Hyperglycemia is prevalent in critical care, as patients experience stress-induced
hyperglycemia, even with no history of diabetes. Hyperglycemia has a significant
impact on patient mortality, outcome and health care cost. Tight regulation
can significantly reduce these negative outcomes, but achieving it remains clinically
elusive, particularly with regard to what constitutes tight control and what
protocols are optimal in terms of results and clinical effort.
Hyperglycemia in critical care is not largely benign, as once thought, and has
a deleterious effect on outcome. Recent studies have shown that tight glucose
regulation to average levels from 6.1–7.75 mmol/L can reduce mortality 17–45%,
while also significantly reducing other negative clinical outcomes. However, clinical
results are highly variable and there is little agreement on what levels of
performance can be achieved and how to achieve them.
A typical clinical solution is to use ad-hoc protocols based primarily on experience,
where large amounts of insulin, up to 50 U/hr, are titrated against
glucose measurements variably taken every 1–4 hours. When combined with the
unpredictable and sudden metabolic changes that characterise this aspect of critical
illness and/or clinical changes in nutritional support, this approach results
in highly variable blood glucose levels. The overall result is sustained periods
of hyper- or hypo- glycemia, characterised by oscillations between these states,
which can adversely affect clinical outcomes and mortality. The situation is exacerbated
by exogenous nutritional support regimes with high dextrose content.
Model-based predictive control can deliver patient specific and adaptive control,
ideal for such a highly dynamic problem. A simple, effective physiological
model is presented in this thesis, focusing strongly on clinical control feasibility.
This model has three compartments for glucose utilisation, interstitial insulin and its transport, and insulin kinetics in blood plasma. There are two patient
specific parameters, the endogenous glucose removal and insulin sensitivity. A
novel integral-based parameter identification enables fast and accurate real-time
model adaptation to individual patients and patient condition.
Three stages of control algorithm developments were trialed clinically in the
Christchurch Hospital Department of Intensive Care Medicine. These control
protocols are adaptive and patient specific. It is found that glycemic control utilising
both insulin and nutrition interventions is most effective. The third stage of
protocol development, SPRINT, achieved 61% of patient blood glucose measurements
within the 4–6.1 mmol/L desirable glycemic control range in 165 patients.
In addition, 89% were within the 4–7.75 mmol/L clinical acceptable range. These
values are percentages of the total number of measurements, of which 47% are
two-hourly, and the rest are hourly. These results showed unprecedented tight
glycemic control in the critical care, but still struggle with patient variability and
dynamics.
Two stochastic models of insulin sensitivity for the critically ill population
are derived and presented in this thesis. These models reveal the highly dynamic
variation in insulin sensitivity under critical illness. The stochastic models can deliver
probability intervals to support clinical control interventions. Hypoglycemia
can thus be further avoided with the probability interval guided intervention assessments.
This stochastic approach brings glycemic control to a more knowledge
and intelligible level.
In “virtual patient” simulation studies, 72% of glycemic levels were within
the 4–6.1 mmol/L desirable glycemic control range. The incidence level of hypoglycemia
was reduced to practically zero. These results suggest the clinical
advances the stochastic model can bring. In addition, the stochastic models reflect
the critical patients’ insulin sensitivity driven dynamics. Consequently, the
models can create virtual patients to simulated clinical conditions. Thus, protocol
developments can be optimised with guaranteed patient safety.
Finally, the work presented in this thesis can act as a starting point for many
other glycemic control problems in other environments. These areas include the
cardiac critical care and neonatal critical care that share the most similarities to
the environment studied in this thesis, to general diabetes where the population is growing exponentially world wide. Furthermore, the same pharmacodynamic
modelling and control concept can be applied to other human pharmacodynamic
control problems. In particular, stochastic modelling can bring added knowledge
to these control systems. Eventually, this added knowledge can lead clinical
developments from protocol simulations to better clinical decision making.
| Identifer | oai:union.ndltd.org:canterbury.ac.nz/oai:ir.canterbury.ac.nz:10092/1570 |
| Date | January 2007 |
| Creators | Lin, Jessica |
| Publisher | University of Canterbury. Mechanical |
| Source Sets | University of Canterbury |
| Language | English |
| Detected Language | English |
| Type | Electronic thesis or dissertation, Text |
| Rights | Copyright Jessica Lin, http://library.canterbury.ac.nz/thesis/etheses_copyright.shtml |
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