Hyperglycaemia is prevalent in critical care and increases the risks of further complications and mortality. Glycaemic control has shown benefits in reducing mortality. However, due in parts to excessive metabolic variability, many studies have found it difficult to reproduce these results. Out-of-Hospital Cardiac Arrest (OHCA) patients have low survival rates and often experience hyperglycaemia. However, these patients belongs to one group who has shown benefit from accurate glycaemic control (AGC), but can be highly insulin resistant and variable, particularly on the first two days of stay.

Hypothermia is often used to treat post-cardiac arrest patients or out of hospital cardiac arrest (OHCA) and these same patients often simultaneously receive insulin. In general, it leads to a lowering of metabolic rate that induces changes in energy metabolism. However, its impact on metabolism and insulin resistance in critical illness is unknown, although one of the adverse events associated with hypothermic therapy is a decrease in insulin sensitivity and insulin secretion. However, this decrease may not be notable in the cohort that is already highly resistant and variable. Hence, understanding metabolic evolution and variability would enable safer and more accurate glycaemic control using insulin in this cohort.

OHCA patients were undergone preliminary analysis during cool and warm, which includes insulin sensitivity (SI), blood glucose (BG), and exogenous insulin and dextrose. Patients were analysed based on overall cohort, sub-cohorts, and 6 and 12 hour time block. Generally, the results show that OHCA patients had very low metabolic activity during cool period but significantly increased over time. In contrast, BG is higher during cool period and decreased over time. The analysis is equally important as the controller development since it provides scientific evidence and understanding of patients’ physiology and metabolic evolution especially during cool and warm.

Model-based methods can deliver control that is patient-specific and adaptive to handle highly dynamic patients. A physiological ICING-2 model of the glucose-insulin regulatory system is presented in this thesis. This model has three compartments for glucose utilisation, effective interstitial insulin and its transport, and insulin kinetics in blood plasma, with emphasis on clinical applicability. The predictive control for the model is driven by the patient-specific and time-varying insulin sensitivity parameter. A novel integral-based parameter identification enables fast and accurate real-time model adaptation to individual patients and patient condition.

Stochastic models and time-series methods for forecasting future insulin sensitivity are presented in this thesis. These methods can deliver probability intervals to support clinical control interventions. The risk of adverse glycaemic outcomes given observed variability from cohort-specific and patient-specific forecasting methods can be quantified to inform clinical staff. Hypoglycaemia can thus be further avoided with the probability interval guided intervention assessments.

Simulation studies of STAR-OHCA control trials on ‘virtual patients’ derived from retrospective clinical data provided a framework to optimise control protocol design in-silico. Comparisons with retrospective control showed substantial improvements in glycaemia within the target 4 - 7 mmol/L range by optimising the infusions of insulin. The simulation environment allowed experimentation with controller parameters to arrive at a protocol that operates within the constraints found earlier during patient analysis.

Overall, the research presented takes model-based OHCA glycaemic control from concept to proof-of-concept virtual trials. The thesis employs the full range of models, tools and methods to optimise the protocol design and problem solution.