<i>In Silico</i> Simulation of Long-Term Type 1 Diabetes Glycemic Control Treatment Outcomes

Wong, Xing-Wei; Chase, J. Geoffrey; Hann, C.E.; Lotz, Thomas; Lin, Jessica; Compte, Aaron J. Le

doi:10.1177/193229680800200313

Cited by 8 publications

(6 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Note that Equations (2)- (3) define an intravenous insulin pathway, per ICU standard insulin delivery, but all pharmaco-dynamics and kinetics are otherwise general and have been used in type 1 diabetes analysis, as well [102][103][104]. The insulin kinetics in Equations (2)-(3) are similar looking to those of the well known Minimal Model, but have very different meaning as defined.…”

Section: Analysing Insulin Sensitivity In the Critically Illmentioning

confidence: 99%

Tight glycemic control in critical care – The leading role of insulin sensitivity and patient variability: A review and model-based analysis

Chase

Compte

Suhaimi

et al. 2011

Computer Methods and Programs in Biomedicine

Self Cite

123

View full text Add to dashboard Cite

Tight glycemic control (TGC) has emerged as a major research focus in critical care due to its potential to simultaneously reduce both mortality and costs. However, repeating initial successful TGC trials that reduced mortality and other outcomes has proven difficult with more failures than successes. Hence, there has been growing debate over the necessity of TGC, its goals, the risk of severe hypoglycemia, and target cohorts.This paper provides a review of TGC via new analyses of data from several clinical trials, including SPRINT, Glucontrol and a recent NICU study. It thus provides both a review of the problem and major background factors driving it, as well as a novel model-based analysis designed to examine these dynamics from a new perspective. Using these clinical results and analysis, the goal is to develop new insights that shed greater light on the leading factors that make TGC difficult and inconsistent, as well as the requirements they thus impose on the design and implementation of TGC protocols.A model-based analysis of insulin sensitivity using data from three different critical care units, comprising over 75,000 hours of clinical data, is used to analyse variability in metabolic dynamics using a clinically validated model-based insulin sensitivity metric (S I ). Variation in S I provides a new interpretation and explanation for the variable results seen (across cohorts and studies) in applying TGC. In particular, significant intra-and inter-patient variability in insulin resistance (1/ S I ) is seen be a major confounder that makes TGC difficult over diverse cohorts, yielding variable results over many published studies and protocols. Further factors that exacerbate this variability in glycemic outcome are found to include measurement frequency and whether a protocol is blind to carbohydrate administration.

show abstract

Section: Analysing Insulin Sensitivity In the Critically Illmentioning

confidence: 99%

Tight glycemic control in critical care – The leading role of insulin sensitivity and patient variability: A review and model-based analysis

Chase

Compte

Suhaimi

et al. 2011

Computer Methods and Programs in Biomedicine

Self Cite

123

View full text Add to dashboard Cite

show abstract

“…In order to analyze the model in a quantitative manner a physiologically correct exogenous glucose input has to be defined. According to the literature, a widely used 33, 34 absorption curve can be seen in the upper figure of Figure 1, which was recorded under extremely strict and precise conditions 32. It allows us to neglect gut‐blood circulation transfer function since it is taken into account by the absorption curve.…”

Section: Brief Review Of the Novel Liu‐tang Modelmentioning

confidence: 99%

Robust servo control of a novel type 1 diabetic model

Kovács

Kulcsár

György

et al. 2010

Optim Control Appl Methods

View full text Add to dashboard Cite

Robust servo control of a model-based biomedical application is presented in the article. The glucoseinsulin control of type 1 diabetic patients is considered to be solved using the results of post-modern robust control principles.The paper uses a recently published glucose-insulin model and presents the transformation of the model to describe the dynamics of type 1 diabetes mellitus. The nonlinear plant is then linearized at a given steady state point. In order to characterize the uncertainty around the nominal model in frequency domain, a parametric nonlinear model sensitivity analysis is performed using gridding method. The aim of the paper is to underline the viability of the robust servo, linear -control algorithm tested in highly nonlinear closed-loop simulation environment. Using two degree-of-freedom robust controller, the structured singular value of the closed-loop is designed to fulfill the robust performance requirements and assure glucose level control.Glucose level tracking is ensured under simulated and realistic exogenous meal disturbances.

show abstract

“…However, despite the rise of computer and model based glycemic control using insulin pumps [13][14][15], there are no current models of insulin Detemir and its unique action for use in model-based control methods for those type 1 and type 2 diabetic individuals using injected insulin (e.g. [16]). In particular, prior models of long acting insulin analogues do not include this albumin binding aspect [17][18][19], which makes the model unique in its own right.…”

Section: Introductionmentioning

confidence: 99%

A subcutaneous insulin pharmacokinetic model for insulin Detemir

Noorden

Knopp

Chase

2019

Computer Methods and Programs in Biomedicine

Self Cite

View full text Add to dashboard Cite

Background and Objective: Type 2 diabetes (T2D) is rapidly increasing in incidence and has significant social and economic costs. Given the increasing cost of complications, even relatively short delays in the onset of T2D can significantly reduce long-term complications and costs. Equally, recent studies have shown the onset of T2D can be delayed by use of long-acting insulin, despite the risk and concomitant low adherence. Thus, there is a strong potential motivation to develop models of longacting insulin analogues to enable safe, effective use in model-based dosing systems. In particular, there are no current models of long-acting insulin Detemir and its unique action for model-based control. The objective of this work is to develop a first model of insulin Detemir and its unique action, and validate it against existing data in the literature.Methods: This study develops a detailed compartment model for insulin Detemir. Model specific parameters are identified using data from a range of published clinical studies on the pharmacokinetic of insulin Detemir. Model validity and robustness are assessed by identifying the model for each study and using average identified parameters over several dose sizes and study cohorts. Comparisons to peak concentration, time of peak concentration and overall error versus measured plasma concentrations are used to assess model accuracy and validity.Results: Almost all studies and cohorts fit literature data to within one standard deviation of error, even when using averaged identified model parameters. However, there appears to be a noticeable dose dependent dynamic not included in this first model, nor reported in the literature studies.Conclusions: A first model of insulin Detemir including its unique albumin binding kinetics is dervied and provisionally validated against clinical pharmacokinetic data. The pharmacokinetic curves are suitable for model-based control and general enough for use. While there are limitations in the studies used for validation that prevent a more complete understanding, the results provide an effective first model and justify the design and implementation of further, more precise human trials.

show abstract

In Silico Simulation of Long-Term Type 1 Diabetes Glycemic Control Treatment Outcomes

Cited by 8 publications

References 43 publications

Tight glycemic control in critical care – The leading role of insulin sensitivity and patient variability: A review and model-based analysis

Tight glycemic control in critical care – The leading role of insulin sensitivity and patient variability: A review and model-based analysis

Robust servo control of a novel type 1 diabetic model

A subcutaneous insulin pharmacokinetic model for insulin Detemir

Contact Info

Product

Resources

About