A 3D insulin sensitivity prediction model enables more patient-specific prediction and model-based glycaemic control. (September 2018)

• Record Type: Journal Article
• Authors: Uyttendaele, Vincent; Knopp, Jennifer L.; Stewart, Kent W.; Desaive, Thomas; Benyó, Balázs; Szabó-Némedi, Noémi; Illyés, Attila; Shaw, Geoffrey M.; Chase, J. Geoffrey
• Is Part Of: Biomedical signal processing and control. Volume 46(2018)
• Page Start: 192
• Page End: 200
• Publication Date: 2018-09
• Subjects: %ΔSI hour-to-hour percentage change in SI -- APACHE acute physiology and chronic health evaluation -- BG blood glucose -- GC glycaemic control -- ICING intensive control insulin-nutrition-glucose -- ICU intensive care unit -- LOS length of stay -- SI insulin sensitivity -- SPRINT specialised relative insulin nutrition tables -- STAR STochastic-TARgeted; Critical care -- Insulin sensitivity -- Glycaemic control -- Blood glucose -- Hyperglycaemia -- Insulin; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2018.05.032
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A 3D model to forecast patient-specific insulin sensitivity variability is proposed. The new model has similar predictive power with much tighter predictive bounds. Tighter prediction bands allow tighter glycaemic control, without compromising safety. 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 model-based 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 68, 629 h 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 (| % Δ SI | ≤ 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.