Glycemic Control Protocol Comparison using Virtual Trials

DTM2011 handbook/programme is given in files and also available as a hard copyBackground: Several accurate glycemic control (AGC) protocols for critical care patients exist but making comparisons is very hard. Objective: This study uses clinically validated virtual patient methods to compare safety and performance for several published AGC protocols. Method: Clinically validated virtual trials were run on 371 patients (39,481 hours, 26,646 measurements) created from the SPRINT AGC cohort. For protocols that do not modulate feed rates enteral nutrition was held at 100% of ACCP goal (25kcal/kg/day) when the patients were clinically fed, and parenteral nutrition rates were matched to clinical data. Performance was defined as %BG within glycemic bands and BG measurement frequency. Safety was defined as the incidence of severe (number patients with BG<40mg/dL) and moderate (%BG<72mg/dL) hypoglycemia. Clinical data from SPRINT is also compared. Results: Clinical SPRINT performance data matched re-simulated SPRINT with 86% vs. 86% BG in 80-145mg/dL, 2.00% vs. 2.07% BG above 180mg/dL and 7.83% vs. 7.29% BG below 72mg/dL, with 14 measurements (over 8 patients) of BG<40mg/dL. Yale results were 83.5%, 3.20%, 5.18%, with 6 severe hypoglycemic patients, using 37,961 measurements (23.0/day). Glucontrol had 75.2%, 3.70%, 9.45%, 52 cases and 26,199 measurements (15.8/day). Braithwaite had 84.2%, 3.00%, 4.22%, 19 cases and 24,396 measurements (14.8/day). The STAR (Stochastic TARgeted) model-based method had 90.6%, 1.67%, 1.33%, 5 cases and 20,591 measurements (12.3/day). Conclusions: Virtual trials provided an effective comparison across protocols with different target bands/values and different clinical cohorts. The model-based STAR protocol provided the best management of patient variability yielding the best performance and safety

Impact of calibration algorithms on hypoglycaemia detection in newborn infants using continuous glucose monitors

invited, 6-pagesNeonatal hypoglycaemia is a common condition that can cause seizures and serious brain injury in infants. It is diagnosed by blood glucose (BG) measurements, often taken several hours apart. Continuous glucose monitoring (CGM) devices can potentially improve hypoglycaemia detection, while reducing the number of BG measurements. Calibration algorithms convert the sensor signal into the CGM output. Thus, these algorithms can have a direct impact on measures used to quantify excursions from normal glycaemic levels. The aim of this study was to quantify the effects of calibration sensor error and non-linear filtering of CGM data on measures of hypoglycaemia (defined as BG < 2.6mmol/L) in neonates. CGM data was recalibrated using an algorithm that explicitly recognised the high accuracy of BG measurements available in this study. Median filtering was also implemented either before or after recalibration. Results for the entire cohort show an increase in the total number of hypoglycaemic events (161 to 193), duration of hypoglycaemia (2.2 to 2.6% of total data), and hypoglycaemic index (4.9 to 7.1µmol/L) after recalibration. With the addition of filtering, the number of hypoglycaemic events was reduced (193 to 131), with little or no change to the other metrics. These results show how reference sensor error and thus calibration algorithms play a significant role in quantifying hypoglycaemia. In particular, metrics such as counting the number of hypoglycaemic events were particularly sensitive to recalibration and filtering effects. While this conclusion might be expected, its potential impact is quantified here, in this case for at-risk neonates for whom hypoglycaemia carries potential long-term negative outcomes

Assessing microcirculation function using a pulse oximeter

1-pageSepsis patients normally suffer microcirculatory dysfunction, which results in organ failure and increased risk of death [1]. Importantly, microcirculatory distress is the only independent factor for predicting patient outcome if it is not treated within 48 hours [2]. Analyzing oxygen transport and utilization could potentially yield insight on microcirculation function to improve sepsis diagnosis. A pulse oximeter is used to extract information on the absorption of red (R) and infrared (IR) light. The IR signal is related to the overall blood volume, (HbO2 + Hb) and the R signal is related to the amount of reduced hemoglobin, (Hb). Differences between these two signals, measured in real-time, thus represent the amount of oxygenated hemoglobin, HbO2 and can be used to estimate microcirculation function and extraction. Healthy individuals participated in vascular occlusion tests (VOT, N=15) and physical exercise (PE, N=10), respectively. These tests artificially create changes in extraction, and are used to prove the concept. This study was approved by the University of Canterbury Human Ethics Committee, Christchurch, New Zealand. In VOT, both red (R) and infrared (IR) signals rapidly decreased during vascular occlusion since less blood flows into the occluded area. During ischemia, the IR signals are almost constant and both the R and IR signals immediately increased after the occlusion is release, as a large amount of blood flows into the occluded area. Median oxygen extraction increases from 39% during rest to 46.5% during following intense exercise. For further (repeated) PE tests yielded extraction of 45%, 44.2%, 45.5% and 44.2%. However, the increased rate varies across subjects showing significant inter-subject variability. The pulse oximeter sensor concept presented is capable of extracting valuable information to assess microcirculation condition. Implementing this concept on ICU patients has the potential to aid sepsis diagnosis and provide more accurate tracking of patient state, sepsis status and response to treatment. [1] P. E. Spronk, D. F. Zandstra, and C. Ince. Bench-to-bedside review: sepsis is a disease of the microcirculation. Crit Care, 8:462–468, Dec 2004. [2] Y. Sakr, M. J. Dubois, D. De Backer, J. Creteur, and J. L. Vincent. Persistent microcirculatory alterations are associated with organ failure and death in patients with septic shock. Crit. Care Med., 32:1825–1831, Sep 2004

Impact of glucocorticoids on insulin resistance in the critically ill

Glucocorticoids (GCs) have been shown to reduce insulin sensitivity in healthy individuals. Widely used in critical care to treat a variety of inflammatory and allergic disorders, they may inadvertently exacerbate stress-hyperglycaemia. This research uses model-based methods to quantify the reduction of insulin sensitivity from GCs in critically ill patients, and thus their impact on glycaemic control. A clinically validated model-based measure of insulin sensitivity (SI) was used to quantify changes between two matched cohorts of 40 intensive care unit (ICU) patients who received GCs and a control cohort who did not. All patients were admitted to the Christchurch hospital ICU between 2005 and 2007 and spent at least 24 hours on the SPRINT glycaemic control protocol. A 31% reduction in whole-cohort median insulin sensitivity was seen between the control cohort and patients receiving glucocorticoids with a median dose equivalent to 200mg/day of hydrocortisone per patient. Comparing percentile-patients as a surrogate for matched patients, reductions in median insulin sensitivity of 20, 25, and 21% were observed for the 25th, 50th and 75th-percentile patients. All these cohort and per-patient reductions are less than or equivalent to the 30-62% reductions reported in healthy subjects especially when considering the fact that the GC doses in this study are 1.3-4 times larger than those in studies of healthy subjects. This reduced suppression of insulin sensitivity in critically ill patients could be a result of saturation due to already increased levels of catecholamines and cortisol common in critically illness. Virtual trial simulation showed that reductions in insulin sensitivity of 20-30% associated with glucocorticoid treatment in the ICU have limited impact on glycaemic control levels within the context of the SPRINT protocol

Development of a Clinical Type 1 Diabetes Metabolic System Model and in Silico Simulation Tool

Invited journal symposium paperObjectives: To develop a safe and effective protocol for the clinical control of Type 1 diabetes using conventional self-monitoring blood glucose (SMBG) measurements, and multiple daily injection (MDI) with insulin analogues. To develop an in silico simulation tool of Type 1 diabetes to predict long-term glycaemic control outcomes of clinical interventions. Methods: The virtual patient method is used to develop a simulation tool for Type 1 diabetes using data from a Type 1 diabetes patient cohort (n=40). The tool is used to test the adaptive protocol (AC) and a conventional intensive insulin therapy (CC) against results from a representative control cohort. Optimal and suboptimal basal insulin replacement are evaluated as a function of self-monitoring blood glucose (SMBG) frequency in conjunction with the (AC and CC) prandial control protocols. Results: In long-term glycaemic control, the AC protocol significantly decreases HbA1c in conditions of suboptimal basal insulin replacement for SMBG frequencies =6/day, and reduced the occurrence of mild and severe hypoglycaemia by 86-100% over controls over all SMBG frequencies in conditions of optimal basal insulin. Conclusions: A simulation tool to predict long-term glycaemic control outcomes from clinical interventions is developed to test a novel, adaptive control protocol for Type 1 diabetes. The protocol is effective and safe compared to conventional intensive insulin therapy and controls. As fear of hypoglycaemia is a large psychological barrier to glycaemic control, the AC protocol may represent the next evolution of intensive insulin therapy to deliver increased glycaemic control with increased safety. Further clinical or experimental validation is needed to fully prove the concept

Overview of Glycemic Control in Critical Care - Relating Performance and Clinical Results

Inagural review article invited for inaugural journalBackground: Hyperglycemia is prevalent in critical care and tight control can save lives. Current ad-hoc clinical protocols require significant clinical effort and produce highly variable results. Model-based methods can provide tight, patient specific control, while addressing practical clinical difficulties and dynamic patient evolution. However, tight control remains elusive as there is not enough understanding of the relationship between control performance and clinical outcome. Methods: The general problem and performance criteria are defined. The clinical studies performed to date using both ad-hoc titration and model-based methods are reviewed. Studies reporting mortality outcome are analysed in terms of standardized mortality ratio (SMR) and a 95th percentile (±2 ) standard error (SE95%) to enable better comparison across cohorts. Results: Model-based control trials lower blood glucose into a 72-110mg/dL band within 10 hours, have target accuracy over 90%, produce fewer hypoglycemic episodes, and require no additional clinical intervention. Plotting SMR versus SE95% shows potentially high correlation (r=0.84) between ICU mortality and tightness of control. Summary: Model-based methods provide tighter, more adaptable “one method fits all” solutions, using methods that enable patient-specific modeling and control. Correlation between tightness of control and clinical outcome suggests that performance metrics, such as time in a relevant glycemic band, may provide better guidelines. Overall, compared to current “one size fits all” sliding scale and ad-hoc regimens, patient-specific pharmacodynamic and pharmacokinetic model-based, or “one method fits all”, control, utilizing computational and emerging sensor technologies, offers improved treatment and better potential outcomes when treating hyperglycemia in the highly dynamic critically ill patient

SPRINT, STAR: Két Hatékony Kezelési Protokoll Az Intenzív Osztályon Kezelt Betegek Vércukorszintjének Normoglikémiás Tartományban Tartásához

(In Hungarian) Paper P0

Glargine as a Basal Insulin Supplement in Recovering Critically Ill Patients - An In Silico Study

Tight glycaemic control is now benefiting medical and surgical intensive care patients by reducing complications associated with hyperglycaemia. Once patients leave this intensive care environment, less acute wards do not continue to provide the same level of glycaemic control. Main reason is that these less acute wards do not have the high levels of nursing resources to provide the same level of glycaemic control. Therefore developments in protocols that are less labour intensive are necessary. This study examines the use of insulin glargine for basal supplement in recovering critically ill patients. These patients represent a group who may benefit from such basal support therapy. In silico study results showed the potential in reducing nursing effort with the use of glargine. However, a protocol using only glargine for glucose control did not show to be effective in the simulated patients. This may be an indication that a protocol using only glargine is more suitable after discharge from critical care

Endogenous insulin secretion in critically ill patients

1-pageGlucose-insulin system models can be used for improved glycemic control of critically ill patients. A key component of glucose-insulin models is pancreatic insulin secretion. There is limited data in the literature quantifying insulin secretion in critically ill patients at physiologic levels. This study presents a model pancreatic insulin secretion in critically ill patients based on data from a critically ill population

Pilot Trials of STAR Target to Range Glycemic Control

ESICM 2011 programme is available in files INTRODUCTION. Tight glycemic control (TGC) has shown benefits in cardiac surgery ICU patients. STAR (Stochastic TARgeted) is a flexible, model-based TGC protocol accounting for patient variability with a stochastically derived maximum 5% risk of blood glucose (BG) below 90 mg/dL. OBJECTIVES. To assess the safety, efficacy and clinical workload of the STAR TGC controller in pilot trials