Long term verification of glucose-insulin regulatory system model dynamics

doi: 10.1109/IEMBS.2004.1403269Hyperglycaemia in critically ill patients increases the risk of further complications and mortality. A long-term verification of a model that captures the essential glucose- and insulin-kinetics is presented, using retrospective data gathered in an Intensive Care Unit (ICU). The model uses only two patient specific parameters, for glucose clearance and insulin sensitivity. The optimization of these parameters is accomplished through a novel integration-based fitting approach, and a piecewise linearization of the parameters. This approach reduces the non-linear, non-convex optimization problem to a simple linear equation system. The method was tested on long-term blood glucose recordings from 17 ICU-patients, resulting in an average error of 7%, which is in the range of the sensor error. One-hour predictions of blood glucose data proved acceptable with an error range between 7- 11%. These results verify the model’s ability to capture longterm observed glucose-insulin dynamics in hyperglycaemic ICU patients

Use of SMS texts for facilitating access to online alcohol interventions: a feasibility study

A41 Use of SMS texts for facilitating access to online alcohol interventions: a feasibility study In: Addiction Science & Clinical Practice 2017, 12(Suppl 1): A4

Derivative weighted active insulin control algorithms and trials

Close control of blood glucose levels significantly reduces vascular complications in diabetes. Heavy derivative controllers utilising the data density available from emerging biosensors are developed to provide tight, optimal control of elevated blood glucose levels. A two-compartment human model is developed for intravenous infusion from physiologically verified subcutaneous infusion models to enable a first of its kind, proof-of-concept clinical trial. Results show tight control with very similar performance to modelled behaviour and strong correlation between modelled insulin used versus the amounts used in clinical trials to validate the models and methods developed

Long term verification of glucose-insulin regulatory system model dynamics

doi: 10.1109/IEMBS.2004.1403269Hyperglycaemia in critically ill patients increases the risk of further complications and mortality. A long-term verification of a model that captures the essential glucose- and insulin-kinetics is presented, using retrospective data gathered in an Intensive Care Unit (ICU). The model uses only two patient specific parameters, for glucose clearance and insulin sensitivity. The optimization of these parameters is accomplished through a novel integration-based fitting approach, and a piecewise linearization of the parameters. This approach reduces the non-linear, non-convex optimization problem to a simple linear equation system. The method was tested on long-term blood glucose recordings from 17 ICU-patients, resulting in an average error of 7%, which is in the range of the sensor error. One-hour predictions of blood glucose data proved acceptable with an error range between 7- 11%. These results verify the model’s ability to capture longterm observed glucose-insulin dynamics in hyperglycaemic ICU patients

Lumped Parameter and Feedback Control Models of the Auto-Regulatory Response in the Circle of Willis

The Circle of Willis (CoW) is a ring-like structure of blood vessels found beneath the hypothalamus at the base of the brain, which distributes blood to the cerebral mass. Simple models of cerebral blood flow dynamics would create a tool capable of diagnosing potential outcomes of surgical or other therapies. A one-dimensional flow model is developed to capture the auto-regulation dynamics by which cerebral blood perfusion is maintained. Figure 1 shows a CoW schematic composed of circulus, afferent (inflow) and efferent (outflow) arteries. Positive flow around the CoW is clockwise but not restricted in direction, while flow in afferent and efferent vessels is restricted to the directions shown. Flow in efferent vessels is regulated by time varying peripheral resistances modelling the effects of auto-regulation in the smaller blood vessels they supply. While many individuals have complete and symmetrical CoW geometry, it is not uncommon for some elements to be restricted or omitted (van der Zwan and Hillen 1991) with the communicating vessels having a higher occurrence of omission (Alpers and Berry 1963)

Cerebral Haemodynamics and Auto-Regulatory Models of the Circle of Willis

Technical Note. Journal is online only.The Circle of Willis (CoW) is a ring-like structure of blood vessels found beneath the hypothalamus at the base of the brain distributing blood to the cerebral hemispheres. A one-dimensional computational fluid dynamic [1-D CFD] model is developed to capture the auto-regulation dynamics that maintain blood perfusion pressure with a goal of developing diagnostic tools for stroke prediction. The afferent and circulus arteries have constant resistances, and the efferent arteries have variable resistors limited to changes in radius of up to 40% to capture auto-regulatory behaviour. Auto-regulation is modelled by feedback control coupled with peripheral resistance dynamics to create a non-linear system. Solutions are obtained far more quickly than higher dimensional CFD models via a unique non-linear solution method. The overall model enables simulation of different CoW geometries for different patient conditions. The CoW is simulated with a 20mmHg arterial pressure drop in the right internal carotid artery for the balanced configuration and each case where a single circulus vessel is omitted. Results match the 20% drop in flowrate, and 20 second response time from published clinical data and prior research. No single omission leads to failure in reaching the required efferent flowrates, highlighting the overall robustness of this arterial structure. A high stroke risk case, however, fails to achieve the required flowrate in the left posterior communicating artery (LPCA2), representing a potential stroke. All of these results agree well with known clinical results, indicating the potential of this model for pre-determining potential outcomes of surgical or other procedures

Derivative weighted active insulin control algorithms and intensive care unit trials

Invited from IFAC Melbourne ConferenceCritically ill-patients often experience stress-induced hyperglycemia. This research demonstrates the effectiveness of a simple automated insulin infusion for controlling the rise and duration of blood glucose excursion in critically ill-patients. Heavy derivative controllers derived from a simple, two-compartment model reduced blood glucose excursion 79–89% after a glucose input in proof-of-concept clinical trials. Modelled performance is very similar to clinical results, including a strong correlation between modelled and actual insulin consumed, validating the fundamental models and methods. However, the need for additional dynamics in the model employed is clearly illustrated despite capturing the essential dynamics for this problem

Impact of insulin-stimulated glucose removal saturation on dynamic modelling and control of hyperglycaemia

Invited special edition on Australasia and Bio-EngineeringReported insulin-stimulated glucose removal saturation levels vary widely between individuals and trade off with insulin sensitivity in model-based control methods. A non-linear model and adaptive insulin infusion protocol enabled high-precision blood glucose control in critically ill patients using a constant insulin-stimulated glucose removal saturation parameter. Analysis of clinical trial results with and without saturation modelling indicates the significant impact of this saturation parameter on controller efficacy. Without accounting for saturation, the time-average prediction error during a five-hour trial was up to 17.6%. The average prediction error between the four patients examined in this study was reduced to 5.8% by approximating the saturation parameter. Hence, saturation is an important dynamic that requires good methods of estimation or identification to enable tight glycemic control

Adaptive bolus-based set-point regulation of hyperglycemia in critical care

Critically ill patients are often hyperglycemic and extremely diverse in their dynamics. Consequently, fixed protocols and sliding scales can result in error and poor control. A two-compartment glucose-insulin system model that accounts for time-varying insulin sensitivity and endogenous glucose removal, along with two different saturation kinetics is developed and verified in proof-of-concept clinical trials for adaptive control of hyperglycemia. The adaptive control algorithm monitors the physiological status of a critically ill patient, allowing real-time tight glycemic regulation. The bolus-based insulin administration approach is shown to result in safe, targeted stepwise glycemic reduction for three critically ill patients

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

Hyperglycaemia in critically ill patients increases the risk of further complications and mortality. This paper introduces a model capable of capturing the essential glucose and insulin kinetics in patients from retrospective data gathered in an Intensive Care Unit (ICU). The model uses two time-varying patient specific parameters for glucose effectiveness and insulin sensitivity. The model is mathematically reformulated in terms of integrals to enable a novel method for identification of patient specific parameters. The method was tested on long-term blood glucose recordings from 17 ICU patients, producing 4% average error, which is within the sensor error. One-hour forward predictions of blood glucose data proved acceptable with an error of 2-11%. All identified parameter values were within reported physiological ranges. The parameter identification method is more accurate and significantly faster computationally than commonly used non-linear, non-convex methods. These results verifl the model's ability to capture long-term observed glucose-insulin dynamics in hyperglycernic ICU patients, as well as the fitting method developed. Applications of the model and parameter identification method for automated control of blood glucose and medical decision support are discussed