A 3D insulin sensitivity prediction model enables more patient-specific prediction and model-based glycaemic control

Abstract

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 modelbased 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 68629 hours of clinical data (819 GC episodes). The 5 th-95 th 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 5 th-95 th 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.

Understanding insulin and nutrition administration in glycemic control with the right side of the brain

“…Figures 6-8 describe the impact of SI variability on glycemic outcome and safety, and demonstrate the potential need to manage nutrition delivery to mitigate hypo-and hyper-glycemic risk. Figure 7 illustrates the risk of SI variability, where critically ill patients have signi cant variability in their hour-hour insulin sensitivity, particularly early in ICU stay [89,105]. Hypoglycemic risk from rising SI ( ) can result in moderate or severe hypoglycemia in up to 10% of hours in the rst 1-3 days of stay, depending on insulin dose [106].…”

“…Clinically, it is possible to quantify and thus account for this variability, creating an objective means to reduce hypoglycemic and hyperglycemic risk [89,105,[107][108][109][110].…”

Risk and reward: extending stochastic glycaemic control intervals to reduce workload

“…Therefore, it is critical for GC protocol design to account for both, using dynamic, personalised solutions [22]. While the use of physiological models allows direct identification of inter-patient variability [20], STAR is the only current protocol [33] also using stochastic modelling to evaluate intra-patient variability [34,35], which it then employs in a unique risk-based dosing strategy [23].…”

Glycemic control in the intensive care unit: A control systems perspective

“…STAR is the only TTR system and controls both insulin and nutrition inputs [188,189], thus using both possible control inputs. It uses a unique risk-based stochastic forecasting [71] based on unique and increasingly complex stochastic models of future insulin sensitivity variability over 1-4 hours ahead [166,167,176,177,190,191]. The approach is used in both the ICU [156,159] and NICU [87,138,158,170], where notably insulin is the only control variable in the NICU for clinical reasons.…”