Hypothermia is often used to treat out of hospital cardiac arrest (OHCA) patients who often simultaneously receive insulin for stress induced hyperglycaemia.Variations in response to insulin reflect dynamic changes in insulin sensitivity (SI), defined by the overall metabolic response to stress and therapy.Thus, tracking and forecasting this parameter is important to provide safe glycaemic control in highly dynamic patients.This study examines stochastic forecasting models of model-based SI variability in OHCA patients to assess the resulting potential impact of this therapy on glycaemic control quality and safety.A retrospective analysis of clinically validated model-based SI profiles identified using data from 240 post-cardiac arrest patients (9988 h) treated with hypothermia, shortly after admission in the Intensive Care Unit (ICU).Data were divided into three periods: (1) Cool (T$35EC), (2) Idle period of 2 h as hypothermia was removed and (3) Warm (T$37EC).The stochastic model captured 60.7 and 90.2% of SI predictions within the (25-75th) and (5-95th) probability forecast intervals during cool period.Equally, it is also recorded 62.8 and 92.1% of SI predictions respectively during the warm period.Maintaining the kernel density variance estimator to c = 1.0 yielded 60.7 and 90.2% for the cool period.Similarly, adjusting a variance estimator of c = 2.0 yields 60.4 and 90.1% for the warm period.A cohort-specific stochastic model of SI provided a conservative forecast for the inter-quartile range and was relatively exact for the 90% range.Adjusting the variance estimator provides a more accurate, cohort-speciWc stochastic model of SI dynamics for the 90% range.These latter results show clearly different levels and distribution of forecasted S I variability between the cold and warm periods.

10.3923/jbs.2016.256.264

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Pilot Trials of the STAR TGC Protocol in a Cardiac Surgery ICU

A.J. LeCompte Sophie Penning Katherine T. Moorhead Paul Massion Jean‐Charles Preiser

Method: The glycemic target was 125mg/dL. Each trial was 24 hours with BG measured 1-2 hourly. Two-hourly measurement was used when BG was between 110-135mg/dL for 3 hours. Each intervention leads to a predicted BG level and outcome range (5-95 percentile). Carbohydrate intake (all sources) was monitored, but not changed from clinical settings except to prevent BG < 100mg/dL. Insulin infusion rates were limited (6U/hour maximum), with limited increases based on current infusion rate (0.5-2.0U/hour). Approval was granted by the Ethics Committee of the Medical Faculty of the University of Liege (Liege, Belgium).

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Concurrent Continuous Glucose Monitoring in Critically Ill Patients: Interim Results and Observations

Journal of Diabetes Science and Technology (2013)

Matthew Signal Liam Fisk Geoffrey M. Shaw J. Geoffrey Chase

Hyperglycemia in intensive care is a prevalent, much debated problem. Glycemic control (GC) can reduce mortality,1 but in some cases, GC has also increased hypoglycemia.2 Continuous glucose monitoring (CGM), with 1–5 min measurements, has the potential to aid GC and provide early warning of potential hypoglycemia.3 Continuous glucose monitoring could also ease nursing burden by reducing blood glucose (BG) measurements to 3–4 per day for CGM device calibration.4 Off-the-shelf CGM devices are currently being trialed in the Christchurch Hospital intensive care unit. Each patient has a Medtronic Guardian Real-Time and a Medtronic iPro2 (retrospective) CGM placed on their abdomen and a second Medtronic iPro2 on their thigh. This configuration allows assessment of interdevice and intersite variability in sensor glucose (SG), and 12–14 independent BG measurements per day provide a comparator. This preliminary analysis uses data from 10 recruited patients. The median (interquartile range) BG levels were 124 (112–137) mg/dl, indicating patients were well controlled. The mean absolute relative difference for the abdomen Guardian SG data was 24.0%, compared with 11.8% and 12.4% for the abdomen and thigh iPro2 SG data, respectively. However, some of the error can be attributed to the reference BG measurements from point-of-care glucometers. Comparing intersite discrepancies in SG to interdevice discrepancies in SG, our data suggest that the CGM device type and thus calibration has a larger impact on observed performance than sensor site. Overall, the reliability/accuracy of CGM devices can vary between patients, and performance could be influenced by illnesses or drugs/therapies among other factors. A particularly interesting observation from this study was a patient with severe edema who had approximately 18 liters of additional fluid located primarily in the abdominal region. Sensor insertion was difficult due to fluid seeping from the insertion site, and two abdominal sensors failed to adhere to the skin (one of which was replaced). The top plot in Figure 1 shows the SG and BG data collected from this patient. However, the main focus of this discussion is the lower plot, which shows the raw current (ISIG) from each sensor. During the first few days, abdominal ISIG is much lower than the thigh, where there was much less excess fluid. As patient condition improved and excess fluid decreased, abdominal sensor ISIG rose to match the thigh sensor, and they tracked each other well for the remainder of monitoring. However, these observations could be due to other factors, such as the sensor itself or drugs/therapies. Further investigation with a larger cohort containing patients with severe edema is required to determine whether or not it has a major effect on CGM performance. Figure 1. The SG and ISIG data from two CGM devices monitoring a patient with severe edema. This limited data set and preliminary analysis indicates that CGM devices have the potential to improve GC in critically ill patients. Such improvements include using SG measurements to drive insulin therapy and/or using SG data for hypoglycemia detection and alarming. However, further understanding of the clinical factors that affect CGM performance is needed before improvements to GC can be realized.

Interim

Blood Glucose Self-Monitoring

10.1177/193229681300700629

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Relationship between Stroke Volume and Pulse Wave Velocity

IFAC-PapersOnLine (2015)

Shun Kamoi Christopher G. Pretty Yeong Shiong Chiew Shaun Davidson Antoine Pironet

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.

Stroke

10.1016/j.ifacol.2015.10.153

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Complexity of Continuous Glucose Monitoring Data in Critically Ill Patients: CGM Devices, Sensor Locations, and DFA Methods

Matthew Signal Felicity Thomas Geoffrey M. Shaw J. Geoffrey Chase

BACKGROUND: Critically ill patients often experience high levels of insulin resistance and stressinduced hyperglycemia, which may negatively impact outcomes. However, evidence surrounding the causes of negative outcomes remains inconclusive. Continuous glucose monitoring (CGM) devices allow researchers to investigate glucose complexity, using detrended fluctuation analysis (DFA), to determine whether it is associated with negative outcomes. AIM: The aim of this study was to investigate the effects of CGM device type/calibration and CGM sensor location on results from DFA. METHODS: This study uses CGM data from critically ill patients who were each monitored concurrently using Medtronic iPro2’s on the thigh and abdomen, and a Medtronic Guardian RealTime on the abdomen. This allowed inter-device/calibration type and inter-sensor site variation to be assessed. DFA is a technique that has previously been used to determine the complexity of CGM data in critically ill patients. Two variants of DFA, monofractal and multifractal, were used to assess the complexity of sensor glucose (SG) data, as well as the pre-calibration raw sensor current. Monofractal DFA produces a scaling exponent (H), where H is inversely related to complexity. The results of multifractal DFA are presented graphically, by the multifractal spectrum. RESULTS: From the 10 patients recruited, 26 CGM devices produced data suitable for analysis. The values of H from abdominal iPro2 data were 0.10 [0.03 – 0.20] higher than those from Guardian Real-Time data, indicating consistently lower complexities in iPro2 data. However, repeating the analysis on the raw sensor current showed little or no difference in complexity. Sensor site had little effect on the scaling exponents in this data set. Finally, multi-fractal DFA revealed no significant associations between the multifractal spectrums and CGM device type/calibration or sensor location. CONCLUSIONS: Monofractal DFA results are dependent on the device/calibration used to obtain CGM data, but sensor location has little impact. Future studies of glucose complexity should consider the findings presented here when designing their investigations. Abbreviations: BG – blood glucose CGM – continuous glucose monitoring FDA – Food and Drug Administration DFA – detrended fluctuation analysis ICU – intensive care unit STAR – Stochastic TARgeted SG – sensor glucose BGA – blood gas analyser IQR – inter-quartile range MARD – mean absolute relative dfference

Multifractal system

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Benchtop to bedside to worldwide: Implementing model-based glycemic control in intensive care

A.J. LeCompte Geoffrey M. Shaw J. Geoffrey Chase

Hyperglycaemia is a common physiological response in critically ill patients, and reflects the perturbed metabolic state associated with severe illness. Regulating blood glucose (BG) levels to pre-ICU concentrations may provide patients with a greater chance of survival and reduced complications. However, despite the potential benefits there is still no universally adopted method for regulating BG levels in the ICU, and several large trials have failed to provide a consistent level of BG regulation across multiple centers. Models of the glucose regulatory system together with specialized controllers can assist clinical staff in therapy decisions by optimizing insulin and nutrition dosing. These systems can be readily implemented using existing or commodity equipment. This article presents experiences in implementing such model-based BG control in eight studies across four clinical units in three countries and highlights challenges faced when translating control systems from design and simulation environments to daily bedside clinical usage. Several practical issues need to be addressed for successful clinical implementation. Patient response to glucose and insulin inputs needs to be characterized, and it has been observed that level of insulin response varies significantly between patients and within patients over time. Clinically desired target ranges for BG control often vary by clinic and by year, and thus control schemes are required to adapt. Finally, the design of the system interface plays an important role in merging with local clinical practices and achieving nursing support for the system. Considerable variation exists, not only in the types of patients and observed responses to treatment, but also in the provision of clinical treatment. Thus a balance is required between flexibility and complexity to reduce training time and costs, improve transparency and promote independent clinical uptake.

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Predicting Pulmonary Distension in a Virtual Patient Model for Mechanical Ventilation

IFAC-PapersOnLine (2021)

Qianhui Sun J. Geoffrey Chase Cong Zhou Merryn H. Tawhai Jennifer L. Knopp

Recruitment maneuvers (RMs) following with positive-end-expiratory-pressure (PEEP) have proved effective in recruiting lung volume and preventing alveoli collapse. To date, standards for optimal patient-specific PEEP are unknown, resulting in variability in care and reduced outcomes, both indicating the need for personalized care. This research extends a well-validated virtual patient model by adding novel elements to model, which is able to utilize bedside available respiratory data, without increasing modelling complexity, to predict patient-specific lung distension and thus to minimise barotrauma risk. Prediction accuracy and robustness are validated against clinical data from 18 volume controlled ventilation (VCV) patients at 7 different baseline PEEP levels (0 to 12cmH2O), where predictions were made up to 12cmH2O of PEEP ahead. Using an exponential basis function set for prediction yields an absolute median peak inspiratory pressure prediction error of 1.50cmH2O for 623 prediction cases. Comparing predicted and clinically measured distension prediction in VCV demonstrated consistent, robust high accuracy with R2=0.90 (623 predictions), which is a measurable improvement in prediction error compared to predictions without using the proposed distension function (R2=0.82). Moreover, the R2 value increases to 0.93-0.95 if only clinically relevant ΔPEEP steps (2-6cmH2O) are considered with an overall median absolute error in peak pressure prediction of 1.04cmH2O. Overall, the results demonstrate the potential and significance for accurately capturing distension mechanics, allowing better risk assessment, as well as extending and more fully validating this virtual mechanical ventilation patient model.

Robustness

10.1016/j.ifacol.2021.10.237

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