthy and ARDS subjects, reflecting the higher pressure required to recruit collapsed alveoli. Mean TCP was effectively unchanged. Conclusion: The minimal model is capable of capturing physiologically relevant TOP, TCP and SD of both healthy and ARDS lungs. The model is able to track disease progression and the response to treatment. [less ▲]

Detailed reference viewed: 34 (5 ULg)
Full Text
Peer Reviewed
See detailNAVA enhances tidal volume and diaphragmatic electro-myographic activity matching: a Range90 analysis of supply and demand
Moorhead, K. T.; Piquilloud, L.; LAMBERMONT, Bernard ULg et al

in Journal of Clinical Monitoring and Computing (2012)

Neurally adjusted ventilatory assist (NAVA) is a ventilation assist mode that delivers pressure in proportionality to electrical activity of the diaphragm (Eadi). Compared to pressure support ventilation ... [more ▼]

Neurally adjusted ventilatory assist (NAVA) is a ventilation assist mode that delivers pressure in proportionality to electrical activity of the diaphragm (Eadi). Compared to pressure support ventilation (PS), it improves patient-ventilator synchrony and should allow a better expression of patient's intrinsic respiratory variability. We hypothesize that NAVA provides better matching in ventilator tidal volume (Vt) to patients inspiratory demand. 22 patients with acute respiratory failure, ventilated with PS were included in the study. A comparative study was carried out between PS and NAVA, with NAVA gain ensuring the same peak airway pressure as PS. Robust coefficients of variation (CVR) for Eadi and Vt were compared for each mode. The integral of Eadi (sh{phonetic}Eadi) was used to represent patient's inspiratory demand. To evaluate tidal volume and patient's demand matching, Range90 = 5-95 % range of the Vt/sh{phonetic}Eadi ratio was calculated, to normalize and compare differences in demand within and between patients and modes. In this study, peak Eadi and sh{phonetic}Eadi are correlated with median correlation of coefficients, R > 0.95. Median sh{phonetic}Eadi, Vt, neural inspiratory time (Ti_ <br /> Neural), inspiratory time (Ti) and peak inspiratory pressure (PIP) were similar in PS and NAVA. However, it was found that individual patients have higher or smaller sh{phonetic}Eadi, Vt, Ti_ <br /> Neural, Ti and PIP. CVR analysis showed greater Vt variability for NAVA (p < 0.005). Range90 was lower for NAVA than PS for 21 of 22 patients. NAVA provided better matching of Vt to sh{phonetic}Eadi for 21 of 22 patients, and provided greater variability Vt. These results were achieved regardless of differences in ventilatory demand (Eadi) between patients and modes. © 2012 Springer Science+Business Media New York. [less ▲]

Detailed reference viewed: 28 (6 ULg)
Full Text
Peer Reviewed
See detailAlgorithmic Processing of Pressure Waveforms to FacilitateEstimation of Cardiac Elastance
Stevenson, D.; Revie, J.; Chase, J. G. et al

in BioMedical Engineering OnLine (2012), 11

Introduction: Cardiac elastances are highly invasive to measure directly, but are clinically useful due tothe amount of information embedded in them. Information about the cardiac elastance, which can be ... [more ▼]

Introduction: Cardiac elastances are highly invasive to measure directly, but are clinically useful due tothe amount of information embedded in them. Information about the cardiac elastance, which can be used toestimate it, can be found in the downstream pressure waveforms of aortic pressure (Pao) and the pulmonaryartery (Ppa). However these pressure waveforms are typically noisy and biased, and require processing in orderto locate the specific information required for the cardiac elastance estimation. This paper presents the methodto algorithmically process the pressure waveforms. Methods: A shear transform is developed in order to helplocate information in the pressure waveforms. This transform turns difficult to locate corners into easy to locatemaximum or minimum points as well as providing error correction. Results: The method located all points 87out of 88 waveforms for Ppa to within the sampling frequency. For Pao, out of 616 total points, 605 were foundwithin 1%, 5 within 5%, 4 within 10% and 2 within 20%. Conclusions: The presented method provides arobust, accurate and dysfunction independent way to locate points on the aortic and pulmonary artery pressurewaveforms, allowing the non-invasive estimation of the left and right cardiac elastance. [less ▲]

Detailed reference viewed: 23 (3 ULg)
Full Text
Peer Reviewed
See detailParameter Identification in a Model of the Cardiovascular System Including the Atria
Pironet, Antoine ULg; Revie, James A.; Paeme, Sabine ULg et al

in 10th Belgian Day on Biomedical Engineering (2011, December 02)

Detailed reference viewed: 22 (7 ULg)
Full Text
Peer Reviewed
See detailParameter Identification in a Model of the Cardiovascular System Including the Atria
Pironet, Antoine ULg; Revie, James A.; Paeme, Sabine ULg et al

Poster (2011, December 02)

Detailed reference viewed: 43 (31 ULg)
Full Text
Peer Reviewed
See detailPatient specific identification of the cardiac driver function in a cardiovascular system model.
Hann, C. E.; Revie, J.; Stevenson, D. et al

in Computer Methods & Programs in Biomedicine (2011)

The cardiac muscle activation or driver function, is a major determinant of cardiovascular dynamics, and is often approximated by the ratio of the left ventricle pressure to the left ventricle volume. In ... [more ▼]

The cardiac muscle activation or driver function, is a major determinant of cardiovascular dynamics, and is often approximated by the ratio of the left ventricle pressure to the left ventricle volume. In an intensive care unit, the left ventricle pressure is usually never measured, and the left ventricle volume is only measured occasionally by echocardiography, so is not available real-time. This paper develops a method for identifying the driver function based on correlates with geometrical features in the aortic pressure waveform. The method is included in an overall cardiovascular modelling approach, and is clinically validated on a porcine model of pulmonary embolism. For validation a comparison is done between the optimized parameters for a baseline model, which uses the direct measurements of the left ventricle pressure and volume, and the optimized parameters from the approximated driver function. The parameters do not significantly change between the two approaches thus showing that the patient specific approach to identifying the driver function is valid, and has potential clinically. [less ▲]

Detailed reference viewed: 12 (2 ULg)
Full Text
Peer Reviewed
See detailTight glycemic control in critical care - The leading role of insulin sensitivity and patient variability: A review and model-based analysis.
Chase, J. G.; Le Compte, A. J.; Suhaimi, F. et al

in Computer Methods & Programs in Biomedicine (2011)

Tight glycemic control (TGC) has emerged as a major research focus in critical care due to its potential to simultaneously reduce both mortality and costs. However, repeating initial successful TGC trials ... [more ▼]

Tight glycemic control (TGC) has emerged as a major research focus in critical care due to its potential to simultaneously reduce both mortality and costs. However, repeating initial successful TGC trials that reduced mortality and other outcomes has proven difficult with more failures than successes. Hence, there has been growing debate over the necessity of TGC, its goals, the risk of severe hypoglycemia, and target cohorts. This paper provides a review of TGC via new analyses of data from several clinical trials, including SPRINT, Glucontrol and a recent NICU study. It thus provides both a review of the problem and major background factors driving it, as well as a novel model-based analysis designed to examine these dynamics from a new perspective. Using these clinical results and analysis, the goal is to develop new insights that shed greater light on the leading factors that make TGC difficult and inconsistent, as well as the requirements they thus impose on the design and implementation of TGC protocols. A model-based analysis of insulin sensitivity using data from three different critical care units, comprising over 75,000h of clinical data, is used to analyse variability in metabolic dynamics using a clinically validated model-based insulin sensitivity metric (S(I)). Variation in S(I) provides a new interpretation and explanation for the variable results seen (across cohorts and studies) in applying TGC. In particular, significant intra- and inter-patient variability in insulin resistance (1/S(I)) is seen be a major confounder that makes TGC difficult over diverse cohorts, yielding variable results over many published studies and protocols. Further factors that exacerbate this variability in glycemic outcome are found to include measurement frequency and whether a protocol is blind to carbohydrate administration. [less ▲]

Detailed reference viewed: 26 (0 ULg)
Full Text
Peer Reviewed
See detailModel-based PEEP optimisation in mechanical ventilation
Chiew, Y. S.; Chase, J. G.; Shaw, G. M. et al

in BioMedical Engineering OnLine (2011), 10

Background: Acute Respiratory Distress Syndrome (ARDS) patients require mechanical ventilation (MV) for breathing support. Patient-specific PEEP is encouraged for treating different patients but there is ... [more ▼]

Background: Acute Respiratory Distress Syndrome (ARDS) patients require mechanical ventilation (MV) for breathing support. Patient-specific PEEP is encouraged for treating different patients but there is no well established method in optimal PEEP selection.Methods: A study of 10 patients diagnosed with ALI/ARDS whom underwent recruitment manoeuvre is carried out. Airway pressure and flow data are used to identify patient-specific constant lung elastance (E <br /> lung) and time-variant dynamic lung elastance (E <br /> drs) at each PEEP level (increments of 5cmH <br /> 2O), for a single compartment linear lung model using integral-based methods. Optimal PEEP is estimated using E <br /> lungversus PEEP, E <br /> drs-Pressure curve and E <br /> drsArea at minimum elastance (maximum compliance) and the inflection of the curves (diminishing return). Results are compared to clinically selected PEEP values. The trials and use of the data were approved by the New Zealand South Island Regional Ethics Committee.Results: Median absolute percentage fitting error to the data when estimating time-variant E <br /> drsis 0.9% (IQR = 0.5-2.4) and 5.6% [IQR: 1.8-11.3] when estimating constant E <br /> lung. Both E <br /> lungand E <br /> drsdecrease with PEEP to a minimum, before rising, and indicating potential over-inflation. Median E <br /> drsover all patients across all PEEP values was 32.2 cmH <br /> 2O/l [IQR: 26.1-46.6], reflecting the heterogeneity of ALI/ARDS patients, and their response to PEEP, that complicates standard approaches to PEEP selection. All E <br /> drs-Pressure curves have a clear inflection point before minimum E <br /> drs, making PEEP selection straightforward. Model-based selected PEEP using the proposed metrics were higher than clinically selected values in 7/10 cases.Conclusion: Continuous monitoring of the patient-specific E <br /> lungand E <br /> drsand minimally invasive PEEP titration provide a unique, patient-specific and physiologically relevant metric to optimize PEEP selection with minimal disruption of MV therapy. © 2011 Chiew et al; licensee BioMed Central Ltd. [less ▲]

Detailed reference viewed: 28 (11 ULg)
Full Text
Peer Reviewed
See detailPatient-ventilator synchrony and tidal volume variability using NAVA and pressure support mechanical ventilation modes
Moorhead, K. T.; Piquilloud, L.; LAMBERMONT, Bernard ULg et al

in Proceedings of the 18th IFAC world congress, 2011 (2011)

Neurally Adjusted Ventilatory Assist (NAVA) is a new ventilatory mode in which ventilator settings are adjusted based on the electrical activity detected in the diaphragm (Eadi). This mode offers ... [more ▼]

Neurally Adjusted Ventilatory Assist (NAVA) is a new ventilatory mode in which ventilator settings are adjusted based on the electrical activity detected in the diaphragm (Eadi). This mode offers significant advantages in mechanical ventilation over standard pressure support (PS) modes, since ventilator input is determined directly from patient ventilatory demand. A comparative study of 22 patients undergoing mechanical ventilation in both PS and NAVA modes was conducted, and it was concluded that for a given variability in Eadi, there is greater variability in tidal volume and correlation between the tidal volume and the diaphragmatic electrical activity with NAVA compared to PS. These results are consistent with the improved patient-ventilator synchrony reported in the literature. © 2011 IFAC. [less ▲]

Detailed reference viewed: 44 (7 ULg)
Full Text
Peer Reviewed
See detailTight Glycemic Control in Intensive Care: From engineering to clinical practice change
Chase, J. G.; Le Compte, A. J.; Evans, A. et al

in 5th European Conference of the International Federation for Medical and Biological Engineering (2011)

Tight glycemic control (TGC) is prevalent in critical care. Providing safe, effective TGC has proven very difficult to achieve with clinically derived protocols. The prob-lem is exacerbated by extreme ... [more ▼]

Tight glycemic control (TGC) is prevalent in critical care. Providing safe, effective TGC has proven very difficult to achieve with clinically derived protocols. The prob-lem is exacerbated by extreme patient variability and the need to minimize clinical effort and burden. These ingredients make an ideal scenario for model-based methods to provide opti-mised solutions. This paper presents the development, clinical-ly validated virtual trials optimisation, and initial clinical implementation of a stochastic targeted (STAR) TGC method and framework. It is compared to a prior successful, model-derived, less flexible and dynamic TGC protocol (SPRINT). The use of stochastic models to safely forecast a range of glu-cose outcomes over 1-3 hours ensures better performance, more dynamic use of the range of insulin and nutrition inputs and thus better glycemic performance and safety from hypo-glycemia, the latter of which was reduced by 3.0x times. Hence, the paper presents an overall engineering approach to TGC from engineering models to clinical implementation and ongo-ing clinical practice change. [less ▲]

Detailed reference viewed: 22 (4 ULg)
Full Text
Peer Reviewed
See detailPatient-specific modelling of the cardiovascular system – application to septic shock with a minimal data set
Desaive, Thomas ULg; Chase, J. G.; Starfinger, C. et al

in World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany (2010)

Detailed reference viewed: 46 (23 ULg)
Full Text
Peer Reviewed
See detailTime varying elastance estimation in an 8 camber cardiovascular system model
Desaive, Thomas ULg; Chase, J. G.; Hann, C. E. et al

in Intensive Care Medicine (2010), 36(2), 151-151

Detailed reference viewed: 23 (3 ULg)
Full Text
Peer Reviewed
See detailNAVA enhances ventilatory variability and diaphragmaticactivity/tidal volume coupling
Moorhead, K.; Piquilloud, L.; Desaive, Thomas ULg et al

in Intensive Care Medicine (2010), 36(2), 326-326

Detailed reference viewed: 23 (2 ULg)
Full Text
Peer Reviewed
See detailReduced organ failure with effective glycemic control
Preiser, Jean-Charles; Chase, J. G.; Pretty, C. G. et al

in Intensive Care Medicine (2010), 36(2), 173-173

Detailed reference viewed: 14 (0 ULg)
Full Text
Peer Reviewed
See detailUnique parameter identification for model-based cardiac diagnosis in critical care
Hann, C. E.; Chase, J. G.; Desaive, Thomas ULg et al

in IFAC Proceedings Volumes (IFAC-PapersOnline) (2009), 7(PART 1), 169-174

Lumped parameter approaches for modeling the cardiovascular system typically have many parameters of which many are not identifiable. The conventional approach is to only identify a small subset of ... [more ▼]

Lumped parameter approaches for modeling the cardiovascular system typically have many parameters of which many are not identifiable. The conventional approach is to only identify a small subset of parameters to match measured data, and to set the remaining parameters at population values. These values are often based on animal data or the "average human" response. The problem, is that setting many parameters at nominal fixed values, may introduce dynamics that are not present in a specific patient. As parameter numbers and model complexity increase, more clinical data is required for validation and the model limitations are harder to quantify. This paper considers the modeling and the parameter identification simultaneously, and creates models that are one to one with the measurements. That is, every input parameter into the model is uniquely optimized to capture the clinical data and no parameters are set at population values. The result is a geometrical characterization of a previously developed six chamber heart model, and a completely patient specific approach to cardiac diagnosis in critical care. In addition, simplified sub-structures of the six chamber model are created to provide very fast and accurate parameter identification from arbitrary starting points and with no prior knowledge on the parameters. Furthermore, by utilizing continuous information from the arterial/pulmonary pressure waveforms and the end-diastolic time, it is shown that only the stroke volumes of the ventricles are required for adequate cardiac diagnosis. This reduced data set is more practical for an intensive care unit as the maximum and minimum volumes are no longer needed, which was a requirement in prior work. The simplified models can also act as a bridge to identifying more sophisticated cardiac models, by providing a generating set of waveforms that the complex models can match to. Most importantly, this approach does not have any predefined assumptions on patient dynamics other than the basic model structure, and is thus suitable for improving cardiovascular management in critical care by optimizing therapy for individual patients. © 2009 IFAC. [less ▲]

Detailed reference viewed: 13 (1 ULg)