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See detailHow Much Do We Gain From Greater Personalisation?
Uyttendaele, Vincent ULiege; Knopp, Jennifer L.; Shaw, Geoffrey M. et al

Poster (2018, November)

Objective: STAR (Stochastic TARgeted) is risk-based glycaemic control (GC) using prediction of future insulin sensitivity (SI) variability to safely dose insulin and nutrition, where SI variability is the ... [more ▼]

Objective: STAR (Stochastic TARgeted) is risk-based glycaemic control (GC) using prediction of future insulin sensitivity (SI) variability to safely dose insulin and nutrition, where SI variability is the key driver in GC difficulty and hypoglycaemia. Currently, STAR uses a 2D stochastic model where current identified patient-specific SI is used to predict future SI variability in a cohort-specific sense. This study assesses the impact on GC performance of a new, more patient-specific 3D stochastic model, using previous and current SI values to predict metabolic variability. Method: Bi-variate and tri-variate Gaussian kernel density methods are used to estimate conditional probability estimation of future SI knowing current SI (2D model) and also previous SI (3D model). Models are built randomly using 411 (70%) of retrospective GC episodes. They are tested using clinically validated virtual trials on the 176 (30%) remaining patients, repeating 3 times (N=528 episodes). Safety, performance, and workload are compared. Results: Out of the total 528 simulated episodes, workload was similar (11.6 measures/day). Performance was similar (90% in 80-145mg/dL band), but tighter for the 3D model (78% vs 74% in 80-125mg/dL band). Median BG level was lower for the 3D model (108 [99, 120] vs. 113 [103, 124]mg/dL), with higher insulin (3.0 [1.5, 5.0] vs 2.5 [1.5, 4.0] U/h) and nutrition (99 [66, 100] vs 92 [70, 100] % goal feed). Safety was very slightly better for the 2D model (2% vs 3% BG<72mg/dL; 1% vs 1.4% BG<40mg/dL). Conclusions: The new, more personalised 3D stochastic model provides moderately improved performance and similar safety and workload. Overall, results suggest greater personalization in predicting variability can improve STAR GC performance and justify implementation to see if it improves outcomes. [less ▲]

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See detailA 3D insulin sensitivity prediction model enables more patient-specific prediction and model-based glycaemic control
Uyttendaele, Vincent ULiege; Knopp, Jennifer L.; Stewart, Kent W. et al

in Biomedical Signal Processing and Control (2018), 45

Background Insulin therapy for glycaemic control (GC) in critically ill patients may improve outcomes by reducing hyperglycaemia and glycaemic variability, which are both associated with increased ... [more ▼]

Background Insulin therapy for glycaemic control (GC) in critically ill patients may improve outcomes by reducing hyperglycaemia and glycaemic variability, which are both associated with increased morbidity and mortality. However, initial positive results have proven difficult to repeat or achieve safely. STAR (Stochastic TARgeted) is a model-based glycaemic control protocol using a risk-based dosing approach. STAR uses a 2D stochastic model to predict distributions of likely future changes in model-based insulin sensitivity (SI) based on its current value, and determines the optimal intervention. Objectives This study investigates the impact of a new 3D stochastic model on the ability to predict more accurate future SI distributions, which would allow more aggressive insulin dosing and improved glycaemic control. Methods The new 3D stochastic model is built using both current SI and its prior variation to predict future SI distribution from 68,629 h of clinical data (819 GC episodes). The 5th-95th percentile range of predicted SI distribution are calculated and compared with the 2D model. Results Results show the 2D model is over-conservative compared to the 3D case for more than 77% of the data, predominantly where SI is stable (|%ΔSI| ≤ 25%). These formerly conservative prediction ranges are now ~30% narrower with the 3D model, which safely enables more aggressive insulin dosing for these patient hours. In addition, distributions of predicted SI within the 5th-95th percentile range are much closer to the ideal value of 90% for more patients with the 3D model. Conclusions The new 3D model better characterises patient specific metabolic variability and patient specific response to insulin, allowing more optimal insulin dosing to increase performance and safety. [less ▲]

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See detailMathematical modeling of extracorporeal CO2 removal therapy. A validation carried out on ten pigs
Habran, Simon ULiege; Desaive, Thomas ULiege; MORIMONT, Philippe ULiege et al

in Medical and Biological Engineering and Computing (2018), 56(3), 421-434

The extracorporeal CO2 removal device (ECCO2RD) is used in clinics to treat patients suffering from respiratory failures like acute respiratory distress syn- drome (ARDS) or chronic obstructive pulmonary ... [more ▼]

The extracorporeal CO2 removal device (ECCO2RD) is used in clinics to treat patients suffering from respiratory failures like acute respiratory distress syn- drome (ARDS) or chronic obstructive pulmonary disease (COPD). The aim of this device is to decarboxylate blood externally with low blood flow. A mathematical model is proposed to describe protective ventilation, ARDS, and an extracorporeal CO2 removal therapy (ECCO2RT). The sim- ulations are compared with experimental data carried out on ten pigs. The results show a good agreement between the mathematical simulations and the experimental data, which provides a nice validation of the model. This model is thus able to predict the decrease of PCO2 during ECCO2RT for different blood flows across the extracorporeal lung support. [less ▲]

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See detailAfterload burden on the right ventricle is enhanced when ARDS is associated with hypercapnic acidosis.
MORIMONT, Philippe ULiege; Habran, Simon ULiege; Desaive, Thomas ULiege et al

in Annals of Intensive Care (2018, February 05), 8(1),

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See detailPreliminary results from the STAR-Liège clinical trial: Virtual trials, safety, performance, and compliance analysis
Uyttendaele, Vincent ULiege; Knopp, Jennifer L.; PIROTTE, Marc ULiege et al

in IFAC-PapersOnLine (2018)

Glycaemic control has been shown to improve outcome in critically ill patients, but hard to achieve in a safe and effective manner. This paper presents the preliminary results of 8 patients controlled at ... [more ▼]

Glycaemic control has been shown to improve outcome in critically ill patients, but hard to achieve in a safe and effective manner. This paper presents the preliminary results of 8 patients controlled at the University Hospital of Liège under STAR-Liège, an insulin-only version of the model-based STAR glycaemic controller framework. Clinical data is compared with virtual trial simulations of the glycaemic control outcomes for the STAR-Liège protocol, and with the standard of care protocol of this intensive care unit, to assess safety, performance, and compliance of the new protocol. Results show 78% of clinical blood glucose measurements in target band. Only 3% of blood glucose measurements were below 4.4 mmol/L (79 mg/dL), with only 1% mild hypoglycaemia and no severe hypoglycaemia. These results are similar to simulation of the protocol, but slightly higher workload is observed clinically due to nursing choice. Compared to standard protocol virtual trial simulations, STAR-Liège achieved tighter and less variable control with similar safety, and less percentage time in higher blood glucose levels. Clinically, 14% of insulin intervention were increased or decreased from recommendation with median [IQR] change of 1 [1, 2] or -2 [-3, -2] U/hr respectively. Clinical and simulation results show STAR-Liège better controls glycaemia to lower ranges compared to the standard protocol, while ensuring safety. Lower time in higher blood glucose ranges potentially improves patient outcomes. Compliance analysis shows potential nurse fears in protocol changes and different insulin dosing. These results are encouraging for the continuation of the clinical trial realised in this medical intensive care unit and its extension to insulin and nutrition control. [less ▲]

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See detailChanges in Identified, Model-based Insulin Sensitivity can be used to Improve Risk and Variability Forecasting in Glycaemic Control
Uyttendaele, Vincent ULiege; Dickson, Jennifer L.; Morton, Sophie et al

in IFAC-PapersOnLine (2018)

Hyperglycaemia, hypoglycaemia and glycaemic variability in critically ill patients are associated with increased mortality and adverse outcomes. Some studies have shown insulin therapy to control ... [more ▼]

Hyperglycaemia, hypoglycaemia and glycaemic variability in critically ill patients are associated with increased mortality and adverse outcomes. Some studies have shown insulin therapy to control glycaemia has improved outcomes, but have proven difficult to repeat or achieve safely. STAR (Stochastic Targeted) is a model-based glycaemic control protocol using a stochastic model to forecast future distributions of insulin sensitivity (SI) based on its current value, to predict the range of future blood glucose outcomes for a given intervention. This study presents an improved 3D stochastic model, forecasting future distributions of SI based on its current value and prior variation. The percentage difference in the 5th, 50th, and 95th percentiles between the current 2D and new 3D models are compared. Results show the original 2D stochastic model is over-conservative for around 77% of the data, predominantly where prior variability was low. For higher prior variation (more than ±25% change in SI), the 3D stochastic model prediction range of future SI is wider. The new 3D model was found to have overall narrower 5th – 95th prediction ranges in SI, but to retain a similar per-patient (60 – 100%) and overall (92%) percentage of SI outcomes correctly predicted within these ranges. These results suggest the new 3D model is more patient-specific and will enable more optimal dosing, to increase both safety and performance. This improvement in forecasting may result in tighter and safer glycaemic control, improving performance within the STAR framework. [less ▲]

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See detailImproved 3D Stochastic Modelling of Insulin Sensitivity Variability for Improved Glycaemic Control
Uyttendaele, Vincent ULiege; Knopp, Jennifer L.; Shaw, Geoff M. et al

in IFAC-PapersOnLine (2018)

Glycaemic control in intensive care unit has been associated with improved outcomes. Metabolic variability is one of the main factors making glycaemic control hard to achieve safely. STAR (Stochastic ... [more ▼]

Glycaemic control in intensive care unit has been associated with improved outcomes. Metabolic variability is one of the main factors making glycaemic control hard to achieve safely. STAR (Stochastic Targeted) is a model-based glycaemic control protocol using a stochastic model to predict likely distributions of future insulin sensitivity based on current patient-specific insulin sensitivity, enabling unique risk-based dosing. This study aims to improve insulin sensitivity forecasting by presenting a new 3D stochastic model, using current and previous insulin sensitivity levels. The predictive power and the percentage difference in the 5th-95th percentile prediction width are compared between the two models. Results show the new model accurately predicts insulin sensitivity variability, while having a median 21.7% reduction of the prediction range for more than 73% of the data, which will safely enable tighter control. The new model also shows trends in insulin sensitivity variability. For previous stable or low insulin sensitivity changes, future insulin sensitivity tends to remain more stable (tighter prediction ranges), whereas for higher previous variation of insulin sensitivity, higher potential future variation of insulin sensitivity is more likely (wider prediction ranges). These results offer the opportunity to better assess and predict future evolution of insulin sensitivity, enabling more optimal risk-based dosing approach, potentially resulting in tighter and safer glycaemic control using the STAR framework. [less ▲]

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See detail3D Stochastic Modelling of Insulin Sensitivity in STAR: Virtual trials analysis
Uyttendaele, Vincent ULiege; Knopp, Jennifer L.; Shaw, Geoffrey M. et al

in IFAC-PapersOnLine (2018)

Glycaemic control has shown beneficial outcomes for critically ill patients, but has been proven hard to achieve safely, increasing risk of hypoglycaemia. Patient metabolic variability is one of the main ... [more ▼]

Glycaemic control has shown beneficial outcomes for critically ill patients, but has been proven hard to achieve safely, increasing risk of hypoglycaemia. Patient metabolic variability is one of the main factor influencing glycaemic control safety and efficacy. STAR is a model-based glycaemic controller using a unique patient-specific risk-based dosing approach. STAR uses a 2D stochastic model, built from population data using kernel density methods, to determine potential forward future evolution in patient-specific insulin sensitivity (SIn+1), based on its current value (SIn). This study uses virtual trial to compare the current 2D stochastic model used in STAR, with a new 3D stochastic model. The new 3D model also uses prior insulin sensitivity value (SIn-1) to determine distribution of likely future SIn+1. A total of 587 virtual patient glycaemic control episodes longer than 24 hours from three different studies are used here. Safety (% blood glucose (BG) measurements < 4.0 and < 2.2 mmol/L), performance (% time in the target 4.4-8.0 mmol/L band), insulin administration and nutrition delivery (% goal feed) are compared. Results show similar performance (90% BG in 4.4-8.0 mmol/L), and similar safety, with slightly higher % BG < 4.0 mmol/L (0.9 vs. 1.4%) and % BG < 2.2 mmol/L (0.02 vs. 0.03%) for the 3D model, was achieved for similar workload. The slightly lower median BG level (6.3 vs. 6.0 mmol/L) for the 3D stochastic model is explained by the higher median insulin rate administered (2.5 vs. 3.0 U/hr). More importantly, simulation results showed higher nutrition delivery using the 3D stochastic model (92 vs. 99 % goal feed). The new 3D stochastic model achieved similar safety and performance than the 2D stochastic model in these virtual simulations, while increasing the total calorific intake. This higher nutritional intake is potentially associated with improved outcome in intensive care units. The 3D stochastic model thus better characterises patient-specific metabolic variability, allowing more optimal insulin and nutritional dosing. Therefore, a pilot clinical trial using the new 3D stochastic model could be realised to assess and compared clinical outcomes using the new stochastic model. [less ▲]

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See detailAssessing mechanical ventilation asynchrony through iterative airway pressure reconstruction
Chiew, Y. S.; Tan, C. P.; Chase, J. G. et al

in Computer Methods and Programs in Biomedicine (2018), 157

Background and objective: Respiratory mechanics estimation can be used to guide mechanical ventilation (MV) but is severely compromised when asynchronous breathing occurs. In addition, asynchrony during ... [more ▼]

Background and objective: Respiratory mechanics estimation can be used to guide mechanical ventilation (MV) but is severely compromised when asynchronous breathing occurs. In addition, asynchrony during MV is often not monitored and little is known about the impact or magnitude of asynchronous breathing towards recovery. Thus, it is important to monitor and quantify asynchronous breathing over every breath in an automated fashion, enabling the ability to overcome the limitations of model-based respiratory mechanics estimation during asynchronous breathing ventilation. Methods: An iterative airway pressure reconstruction (IPR) method is used to reconstruct asynchronous airway pressure waveforms to better match passive breathing airway waveforms using a single compartment model. The reconstructed pressure enables estimation of respiratory mechanics of airway pressure waveform essentially free from asynchrony. Reconstruction enables real-time breath-to-breath monitoring and quantification of the magnitude of the asynchrony (MAsyn). Results and discussion: Over 100,000 breathing cycles from MV patients with known asynchronous breathing were analyzed. The IPR was able to reconstruct different types of asynchronous breathing. The resulting respiratory mechanics estimated using pressure reconstruction were more consistent with smaller interquartile range (IQR) compared to respiratory mechanics estimated using asynchronous pressure. Comparing reconstructed pressure with asynchronous pressure waveforms quantifies the magnitude of asynchronous breathing, which has a median value MAsyn for the entire dataset of 3.8%. Conclusion: The iterative pressure reconstruction method is capable of identifying asynchronous breaths and improving respiratory mechanics estimation consistency compared to conventional model-based methods. It provides an opportunity to automate real-time quantification of asynchronous breathing frequency and magnitude that was previously limited to invasively method only. © 2018 Elsevier B.V. [less ▲]

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See detailNext-generation, personalised, model-based critical care medicine: A state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them
Chase, J. G.; Preiser, J.-C.; Dickson, J. L. et al

in BioMedical Engineering OnLine (2018), 17(1),

Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to ... [more ▼]

Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to treatment, and increasingly aging populations mean ICUs are under increasing demand and their cohorts are increasingly ill. Equally, patient expectations are growing, while the economic ability to deliver care to all is declining. Better, more productive care is thus the big challenge. One means to that end is personalised care designed to manage the significant inter- and intra-patient variability that makes the ICU patient difficult. Thus, moving from current "one size fits all" protocolised care to adaptive, model-based "one method fits all" personalised care could deliver the required step change in the quality, and simultaneously the productivity and cost, of care. Computer models of human physiology are a unique tool to personalise care, as they can couple clinical data with mathematical methods to create subject-specific models and virtual patients to design new, personalised and more optimal protocols, as well as to guide care in real-time. They rely on identifying time varying patient-specific parameters in the model that capture inter- and intra-patient variability, the difference between patients and the evolution of patient condition. Properly validated, virtual patients represent the real patients, and can be used in silico to test different protocols or interventions, or in real-time to guide care. Hence, the underlying models and methods create the foundation for next generation care, as well as a tool for safely and rapidly developing personalised treatment protocols over large virtual cohorts using virtual trials. This review examines the models and methods used to create virtual patients. Specifically, it presents the models types and structures used and the data required. It then covers how to validate the resulting virtual patients and trials, and how these virtual trials can help design and optimise clinical trial. Links between these models and higher order, more complex physiome models are also discussed. In each section, it explores the progress reported up to date, especially on core ICU therapies in glycemic, circulatory and mechanical ventilation management, where high cost and frequency of occurrence provide a significant opportunity for model-based methods to have measurable clinical and economic impact. The outcomes are readily generalised to other areas of medical care. © 2018 The Author(s). [less ▲]

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See detailBeat-by-Beat Estimation of the Left Ventricular Pressure–Volume Loop Under Clinical Conditions
Davidson, S.; Pretty, C.; Kamoi, S. et al

in Annals of Biomedical Engineering (2018), 46(1), 171-185

This paper develops a method for the minimally invasive, beat-by-beat estimation of the left ventricular pressure–volume loop. This method estimates the left ventricular pressure and volume waveforms that ... [more ▼]

This paper develops a method for the minimally invasive, beat-by-beat estimation of the left ventricular pressure–volume loop. This method estimates the left ventricular pressure and volume waveforms that make up the pressure–volume loop using clinically available inputs supported by a short, baseline echocardiography reading. Validation was performed across 142,169 heartbeats of data from 11 Piétrain pigs subject to two distinct protocols encompassing sepsis, dobutamine administration and clinical interventions. The method effectively located pressure–volume loops, with low overall median errors in end-diastolic volume of 8.6%, end-systolic volume of 17.3%, systolic pressure of 19.4% and diastolic pressure of 6.5%. The method further demonstrated a low overall mean error of 23.2% predicting resulting stroke work, and high correlation coefficients along with a high percentage of trend compass ‘in band’ performance tracking changes in stroke work as patient condition varied. This set of results forms a body of evidence for the potential clinical utility of the method. While further validation in humans is required, the method has the potential to aid in clinical decision making across a range of clinical interventions and disease state disturbances by providing real-time, beat-to-beat, patient specific information at the intensive care unit bedside without requiring additional invasive instrumentation. © 2017, Biomedical Engineering Society. [less ▲]

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See detailA mathematical model of respiration under protective ventilation and extracorporeal CO2 removal therapy
Habran, Simon ULiege; Desaive, Thomas ULiege; MORIMONT, Philippe ULiege et al

Conference (2017, September 27)

The aim of the present study is to build a mathematical model of the respiratory system connected to an extracorporeal CO2 removal device (ECCO2RD) to optimize the gas exchanges efficiency. The model must ... [more ▼]

The aim of the present study is to build a mathematical model of the respiratory system connected to an extracorporeal CO2 removal device (ECCO2RD) to optimize the gas exchanges efficiency. The model must be simple enough to provide rapid solutions and to estimate specific parameters from available clinical data. But it also must be complex enough to be able to simulate the respiratory system when protective ventilation is used and when this system is assisted by an ECCO2RD. [less ▲]

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See detailUntangling glycaemia and mortality in critical care
Uyttendaele, Vincent ULiege; Dickson, Jennifer L.; Shaw, Geoffrey M. et al

in Critical Care (2017), 21(1), 152

Background: Hyperglycaemia is associated with adverse outcomes in the intensive care unit, and initial studies suggested outcome benefits of glycaemic control (GC). However, subsequent studies often ... [more ▼]

Background: Hyperglycaemia is associated with adverse outcomes in the intensive care unit, and initial studies suggested outcome benefits of glycaemic control (GC). However, subsequent studies often failed to replicate these results, and they were often unable to achieve consistent, safe control, raising questions about the benefit or harm of GC as well as the nature of the association of glycaemia with mortality and clinical outcomes. In this study, we evaluated if non-survivors are harder to control than survivors and determined if glycaemic outcome is a function of patient condition and eventual outcome or of the glycaemic control provided. Methods: Clinically validated, model-based, hour-to-hour insulin sensitivity (SI) and its hour-to-hour variability (%ΔSI) were identified over the first 72 h of therapy in 145 patients (119 survivors, 26 non-survivors). In hypothesis testing, we compared distributions of SI and %ΔSI in 6-hourly blocks for survivors and non-survivors. In equivalence testing, we assessed if differences in these distributions, based on blood glucose measurement error, were clinically significant. Results: SI level was never equivalent between survivors and non-survivors (95% CI of percentage difference in medians outside ±12%). Non-survivors had higher SI, ranging from 9% to 47% higher overall in 6-h blocks, and this difference became statistically significant as glycaemic control progressed. %ΔSI was equivalent between survivors and non-survivors for all 6-hourly blocks (95% CI of difference in medians within ±12%) and decreased in general over time as glycaemic control progressed. Conclusions: Whereas non-survivors had higher SI levels, variability was equivalent to that of survivors over the first 72 h. These results indicate survivors and non-survivors are equally controllable, given an effective glycaemic control protocol, suggesting that glycaemia level and variability, and thus the association between glycaemia and outcome, are essentially determined by the control provided rather than by underlying patient or metabolic condition. [less ▲]

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See detailMinimally invasive estimation of ventricular dead space volume through use of Frank-Starling curves.
Davidson, Shaun; Pretty, Chris; Pironet, Antoine et al

in PLoS ONE (2017), 12(4), 1-11

This paper develops a means of more easily and less invasively estimating ventricular dead space volume (Vd), an important, but difficult to measure physiological parameter. Vd represents a subject and ... [more ▼]

This paper develops a means of more easily and less invasively estimating ventricular dead space volume (Vd), an important, but difficult to measure physiological parameter. Vd represents a subject and condition dependent portion of measured ventricular volume that is not actively participating in ventricular function. It is employed in models based on the time varying elastance concept, which see widespread use in haemodynamic studies, and may have direct diagnostic use. The proposed method involves linear extrapolation of a Frank-Starling curve (stroke volume vs end-diastolic volume) and its end-systolic equivalent (stroke volume vs end-systolic volume), developed across normal clinical procedures such as recruitment manoeuvres, to their point of intersection with the y-axis (where stroke volume is 0) to determine Vd. To demonstrate the broad applicability of the method, it was validated across a cohort of six sedated and anaesthetised male Pietrain pigs, encompassing a variety of cardiac states from healthy baseline behaviour to circulatory failure due to septic shock induced by endotoxin infusion. Linear extrapolation of the curves was supported by strong linear correlation coefficients of R = 0.78 and R = 0.80 average for pre- and post- endotoxin infusion respectively, as well as good agreement between the two linearly extrapolated y-intercepts (Vd) for each subject (no more than 7.8% variation). Method validity was further supported by the physiologically reasonable Vd values produced, equivalent to 44.3-53.1% and 49.3-82.6% of baseline end-systolic volume before and after endotoxin infusion respectively. This method has the potential to allow Vd to be estimated without a particularly demanding, specialised protocol in an experimental environment. Further, due to the common use of both mechanical ventilation and recruitment manoeuvres in intensive care, this method, subject to the availability of multi-beat echocardiography, has the potential to allow for estimation of Vd in a clinical environment. [less ▲]

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See detailImproved Blood Glucose Forecasting Models using Changes in Insulin Sensitivity in Intensive Care Patients
Uyttendaele, Vincent ULiege; Dickson, Jennifer; Shaw, Geoff et al

Poster (2017, February 01)

Introduction: Hyperglycaemia, hypoglycaemia and glycaemic variability are associated with worsened outcomes and increased mortality in intensive care units. Glycaemic control (GC) using insulin therapy ... [more ▼]

Introduction: Hyperglycaemia, hypoglycaemia and glycaemic variability are associated with worsened outcomes and increased mortality in intensive care units. Glycaemic control (GC) using insulin therapy has shown improved outcomes, but have been proven difficult to repeat or achieve safely. STAR (Stochastic TARgeted) is a model-based glycaemic control protocol using a stochastic model to forecast distributions of likely future changes in insulin sensitivity (SI) based on its current value. This can be used to determine likely future blood glucose (BG) levels for a given intervention, enabling the most optimal dose selection that best overlaps a clinically defined BG target band. This study presents a novel 3D model capable to predict likely future distribution of SI using both current SI and its prior variability (%ΔSI). Methods: Metabolic data from 3 clinical ICU cohorts totalling 819 episodes and 68629 hours of treatment under STAR and SPRINT protocols are used in this study. Data triplets (%ΔSIn, SIn, SIn+1) are created and binned together in a range of %ΔSI = [-100%, 200%] and SIn = [1.0e-7, 2.1e-3] in bin sizes of %ΔSI = 10% and SIn = 0.5e-4. The 5th, 50th, and 95th percentile of SIn+1 are determined for each bin where data density is high enough (>100 triplets) and compared to the previous stochastic model. The predictive power of the two models are compared by computing median [IQR] per-patient percentage prediction of SI within the 5th-95th and 25th-75th percentile ranges of model predictions. Results: Results show the previous model is over-conservative for ~77% of the data, mainly where %ΔSI is within an absolute 25% change. The percentage change in the 90% CI width in this region is reduced by ~25-40%. Conversely, non-conservative regions are also identified, with 90% CI width increased up to ~80%. Predictive power is similar for both model (60.3% [47.8%, 71.5%] vs. 51.2 [42.9%, 59.2%] within 25th-75th and 93.6% [85.7%, 97.3%] vs. 90.7% [84.4%, 94.6%] within 5th-95th range). Conclusions: The new 3D model achieved similar predictive power as the previous model by reducing the 5th-95th percentile prediction range for 77% of the data, predominantly where SI is stable. If the conservatism of the previous model reduces risk of hypoglycaemia, it also inhibits the controller’s ability to reduce BG to the normal range by safely using more aggressive dosing. The 3D new model thus better characterises patient-specific response to insulin, and allows more optimal dosing, increasing performance and safety. [less ▲]

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See detailContinuous glucose monitoring in the ICU: Clinical considerations and consensus
Krinsley, J. S.; Chase, J. G.; Gunst, J. et al

in Critical Care (2017), 21(1),

Glucose management in intensive care unit (ICU) patients has been a matter of debate for almost two decades. Compared to intermittent monitoring systems, continuous glucose monitoring (CGM) can offer ... [more ▼]

Glucose management in intensive care unit (ICU) patients has been a matter of debate for almost two decades. Compared to intermittent monitoring systems, continuous glucose monitoring (CGM) can offer benefit in the prevention of severe hyperglycemia and hypoglycemia by enabling insulin infusions to be adjusted more rapidly and potentially more accurately because trends in glucose concentrations can be more readily identified. Increasingly, it is apparent that a single glucose target/range may not be optimal for all patients at all times and, as with many other aspects of critical care patient management, a personalized approach to glucose control may be more appropriate. Here we consider some of the evidence supporting different glucose targets in various groups of patients, focusing on those with and without diabetes and neurological ICU patients. We also discuss some of the reasons why, despite evidence of benefit, CGM devices are still not widely employed in the ICU and propose areas of research needed to help move CGM from the research arena to routine clinical use. © 2017 The Author(s). [less ▲]

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See detailA proof of concept study of acoustic sensing of lung recruitment during mechanical ventilation
Rodgers, G. W.; Lau Young, J. B.; Desaive, Thomas ULiege et al

in Biomedical Signal Processing and Control (2017), 32

Advancements in health technologies are crucial to support healthcare professionals, improve patient outcomes, and best utilize increasingly scarce and under-demand healthcare resources. This research ... [more ▼]

Advancements in health technologies are crucial to support healthcare professionals, improve patient outcomes, and best utilize increasingly scarce and under-demand healthcare resources. This research presents an initial proof-of-concept study of simple, non-invasive monitoring techniques used in Mechanical Ventilation (MV), which is the primary therapy for Acute Respiratory Distress Syndrome (ARDS). The high levels of inter-patient variability seen in patients with ARDS have resulted in much speculation about the ideal method of determining ventilation settings, such as tidal volume (Vt) and Positive End Expiratory Pressure (PEEP). One of the oldest and simplest methods is acoustic sensing of recruitment and lung condition. This project involves using a digital recording stethoscope to monitor the acoustic output of patients in the Intensive Care Unit (ICU) during mechanical lung ventilation. During lung recruitment, ‘crackles’ can be heard within the chest cavity with a stethoscope. These crackles vary significantly, depending on the status of the patient's respiratory system and are used as an indicator of the level of alveolar recruitment. This preliminary, proof-of-concept study focused on crackle detection and involved gathering sound samples from patients in the Christchurch Hospital ICU with evidence of crackles in the chest cavity. Frequency based analysis showed that crackles can be detected as emissions with higher power levels between 100 and 300 Hz (subject to patient variability). The ability to non-invasively record, detect and quantify the intensity of crackles could provide immediate feedback to clinicians and, in the long term, aid in the optimization of ventilator therapy. © 2016 Elsevier Ltd [less ▲]

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See detailEditorial
Chase, J. G.; Benyo, B.; Cinar, A. et al

in Control Engineering Practice (2017), 58

[No abstract available]

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