The STAR Controller

The Stochastic TARgeted (STAR) protocol is a unique, model-based TGC protocol (Chase et al., 2011b, Evans et al., 2011, Evans et al., 2012, Fisk et al., 2012) for insulin therapy that uses clinically validated metabolic and stochastic models (Lin et al., 2006, Lin et al., 2008) to optimize treatment in the context of possible future patient variation. Probabilistic forecasting enables more adaptive, optimized patient-specific care with clinically specified maximum risk(s) of hyper- and hypoglycaemia. This protocol implements insulin and nutrition interventions based on the current patient-specific insulin sensitivity (SI(t)). Insulin

sensitivity is identified hourly for each patient using recent BG measurements and a computerized metabolic system model. With this value, the predicted blood glucose response to a particular intervention can be calculated. The algorithm for STAR is illustrated in the Figure 2.10.

The stochastic forecasting is unique and enables a maximum likelihood approach to targeting a desired glycaemic range while enabling the clinical risk of hypo- or hyperglycaemia to be directly managed. It also enables patients with very different metabolic (intra- and inter- patient) variability to be directly managed and controlled within a single (STAR) model- based framework. Summary of protocol is shown at the Table 2.6.

The STAR protocol has the ability to specify risk of hypoglycemia below a clinically set threshold, and the ability to enable multiple hourly measurements based on clinically set glycemic thresholds. Within that framework, clinical or site-specific constraints may be added for how control is provided, which is via insulin and nutrition control. This approach can provide quality control performance that is tighter across patients and thus more patient- specific reduced light hypoglycaemia using a clinically specified maximum risk with stochastic forecasting of metabolic variation. However, there is no guarantee that all ICU patients would have similar metabolic variability (Le Compte et al., 2010, Penning et al., 2012).

30

Table 2.5: Summary of STAR protocol

Target Particular STAR protocol

i) Blood Glucose Range within 4 – 6.5 mmol/L as specified in 5-95th percentiles range.

ii) Clinical risk of hypo- or hyperglycemia

Maximum 5% risk of BG < 4.0

iii) Measurement interval a) 1 - 3 hours when BG levels are within 4 – 7.5 mmol/L. b) Every hour when BG levels are outside range.

iv) Control intervention Intervention of insulin and nutrition are based on the current patient-specific insulin sensitivity (SI(t)) to maximize the

likelihood of BG in a clinically specific range and maximum acceptable risk of hypoglycaemia.

31

32

2.5 Summary

This chapter discusses the basis and background of the glucose-insulin system models dealing to the model used in this thesis, and reviews several other models that have been developed and used for glycaemic understanding, control and management. These models have been used clinically for various studies for understanding or intervention. The use for understanding versus intervention requires differences in model capability and complexity that may not translate directly from one use to another. However, not all of these models were physiologically complete and some failed to capture inter- and intra- patient variability. The ICING-2 model presented in this chapter provides an overall measure of a patient’s insulin sensitivity, particularly to exogenous insulin and nutrition inputs that guide and determine the metabolic balance in ICU patients. It is also already proven to be suitable for clinical control, while accurately accounting for all relevant and observed physiological behaviour.

The overall glycaemic control system model and its key components, including input, output, actuators, patient and controller is also introduced to indicate the relation between glucose- insulin model and glycaemic control system model. The existing controllers such as SPRINT and STAR are explained since these controllers will be used during virtual trials. Control performance measures are defined to standardize the criteria assessment of the various controllers used in these studies.

34

Chapter 3: Patient Demography

This chapter presents the OHCA patient data used in this thesis. Patient data is analysed statistically to summarize the demography by whole cohort and by hospital. Additionally, the cohort is sub-divided and analysed by gender, diabetes status, mortality, and return of spontaneous circulation (ROSC). This data and analysis will be used in Chapter 4 – 7.

3.1 Introduction

A retrospective analysis of glycaemic control data from 180 OHCA patients (7812 hours) treated with hypothermia, shortly after admission to the Intensive Care Units (ICUs) of Erasme Hospital, Belgium and Lausanne Hospital, Switzerland. All patients were on local glycaemic protocols. Therapeutic Hypothermia (TH) was applied following a standardized written protocol.

All patients were treated with mild TH to 33 ± 1oC for 24 hours, irrespective of age, initial arrest rhythm and other physiological conditions. TH was started immediately after admission and was induced with ice-cold packs and intravenous ice-cold fluids. Body temperature was maintained at hypothermia using a surface cooling device with a computerized adjustment of patient temperature target. During this time, some short-acting drugs, such as midazolam (0.1mg/kg.hr), fentanyl (1.5µg/kg/hr) and vecuronium (0.1mg/kg boluses), were used to administer sedation, analgesia and control shivering. Rewarming was achieved passively, and sedation-analgesia was stopped when patient temperature was greater than 35oC.

Blood glucose (BG) and temperature readings were taken 1-2 hourly. Data were divided into three periods: 1) cool (T<=35oC); 2) idle period of 2 hours as hypothermia was removed; and 3) warm (T>35oC). A maximum of 24 and a minimum of 15 hours for the cool and warm periods were considered, ensuring a balance of contiguous data across the periods and transition. The idle period is not considered in the analysis.

35