Analysis, classification and management of insulin sensitivity variability in a glucose-insulin system model for critical illness
Thesis DisciplineMechanical Engineering
Degree GrantorUniversity of Canterbury
Degree NameDoctor of Philosophy
Hyperglycaemia in critical care is common and has been linked to increased mortality and morbidity. Tight control of blood glucose concentrations to more normal levels can significantly reduce the negative outcomes associated with hyperglycaemia. However, hypoglycaemia and glycaemic variability have also been independently shown to increase mortality in critically ill patients. Further complicating the matter, critically ill patients exhibit high inter- and intra patient metabolic variability and thus consistent, safe control of glycaemia has proved very difficult.
Model-based and model-derived tight glycaemic control methods have shown significant ability to provide very tight control with little or no hypoglycaemia in the intensive care unit (ICU). The model-based control practised in the Christchurch Hospital ICU uses a physiological model that relies on a single, time-varying parameter, SI, to capture the patient-specific glycaemic response to insulin. As an identified parameter, SI is prone to also capturing other, unintended, dynamics that add variability on multiple timescales. The objective of this research was to enable enhanced glycaemic control by addressing this variability of the SI parameter through better modelling and implementation.
An improved model of insulin secretion as a function of blood glucose concentration was developed using data collected from a recent study at the Christchurch Hospital ICU. Separate models were identified for non-diabetic patients and diagnosed, or suspected type II diabetic patients, with R2 = 0.61 and 0.69, respectively. The gradients of the functions identified were comparable to data published in a number of other studies on healthy and diabetic subjects.
The transcapilliary diffusion (nI) and cellular clearance (nC) rate parameters were optimised using data from published microdialysis studies. Interactions between these key parameters determine maximum interstitial insulin concentrations available for glucose disposal, and thus directly influence SI. The optimal values of these parameters were determined to be nI = nC = 0.0060 1/min. Models of endogenous glucose production (EGP), as functions of blood glucose concentration and time, were assessed. These models proved unsatisfactory due to difficulties in identifying reliable functions with the available data set. Thus, it was determined that EGP should continue to be treated as a population constant, except during real-time glycaemic control, where the value may be adjusted temporarily to ensure valid SI values.
The first 24 hours of ICU stay proved to be a period of significantly increased SI variability, both in terms of hour-to-hour changes and longer-term evolution of level. This behaviour was evident for the entire study cohort as a whole and was particularly pronounced during the first 12-18 hours. The subgroup of cardiovascular surgery patients, in which there was sufficient data for analysis, mirrored the results of the whole cohort, but was found to have even lower and more variable SI. Glucocorticoid steroids were also found to be associated with clinically significant reductions in overall level and increases in hour-to-hour variability of SI.
To manage variability caused by factors external to the physiological model, the use of several stochastic models was proposed. Using different models for the early part of ICU stay and for different diagnostic subgroups as well as when patients were receiving certain drug therapies would permit control algorithms to reduce the impact of the SI variability on outcome glycaemia.
The impact of measurement timing and BG concentration errors on the variability of SI was assessed. Results indicated that the impact of both sources of errors on SI level was unlikely to be clinically significant. The impact of BG sensor errors on hour-to-hour SI variability was more pronounced. Understanding the effect of sensor and timing errors on SI allows their impact to be reduced by using the 5-95 percentile forecast range of stochastic models during glycaemic control.
The performance of the model incorporating the proposed insulin kinetic parameters and secretion enhancements was validated for clinical glycaemic control and virtual trial purposes. This validation was conducted by self- and cross validation on a cohort independent to that with which the model was developed. The use of multiple stochastic models to reduce the impact of this extrinsic variability during glycaemic control was validated using virtual trials.