AIDA Technical Guide
(Re)-Load AIDA Frames / Menus
by Dr. Eldon D. Lehmann and Dr. Tibor Deutsch
A clinical model of glucose-insulin interaction in insulin-dependent (type 1) diabetes mellitus has been developed for patient and medical staff education. The model attempts to reflect the underlying (patho)physiology of insulin action and carbohydrate absorption in quantitative terms such as insulin sensitivity, volume of glucose and insulin distribution and maximal rate of gastric emptying. The model's predictions allow a 24 hour simulation of blood glucose profiles for hypothetical patients to be generated. The mathematics underlying the model are described and the results of validation work to test the model against real patient data are provided. It is concluded that the model is not refined enough for individual patient simulation and as such the system can only be applied as an educational / demonstration tool.
The purpose of this on-line document is to provide some background information and technical details about how AIDA actually works. Further information, examples and validation results can be obtained from published articles in the diabetes / medical / computing literature [1-18] where full details of all AIDA's functions and components are open to scrutiny.
In 1993 compelling evidence was published from the Diabetes Control and Complications Trial (DCCT)  that maintaining tight blood glucose control, certainly in patients with insulin-dependent (type 1) diabetes mellitus, can delay the onset and slow the progression of the later life complications usually associated with the disease. As we have entered the 21st century, one way of achieving the goals demonstrated by the DCCT in routine clinical practice may well be through the use of Information Technology (IT).
Many workers have suggested that IT may be able to assist in the transfer of knowledge and expertise from specialist diabetes centres to primary care physicians (general practitioners) and practice nurses as well as directly to patients [see refs 20-26 for reviews]. The concept of providing a type 1 diabetic patient with a hand-held electronic log which could store blood glucose readings and give the patient advice about the next meal or insulin injection is clearly an exciting one. However, the development of a hand-held log which could provide patient specific advice and be able to justify its reasoning is a non-trivial problem which has not yet really been solved. Furthermore, such a system even if developed and validated would only really be of use in health-care systems which could afford the costs of the high technology involved. A complimentary approach, however, would be to use similar IT techniques as a way of teaching patients about insulin dosage, dietary and exercise adjustments in type 1 diabetes mellitus. Once properly educated with a deeper understanding as to how changes in their therapeutic regimen could affect their glycaemic control, motivated patients should be in a better position to optimise their own therapy with guidance from their physician. Such educational application of these techniques may be particularly pertinent because - rather than doing something for a patient all the time - teaching them how to do it for themselves may lead to a much more lasting (and possibly better) result .
Anderson et al  in 1992 reported a survey of some 400 nurse and dietician members of the American Association of Diabetes Educators regarding their attitudes, use and knowledge of computers. They found that even diabetes educators who used computers infrequently had a generally favourable attitude towards them. However, even those who reported frequent use of computers did not view themselves as adequately skilled, even in the most straightforward computer applications. Not surprisingly, the highest use of computers was for non-educational applications - confined mostly to word processing and record keeping.
Anderson and co-workers  reached the conclusion that their data were consistent with a perception among U.S. patient educators that there are, at present, few computer applications relevant to the field of diabetes education, and even fewer with any degree of broad acceptance. This survey indicated that the respondents (only 50% of the 800 members sent questionnaires actually replied) felt that computers have yet to make a major contribution to the teaching and learning process in diabetes education.
Furthermore, patient educators failed to view themselves as adequately prepared for the creative use or development of computer applications - leading the authors to suggest that "the present role of computers in support of patient education will not change significantly without encouragement, support and demonstrations of efficacy by health care institutions and professional organizations" .
Juge and Assal  have developed a computer assisted instruction program on hypoglycaemia which has been used in a number of European countries for patients with type 1 diabetes mellitus. Their perception is that patients suffering from a chronic disease, such as diabetes, require specific skills which are very different in nature to the theoretical knowledge they usually receive from different sources, including health-care providers. In order to be really useful, the authors  felt that educational programs for patients must help them to cope with their disease and, as far as possible, take into account their concerns, fears and misconceptions.
Clearly the provision of education about diabetes mellitus should be needs directed. Health-care professionals and students need education in diabetes care, while patients need training in diabetes self-care. The latter can be compared to learning to drive a car, whereas the former is more akin to understanding the physical principles on which the internal combustion engine is based (but never actually needing to acquire the skills of driving). Society should not try to teach all diabetic patients the patho-physiology of diabetes mellitus; yet we must endeavour to teach this to health-care professionals and students. Computers undoubtedly can help in both these processes .
The advent of home self-monitoring blood glucose (SMBG) measurements in the late 1970s brought with it the hope of improved glycaemic control for patients with type 1 diabetes mellitus. By the end of the 1980s the optimism encouraged by initial reports of improved control using SMBG had faded. In the majority of patients it appeared that the introduction of SMBG had not resulted in improved diabetic control [30,31].
SMBG data are conventionally analysed by the physician visually scanning the patient's logbook. As a consequence, clinical decisions are often based on inadequate assessments of the available data. The ability of health-care professionals to efficiently access and analyse SMBG data is crucial if all the potential benefits of such data collection are to be fully realised . However, the problem runs deeper than this. Patients also need to be able to interpret their own data - and act on them accordingly. Page and Peacock  make a powerful case for the fact that, on its own, SMBG will not lead to improved glycaemic control without appropriate assessment and modification of the treatment regimen. The problem may be that not enough educational effort has gone into teaching patients how to 'close the loop' and adjust their own insulin injections, diet and lifestyle on the basis of their SMBG data.
Accurate and immediate feedback regarding performance is essential to the learning and maintenance of any skill. The provision of such rapid feedback is well suited to currently available computer technology. However, at present patients often perform SMBG month after month, producing large volumes of data, without any appreciable or appropriate feedback from health professionals . Eventually, many can become disenchanted and discontinue monitoring .
Many prospective studies of the impact of standard diabetes patient education have found that while knowledge is increased, metabolic control improves little, if at all [34-36]. The lack of formalised insulin adjustment methods may be a major reason for this - explaining why many diabetes control programs fail to demonstrate significantly better metabolic control in their patients [37,38]. By contrast, using computer-based interactive education in out-patient clinics, some improvement in glycaemic control has been demonstrated in patients with type 1 diabetes mellitus .
So where do we go from here? That computers can 'close the loop' for in-patient and peri-operative blood glucose control in diabetes mellitus has been well documented. The problem is how to achieve similar ambulatory control through educational means. Providing diabetic patients with the knowledge and confidence to 'close the loop' and act on their SMBG measurements is probably the single most important area in which computer-based simulation and education techniques can be applied with existing computer technology, today. The need for such systems is clear because even amongst interested and motivated patients, knowledge of the principles of insulin self-adjustment at present appear limited .
In the words of the DCCT investigators "Because the resources needed are not widely available, new strategies are needed to adapt methods of intensive treatment for use in the general community at less cost and effort" . It is against this background that the current educational versions of AIDA (v4) and AIDA on-line have been produced.
In parallel with use for patient education such a system may also have potential for the teaching of medical and nursing students, as well as other health-care workers. Michael and Rovick  have highlighted some of the features of computer-based simulations which make them so attractive as teaching tools for developing problem-solving expertise:
(i) they can be used in scheduled sessions with or without teachers present, but are equally useful as unscheduled, ad lib learning resources for individual students.Time will tell whether such a teaching tool - in the diabetes field - will prove of value for the education of medical and nursing students about insulin dosage and dietary adjustments in type 1 diabetes mellitus; this possibly being a novel way of providing a larger number of suitably trained health-care workers to disseminate the benefits of the DCCT trial more widely.
(ii) such programs can be designed to be highly interactive, providing nearly immediate feedback at every point; much of their success as teaching tools relying on their ability to command active learning while providing on-line correction of errors.
(iii) the interactive nature of such exercises also allows the experience to be individualised for each user; 'good' students can be immediately reinforced and their knowledge-base and skills increased, while students having difficulties can have factual and / or problem-solving errors corrected immediately .
The concept underlying the current release of AIDA (v4) and AIDA on-line is to allow patients, their relatives, students and health-care workers to experiment with insulin dosage and dietary adjustments, and perhaps as a result of their experience gain an increased knowledge and a deeper understanding of the interplay between the processes involved.
The glycaemic response of an insulin-treated diabetic patient goes through transitory phases leading to a steady state glycaemic profile following a change in either the insulin regimen or diet. The purpose of the AIDA model is to simulate these steady state glycaemic and plasma insulin responses independently of the initial values from which the simulation is started. The accompanying figures to this text provide a schematic showing the anatomical basis of the model which assumes a patient completely lacking endogenous insulin secretion - i.e. an insulin-dependent (type 1) diabetic patient.
The model contains a single glucose pool representing extracellular glucose (including blood glucose) into which glucose enters via both intestinal absorption and hepatic glucose production. Glucose is removed from this space by insulin-independent glucose utilisation in red blood cells (RBCs) and the central nervous system (CNS, see figure) as well as by insulin-dependent glucose utilisation in the liver and periphery; the latter taking place mostly in muscle and adipose tissue. Hepatic and peripheral handling of glucose in the model are dealt with separately. Glucose excretion from the extracellular space takes place above the renal threshold of glucose as a function of the creatinine clearance (glomerular filtration) rate.
By separating the hepatic and peripheral handling of glucose in the model it is possible to assign different case scenario specific insulin sensitivity parameters to glucose-insulin interactions in the liver and periphery.
As shown schematically in the figure, peripheral glucose uptake takes place as a function of both insulin and plasma glucose levels; the former enhancing glucose utilisation according to the peripheral insulin sensitivity parameter, Sp, which has a normalised value between 0 and 1. Sp multiplied by the insulin level gives the effective insulin level responsible for the control action.
As the liver both produces and utilises glucose depending on the blood glucose and insulin levels we have modelled hepatic glucose handling in terms of the 'net hepatic glucose balance' which is computed as the sum of gluconeogenesis, glycogen breakdown and glycogen synthesis data derived for different blood glucose and insulin levels from nomograms given in Guyton et al . This representation of hepatic glucose handling was chosen in order to avoid the use of non-physiologically based mathematical functions to describe hepatic glucose handling. Table 1 shows how the net hepatic glucose balance varies as a function of glucose and normalised insulin levels. Sh, the hepatic insulin sensitivity parameter, which also has a normalised value between 0 and 1, allows computation of the effective insulin level which controls hepatic glucose handling.
The net hepatic glucose balance for any arterial blood glucose level between 1.1 mmol/l and 4.4 mmol/l is computed by interpolation between the values shown on the nomogram. Capillary blood glucose values measured using home monitoring blood glucose meters are approximately 25% lower than the arterial blood glucose levels which are given in . Hence, in the AIDA model we use the capillary blood glucose levels which correspond to the arterial blood glucose data given in Table 1.
Effective plasma insulin
(Sh * I / Ibasal)
AG <= 1.1 mmol/l AG = 3.3 mmol/l AG >= 4.4 mmol/l 0 291.6 160.0 78.3 1 194.6 114.6 53.3 2 129.3 66.0 -1.7 3 95.7 46.3 -54.3 4 85.0 22.6 -76.0 5 76.3 4.3 -85.0 6 69.0 -10.0 -92.0 7 62.0 -25.3 -97.3 8 52.0 -43.3 -101.0 9 48.0 -47.3 -104.0 10 41.7 -49.3 -106.7
Table 1. Net hepatic glucose balance (mmol/hr) as a function of the arterial blood glucose level, AG, and plasma insulin level, I; calculated from Guyton et al . Sh is a case scenario specific hepatic insulin sensitivity parameter which has a normalised value between 0 and 1.
The data shown in Table 1 are based on the steady state plasma insulin level which is normalised with respect to a basal level, Ibasal. Note that for low blood glucose values there is an automatic compensatory increase in hepatic glucose production (positive balance) and at high blood glucose levels the net action of the liver is to take up glucose from the blood (negative balance).
Glucose enters the portal circulation via first-order absorption from the gut. The rate of gastric emptying which provides the glucose flux into the small intestine in the model is assumed to be controlled by a complex process maintaining a relatively constant glucose supply to the gut during carbohydrate absorption apart from the ascending and descending phases of the gastric emptying process.
The duration of the period in which glucose entry from the stomach into the duodenum is constant and maximal has been defined as a function of the carbohydrate content of the meal ingested. Thus the time course of the systemic appearance of glucose is described by either a modified trapezoidal or triangular function depending on the quantity of carbohydrate in the meal (See figure).
The function of the kidney to excrete glucose has been modelled in terms of two case scenario specific model parameters; the renal threshold of glucose and the creatinine clearance (glomerular filtration) rate (See figure).
The model contains separate compartments for plasma and 'active' insulin. Insulin is removed from the former by hepatic degradation while the latter is responsible for glycaemic control. The activation and deactivation of insulin are assumed to obey first-order kinetics. The only insulin input into the model comes from the absorption site following subcutaneous injection. (See figure).
Four differential equations along with twelve auxiliary relations and the experimental data from  constitute the model which is solved by numerical integration. The change in the plasma insulin concentration, I, is given by the following equation:
where ke is the first-order rate constant of insulin elimination, Iabs is the rate of insulin absorption and Vi is the volume of insulin distribution. The build-up and the deactivation of the 'active' insulin pool, Ia, is assumed to obey first-order kinetics.
where k1 and k2 are first order rate constants which serve to describe the delay in insulin action. The rate of insulin absorption is modelled according to Berger and Rodbard .
where t is the time elapsed from the injection, T50 is the time at which 50% of the dose, D, has been absorbed and s is a preparation specific parameter defining the insulin absorption pattern of the different types of insulin catered for in the model (short-, intermediate- and long-acting).
A linear dependency of T50 on dose is defined as:
where a and b are preparation specific parameters, the values of which are given in  along with values for s. If the insulin regimen consists of more than one injection and / or components, Iabs becomes the sum of the individual Iabs contributions resulting from the different multicomponent injections.
The steady state insulin profile, Iss, corresponding to a given regimen is computed by using the superposition principle assuming three days to be enough to reach steady state conditions:
i.e. the steady state response results from the composite effect of injections given for three subsequent days. It is evident that this summation is not needed for short-acting insulin preparations (e.g. Actrapid) but it should be used for other, longer acting, insulin preparations whose half time of absorption is higher, especially when larger doses are given.
Since the experimental data provided by Guyton et al  refers to equilibrium conditions, the insulin level equilibrated with the steady state active insulin is considered when computing the net hepatic glucose balance and peripheral glucose uptake. In other words, at any time during the simulation, we have steady state Iss(t) and Ia,ss(t) values, but use:
(6)as the insulin level responsible for the hepatic and peripheral control action, where I#eq(t) is the insulin level in equilibrium with Ia,ss(t).
Assuming a single compartment for extracellular glucose, the change in glucose concentration with time is given by the differential equation:
where G is the plasma glucose level, Gin is the systemic appearance of glucose via glucose absorption from the gut, Gout is the overall rate of peripheral and insulin-independent glucose utilisation, NHGB is the net hepatic glucose balance, Gren is the rate of renal glucose excretion and Vg is the volume of distribution for glucose.
Assuming a classical Michaelis-Menten relationship between glucose utilisation and the plasma glucose concentration, with a constant Km such that insulin concentration is reflected in different values of the maximal rate of the transport process, we can write :
where c is the slope of the peripheral glucose utilisation vs insulin level relationship, GI is the insulin-independent glucose utilisation and GX is a reference glucose level. The NHGB value at any combination of G and I#eq has been derived from the data summarised in Table 1 using Sh * I#eq as the effective insulin level. The amount of glucose in the gut, Ggut, following the ingestion of a meal containing Ch millimoles of glucose equivalent carbohydrate is defined as:
where kgabs is the rate constant of glucose absorption from the gut into the systemic circulation and Gempt is the rate of gastric emptying. The duration of the period Tmaxge for which gastric emptying is constant and maximal (Vmaxge) is a function of the carbohydrate content of the meal ingested:
(10)where Vmaxge is the maximal rate of gastric emptying and Tascge and Tdesge are the respective lengths of the ascending and descending branches of the gastric emptying curve which have default values in the model of 30 mins (0.5 hrs).
However, for small quantities of carbohydrate (below approximately 10 g) such values cannot be used because there will never be time for the gastric emptying curve to plateau out. In such cases Tascge and Tdesge are defined as:
giving approximately a triangular function (See figure). Equation (11) is only used when the quantity of carbohydrate ingested falls below a critical level (Chcrit) which is defined as:
Using linear interpolation the rate of gastric emptying for meals containing Ch millimoles of carbohydrate greater than Chcrit, can therefore be defined, according to the time elapsed from the start of the meal, t, as follows:
(13)Glucose input via the gut wall, Gin, can be modelled by:
Values for these model parameters which have been derived from [42,43] are given in Table 2. All parameters except Sp and Sh are assumed to be case scenario independent.
As shown in the figure, the rate of renal glucose excretion, Gren, in the model is defined as:
for blood glucose values (G) above the renal threshold of glucose (RTG) where GFR is the glomerular filtration (creatinine clearance) rate. Default parameter values in the model have been set for RTG and GFR at 9.0 mmol/l and 100 ml/min respectively. These default values are used for all case scenarios except where renal dysfunction is to be simulated and the clinical parameters are chosen. The renal excretion of glucose (Gren) is zero for blood glucose values below the renal threshold of glucose [Equation (15b)].
ke = 5.4 l/hr insulin elimination rate constant k1 = 0.025 /hr parameter for insulin pharmacodynamics k2 = 1.25 /hr parameter for insulin pharmacodynamics Ibasal = 10 mU/l reference basal level of insulin Km = 10 mmol/l Michaelis constant for enzyme mediated glucose uptake GI = 0.54 mmol/hr/kg insulin-independent glucose utilisation per kg body weight GX = 5.3 mmol/l reference value for glucose utilisation c = 0.015 mmol/hr/kg/mU * l slope of peripheral glucose utilisation vs insulin line kgabs = 1 /hr rate constant for glucose absorption from the gut Vmaxge = 120 mmol/hr maximal rate of gastric emptying VI = 0.142 l/kg volume of distribution for insulin per kg body weight Vg = 0.22 l/kg volume of distribution for glucose per kg body weight
Table 2. Case scenario independent model parameter values calculated from [42,43].
It is noted that the insulin and glucose parts of the model are only linked by equation (8) and when computing the net hepatic glucose balance as a function of G and I#eq. This means that the plasma and 'active' insulin profiles as well as the glucose absorption profiles for any meal can be computed separately. This characteristic of the model is utilised when implementing the system for computer simulations.
Please note: Parameter estimation facilities are *not* available for AIDA on-line. They are are only available in the downloadable release of AIDA (v4) for the PC.
The algorithm used for parameter estimation of values for the hepatic and peripheral insulin sensitivities (Sh and Sp) determines estimates which give the best 'fit' between the observed and predicted data. Fit is assessed using data-trend sensitive least squares criteria to calculate the root mean square (RMS) deviation between the observed and predicted blood glucose data sets at the observed blood glucose time points. RMS values are calculated using the equation:
where d is the difference between each pair of observed and predicted blood glucose readings, n is the number of pairs of blood glucose values and np is the number of parameters in the parameter estimation procedure (2; Sh and Sp). In determining the fit hypoglycaemic episodes in AIDA v4 are assigned a blood glucose value of 1.0 mmol/l. Parameter values for which there is a conflict of trends between the two data sets in any time period are assigned a very poor fit by using an empirical, 'penalty' score for such cases. For example if the observed data shows a marked decrease in a given period while there is an increase in the simulated glycaemic profile in the same period, a penalty score is associated with the blood glucose level at the end of this period although the absolute deviation might be minor. Following parameter estimation if the best fit obtainable is greater than 3 mmol/l then the user is informed that it is not possible to fit the model to the data sufficiently accurately to permit individual case scenario parameterisation and simulation to be performed. A best fit value < 3 mmol/l was found, by inspection, to be the upper limit of acceptable parameter estimation. It should be apparent that the 'brute force' enumeration algorithm applied for parameter estimation precludes application of the model and fitting routine for *individual* patient parameterisation and simulation.
In order to carry out a preliminary validation of the AIDA model and permit a quantitative assessment to be made of its predictive accuracy blood glucose data, insulin dosage information and carbohydrate intake meal-related data were collected over a 5-6 day period from 30 insulin-dependent diabetic patients attending diabetes out-patient clinics in the Erzsebet Hospital [Budapest] as well as in the Diabetes Centre Bogenhausen [Munich], Istituto Patologia e Metodologica Clinica [Perugia], St. Thomas' Hospital [London] and Hospital Ramon y Cajal [Madrid] .
The data for a particular patient were entered into the system for the day before a change in the insulin injection or dietary regimen (defined as day 1). Parameter estimation was performed on these data and the values of Sh and Sp determined in this way were used to simulate the effect of changes in the therapeutic regimen for day 2. Parameter estimation could only be performed on data from 24 (80%) of the 30 patients in the study; crossover trends between observed and predicted data preventing the model from being used with data from the other 6 patients.
Upon simulation of the new insulin dosage or dietary regimen the RMS deviation between observed and predicted blood glucose profiles was automatically calculated. These calculations were performed at each observed blood glucose time point and a mean value for the RMS fit determined for that particular simulation. This process was repeated for all 24 patients over a period of 4-5 consecutive days yielding a total of 578 pairs of blood glucose measurements for simulation over 94 days; data from the first day of the study not being used in order to allow theoretical steady state conditions to apply. The RMS values for the fit obtained ranged from 0.8 to 4.6 mmol/l with a mean error (+/-SD) of 1.93 +/- 0.86 mmol/l .
The model presented here focuses on the adjustment of insulin and / or diet in the insulin-dependent diabetic patient. In contrast to previously developed heuristic rule based expert systems and linear models for insulin dosage or dietary adjustment [45-50] this model can be interpreted in physiological terms and is therefore more readily understandable to a health-care worker or patient; the anatomical basis of the model (figure) further aiding its interpretation by providing explicit functions for different organs within the body.
In developing the model we have followed the principles usually associated with the minimal-model approach, to find a concise mathematical formulation to represent the major physiological systems with the fewest possible parameters. As such the model has intentionally been kept simple. For example we have not, at present, attempted to model the role of ketones in the fasting type 1 diabetic patient, nor have we tried to model the change in the renal threshold of glucose which takes place with age. We have also avoided modelling any of the counter-regulatory hormones when simulating low blood glucose levels. Other complicating factors such as glucose transporters which help mediate insulin-independent glucose utilisation in certain muscle beds within the body have not been modelled. With increasing complexity the number of parameters for the model increases and so do the difficulties of determining their values in real life. Educationally there does not seem to be much to be gained by such added complexity. Despite these limitations we believe this model has potential application as a teaching tool for patients and medical personnel regarding insulin dosage and dietary adjustments in diabetes.
The validation work has shown that a set of differential equations with individually tailored parameters cannot be used to model all patients in any conditions. Hence the system should not be used for individual patient simulation.
The current version of the system takes account of both insulin therapy and the dietary regimen. However, physical activity, stress and other lifestyle related events are not, at present, included as it is assumed that they will remain relatively constant for the duration of the simulation period. Methods of incorporating such parameters into clinical models are currently being investigated.
Automated estimation of clinical parameters is a key requirement for the addition to the downloadable program of extra example case scenarios. We have developed a parameter estimation approach for AIDA v4 which not only minimises the least squares difference between observed and predicted data sets but also assesses the direction of change in the data. In this way it is possible for the computer to reject parameter values for which there is a good 'traditional fit' as assessed by least squares criteria, but clearly contradictory trends in the observed and simulated data. If no parameter values satisfy both criteria then the computer informs the user that the model cannot be fitted to the example case scenario data. Such a situation might occur, for example, if an attempt is made to fit the model to data where rebound hyperglycaemia follows a hypoglycaemic episode.
From a clinical viewpoint the work described here, allowing a user to experiment 'on-line' with common clinical situations, should be useful as an educational tool for patients, their relatives, students and / or medical staff. Furthermore it should be very easy for a clinician or diabetic specialist nurse to experiment with various ideas in order to identify possible changes in the treatment regimen for an individual patient. With the patients themselves present this might provide a very powerful educational opportunity for demonstrating to patients the glycaemic effects of certain changes in therapy. We stress, however, that such experimentation should only be performed by a suitably qualified and experienced diabetes specialist.
LIMITATIONS OF THE AIDA APPROACH
Limitations of the current AIDA approach can be related both to conceptual problems and problems arising from the current implementation. The model itself is clearly not refined enough, not coping with important processes such as exercise or stress - which greatly affect the lives of many diabetic patients. Also it does not allow the simulation of transient conditions. Furthermore, current modelling of the glycaemic effect of food in terms of the overall carbohydrate content of the meal is a well recognised simplification of a very complex physiological process. In this latter case, however, data are simply not available in the literature about the glycaemic indices and absorption times to peak of a wide variety of foods. For example, the glucose absorption profile following the ingestion of, say, 50 grams of carbohydrate in a hamburger, on its own, may differ quite substantially from that when an identical quantity of carbohydrate is ingested as a meal of chips. Also the glucose absorption profile will be completely different, once again, if the hamburger and chips are eaten together. Quite what happens to glucose absorption when tomato sauce and vinegar are added to this meal no one quite knows!
Clearly, current modelable knowledge of the processes involved in the absorption of food from the gut is severely limited, and this needs to be recognised in any attempts to utilise such models clinically for glycaemic prediction. Also, it is clear that the current parameter estimation technique employed within AIDA v4 is not, at present, refined enough since it provides no error estimates for parameter values. The lack of a flexible interface to external data collection devices and other pre-processing systems is also often commented upon - however as AIDA is intended purely as an educational tool, we do not want it to be too easy for patients to enter their own regimen and blood glucose data!
We have shown in previous work that the hepatic and peripheral insulin sensitivity parameters (Sh and Sp) estimated for one set of patient data on one day may not necessarily be accurate several days later. We have also shown that the RMS deviation between the observed and predicted blood glucose values became systematically worse as time progressed from the date of the original parameter estimation [4,13]. Given this, at the risk of labouring the point, neither AIDA v4 nor AIDA on-line are intended for individual patient simulation and should not be used for generating therapeutic advice [51-54].
For further information about how AIDA works we refer interested readers to published articles in the diabetes / medical / computing literature [1-18] where full details of all AIDA's functions and components are open to scrutiny.
The AIDA freeware software is provided 'as is' and the authors disclaim all warranties relating to the software program, whether express or implied, including but not limited to any implied warranties of merchantability and fitness for a particular purpose, and all such warranties are expressly and specifically disclaimed. Neither the authors nor distributors shall be liable for any indirect, consequential, or incidental damages arising out of the use or inability to use this freeware software even if the authors have been advised of the possibility of such damages or claims. In no event shall the authors have any liability for any damages regardless of the form of claim. The person using the software bears all risk as to the quality and performance of the software.
AIDA v4 and AIDA on-line are prototype computer systems. They are not finished pieces of work and therefore could contain bugs. Users should be aware that the simulations provided have not been formally validated and therefore could be erroneous.
While we have experimented with the use of parameter estimation techniques for fitting the model to individual patient data, the glycaemic predictions provided may in no way reflect an individual's glycaemic response. As such the authors hereby disclaim any liability for the use of this software.
Users are strongly advised to read the relevant scientific literature [1-18] to understand the limitations and assumptions underlying this work.
So that users can be informed of all updates and developments to the system (as well as bugs) they are cordially requested to register their use of AIDA, without charge.
To register all you need to do is enter your email id below and press the Submit button
or send a blank email note to: firstname.lastname@example.org
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22. Lehmann ED. Application of information technology in clinical diabetes care - a Special Issue. Part 1. Databases, algorithms and decision support. Medical Informatics 1996; 21: 255-258 [Editorial].
23. Lehmann ED. (Ed.) Special Issue: Application of information technology in clinical diabetes care. Part 1. Databases, algorithms and decision support. Medical Informatics 1996; 21: 255-378.
24. Lehmann ED. Application of information technology in clinical diabetes care - a Special Issue. Part 2. Models and education. Medical Informatics 1997; 22: 1-3 [Editorial].
25. Lehmann ED. (Ed.) Special Issue: Application of information technology in clinical diabetes care. Part 2. Models and education. Medical Informatics 1997; 22: 1-120.
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27. Lehmann ED. The Diabetes Control and Complications Trial (DCCT): A role for computers in patient education? Diabetes Nutrition and Metabolism 1994; 7: 308-316.
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Graphical summaries of the AIDA model structure are available via these links: AIDA model graphics (with links to further graphs) and AIDA model graphics (more detailed / printable graph).
A wide range of general diabetes computing (and AIDA-related) full text references can also be accessed in either HTML format or as portable document format (PDF) files by clicking on this link here.
For more information about AIDA or AIDA on-line please contact:
Dr. Eldon D. Lehmann
Department of Imaging (MR Unit)
Imperial College of Science, Technology and Medicine
National Heart and Lung Institute
Royal Brompton Hospital
London SW3 6NP
or via the on-line AIDA contact form
Source: Printed from the AIDA Website
This AIDA Technical Guide is based on: J. Biomed. Eng. 1992; 14: 235-242; Diab. Nutr. Metab. 1994; 7: 308-316; Med. Eng. Phys. 1994; 16: 193-202
The primary reference describing the AIDA model is reference 3 above.
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Return to AIDA Website Home Page AIDA is a freeware diabetic software simulator program of glucose-insulin action + insulin dose & diet adjustment in diabetes mellitus. It is intended purely for education, self-learning and / or teaching use. It is not meant for individual blood glucose prediction or therapy planning. Caveats
This Web page was last updated on 8th January, 2003. (c) www.2aida.org, 2000-2003. All rights reserved. Disclaimer. For the AIDA European Website, please click here. For the Diabetes / Insulin Tutorial, please click here.