3D Stochastic Modelling of Insulin Sensitivity in STAR: Virtual trials analysis

[en] Glycaemic control has shown beneficial outcomes for critically ill patients, but has been proven hard to achieve safely, increasing risk of hypoglycaemia. Patient metabolic variability is one of the main factor influencing glycaemic control safety and efficacy. STAR is a model-based glycaemic controller using a unique patient-specific risk-based dosing approach. STAR uses a 2D stochastic model, built from population data using kernel density methods, to determine potential forward future evolution in patient-specific insulin sensitivity (SIn+1), based on its current value (SIn). This study uses virtual trial to compare the current 2D stochastic model used in STAR, with a new 3D stochastic model. The new 3D model also uses prior insulin sensitivity value (SIn-1) to determine distribution of likely future SIn+1. A total of 587 virtual patient glycaemic control episodes longer than 24 hours from three different studies are used here. Safety (% blood glucose (BG) measurements < 4.0 and < 2.2 mmol/L), performance (% time in the target 4.4-8.0 mmol/L band), insulin administration and nutrition delivery (% goal feed) are compared. Results show similar performance (90% BG in 4.4-8.0 mmol/L), and similar safety, with slightly higher % BG < 4.0 mmol/L (0.9 vs. 1.4%) and % BG < 2.2 mmol/L (0.02 vs. 0.03%) for the 3D model, was achieved for similar workload. The slightly lower median BG level (6.3 vs. 6.0 mmol/L) for the 3D stochastic model is explained by the higher median insulin rate administered (2.5 vs. 3.0 U/hr). More importantly, simulation results showed higher nutrition delivery using the 3D stochastic model (92 vs. 99 % goal feed). The new 3D stochastic model achieved similar safety and performance than the 2D stochastic model in these virtual simulations, while increasing the total calorific intake. This higher nutritional intake is potentially associated with improved outcome in intensive care units. The 3D stochastic model thus better characterises patient-specific metabolic variability, allowing more optimal insulin and nutritional dosing. Therefore, a pilot clinical trial using the new 3D stochastic model could be realised to assess and compared clinical outcomes using the new stochastic model.

Research center :

GIGA-In silico Medicine
Department of Mechanical Engineering, University of Canterbury, Chrsitchurch, New Zealand

Disciplines :

Anesthesia & intensive care
Endocrinology, metabolism & nutrition
Engineering, computing & technology: Multidisciplinary, general & others

Author, co-author :

Uyttendaele, Vincent ; Université de Liège - ULiège > Département d'astrophys., géophysique et océanographie (AGO) > Thermodynamique des phénomènes irréversibles

Knopp, Jennifer L.

Shaw, Geoffrey M.

Desaive, Thomas ; Université de Liège - ULiège > Département d'astrophys., géophysique et océanographie (AGO) > Thermodynamique des phénomènes irréversibles

Chase, J. Geoffrey

Language :

English

Title :

3D Stochastic Modelling of Insulin Sensitivity in STAR: Virtual trials analysis

Publication date :

2018

Event name :

10th IFAC Symposium on Biological and Medical Systems

Event place :

Sao Paulo, Brazil

Event date :

3-5 September 2018

Audience :

International

Journal title :

IFAC-PapersOnLine

ISSN :

2405-8971

eISSN :

2405-8963

Publisher :

IFAC Secretariat, Austria

Pages :

128-133

Peer reviewed :

Peer Reviewed verified by ORBi

Funders :

F.R.S.-FNRS - Fonds de la Recherche Scientifique [BE]

Available on ORBi :

since 04 October 2018

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