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

Abstract

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. Methods: 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.

mAGiC DRAGONS: A Protocol for Accurate Glycaemic Control in General Wards

“…Four of the patients were treated exclusively with subcutaneous insulin as part of an on-going trial, 30 patients had been treated by the SPRINT table-based protocol (Specialized Relative Insulin and Nutrition Tables) (Chase et al, 2008b) and the remaining 29 patients were treated with the tabletbased STAR protocol (Stochastic TARgeted) (Evans et al, 2012). Cohort demographics are presented in Table 1.…”

“…Hyperglycaemia worsens outcomes leading to further risk of complication, including sepsis (Bistrian, 2001), myocardial infarction (Capes et al, 2000), polyneuropathy, and multiple organ failure (van den Berghe et al, 2001). Adaptive model-based protocols for accurate glycaemic control (AGC) that modulate insulin and nutrition have shown considerable promise in the intensive care unit (ICU) (Chase et al, 2008b, Chase et al, 2005, Evans et al, 2012, Hovorka et al, 2007, Kulnik et al, 2008), but are not suitable for less acute wards as they can be relatively demanding on clinical staff.…”

Virtual Trials of the NICE-SUGAR Protocol: The Impact on Performance of Protocol and Protocol Compliance

“…STAR differs from NICE-SUGAR as it is a computerised model-based glycaemic control protocol, and uses a stochastic model to assess likely future change of patient-specific timevarying insulin sensitivity (SI) (Evans et al, 2012, Lin et al, 2008, Fisk et al, 2012. Current SI is determined from the clinically validated ICING physiological model , using integral-based fitting methods (Hann et al, 2005).…”

“…STAR (Stochastic TARgeted) is a clinically validated modelbased glycaemic control framework (Evans et al, 2012, Fisk et al, 2012, Stewart et al, 2016. It uses model-based insulin sensitivity to characterise and forward predict changes in metabolic state.…”

The Future is Now: Software-Guided Intensive Insulin Therapy in the Critically Ill

“…60,61 Nevertheless, MPCs such as Stochastic Targeted glycemic control and Space GlucoseControl (B. Braun, Melsungen, Germany) show promise with faster entry into the optimum range, minimal hypoglycemia, and, in some cases, decreased workload compared with paper-based protocols. [62][63][64][65] The ideal method for controlling glucose in the intensive care unit is characterized by its ease of use, minimal burden on staff, automated data entry, high adherence rate, and use of a proven algorithm to calculate insulin dosage. It would quickly correct hyperglycemia, consistently maintain glucose within the predetermined optimal range with minimal variability, and not result in episodes of hypoglycemia.…”