3D kernel-density stochastic model for more personalized glycaemic control: Development and in-silico validation (2019)
Type of ContentJournal Article
PublisherSpringer Science and Business Media LLC
Background: The challenges of glycaemic control in critically ill patients have been debated for 20 years. While glycaemic control shows benefits inter- A nd intra-patient metabolic variability results in increased hypoglycaemia and glycaemic variability, both increasing morbidity and mortality. Hence, current recommendations for glycaemic control target higher glycaemic ranges, guided by the fear of harm. Lately, studies have proven the ability to provide safe, effective control for lower, normoglycaemic, ranges, using model-based computerised methods. Such methods usually identify patient-specific physiological parameters to personalize titration of insulin and/or nutrition. The Stochastic-Targeted (STAR) glycaemic control framework uses patient-specific insulin sensitivity and a stochastic model of its future variability to directly account for both inter- A nd intra-patient variability in a risk-based insulin-dosing approach. Results: In this study, a more personalized and specific 3D version of the stochastic model used in STAR is compared to the current 2D stochastic model, both built using kernel-density estimation methods. Fivefold cross validation on 681 retrospective patient glycaemic control episodes, totalling over 65,000 h of control, is used to determine whether the 3D model better captures metabolic variability, and the potential gain in glycaemic outcome is assessed using validated virtual trials. Results show that the 3D stochastic model has similar forward predictive power, but provides significantly tighter, more patient-specific, prediction ranges, showing the 2D model over-conservative > 70% of the time. Virtual trial results show that overall glycaemic safety and performance are similar, but the 3D stochastic model reduced median blood glucose levels (6.3 [5.7, 7.0] vs. 6.2 [5.6, 6.9]) with a higher 61% vs. 56% of blood glucose within the 4.4-6.5 mmol/L range. Conclusions: This improved performance is achieved with higher insulin rates and higher carbohydrate intake, but no loss in safety from hypoglycaemia. Thus, the 3D stochastic model developed better characterises patient-specific future insulin sensitivity dynamics, resulting in improved simulated glycaemic outcomes and a greater level of personalization in control. The results justify inclusion into ongoing clinical use of STAR.
CitationUyttendaele V, Knopp JL, Davidson S, Desaive T, Benyo B, Shaw GM, Chase JG (2019). 3D kernel-density stochastic model for more personalized glycaemic control: Development and in-silico validation. BioMedical Engineering Online. 18(1). 102-.
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KeywordsGlycaemic control; Hyperglycaemia; Blood glucose; Insulin; Insulin sensitivity; Kernel density
ANZSRC Fields of Research40 - Engineering::4003 - Biomedical engineering::400306 - Computational physiology
32 - Biomedical and clinical sciences::3202 - Clinical sciences::320212 - Intensive care
32 - Biomedical and clinical sciences::3202 - Clinical sciences::320208 - Endocrinology
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3D kernel-density stochastic model for more personalized glycaemic control: Development and in-silico validation Uyttendaele V; Knopp JL; Davidson S; Desaive T; Benyo B; Shaw GM; Chase, Geoff (Springer Science and Business Media LLC, 2019)Background: The challenges of glycaemic control in critically ill patients have been debated for 20 years. While glycaemic control shows benefits inter- and intra-patient metabolic variability results in increased hypoglycaemia ...
Uyttendaele V; Shaw, Geoff; Desaive T; Knopp, Jennifer; Chase, Geoff (Springer Science and Business Media LLC, 2020)Background: STAR is a model-based, personalised, risk-based dosing approach for glycaemic control (GC) in critically ill patients. STAR provides safe, effective control to nearly all patients, using 1-3 hourly measurement ...
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