Glycemic control; Hyperglycemia; Blood Glucose; Insulin; Insulin Sensitivity; Kernel Density
Abstract :
[en] 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 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- and 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 overconservative
> 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.
Research center :
GIGA-In-Silico Medicine
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others Human health sciences: Multidisciplinary, general & others Endocrinology, metabolism & nutrition Anesthesia & intensive care
Author, co-author :
Uyttendaele, Vincent ; Université de Liège - ULiège > In silico-Model-based therapeutics, Critical Care Basic Sc.
Knopp, Jennifer L.
Davidson, Shaun
Desaive, Thomas ; Université de Liège - ULiège > Département d'astrophys., géophysique et océanographie (AGO) > Thermodynamique des phénomènes irréversibles
Benyo, Balazs
Shaw, Geoffrey M.
Chase, J. Geoffrey
Language :
English
Title :
3D kernel-density stochastic model for more personalized glycaemic control: development and in-silico validation
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