References of "Desaive, Thomas"
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See detailStructural identifiability analysis of a cardiovascular system model
Pironet, Antoine ULg; Dauby, Pierre ULg; Chase, J. Geoffrey et al

in Medical engineering & physics (2016)

The six-chamber cardiovascular system model of Burkhoff and Tyberg has been used in several theoretical and experimental studies. However, this cardiovascular system model (and others derived from it) are ... [more ▼]

The six-chamber cardiovascular system model of Burkhoff and Tyberg has been used in several theoretical and experimental studies. However, this cardiovascular system model (and others derived from it) are not identifiable from any output set. In this work, two such cases of structural non-identifiability are first presented. These cases occur when the model output set only contains a single type of information (pressure or volume). A specific output set is thus chosen, mixing pressure and volume information and containing only a limited number of clinically available measurements. Then, by manipulating the model equations involving these outputs, it is demonstrated that the six-chamber cardiovascular system model is structurally globally identifiable. A further simplification is made, assuming known cardiac valve resistances. Because of the poor practical identifiability of these four parameters, this assumption is usual. Under this hypothesis, the six-chamber cardiovascular system model is structurally identifiable from an even smaller dataset. As a consequence, parameter values computed from limited but well-chosen datasets are theoretically unique. This means that the parameter identification procedure can safely be performed on the model from such a well-chosen dataset. Thus, the model may be considered suitable for use in diagnosis. [less ▲]

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See detailImportance of Metabolism Variations in a Model of Extracorporeal Carbon Dioxide Removal
Habran, Simon ULg; Desaive, Thomas ULg; MORIMONT, Philippe ULg et al

in Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (2016)

Extracorporeal CO2 Removal device is used in clinics when a patient suffers from a pulmonary insufficiency like Acute Respiratory Distress Syndrome and allows to decarboxylate blood externally. In this ... [more ▼]

Extracorporeal CO2 Removal device is used in clinics when a patient suffers from a pulmonary insufficiency like Acute Respiratory Distress Syndrome and allows to decarboxylate blood externally. In this work, a model of the respiratory system coupled with such a device is proposed to analyze the decrease of CO2 partial pressure in blood. To validate the model, some parameters are estimated thanks to experimental data. Metabolism is a crucial parameter and we show that its time evolution must be taken into account in order to have correct CO2 partial pressure simulations in arteries and in veins. [less ▲]

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See detailWhen NICE is Not Nice: Performance of Two ICU Glycaemic Control Protocols
Uyttendaele, Vincent ULg; Dickson, Jennifer; Stewart, Kent et al

Poster (2016)

Objective: Hypoglycemia, hyperglycemia and blood glucose (BG) variability are associated with worsened outcomes in critical care. However, NICE-SUGAR trial (unexpectedly?) showed no clinical benefit from ... [more ▼]

Objective: Hypoglycemia, hyperglycemia and blood glucose (BG) variability are associated with worsened outcomes in critical care. However, NICE-SUGAR trial (unexpectedly?) showed no clinical benefit from intensive insulin therapy. This study compares the table-based NICE-SUGAR and model-based STAR protocols to assess their relative capability to achieve safe, effective control for all patients. Method: Validated virtual patients (n=443) were used to simulate glycemic outcomes of the NICE-SUGAR and STAR protocols. Key outcomes assess tightness and safety of control for all patients: %BG in 80–144 mg/dL range (PTR); Per-Patient Mean BG (PPM_BG); and Incidence Hypoglycemia (BG<40 mg/dL). These metrics assess performance overall, for each patient, and safety. Results are assessed for NICE-SUGAR measuring per-protocol (~24/day) and at reported average rate (~3-hourly; ~8/day). STAR measures 1-3-hourly, averaging 12/day. Result: Per-protocol, STAR provided tight control, with higher PTR (90.7% vs. 78.3%) and tighter median [IQR] PPM_BG (112[106-119] vs. 117[106–137] mg/dL), and greater safety from hypoglycemia (5 (1%) vs. 10 patients (2.5%)). The 5-95th percentile range PPM_BG for NICE-SUGAR (97–185 mg/dL) shows ~5% of NICE-SUGAR patients had mean BG above 180mg/dL matching clinically reported performance. STAR’s 90th percentile PPM_BG range was (97–146 mg/dL). Measuring as recorded clinically, NICE-SUGAR had PTR of 77%, PPM_BG of 122 [110-140] mg/dL and 24(6%) of patients experienced hypoglycemia. These results match clinically reported values well (mean BG 115 vs. 118 mg/dL clinically vs. simulation, clinically 7% of patients had a hypoglycemic event) Conclusions: Glycemic control protocols need to be both safe and effective for all patients before potential clinical benefits can be assessed. NICE-SUGAR (measured ~24/day or as reported ~8/day) was unable to achieve this outcome for all patients. [less ▲]

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See detailVirtual patients and virtual cohorts: a new way to think about the design and implementation of personalized ICU treatments
Chase, JG; Desaive, Thomas ULg; Preiser, JC

in Annual update in intensive care and emergency medicine 2016 (2016)

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See detailAre Survivors Easier to Control? Why the Association of Glycemia and Mortality in Critical Care is Real
Uyttendaele, Vincent ULg; Dickson, Jennifer; Stewart, Kent et al

Poster (2016)

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See detailBlood Glucose Levels of Subelite Athletes During 6 Days of Free Living.
Thomas, Felicity Louise ULg; Pretty, Chris G.; Desaive, Thomas ULg et al

in Journal of Diabetes Science and Technology (2016)

BACKGROUND: Continuous glucose monitoring (CGM) devices, with their 1-5 min measurement interval, allow blood glucose (BG) concentration dynamics to be captured more frequently and less invasively than ... [more ▼]

BACKGROUND: Continuous glucose monitoring (CGM) devices, with their 1-5 min measurement interval, allow blood glucose (BG) concentration dynamics to be captured more frequently and less invasively than traditional BG measures. One cohort CGM could provide insight is athletes. This study investigates what impact their heightened energy expenditure and dietary intake may have on their ability to achieve optimal BG. METHODS: Ten subelite athletes (resting HR<60 bpm, training>6 hrs per week) were recruited. Two Ipro2 CGM devices (Medtronic Minimed, Northridge, CA) were inserted into the abdomen and remained in place for ~6 days. Time in band was calculated as the percentage of CGM BG measurements with in the 4.0-6.0 mmol/L. Fasting glucose was calculated using CGM calibration BG measurements and postprandial glucose response was also calculated using the CGM values. RESULTS: 4/10 athletes studied spent more than 70% of the total monitoring time above 6.0 mmol/L even with the 2-hour period after meals is excluded. Fasting BG was also in the ADA defined prediabetes range for 3/10 athletes. Only 1 participant spent substantial time below 4.0 mmol/L which was largely due to significantly lower energy intake compared to recommendations. CONCLUSIONS: Contrary to expectations high BG appears to be more of a concern for athletes then low BG even in those with the highest energy expenditure and consuming below the recommended carbohydrate intake. This study warrants further investigation on the recommended diets and the BG of athletes to better determine the causes and impact of this hyperglycemia on overall athlete health. [less ▲]

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See detailAssessing respiratory mechanics using pressure reconstruction method in mechanically ventilated spontaneous breathing patient.
Damanhuri, Nor Salwa; Chiew, Yeong Shiong; Othman, Nor Azlan et al

in Computer Methods & Programs in Biomedicine (2016), 130

BACKGROUND: Respiratory system modelling can aid clinical decision making during mechanical ventilation (MV) in intensive care. However, spontaneous breathing (SB) efforts can produce entrained "M-wave ... [more ▼]

BACKGROUND: Respiratory system modelling can aid clinical decision making during mechanical ventilation (MV) in intensive care. However, spontaneous breathing (SB) efforts can produce entrained "M-wave" airway pressure waveforms that inhibit identification of accurate values for respiratory system elastance and airway resistance. A pressure wave reconstruction method is proposed to accurately identify respiratory mechanics, assess the level of SB effort, and quantify the incidence of SB effort without uncommon measuring devices or interruption to care. METHODS: Data from 275 breaths aggregated from all mechanically ventilated patients at Christchurch Hospital were used in this study. The breath specific respiratory elastance is calculated using a time-varying elastance model. A pressure reconstruction method is proposed to reconstruct pressure waves identified as being affected by SB effort. The area under the curve of the time-varying respiratory elastance (AUC Edrs) are calculated and compared, where unreconstructed waves yield lower AUC Edrs. The difference between the reconstructed and unreconstructed pressure is denoted as a surrogate measure of SB effort. RESULTS: The pressure reconstruction method yielded a median AUC Edrs of 19.21 [IQR: 16.30-22.47]cmH2Os/l. In contrast, the median AUC Edrs for unreconstructed M-wave data was 20.41 [IQR: 16.68-22.81]cmH2Os/l. The pressure reconstruction method had the least variability in AUC Edrs assessed by the robust coefficient of variation (RCV)=0.04 versus 0.05 for unreconstructed data. Each patient exhibited different levels of SB effort, independent from MV setting, indicating the need for non-invasive, real time assessment of SB effort. CONCLUSION: A simple reconstruction method enables more consistent real-time estimation of the true, underlying respiratory system mechanics of a SB patient and provides the surrogate of SB effort, which may be clinically useful for clinicians in determining optimal ventilator settings to improve patient care. [less ▲]

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See detailGlucose Control in the ICU: A Continuing Story.
Preiser, Jean-Charles; Chase, J. Geoffrey; Hovorka, Roman et al

in Journal of diabetes science and technology (2016)

In the present era of near-continuous glucose monitoring (CGM) and automated therapeutic closed-loop systems, measures of accuracy and of quality of glucose control need to be standardized for licensing ... [more ▼]

In the present era of near-continuous glucose monitoring (CGM) and automated therapeutic closed-loop systems, measures of accuracy and of quality of glucose control need to be standardized for licensing authorities and to enable comparisons across studies and devices. Adequately powered, good quality, randomized, controlled studies are needed to assess the impact of different CGM devices on the quality of glucose control, workload, and costs. The additional effects of continuing glucose control on the general floor after the ICU stay also need to be investigated. Current algorithms need to be adapted and validated for CGM, including effects on glucose variability and workload. Improved collaboration within the industry needs to be encouraged because no single company produces all the necessary components for an automated closed-loop system. Combining glucose measurement with measurement of other variables in 1 sensor may help make this approach more financially viable. [less ▲]

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See detailSize of cannula for extracorporeal CO2 removal therapies
Habran, Simon ULg; Desaive, Thomas ULg; MORIMONT, Philippe ULg et al

Conference (2015, November 26)

Extracorporeal CO2 removal devices (ECCO2R) can be used in clinics to decarboxylate blood externally for patients suffering from pulmonary insufficiencies like acute respiratory distress syndrome. In this ... [more ▼]

Extracorporeal CO2 removal devices (ECCO2R) can be used in clinics to decarboxylate blood externally for patients suffering from pulmonary insufficiencies like acute respiratory distress syndrome. In this work, we propose a model of the respiratory system coupled with such a device to analyze the decrease of CO2 partial pressure in the blood as a function of the blood flow through the device. Thanks to this information, the size of the cannulae can be optimized. [less ▲]

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See detailCardio-pulmonary mechanics and minimal modelling in critical care
de Bournonville, Sébastien; Pironet, Antoine ULg; Desaive, Thomas ULg et al

Poster (2015, November 26)

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See detailCardio-pulmonary mechanics and minimal modelling in critical care
de Bournonville, Sébastien; Pironet, Antoine ULg; Desaive, Thomas ULg et al

in Proceedings of the IEEE-EMBS Benelux chapter 2015 meeting (2015, November 26)

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See detailParameter Correlation Analysis in Four Mathematical Models of Left Ventricular Relaxation
Pironet, Antoine ULg; MORIMONT, Philippe ULg; Dauby, Pierre ULg et al

in Proceedings of the IEEE-EMBS Benelux chapter 2015 meeting (2015, November 26)

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See detailParameter Identification Methods in a Model of the Cardiovascular System
Pironet, Antoine ULg; Desaive, Thomas ULg; Dauby, Pierre ULg et al

Poster (2015, September 01)

To be clinically relevant, mathematical models have to be patient-specific, meaning that their parameters have to be identified from patient data. To achieve real time monitoring, it is important to ... [more ▼]

To be clinically relevant, mathematical models have to be patient-specific, meaning that their parameters have to be identified from patient data. To achieve real time monitoring, it is important to select the best parameter identification method, in terms of speed, efficiency and reliability. This work presents a comparison of seven parameter identification methods applied to a lumped-parameter cardiovascular system model. The seven methods are tested using in silico and experimental reference data. To do so, precise formulae for initial parameter values first had to be developed. The test results indicate that the trust-region reflective method seems to be the best method for the present model. This method (and the proportional method) are able to perform parameter identification in two to three minutes, and will thus benefit cardiac and vascular monitoring applications. [less ▲]

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See detailModel-Based Stressed Blood Volume is an Index of Fluid Responsiveness
Pironet, Antoine ULg; Dauby, Pierre ULg; Chase, J. Geoffrey et al

Conference (2015, September 01)

Fluid therapy is frequently used to manage acute circulatory failure. This therapy aims to restore cardiac output by fluid administration, which increases the quantity of fluid in the circulation. However ... [more ▼]

Fluid therapy is frequently used to manage acute circulatory failure. This therapy aims to restore cardiac output by fluid administration, which increases the quantity of fluid in the circulation. However, it has been shown to be effective only in certain cases, leading to the need for indices of fluid responsiveness. Total stressed blood volume has recently been shown to be such an index of fluid responsiveness. However, the current methods to determine this parameter require specific procedures. In this work, a more straightforward method is developed using data available in the intensive care unit. A simple three-chamber cardiovascular system model is used, of which total stressed blood volume is a parameter. All model parameters (including total stressed blood volume) are adjusted to pig experimental data during fluid administrations. The resulting value of total stressed blood volume is always negatively associated with the relative change in cardiac output after fluid administration. This finding confirms that total stressed blood volume is an index of fluid responsiveness. Another finding of this study is that the response curves are subject-specific. The method developed in this work can be applied to humans, since the data required is typically available in an intensive care unit. [less ▲]

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See detailRelationship between Stroke Volume and Pulse Wave Velocity
Kamoi, Shun; Pretty, Christopher; Chiew, Yeong Shiong et al

Conference (2015, September 01)

Stroke Volume (SV) measurements are essential for evaluating patient hemodynamic status and response to therapy. However, current methods for monitoring SV require either invasive or non- invasive ... [more ▼]

Stroke Volume (SV) measurements are essential for evaluating patient hemodynamic status and response to therapy. However, current methods for monitoring SV require either invasive or non- invasive additional measurements. This study investigates the relationship between SV and Pulse Wave Velocity (PWV) to examine whether the value of PWV can capture the changes in SV. The analysis was performed using data from six porcine experiments (N=6 Pietrain Pigs, 20-29 kg) in which left ventricular volume, aortic arc pressure, and descending aortic pressure waveforms were measured simultaneously. From the measured data, correlation coefficients were determined between absolute value of aortic PWV, SV and trend value ‘PWV – mean PWV’, ‘SV – mean SV’ during periods when changes in SV were induced from preload changes, as well as infusion of dobutamine. The results showed good correlation (r = 0.59) for trend value, however, the correlation coefficient were poor with r = 0.028 for absolute value across all pigs. The analysis showed that value of PWV is reliable for capturing trend value of SV in preload changes. However, it is unreliable for capturing absolute value of SV or changes in SV made from dobutamine. [less ▲]

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See detailRelationship between Stroke Volume and Pulse Wave Velocity
Kamoi, Shun; Pretty, Christopher; Chiew, Yeong Shiong et al

in Preprints of the 9th IFAC Symposium on Biological and Medical Systems (2015, September)

Stroke Volume (SV) measurements are essential for evaluating patient hemodynamic status and response to therapy. However, current methods for monitoring SV require either invasive or non- invasive ... [more ▼]

Stroke Volume (SV) measurements are essential for evaluating patient hemodynamic status and response to therapy. However, current methods for monitoring SV require either invasive or non- invasive additional measurements. This study investigates the relationship between SV and Pulse Wave Velocity (PWV) to examine whether the value of PWV can capture the changes in SV. The analysis was performed using data from six porcine experiments (N=6 Pietrain Pigs, 20-29 kg) in which left ventricular volume, aortic arc pressure, and descending aortic pressure waveforms were measured simultaneously. From the measured data, correlation coefficients were determined between absolute value of aortic PWV, SV and trend value ‘PWV – mean PWV’, ‘SV – mean SV’ during periods when changes in SV were induced from preload changes, as well as infusion of dobutamine. The results showed good correlation (r = 0.59) for trend value, however, the correlation coefficient were poor with r = 0.028 for absolute value across all pigs. The analysis showed that value of PWV is reliable for capturing trend value of SV in preload changes. However, it is unreliable for capturing absolute value of SV or changes in SV made from dobutamine. [less ▲]

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See detailModel-Based Stressed Blood Volume is an Index of Fluid Responsiveness
Pironet, Antoine ULg; Dauby, Pierre ULg; Chase, J. Geoffrey et al

in IFAC PapersOnLine (2015, September)

Fluid therapy is frequently used to manage acute circulatory failure. This therapy aims to restore cardiac output by fluid administration, which increases the quantity of fluid in the circulation. However ... [more ▼]

Fluid therapy is frequently used to manage acute circulatory failure. This therapy aims to restore cardiac output by fluid administration, which increases the quantity of fluid in the circulation. However, it has been shown to be effective only in certain cases, leading to the need for indices of fluid responsiveness. Total stressed blood volume has recently been shown to be such an index of fluid responsiveness. However, the current methods to determine this parameter require specific procedures. In this work, a more straightforward method is developed using data available in the intensive care unit. A simple three-chamber cardiovascular system model is used, of which total stressed blood volume is a parameter. All model parameters (including total stressed blood volume) are adjusted to pig experimental data during fluid administrations. The resulting value of total stressed blood volume is always negatively associated with the relative change in cardiac output after fluid administration. This finding confirms that total stressed blood volume is an index of fluid responsiveness. Another finding of this study is that the response curves are subject-specific. The method developed in this work can be applied to humans, since the data required is typically available in an intensive care unit. [less ▲]

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See detailParameter Identification Methods in a Model of the Cardiovascular System
Pironet, Antoine ULg; Desaive, Thomas ULg; Dauby, Pierre ULg et al

in IFAC PapersOnLine (2015, September)

To be clinically relevant, mathematical models have to be patient-specific, meaning that their parameters have to be identified from patient data. To achieve real time monitoring, it is important to ... [more ▼]

To be clinically relevant, mathematical models have to be patient-specific, meaning that their parameters have to be identified from patient data. To achieve real time monitoring, it is important to select the best parameter identification method, in terms of speed, efficiency and reliability. This work presents a comparison of seven parameter identification methods applied to a lumped-parameter cardiovascular system model. The seven methods are tested using in silico and experimental reference data. To do so, precise formulae for initial parameter values first had to be developed. The test results indicate that the trust-region reflective method seems to be the best method for the present model. This method (and the proportional method) are able to perform parameter identification in two to three minutes, and will thus benefit cardiac and vascular monitoring applications. [less ▲]

Detailed reference viewed: 107 (10 ULg)