Integral-based parameter identification for long-term dynamic verification of a glucose–insulin system model

Hann, C.E.; Chase, J. Geoffrey; Lin, Jessica; Lotz, Thomas; Doran, Carmen V.; Shaw, Geoffrey M.

doi:10.1016/j.cmpb.2004.10.006

Cited by 229 publications

(294 citation statements)

References 15 publications

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“…The Christchurch Women's Hospital (CWH) cohort consists of 67 very/extremely premature patients who received insulin either under sliding scale-based or modelbased protocols [60]. The HINT cohort [28] was derived from detailed BG, insulin and nutrition data charted during the HINT trial in Auckland, NZ.…”

Section: Model Validation Cohortsmentioning

confidence: 99%

“…SI profiles were fit to clinical data from the CWH and HINT validation cohorts using the integral based fitting method [60] and Equations 1-4. Ordinary differential equation solutions for Equations 1 -3 (G,I,Q) were generated using a Runga-Kutta 4 based method (Matlab ® , ode45).…”

Section: Model Validation Cohortsmentioning

confidence: 99%

“…Where fitting error is high, the model fails to fit clinical data and is therefore inappropriate. However, for appropriately constructed model dynamics, fitting error saturates at some lower value as the fitted insulin sensitivity ensures model fit over a reasonable range of parameter values [60]. For this reason, prediction error is also analysed.…”

Section: Fitting and Prediction Errormentioning

confidence: 99%

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Insulin kinetics and the Neonatal Intensive Care Insulin–Nutrition–Glucose (NICING) model

Dickson

Pretty

Alsweiler

et al. 2017

Mathematical Biosciences

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Background: Models of human glucose-insulin physiology have been developed for a range of uses, with similarly different levels of complexity and accuracy. STAR (Stochastic Targeted) is a modelbased approach to glycaemic control. Elevated blood glucose concentrations (hyperglycaemia) are a common complication of stress and prematurity in very premature infants, and have been associated with worsened outcomes and higher mortality. This research identifies and validates the model parameters for model-based glycaemic control in neonatal intensive care.

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Section: Model Validation Cohortsmentioning

confidence: 99%

Section: Model Validation Cohortsmentioning

confidence: 99%

Section: Fitting and Prediction Errormentioning

confidence: 99%

See 1 more Smart Citation

Insulin kinetics and the Neonatal Intensive Care Insulin–Nutrition–Glucose (NICING) model

Dickson

Pretty

Alsweiler

et al. 2017

Mathematical Biosciences

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“…Finally, the cytokines and hormones that drive these affects that result in hyperglycemia are physiologically linked to lowered insulin sensitivity and vary continuously over time as patient condition evolves. Hence, this overall effective insulin sensitivity is dynamic and time-varying [86][87][88].…”

Section: Insulin Sensitivity Patient Variability and Impact On Tgcmentioning

confidence: 99%

“…For each cohort, insulin sensitivity, S I , is identified hourly from the clinical data [88] using a method that with this model yields correlations of R > 0.97 for insulin resistant and normal subjects in euglycemic clamp testing where enough data are known to assess the accuracy of this identified value [91]. In each cohort, the resulting hour to hour variation in S I (t) for each patient is then used to generate a stochastic model giving the probability distribution for hourly variation in S I from any current value of S I [86,87].…”

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

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123

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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.

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Part A: Type 1 Diabetes Mellitus

Zavitsanou

Mantalaris

Georgiadis

et al. 2017

Modelling Optimization and Control of Biomedical Systems

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Integral-based parameter identification for long-term dynamic verification of a glucose–insulin system model

Cited by 229 publications

References 15 publications

Insulin kinetics and the Neonatal Intensive Care Insulin–Nutrition–Glucose (NICING) model

Insulin kinetics and the Neonatal Intensive Care Insulin–Nutrition–Glucose (NICING) model

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

Part A: Type 1 Diabetes Mellitus

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