Analysis of non-steady state physiological and pathological processes
Keywords: automated retinopathy
time series analysis
endocrinology
diabetes
non linear dynamics
systems modelling
machine vision
glycaemic variability

The analysis of non steady state physiological and pathological processes concerns the abstraction, extraction, formalisation and analysis of information from physiological systems that is obscured, hidden or unable to be assessed using traditional methods. Time Series Analysis (TSA) techniques were developed and built into a software program, Easy TSA, with the aim of examining the oscillations of hormonal concentrations in respect to their temporal aspects – periodicity, phase, pulsatility. The Easy TSA program was validated using constructed data sets and used in a clinical study to examine the relationship between insulin and obesity in people without diabetes. In this study fifty-six non-diabetic subjects (28M, 28F) were examined using data from a number of protocols. Fourier Transform and Autocorrelation techniques determined that there was a critical effect of the level of BMI on the frequency, amplitude and regularity of insulin oscillations. Second, information systems formed the background to the development of an algorithm to examine glycaemic variability and a new methodology termed the Glycaemic Risk in Diabetes Equation (GRADE) was developed. The aim was to report an integrated glycaemic risk score from glucose profiles that would complement summary measures of glycaemia, such as the HbA1c. GRADE was applied retrospectively to blood glucose data sets to determine if it was clinically relevant. Subjects with type 1 and type 2 diabetes had higher GRADE scores than the non-diabetic population and the contribution of hypo- and hyperglycaemic episodes to risk was demonstrated. A prospective study was then designed with the aim to apply GRADE in a clinical context and to measure the statistical reproducibility of using GRADE. Fifty-three (Male 26, Female 27) subjects measured their blood glucose 4 times daily for twenty-one days. The results were that lower HbA1c’s correlated with an increased risk of hypoglycaemia and higher HbA1c’s correlated with an increased risk of hyperglycaemia. Some subjects had HbA1c of 7.0 but had median GRADE values ranging from 2.2 to 10.5. The GRADE score summarized diverse glycaemic profiles into a single assessment of risk. Well-controlled glucose profiles yielded GRADE scores <= 5 and higher GRADE scores represented increased clinical risk from hypo or hyperglycaemia. Third, an information system was developed to analyse data-rich multi-variable retinal images using the concept of assessment of change rather than specific lesion recognition. A fully Automated Retinal Image Differencing (ARID) computer system was developed to highlight change between retinal images over time. ARID was validated using a study and then a retrospective study sought to determine if the use of the ARID software was an aid to the retinal screener. One hundred and sixty images (80 image pairs) were obtained from Gloucestershire Diabetic Eye Screening Programme. Images pairs were graded manually and categorised according to how each type of lesion had progressed, regressed, or not changed between image A and image B. After a 30 day washout period image pairs were graded using ARID and the results compared. The comparison of manual grading to grading using ARID (Table 4.3) demonstrated an increased sensitivity and specificity. The mean sensitivity of ARID (87.9%) was increased significantly in comparison to manually grading sensitivity (84.1%) (p<0.05). The specificity of the automated analysis (87.5%) increased significantly from the specificity (56.3%) achieved by manually grading (p<0.05). The conclusion was that automatic display of an ARID differenced image where sequential photographs are available would allow rapid assessment and appropriate triage. Forth, non-linear dynamic systems analysis methods were utilised to build a system to assess the extent of chaos characteristics within the insulin-glucose feedback domain. Biological systems exist that are deterministic yet are neither predictable nor repeatable. Instead they exhibit chaos, where a small change in the initial conditions produces a wholly different outcome. The glucose regulatory system is a dynamic system that maintains glucose homeostasis through the feedback mechanism of glucose, insulin, and contributory hormones and was ideally suited to chaos analysis. To investigate this system a new algorithm was created to assess the Normalised Area of Attraction (NAA). The NAA was calculated by defining an oval using the 95% CI of glucose & Insulin (the limit cycle) on a phasic plot. Thirty non-diabetic subjects and four subjects with type 2 diabetes were analysed. The NAA indicated a smaller range for glucose and insulin excursions with the non-diabetics subjects (p<0.05). The conclusion was that the evaluation of glucose metabolism in terms of homeostatic integrity and not in term of cut-off values may enable a more realistic approach to the effective treatment and prevention of diabetes and its complications.