Stochastic Targeted Research Articles

Safety, efficacy and clinical generalization of the STAR protocol: a retrospective analysis.

BackgroundThe changes in metabolic pathways and metabolites due to critical illness result in a highly complex and dynamic metabolic state, making safe, effective management of hyperglycemia and hypoglycemia difficult. In addition, clinical practices can vary significantly, thus making GC protocols difficult to generalize across units.The aim of this study was to provide a retrospective analysis of the safety, performance and workload of the stochastic targeted (STAR) glycemic control (GC) protocol to demonstrate that patient-specific, safe, effective GC is possible with the STAR protocol and that it is also generalizable across/over different units and clinical practices.MethodsRetrospective analysis of STAR GC in the Christchurch Hospital Intensive Care Unit (ICU), New Zealand (267 patients), and the Gyula Hospital, Hungary (47 patients), is analyzed (2011–2015). STAR Christchurch (BG target 4.4–8.0 mmol/L) is also compared to the Specialized Relative Insulin and Nutrition Tables (SPRINT) protocol (BG target 4.4–6.1 mmol/L) implemented in the Christchurch Hospital ICU, New Zealand (292 patients, 2005–2007). Cohort mortality, effectiveness and safety of glycemic control and nutrition delivered are compared using nonparametric statistics.ResultsBoth STAR implementations and SPRINT resulted in over 86 % of time per episode in the blood glucose (BG) band of 4.4–8.0 mmol/L. Patients treated using STAR in Christchurch ICU spent 36.7 % less time on protocol and were fed significantly more than those treated with SPRINT (73 vs. 86 % of caloric target). The results from STAR in both Christchurch and Gyula were very similar, with the BG distributions being almost identical. STAR provided safe GC with very few patients experiencing severe hypoglycemia (BG < 2.2 mmol/L, <5 patients, 1.5 %).ConclusionsSTAR outperformed its predecessor, SPRINT, by providing higher nutrition and equally safe, effective control for all the days of patient stay, while lowering the number of measurements and interventions required. The STAR protocol has the ability to deliver high performance and high safety across patient types, time, clinical practice culture (Christchurch and Gyula) and clinical resources.

Generalizability of a Nonlinear Model-based Glycemic Controller

Critically ill patients exhibit a highly complex and dynamic metabolic state, making safe, effective management of hyperglycemia difficult. Clinical practices vary significantly, thus making glycemic control (GC) protocols difficult to generalize across units. This study examines the generalizability of a nonlinear, model-based GC protocol, STAR (Stochastic TARgeted), across two different intensive care units (ICUs) with very different practices and use of GC. Patient data from the ICUs at Christchurch Hospital, New Zealand (267 Patients) and Kalman Pandy Hospital, Hungary (47 Patients) are examined. Safety and performance are examined, where complete generalizability would be indicated by similar glycemic performance distributions using non-parametric statistics as appropriate. STAR spent over 86% of time in the target BG band of 4.4-8.0 mmol/L per-episode in both Christchurch and Gyula, with the BG (blood glucose) distributions being almost identical. STAR provided safe GC with very few patients experiencing mild hypoglycemia (< 5 patients, 1.5%). The nonlinear model-based STAR GC protocol delivered equivalent high performance and safety across patient types, time, clinical practice culture, and clinical resources.

The effects of ICU specific nutrition management, as a human factor by using Stochastic Targeted glycaemic control

The accurate glycaemic control in Intensive Care Units (ICUs) is a critical issue for physicians as a result of stress-induced blood glucose variability and high risk of hypoglycaemia. The STAR (Stochastic TARgeted) glycaemic control protocol is a model-based protocol, providing reduced risk of hypoglycaemia and stabilized blood glucose level by modulating insulin and nutrition inputs. The STAR framework is flexible, it allows clinicians to specify the target blood glucose range and the targeted nutrition intake of the patients (i.e. goal feed) according to the hospital’s feeding regime and the actual need of the patient. Thus, the differences in the clinical application of STAR in different hospitals can effect the clinical outcomes and the average delivered amount of nutritions during the patient’s treatment. This paper aims to analyse these effects by comparing the treatment outcomes of patients treated in two different hospitals, one in Christchurch, New Zealand and one in Gyula, Hungary.

Continuous Glucose Monitoring: Using CGM to Guide Insulin Therapy Virtual Trials Results

Continuous glucose monitoring (CGM) devices can measure blood glucose levels through interstitial measurements almost continuously (1-5min sampling period). However, they are not as accurate as glucose readings from blood measurements. The relation between tissue and blood glucose is dynamic and the sensor signal can degrade over time. In addition, CGM readings contains high frequency noise and can drift between measurements. However, maintaining continuous glucose monitoring has the potential to improve the level of glycemic control achieved and reduce nurse workload. For this purpose, a simple model was designed and tested to see the effect of inherent CGM error on the insulin therapy protocol, STAR (Stochastic TARgeted).An error model was generated from 9 patients that had one Guardian Real-Time CGM device (Medtronic Minimed, Northridge, CA, USA) inserted into their abdomen as part of an observation trial assesing the accuracy of CGM measurements compared to a blood gas analyser and glucometer readings. A resulting error model was then used to simulate the outcomes if the STAR protocol was guided by CGM values on 183 virtual patients. CGM alarms for hyper- and hypo-glycaemic region were included to improve patient safety acting as 'guardrails'. The STAR CGM protocol gave good performance and reduced workload by ~50%, reducing the number of measurements per day per patient from 13 to 7. The number of hypoglycaemic events increased compared to the current STAR from 0.03% <2.2mmol/L to 0.32%. However, in comparison to other published protocols it is still a very low level of hypoglycaemia and less than clinically acceptable value of 5% <4.0mmol/L. More importantly this study shows great promise for the future of CGM and their use in clinic. With the a newer generation of sensors, specifically designed for the ICU, promising less noise and drift suggesting that a reduced nurse workload without compromising safety or performance is with in reach.

Modelling Insulin Clearance and Degradation in Very Premature Infants

Inability to maintain blood glucose concentrations (BG) to a normal range is a common complication of prematurity and stress in neonatal intensive care units (NICUs). STAR (Stochastic TARgeted) glycaemic control uses physiological models to capture and predict patient glycaemic behaviour, and thus dose insulin safely and effectively for tight glycaemic control. STAR in the NICU has in past been based on a simple NICU model. In this study, clinical data is used to identify and validate insulin kinetic parameters of the more physiologically descriptive NICING (Neonatal Intensive Care Insulin-Nutrition-Glucose) model. C-peptide, plasma insulin and BG from a cohort of 41 extremely pre-term (median age 27.2 [26.2 - 28.7] weeks) and very low birth-weight infants (median birth weight 839 [735 - 1000] g) was used alongside C-peptide kinetic models to identify model parameters associated with insulin kinetics in the NICING model. These kinetic parameters in the NICING model were validated by fitting the model to a cohort of 160 glucose, insulin, and nutrition data records from extremely premature infants from two different NICUs. The model fits data and predicts changes in BG in a manner similar to the previous NICU model, indicating that it will perform safely and effectively in the clinical setting. However, the NICING model is more physiologically descriptive, capturing more insulin kinetics and allowing consistency with existing adult ICU glycaemic control protocols and other diabetes models.

Performance and Safety of STAR Glycaemic Control in Neonatal Intensive Care: Further Clinical Results Including Pilot Results from a New Protocol Implementation

Elevated blood glucose concentrations (BG) (Hyperglycaemia) are a common complication of prematurity in extremely low birth weight neonates in the neonatal intensive care unit (NICU), and are associated with increased mortality and morbidity. Insulin therapy allows glucose tolerance and weight gain to be increased. However, insulin therapy is commonly associated with a significant increase in low BG events (hypoglycaemia), which is also associated with adverse outcomes. Controlling BG levels via nutrition restriction reduces infant growth and is thus undesirable. STAR (Stochastic TARgeted) is a model-based glycaemic framework that mitigates the risks of hypoglycaemia through quantification of current insulin sensitivity and future variability. From August 2008 to December 2012 40 patients totaling 61 glycaemic episodes were treated with STAR in the NICU (STAR-NICU). Percentage time in the clinically targeted 4.0-8.0 mmol/L band was 62%, a 14% increase compared to retrospective data and hyperglycaemia (BG>10.0mmol/L) was halved. Overall incidence of severe hypoglycemia (BG<2.6 mmol/L) was slightly increased from 0.4% to 0.7%, and a concomitant increase in the number of patients experiencing severe hypoglycaemia (8 of 62 control episodes in comparison to 1 of 25 retrospective). Results from 5 patient episodes under the new STAR-GRYPHON protocol were significantly better, with 83% of measurements in the targeted band (35% better than retrospective data and 17% better than original STAR NICU protocols), and 0% incidence of BG<4.0 mmol/L. STAR-GRYPHON is expected to maintain this level of tight glycaemic control. Thus, model based control can be safely and effectively used to control BG in extremely premature infants in intensive care.

In silico assessment of a computerized model-based glycaemic control approach in a Belgian medical intensive care unit

Glycaemic control can be used to enhance critically ill patient outcome. This paper presents the in-silico design of a computerized model-based controller for a Belgian medical intensive care unit (CHU of Liege, Belgium). In silico trials are used to assess the current clinical protocol efficiency and safety and to compare this protocol with the existing Stochastic Targeted (STAR) control approach. The objective of this research is to optimize a glycaemic controller for its future clinical implementation and clinical workflow requirements. Results suggest that the currently used, paper-based sliding scale protocol is too general to achieve safe and effective glycaemic control. The computerized model-based protocol STAR leads to better glycaemic outcomes associated with increased safety. In particular, time in target band is higher than 80% with STAR targeting 90-150 mg/dL and 100-160 mg/dL. Time in the desired 100-150 mg/dL band is improved using STAR, and BG < 80 mg/dL is reduced. Results suggest that control targeting 100-160 mg/dL is associated with increased time in band and increased safety.

On the Problem of Patient-Specific Endogenous Glucose Production in Neonates on Stochastic Targeted Glycemic Control

Both stress and prematurity can induce hyperglycemia in the neonatal intensive care unit, which, in turn, is associated with worsened outcomes. Endogenous glucose production (EGP) is the formation of glucose by the body from substrates and contributes to blood glucose (BG) levels. Due to the inherent fragility of the extremely low birth weight (ELBW) neonates, true fasting EGP cannot be explicitly determined, introducing uncertainty into glycemic models that rely on quantifying glucose sources. Stochastic targeting, or STAR, is one such glycemic control framework. A literature review was carried out to gather metabolic and EGP values on preterm infants with a gestational age (GA) &lt;32 weeks and a birth weight (BW) &lt;2 kg. The data were analyzed for EGP trends with BW, GA, BG, plasma insulin, and glucose infusion (GI) rates. Trends were modeled and compared with a literature-derived range of population constant EGP models using clinically validated virtual trials on retrospective clinical data. No clear relationship was found for EGP and BW, GA, or plasma insulin. Some evidence of suppression of EGP with increasing GI or BG was seen. Virtual trial results showed that population-constant EGP models fit clinical data best and gave tighter control performance to a target band in virtual trials. Variation in EGP cannot easily be quantified, and EGP is sufficiently modeled as a population constant in the neonatal intensive care insulin-nutrition-glucose model. Analysis of the clinical data and fitting error suggests that ELBW hyperglycemic preterm neonates have unsuppressed EGP in the higher range than that seen in literature.

Nasogastric Aspiration as an Indicator for Feed Absorption in Model-Based Glycemic Control in Neonatal Intensive Care

STAR (stochastic targeted) is a glycemic control model-based framework for critically ill neonates that has shown benefits in reducing hypoglycemia and hyperglycemia. STAR uses a stochastic matrix method to forecast future changes in a patient's insulin sensitivity and then applies this result to a physiological model to select an optimal insulin treatment. Nasogastric aspiration may be used as an indicator to suggest periods of care when enteral feed absorption is compromised, improving the performance of glycemic control. An analysis has been carried out to investigate the effect of poorly absorbed feeds on glycemic control. Clinical data were collected from eight patients on insulin therapy and enteral feed, which included large or significantly milky aspirates. Patients had a median gestational age of 25 weeks and postnatal age of 5.5 days. Virtual patients were created using the NICING model, and insulin sensitivity (SI) profiles were fit. Alternative feed profiles were generated whereby enteral feed absorption was redistributed with time to account for poor feed absorption. The effect of poor feed absorption, as indicated by aspirates, is investigated. The average percentage change of SI 4 h before a significant aspirate was 1.16%, and 1.49% in the 4 h following the aspirate. No distinct relationship was found between the fractional change in SI and the volume of the aspirate. Accounting for aspirates had a clinically negligible impact on glycemic control in virtual trials. Accounting for aspirates by manipulating enteral feed profiles had a minimal influence on both modeling and controlling glycemia in neonates. The impact of this method is clinically insignificant, suggesting that a population constant for the rate of glucose absorption in the gut adequately models feed absorption within the STAR framework.

Development and optimisation of stochastic targeted (STAR) glycaemic control for pre-term infants in neonatal intensive care

Hyperglycaemia is a common complication of prematurity and stress in neonatal intensive care units (NICUs). It has been linked to worsened outcomes and mortality. There is currently no universally accepted best practice glycaemic control method, with many protocols lacking patient specificity or relying heavily on ad hoc clinical judgment from clinical staff who may be caring or overseeing care for several patients at once. The result is persistent hypoglycaemia and poor control. This research presents the virtual trial design and optimisation of a stochastic targeted (STAR) approach to improve performance and reduce hypoglycaemia. Clinically validated virtual trials based on NICU patient data (N=61 patients, 7006h) are used to develop and optimise a STAR protocol that improves on current STAR-NICU performance and reduce hypoglycaemia. Five approaches are used to maximise the stochastic range of BG outcomes within 4.0–8.0mmol/L, and are designed based on an overall cohort risk to provide clinically specified risk (5%) of BG above or below a clinically specified level. The best protocol placed the 5th percentile BG outcome for an intervention on 4.0mmol/L band. The optimised protocol increased %BG in the 4.0–8.0mmol/L band by 3.5% and the incidence of BG<2.6mmol/L by 1 patient (50%). Significant intra- and inter-patient variability limited possible performance gains so that they are unlikely to be clinically substantial, indicating a need for a further increase patient-specific or sub-cohort specific approaches to manage variability. This result sets the direction for future research.

STAR Development and Protocol Comparison

Accurate glycemic control (AGC) is difficult due to excessive hypoglycemia risk. Stochastic TARgeted (STAR) glycemic control forecasts changes in insulin sensitivity to calculate a range of glycemic outcomes for an insulin intervention, creating a risk framework to improve safety and performance. An improved, simplified STAR framework was developed to reduce light hypoglycemia and clinical effort, while improving nutrition rates and performance. Blood glucose (BG) levels are targeted to 80-145mg/dL, using insulin and nutrition control for 1-3h interventions. Insulin changes are limited to +3U/h and nutrition to ±30% of goal rate (minimum 30%). All targets and rate change limits are clinically specified and generalizable. Clinically validated virtual trials were run on using clinical data from 371 patients (39841h) from the Specialized Relative Insulin and Nutrition Tables (SPRINT) cohort. Cohort and per-patient results are compared to clinical SPRINT data, and virtual trials of three published protocols. Performance was measured as time within glycemic bands, and safety by patients with severe (BG < 40mg/dL) and mild (%BG < 72mg/dL) hypoglycemia. Pilot trial results from the first ten patients (1486h) are included to support the in-silico findings. In both virtual and clinical trials, mild hypoglycemia was below 2% versus 4% for SPRINT. Severe hypoglycemia was reduced from 14 (SPRINT) to 6 (STAR), and 0 in the pilot trial. AGC was tighter than both SPRINT clinical data and in-silico comparison protocols, with 91% BG within the specified target (80-145mg/dL) in virtual trials and 89.4% in pilot trials. Clinical effort (measurements) was reduced from 16.2/day to 11.8/day (13.5/day in pilot trials). This STAR framework provides safe AGC with significant reductions in hypoglycemia and clinical effort due to stochastic forecasting of patient variation-a unique risk-based approach. Initial pilot trials validate the in-silico design methods and resulting protocol, all of which can be generalized to suit any given clinical environment.

Stochastic Targeted (STAR) Glycemic Control: Design, Safety, and Performance

Tight glycemic control (TGC) has shown benefits but has been difficult to achieve consistently. STAR (Stochastic TARgeted) is a flexible, model-based TGC approach that directly accounts for intra- and interpatient variability with a stochastically derived maximum 5% risk of blood glucose (BG) below 72 mg/dl. This research assesses the safety, efficacy, and clinical burden of a STAR TGC controller modulating both insulin and nutrition inputs in virtual and clinical pilot trials. Clinically validated virtual trials using data from 370 patients in the SPRINT (Specialized Relative Insulin and Nutrition Titration) study were used to design the STAR protocol and test its safety, performance, and required clinical effort prior to clinical pilot trials. Insulin and nutrition interventions were given every 1-3 h as chosen by the nurse to allow them to manage workload. Interventions were designed to maximize the overlap of the model-predicted (5-95(th) percentile) range of BG outcomes with the 72-117 mg/dl band and thus provide a maximum 5% risk of BG <72 mg/dl. Interventions were calculated using clinically validated computer models of human metabolism and its variability in critical illness. Carbohydrate intake (all sources) was selected to maximize intake up to 100% of the American College of Chest Physicians/Society of Critical Care Medicine (ACCP/SCCM) goal (25 kg/kcal/h). Insulin doses were limited (8 U/h maximum), with limited increases based on current rate (0.5-2.0 U/h). Initial clinical pilot trials involved 3 patients covering ~450 h. Approval was granted by the Upper South A Regional Ethics Committee. Virtual trials indicate that STAR provides similar glycemic control performance to SPRINT with 2-3 h (maximum) measurement intervals. Time in the 72-126 mg/dl and 72-145 mg/dl bands was equivalent for all controllers, indicating that glycemic outcome differences between protocols were only shifted in this range. Safety from hypoglycemia was improved. Importantly, STAR using 2-3 h (maximum) intervention intervals reduced clinical burden up to 30%, which is clinically very significant. Initial clinical trials showed glycemic performance, safety, and management of inter- and intrapatient variability that matched or exceeded the virtual trial results. In virtual trials, STAR TGC provided tight control that maximized the likelihood of BG in a clinically specified glycemic band and reduced hypoglycemia with a maximum 5% (or lower) expected risk of light hypoglycemia (BG <72 mg/dl) via model-based management of intra- and interpatient variability. Clinical workload was self-managed and reduced up to 30% compared with SPRINT. Initial pilot clinical trials matched or exceeded these virtual results.

Development and Pilot Trial Results of Stochastic Targeted (STAR) Glycemic Control in a Medical ICU

Accurate glycemic control (AGC) is difficult due to excessive hypoglycemia risk. Stochastic TARgeted (STAR) glycemic control forecasts changes in insulin sensitivity to calculate a range of glycemic outcomes for an insulin intervention, creating a risk framework to improve safety and performance. An improved, simplified STAR framework was developed to reduce light hypoglycemia and clinical effort, while improving nutrition rates and performance. Blood glucose (BG) levels are targeted to 80 – 145mg/dL, using insulin and nutrition control for 1-3 hour interventions. Insulin changes are limited to +3U/hour and nutrition to ±30% of goal rate (minimum 30%). All targets and rate change limits are clinically specified and generalizable. Clinically validated virtual trials were run using clinical data from 371 patients (39,841hours) from the SPRINT cohort. Cohort and per-patient results are compared to clinical SPRINT data. Performance was measured as time within glycemic bands, and safety by patients with severe (BG<40mg/dL) and mild (%BG<72mg/dL) hypoglycemia. Pilot trial results from the first 10 patients (1,458 hours) are included to support the in-silico findings. In both virtual and clinical trials, mild hypoglycemia was below 2% versus 4% for SPRINT. Severe hypoglycemia was reduced from 14 (SPRINT) to 6 (STAR), and 0 in the pilot trial. BG results tighter than SPRINT clinical data, with 91.6% BG within the specified target (80–145mg/dL) in virtual trials and 89.4% in pilot trials. Clinical effort (measurements) was reduced from 16.1/day to 11.8/day (13.5/day in pilot trials). This STAR framework provides safe, accurate glycemic control with significant reductions in hypoglycemia and clinical effort due to stochastic forecasting of patient variation – a unique risk-based approach. Initial pilot trials validate the in silico design methods and resulting protocol, all of which can be generalized to suit any given clinical environment.

Development and optimisation of stochastic targeted (STAR) glycaemic control for neonatal intensive care

Hyperglycaemia is a common complication of prematurity and stress in neonatal intensive care units (NICUs). It has been linked to worsened outcomes and mortality. There is currently no universally accepted best practice glycaemic control method, with many protocols lacking patient specificity and relying heavily on clinical judgment. The result is persistent hypoglycaemia and poor control. This research presents the virtual trial design and optimisation of a stochastic targeted (STAR) approach to improve performance and reduce hypoglycaemia. Clinically validated virtual trials based on NICU patient data (N = 61 patients, 7006 hours) are used to develop and optimise a STAR protocol that improves on current STAR-NICU performance and reduce hypoglycaemia. Five approaches are used to maximize the stochastic range of BG outcomes within 4.0-8.0mmol/L, and are designed based on an overall cohort risk to provide clinically specified risk (5%) of BG above or below a clinically specified level. The best protocol placed the 5 th percentile BG outcome for an intervention on 4.0mmol/L band. The optimised protocol increased %BG in the 4.0-8.0mmol/L band by 3.5% and decreased the incidence of BG<2.6mmol/L by 1 patient (50%). Significant intra- and inter- patient variability reduced possible performance gains, indicating a need for patient-specific or sub-cohort specific approaches to manage variability.

Pilot proof of concept clinical trials of Stochastic Targeted (STAR) glycemic control

IntroductionTight glycemic control (TGC) has shown benefits but has been difficult to achieve consistently. STAR (Stochastic TARgeted) is a flexible, model-based TGC approach directly accounting for intra- and inter- patient variability with a stochastically derived maximum 5% risk of blood glucose (BG) < 4.0 mmol/L. This research assesses the safety, efficacy, and clinical burden of a STAR TGC controller modulating both insulin and nutrition inputs in pilot trials.MethodsSeven patients covering 660 hours. Insulin and nutrition interventions are given 1-3 hourly as chosen by the nurse to allow them to manage workload. Interventions are calculated by using clinically validated computer models of human metabolism and its variability in critical illness to maximize the overlap of the model-predicted (5-95th percentile) range of BG outcomes with the 4.0-6.5 mmol/L band while ensuring a maximum 5% risk of BG < 4.0 mmol/L. Carbohydrate intake (all sources) was selected to maximize intake up to 100% of SCCM/ACCP goal (25 kg/kcal/h). Maximum insulin doses and dose changes were limited for safety. Measurements were made with glucometers. Results are compared to those for the SPRINT study, which reduced mortality 25-40% for length of stay ≥3 days. Written informed consent was obtained for all patients, and approval was granted by the NZ Upper South A Regional Ethics Committee.ResultsA total of 402 measurements were taken over 660 hours (~14/day), because nurses showed a preference for 2-hourly measurements. Median [interquartile range, (IQR)] cohort BG was 5.9 mmol/L [5.2-6.8]. Overall, 63.2%, 75.9%, and 89.8% of measurements were in the 4.0-6.5, 4.0-7.0, and 4.0-8.0 mmol/L bands. There were no hypoglycemic events (BG < 2.2 mmol/L), and the minimum BG was 3.5 mmol/L with 4.5% < 4.4 mmol/L. Per patient, the median [IQR] hours of TGC was 92 h [29-113] using 53 [19-62] measurements (median, ~13/day). Median [IQR] results: BG, 5.9 mmol/L [5.8-6.3]; carbohydrate nutrition, 6.8 g/h [5.5-8.7] (~70% goal feed median); insulin, 2.5 U/h [0.1-5.1]. All patients achieved BG < 6.1 mmol/L. These results match or exceed SPRINT and clinical workload is reduced more than 20%.ConclusionsSTAR TGC modulating insulin and nutrition inputs provided very tight control with minimal variability by managing intra- and inter- patient variability. Performance and safety exceed that of SPRINT, which reduced mortality and cost in the Christchurch ICU. The use of glucometers did not appear to impact the quality of TGC. Finally, clinical workload was self-managed and reduced 20% compared with SPRINT.

First pilot trial of the STAR-Liege protocol for tight glycemic control in critically ill patients

Tight glycemic control (TGC) has shown benefits in ICU patients, but been difficult to achieve consistently due to inter- and intra- patient variability that requires more adaptive, patient-specific solutions. STAR (Stochastic TARgeted) is a flexible model-based TGC framework accounting for patient variability with a stochastically derived maximum 5% risk of blood glucose (BG) below 72mg/dL. This research describes the first clinical pilot trial of the STAR approach and the post-trial analysis of the models and methods that underpin the protocol.The STAR framework works with clinically specified targets and intervention guidelines. The clinically specified glycemic target was 125mg/dL. Each trial was 24h with BG measured 1–2hourly. Two-hourly measurement was used when BG was between 110–135mg/dL for 3h. In the STAR approach, each intervention leads to a predicted BG level and outcome range (5–95th percentile) based on a stochastic model of metabolic patient variability. Carbohydrate intake (all sources) was monitored, but not changed from clinical settings except to prevent BG<100mg/dL when no insulin was given. Insulin infusion rates were limited (6U/h maximum), with limited increases based on current infusion rate (0.5–2.0U/h), making this use of the STAR framework an insulin-only TGC approach. Approval was granted by the Ethics Committee of the Medical Faculty of the University of Liege (Liege, Belgium).Nine patient trials were undertaken after obtaining informed consent. There were 205 measurements over all 9 trials. Median [IQR] per-patient results were: BG: 138.5 [130.6–146.0]mg/dL; carbohydrate administered: 2–11g/h; median insulin:1.3 [0.9–2.4]U/h with a maximum of 6.0 [4.7–6.0]U/h. Median [IQR] time in the desired 110–140mg/dL band was: 50.0 [31.2–54.2]%. Median model prediction errors ranged: 10–18%, with larger errors due to small meals and other clinical events. The minimum BG was 63mg/dL and no other measurement was below 72mg/dL, so only 1 measurement (0.5%) was below the 5% guaranteed minimum risk level.Post-trial analysis showed that patients were more variable than predicted by the stochastic model used for control, resulting in some of the prediction errors seen. Analysis and (validated) virtual trial re-simulating the clinical trial using stochastic models relevant to the patient's particular day of ICU stay were seen to be more accurate in capturing the observed variability. This analysis indicated that equivalent control and safety could be obtained with similar or lower glycemic variability in control using more specific stochastic models.STAR effectively controlled all patients to target. Observed patient variability in response to insulin and thus prediction errors were higher than expected, likely due to the recent insult of cardiac surgery or a major cardiac event, and their immediate recovery. STAR effectively managed this variability with no hypoglycemia. Improved stochastic models will be used to prospectively test these outcomes in further ongoing clinical pilot trials in this and other units.