References of "Desaive, Thomas"
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See detailNeurally Adjusted Ventilatory Assist (NAVA) improves the matching of diaphragmatic electrical activity and tidal volume in comparison to pressure support (PS)
Piquilloud, L; Chiew, YS; Bialais, E et al

in Intensive Care Medicine (2011), 37 (Suppl 1)

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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 ▲]

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See detailInsulin Sensitivity, Its Variability and Glycemic Outcome: A model-based analysis of the difficulty in achieving tight glycemic control in critical care
Chase, J. Geoffrey; Le Compte, Aaron J.; Preiser, Jean-Charles et al

in 18th World Congress of the International Federation of Automatic Control (IFAC) (2011)

Effective tight glycemic control (TGC) can improve outcomes in intensive care unit (ICU) <br />patients, but is difficult to achieve consistently. Glycemic level and variability, particularly early in a ... [more ▼]

Effective tight glycemic control (TGC) can improve outcomes in intensive care unit (ICU) <br />patients, but is difficult to achieve consistently. Glycemic level and variability, particularly early in a <br />patient’s stay, are a function of variability in insulin sensitivity/resistance resulting from the level and <br />evolution of stress response, and are independently associated with mortality. This study examines the <br />daily evolution of variability of insulin sensitivity in ICU patients using patient data (N = 394 patients, <br />54019 hours) from the SPRINT TGC study. Model-based insulin sensitivity (SI) was identified each hour <br />and hour-to-hour percent changes in SI were assessed for Days 1-3 individually and Day 4 Onward, as <br />well as over all days. Cumulative distribution functions (CDFs), median values, and inter-quartile points <br />(25th and 75th percentiles) are used to assess differences between groups and their evolution over time. <br />Compared to the overall (all days) distributions, ICU patients are more variable on Days 1 and 2 (p < <br />0.0001), and less variable on Days 4 Onward (p < 0.0001). Day 3 is similar to the overall cohort (p = 0.74). <br />Absolute values of SI start lower and rise for Days 1 and 2, compared to the overall cohort (all days), (p < <br />0.0001), are similar on Day 3 (p = .72) and are higher on Days 4 Onward (p < 0.0001). ICU patients have <br />lower insulin sensitivity (greater insulin resistance) and it is more variable on Days 1 and 2, compared to <br />an overall cohort on all days. This is the first such model-based analysis of its kind. Greater variability <br />with lower SI early in a patient’s stay greatly increases the difficulty in achieving and safely maintaining <br />glycemic control, reducing potential positive outcomes. Clinically, the results imply that TGC patients will <br />require greater measurement frequency, reduced reliance on insulin, and more explicit specification of <br />carbohydrate nutrition in Days 1-3 to safely minimise glycemic variability for best outcome. [less ▲]

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See detailSafety and Performance of Stochastic Targeted (STAR) TGC of Insulin and Nutrition
Shaw, GM; Le Compte, Aaron; Evans, A et al

in Proceedings of SQAO 2011 (2011)

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See detailPilot Trials of STAR Target to Range Glycemic Control
Penning, Sophie ULg; Le Compte, Aaron; Massion, Paul et al

in Intensive Care Medicine (2011), 37 (Suppl 1)

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See detailVariability of insulin sensitivity for diabetics and non-diabetics during the first 3 days of ICU stay
Pretty, Christopher G.; Le Compte, Aaron; Preiser, Jean-Charles et al

in Intensive Care Medicine (2011), 37 (Suppl 1)

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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 ▲]

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See detailModel-based diagnosis of acute pulmonary embolism and septic shock in porcine trials
Revie, JA; Stevenson, D; Chase, JG et al

in Proceedings of the Health Research Society of Christchurch Annual Scientific Session 2011 (2011)

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See detailEnteral nutrition-associated incretin effect in the critically ill
Preiser, JC; Jamaludin, U; Docherty, P et al

in Proceedings of the 33rd Congress of Clinical Nutrition and Metabolism (ESPEN 2011) (2011)

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See detailObservation of the Incretin Effect in Critically Ill patients
Jamaludin, U.; Docherty, P; Chase, JG et al

in Proceedings of the 11th Annual Diabetes Technology Meeting (DTM2011) (2011)

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See detailProcessing aortic and pulmonary artery waveforms to derive the ventricle time-varying elastance
Stevenson, D. J.; Hann, C. E.; Chase, G. J. et al

in IFAC Proceedings Volumes (IFAC-PapersOnline) (2011), 18(PART 1), 587-592

Time-varying elastance of the ventricles is an important metric both clinically and as an input for a previously developed cardiovascular model. However, currently time-varying elastance is not normally ... [more ▼]

Time-varying elastance of the ventricles is an important metric both clinically and as an input for a previously developed cardiovascular model. However, currently time-varying elastance is not normally available in an Intensive Care Unit (ICU) setting, as it is an invasive and ethically challenging metric to measure. A previous paper developed a method to map less invasive metrics to the driver function, enabling an estimate to be achieved without invasive measurements. This method requires reliable and accurate processing of the aortic and pulmonary artery pressure waveforms to locate the specific points that are required to estimate the driver function. This paper details the method by which these waveforms are processed, using a data set of five pigs induced with pulmonary embolism, and five pigs induced with septic shock (with haemofiltration), adding up to 88 waveforms (for each of aortic and pulmonary artery pressure), and 616 points in total to locate. 98.2% of all points were located to within 1% of their true value, 0.81% were between 1% and 5%, 0.65% were between 5% and 10%, the remaining 0.32% were below 20%.© 2011 IFAC. [less ▲]

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See detailClinical detection and monitoring of acute pulmonary embolism: proof of concept of a computer-based method.
Revie, James A; Stevenson, David J; Chase, J Geoffrey et al

in Annals of Intensive Care (2011), 1(1), 33

ABSTRACT: BACKGROUND: The diagnostic ability of computer-based methods for cardiovascular system (CVS) monitoring offers significant clinical potential. This research tests the clinical applicability of a ... [more ▼]

ABSTRACT: BACKGROUND: The diagnostic ability of computer-based methods for cardiovascular system (CVS) monitoring offers significant clinical potential. This research tests the clinical applicability of a newly improved computer-based method for the proof of concept case of tracking changes in important hemodynamic indices due to the influence acute pulmonary embolism (APE). METHODS: Hemodynamic measurements from a porcine model of APE were used to validate the method. Of these measurements, only those that are clinically available or inferable were used in to identify pig-specific computer models of the CVS, including the aortic and pulmonary artery pressure, stroke volume, heart rate, global end diastolic volume, and mitral and tricuspid valve closure times. Changes in the computer-derived parameters were analyzed and compared with experimental metrics and clinical indices to assess the clinical applicability of the technique and its ability to track the disease state. RESULTS: The subject-specific computer models accurately captured the increase in pulmonary resistance (Rpul), the main cardiovascular consequence of APE, in all five pigs trials, which related well (R2 = 0.81) with the experimentally derived pulmonary vascular resistance. An increase in right ventricular contractility was identified, as expected, consistent with known reflex responses to APE. Furthermore, the modeled right ventricular expansion index (the ratio of right to left ventricular end diastolic volumes) closely followed the trends seen in the measured data (R2 = 0.92) used for validation, with sharp increases seen in the metric for the two pigs in a near-death state. These results show that the pig-specific models are capable of tracking disease-dependent changes in pulmonary resistance (afterload), right ventricular contractility (inotropy), and ventricular loading (preload) during induced APE. Continuous, accurate estimation of these fundamental metrics of cardiovascular status can help to assist clinicians with diagnosis, monitoring, and therapy-based decisions in an intensive care environment. Furthermore, because the method only uses measurements already available in the ICU, it can be implemented with no added risk to the patient and little extra cost. CONCLUSIONS: This computer-based monitoring method shows potential for real-time, continuous diagnosis and monitoring of acute CVS dysfunction in critically ill patients. [less ▲]

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See detailVariability of insulin sensitivity for diabetics and non-diabetics during the first 3 days of ICU stay
Pretty, Christopher G.; Le Compte, Aaron; Preiser, Jean-Charles et al

Poster (2011)

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See detailSafety and Performance of Stochastic Targeted (STAR) Glycemic Control of Insulin and Nutrition - First Pilot Results
Shaw, Geoffrey M.; Le Compte, Aaron; Evans, Alicia et al

Poster (2011)

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See detailSafety and Performance of Stochastic Targeted (STAR) Glycemic Control of Insulin and Nutrition – First Pilot Results
Shaw, Geoffrey M.; Le Compte, Aaron; Evans, Alicia et al

in Intensive Care Medicine (2011)

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See detailPilot Trials of STAR Target to Range Glycemic Control
Penning, Sophie ULg; Le Compte, Aaron; Massion, Paul et al

Poster (2011)

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See detailGlycemic Variability, Hypoglycemia and Organ Failure in the Glucontrol Study
Penning, Sophie ULg; Le Compte, Aaron J.; Preiser, Jean-Charles et al

in 10th Belgian Day on Biomedical Engineering (2011)

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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 ▲]

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See detailModel-based optimal PEEP in mechanically ventilated ARDS patients in the Intensive Care Unit
Sundaresan, Ashwath; Chase, J Geoffrey; Shaw, Geoffrey M et al

in Biomedical Engineering Online (2011), 10

Background: The optimal level of positive end-expiratory pressure (PEEP) is still widely debated in treating acute respiratory distress syndrome (ARDS) patients. Current methods of selecting PEEP only ... [more ▼]

Background: The optimal level of positive end-expiratory pressure (PEEP) is still widely debated in treating acute respiratory distress syndrome (ARDS) patients. Current methods of selecting PEEP only provide a range of values and do not provide unique patient-specific solutions. Model-based methods offer a novel way of using non-invasive pressure-volume (PV) measurements to estimate patient recruitability. This paper examines the clinical viability of such models in pilot clinical trials to assist therapy, optimise patient-specific PEEP, assess the disease state and response over time. Methods: Ten patients with acute lung injury or ARDS underwent incremental PEEP recruitment manoeuvres. PV data was measured at increments of 5 cmH(2)O and fitted to the recruitment model. Inspiratory and expiratory breath holds were performed to measure airway resistance and auto-PEEP. Three model-based metrics are used to optimise PEEP based on opening pressures, closing pressures and net recruitment. ARDS status was assessed by model parameters capturing recruitment and compliance. Results: Median model fitting error across all patients for inflation and deflation was 2.8% and 1.02% respectively with all patients experiencing auto-PEEP. In all three metrics' cases, model-based optimal PEEP was higher than clinically selected PEEP. Two patients underwent multiple recruitment manoeuvres over time and model metrics reflected and tracked the state or their ARDS. Conclusions: For ARDS patients, the model-based method presented in this paper provides a unique, non-invasive method to select optimal patient-specific PEEP. In addition, the model has the capability to assess disease state over time using these same models and methods. [less ▲]

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See detailA graphical method for practical and informative identifiability analyses of physiological models: A case study of insulin kinetics and sensitivity
Docherty, Paul D.; Chase, J Geoffrey; Lotz, Thomas F. et al

in Biomedical Engineering Online (2011), 10

Background: Derivative based a-priori structural identifiability analyses of mathematical models can offer valuable insight into the identifiability of model parameters. However, these analyses are only ... [more ▼]

Background: Derivative based a-priori structural identifiability analyses of mathematical models can offer valuable insight into the identifiability of model parameters. However, these analyses are only capable of a binary confirmation of the mathematical distinction of parameters and a positive outcome can begin to lose relevance when measurement error is introduced. This article presents an integral based method that allows the observation of the identifiability of models with two-parameters in the presence of assay error. Methods: The method measures the distinction of the integral formulations of the parameter coefficients at the proposed sampling times. It can thus predict the susceptibility of the parameters to the effects of measurement error. The method is tested in-silico with Monte Carlo analyses of a number of insulin sensitivity test applications. Results: The method successfully captured the analogous nature of identifiability observed in Monte Carlo analyses of a number of cases including protocol alterations, parameter changes and differences in participant behaviour. However, due to the numerical nature of the analyses, prediction was not perfect in all cases. Conclusions: Thus although the current method has valuable and significant capabilities in terms of study or test protocol design, additional developments would further strengthen the predictive capability of the method. Finally, the method captures the experimental reality that sampling error and timing can negate assumed parameter identifiability and that identifiability is a continuous rather than discrete phenomenon. [less ▲]

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