AIDA: an interactive diabetes advisor

computer methods and programs in biomedicine ELSEVIER SCIENTIFIC P t l B L I S H ~ RS I R E I A N [ )

Computer Methods and Programs in Biomedicine 41 (1994) 183-203

AIDA: an interactive diabetes advisor E.D. Lehmann

,a

, T . D e u t s c h b'c, E . R .

Carson',

ca

P . H . S 6 n k s e n a'c

aDepartment of Endocrinology and Chemical Pathology, United Medical and Dental Schools of Guy's and St. Thomas's Hospitals (University of London), St. Thomas's Campus, Lambeth Palace Road, London SEI 7EH, UK bComputer Centre, Semmelweis University of Medicine, Budapest, Hungary CDepartment of Systems Science, Centrefor Measurement and Information in Medicine, City University, London, UK

Abstract AIDA is a prototype computer system that incorporates a model of glucose-insulin interaction in type I diabetes mellitus alongside a knowledge-based system to make glycaemic predictions and to generate insulin dosage adjustment advice. The model attempts to reflect the underlying (patho)physiology of insulin action and carbohydrate absorption in quantitative terms. The prototype is intended to be used as a decision support system by clinical personnel in the context of day-to-day management of insulin-dependent diabetic patients. It is designed for use during consultations, as a simulator of patient response following changed insulin and dietary regimen and as a system for providing education on planning insulin therapy. Joe Daniels is a 41-year-old, 70-kg, male insulin-dependent diabetic patient who was diagnosed as being diabeti c in 1972, at the age of 22. Joe recently found that he was having hypoglycaemic symptoms. Using self-monitoring blood glucose equipment, glycaemic levels below 3.0 mmol/l were recorded at least once a week, while hyperglycaemic readings (> 16 mmol/l) were observed two to three times per week. Joe came into hospital to have his glycaemic control improved, as doctors were concerned about the risks of him suffering a serious hypoglycaemic attack. Using some of the data collected by Joe while in hospital, we will demonstrate how AIDA might be applied either in a clinical setting to provide therapeutic advice or in an educational setting to interactively teach diabetic patients about their diabetes and educate them to adjust their own insulin injections and diet. Key words: Diabetes mellitus; Insulin dosage adjustment; Dynamic simulation; Knowledge-based system; Education

1. Introduction Diabetes mellitus is a major chronic disease that results from underproduction or reduced action o f the h o r m o n e insulin and is characterised by high blood glucose levels. It is a lifelong condition that * Corresponding author. tThis work was presented in part in Baltimore, Maryland, at the 16th Annual Symposium on Computer Applications in Medical Care (SCAMC), 8-11 November 1992, organised by the American Medical Informatics Association (AMIA). This paper is an extended version of Ref. 24.

has a variety o f debilitating and life-threatening complications. While the incidence o f the disease is currently on the increase in Western society, the incidence and severity o f the later-life complications that accompany it can be considerably reduced if the diabetic patient receives effective treatment leading to good glycaemic control [13,15]. In general, such treatment attempts to achieve normoglycaemia by maintaining a careful balance between diet, physical activity and insulin therapy. However, education o f the diabetic patient to achieve this balance requires a level of

0169-2607/94/$06.00 © 1994 Elsevier Scientific Publishers Ireland Ltd. All rights reserved. SSD1 0169-2607(93)01406-R

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clinical expertise that, although present in specialised diabetes units and some general practices with an interest in diabetes, is not always to be found in other sectors of the health service. One way of making this clinical expertise more widely available might be to use information technology [5,11,17,33]. A number of computer-based approaches to assist in the treatment or long-term management of diabetic patients have been previously reported in the literature [7]. For example, Berger and Rodbard [4] have developed a computer program that incorporates a pharmacodynamic model of insulin action to predict the expected time course of plasma glucose in response to a change in the insulin dose, timing or regimen. However, their system does not allow dietary input and has no explicit representation for endogenous glucose production or carbohydrate absorption. Numerous other stand-alone mathematical models of the glucoregulatory system in insulin-dependent (type I) diabetes mellitus also exist [7]. We have, however, recently developed a physiological model of glucose-insulin interaction for patient and medical staff education about insulin-dependent diabetes [21,24] as part of a more complex diabetes data management system [22,25-29]. The individually tailored glucoregulatory model in theory allows glycaemic predictions to be made following changes in a patient's treatment regimen. As well as helping to identify problems in glycaemic control, the model can also be used with a knowledgebased system (KBS) to provide therapeutic suggestions to try to help stabilise a patient's carbohydrate metabolism. Joe Daniels is a 41-year-old, 70 kg, male insulindependent diabetic patient who was diagnosed as being diabetic in 1972, at the age of 22. Joe recently found that he was having hypoglycaemic symptoms. Using self-monitoring blood glucose equipment, glycaemic levels below 3.0 mmol/1 were recorded at least once a week, while hyperglycaemic readings (> 16 mmol/i) were observed two to three times per week. Joe came into hospital to have his glycaemic control improved, as doctors were concerned about the risks of him suffering a serious hypoglycaemic attack. Using some of the data collected by Joe while in hospital, we will

demonstrate how our prototype computer system [20-291 might be applied either in a clinical setting to provide therapeutic advice to try to improve glycaemic control or in an educational setting to interactively teach diabetic patients about their diabetes and educate them to adjust their own insulin injections and diet. 2. System overview The system combines a mathematical model and a rule-based inference engine to interpret blood glucose data and recommend therapeutic actions following a change in either the diet or insulin regimen. The model, which assumes a patient completely lacking endogenous insulin secretion, contains a single extracellular glucose compartment into which glucose enters via both intestinal absorption and hepatic glucose production. Glucose enters the portal circulation via first-order absorption from the gut and is removed from the extracellular space by insulin-independent glucose utilisation in the central nervous system and red blood cells as well as by insulin-dependent glucose utilisation in the liver and periphery. Hepatic and peripheral handling of glucose in the model are dealt with separately, the net hepatic glucose balance being 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. [14]. Glucose excretion from the extracellular space takes place above the renal threshold of glucose as a function of the creatinine clearance rate. The only insulin input into the model comes from the absorption site following subcutaneous injection, the pharmacokinetics of insulin absorption being derived from a recent description of that process by Berger and Rodbard [4]. The model contains separate compartments for plasma and 'active' insulin, the latter being responsible for glycaemic control, while insulin is removed from the former by hepatic degradation. Insulin affects the net hepatic glucose balance characterised by a liver sensitivity parameter, Sh, as well as enhancing peripheral glucose utilisation described by a peripheral sensitivity parameter, Sp. Fig. 1 sum-

E.D. Lehmann et al./ Comput. Methods Programs Biomed. 41 (1994) 183-203

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Fig. 1. Summary of the compartmental structure of the model. CNS, central nervous system; RBC, red blood cells.

marises the compartmental structure of the model, while Fig. 2 provides an overview of the model's main functions, the mathematics of which have been described in detail elsewhere [21]. The model parameters need to be adjusted for individual patients. The algorithm used by the system for parameter estimation determines values for Sh and Sp that give the best fit between the observed (home monitoring) and predicted blood glucose data. Fit is assessed using data-trendsensitive 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. In determining the fit, hypoglycaemic episodes 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 a 'penalty' score for such cases. Following parameter estimation, if the best fit obtainable is greater than 3 mmol/l, then the clinician is informed that it is not possible to fit the model to the data sufficiently accurately to permit individual patient parameterisation and simulation to be performed [21]. The aim of the knowledge-based system (KBS) that has been linked to the model is to provide alternative suggestions whereby the quality of the patient's glycaemic control can be improved [26]. The reasoning approach used by the KBS is to (i)

locate hypoglycaemic episodes and blood glucose readings in the different daily periods, (ii) select patient-specific blood glucose targets in the different daily periods (target list), (iii) classify the blood glucose response in the different daily periods and select out-of-range type blood glucose problems to be solved (problem list), (iv) generate all candidate adjustments in the current insulin therapy that are expected to solve at least one problem on the problem list, (v) filter these suggestions for contradictions and possible side effects, (vi) rank-order alternative control actions and (vii) present final suggestions in a problem-oriented and control-oriented way, For the purposes of generating therapeutic advice, each day is divided into 13 consecutive time periods of variable duration [27]. Before reasoning starts, all hypoglycaemic episodes and the blood glucose readings made by the patient are allocated to one of these periods. Therapeutic targets are then defined in terms of lower and upper limits for the blood glucose level in these different time periods. Usually higher values are permitted for periods overnight, in order to reduce the risk of nocturnal hypoglycaemia. In order to correct blood glucose values that are either too high or too low, appropriate therapeutic adjustments in the current insulin regimen need to be selected. Changes that can be made include adjusting the dose and/or timing of the insulin injection(s) and shifting the inter-

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E.D. Lehmann et al./ Comput. Methods Programs Biomed. 41 (1994) 183-203

mediate-acting insulin injection (if present) between the evening meal and bedtime [27]. Alterations to the insulin regimen are generated on the basis of the precomputed glycaemic effect of insulin dosage (d) or timing (t) adjustments in different periods of the day, as summarised in Fig. 3. This figure shows the glycaemic effects of all possible therapeutic insulin regimen adjustments catered for by the current version of the KBS in the negative (d- or t-) direction. If the dose of an insulin preparation is increased (d+) or injected

later (t+), the direction of the changes shown on the graphs in Fig. 3 are reversed, the direction of the changes shown indicating the direction of the effects that adjustments in dosage or timing would have on the patient's blood glucose level in the corresponding time intervals. For example, a reduction in the Actrapid dose injected before breakfast (bre Act d-) would lead to a rise in the blood glucose profile between breakfast and lunch (Fig. 3). A list of candidate adjustments to the current

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insulin regimen that are expected to solve at least one problem in the patient's blood glucose profile is generated by an exhaustive search of all the adjustment options shown in Fig. 3. The selection of control actions for individual problems does not, however, represent the end of the reasoning process, as suggestions for different problems may be contradictory, e.g. one problem may require an increase in the insulin dose while another problem may require a decrease in the dose of the same insulin preparation. Furthermore, an adjustment that is beneficial for a particular problem may

precipitate some adverse side effects. Therefore the individual recommendations are filtered for contradictions and contra-indications before being presented to the clinician [27]. In order to facilitate the selection of final recommendations, the control actions that 'survive' the filtering process can be quantified and rankordered by the use of the model. Furthermore, as the KBS can provide a number of different pieces of advice for any one set of patient data, this link provides an opportunity to identify the 'optimal' suggestion for an individual patient. This is done

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by the use of an insulin dosage optimisation algorithm that adjusts the insulin regimen in the direction advised by the KBS and assesses the quality of glycaemic control that results upon simulation of this advice, the appropriateness of each suggestion being determined in terms of a deviation from normoglycaemia (DFN) parameter that provides a value for the difference between the predicted blood glucose profile and a pre-defined acceptable normoglycaemic range [26]. The default therapeutic target range used by the current version of the system is 3-10 mmol/l, but this can be adjusted as required for each individual patient. The DFN value itself is determined in terms of the area of the predicted blood glucose curve that lies outside the normoglycaemic range, blood glucose values below the lower normoglycaemic threshold carrying a penalty weighting of a factor of 2.5 (compared with hyperglycaemic values), as hypoglycaemic episodes are such potentially life-

threatening events, which the system needs to be able to avoid. The current version of the optimisation algorithm iterates through the model and the DFN calculator, assessing the deviation from normoglycaemia and then adjusting the insulin regimen in one-unit increments in the direction suggested by the KBS. The iterations for that particular piece of advice cease when the DFN parameter starts to increase (i.e. worsen). Having completed its iterations, the system then presents its advice in an easy-to-interpret format, selecting the insulin dosage adjustment that results in the smallest predicted deviation from normoglycaemia.

3. C a ~ smdy Fig. 4a shows some clinical and nutritional data collected by Joe F. Daniels while on intensive in-

E.D. Lehmann et al./Comput. Methods Programs Biomed. 41 (1994) 183-203

sulin therapy (injections three times a day). Fig. 4b displays these data graphically, showing the front end of the system used for accessing the model. The upper panel demonstrates the observed (measured) blood glucose readings recorded by Joe, while the lower panel represents a composite display of information regarding Joe's insulin and carbohydrate intake. The distribution of the carbohydrate units that he ate can be seen in this panel, along with the three-times-daily (split evening dose) regular (Actrapid) and intermediateacting (NPH) insulin regimen that he was prescribed. Fig. 4b shows the screen display while parameter estimation is in progress. Given the insulin and carbohydrate intake shown in the lower panel, the system performs simulations at 10% increments of the values of both S h and Sp, as described in Ref. 21. Given this, the red area on the graph in the upper panel constitutes the 'search space' for the parameter estimation routine. Fig. 4c shows the best-fit curve once parameter estimation is complete, the close correspondence between observed and predicted blood glucose values for this individual patient being clear to see. Having fitted the modei to Joe's observed blood glucose data, it is now possible to use the model to simulate the glycaemic effect of alterations to his therapeutic regimen. Fig. 5a shows how the model can be used to demonstrate to patients, students and medical staff the effect on Joe's blood glucose profile of taking his early morning insulin injection but missing breakfast. When this is done the model predicts 'Patient at risk of H YPO', with a nadir in glycaemia of 1.3 mmol/l at 9:45 a.m. Fig. 5b shows the opposite clinical situation, where Joe has breakfast but fails to take his early morning insulin. In this case his blood glucose profile is predicted to reach a peak of 16.6 mmol/I at 2:15 p.m., which would be clinically far from ideal. Fig. 6a shows how the model can also be used to demonstrate the glycaemic effect of dietary changes in Joe's therapeutic regimen. In this example the carbohydrate content of lunch has been increased in 10-g increments from its original 20 g to 60 g and the effect on his blood glucose profile simulated. As might be expected, eating three times his normal carbohydrate intake for lunch re-

189

suits in a marked hyperglycaemia (16.9 mmol/1 at 4:15 p.m.). Supposing that Joe planned to go for an important lunch where it would be difficult for him not to eat a lot, Figs. 6b and 6c show how the model could be used interactively to try to identify ways of controlling the hyperglycaemia that would result. In Fig. 6b the clinician has interactively added a short-acting insulin (Actrapid) injection to the therapeutic regimen. The amount of insulin to be injected at 12:30 p.m. (just before lunch), can, however be determined using the insulin dosage optimisation algorithm. Fig. 6b shows this optimisation procedure 'in progress', the strategy being to increase the Actrapid dose before lunch ('Actrapid injection number 21 because hyperglycaemia is present. Given this, in Fig. 6b the 12:30 p.m. Actrapid dose is being increased in oneunit increments, and for each increment a simulation is performed. A value for the deviation from normoglycaemia (DFN) parameter is then automatically calculated, iterations through the simulator and DFN calculator being performed until the value of the DFN parameter starts to increase. Fig. 6c shows insulin dosage optimisation once it is complete, the final advice being to inject 8 units of Actrapid at 12:30 p.m. As can be seen, the blood glucose profile that is predicted to result would be clinically perfectly acceptable. Fig. 7a shows another way in which the model might prove useful. In this case we see Joe's same data but simulating the effect of his going on the night shift at the local factory. In such a situation it might be very much harder for a physician, diabetic specialist nurse or patient to handle the new injection and meal times than for a computer. As can be seen, the model predicts that Joe will have a relatively low blood glucose (of 3.1 mmol/l) at 11:00 p.m., when he may be quite tired, having just transferred from the day shift. Fig. 7b shows how the model can be used interactively by a physician to help improve Joe's blood glucose profile. In this case suggestions from the clinician to adjust the 7:15 p.m. insulin injection, increasing his NPH dose by 4 units and decreasing his Actrapid dose by 2 units results in a predicted near-normoglycaemic blood glucose profile. As well as allowing one to experiment with an

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existing insulin regimen, the model also facilitates experimentation with the glycaemic effect of totally new insulin regimens. Fig. 8a shows an example of this, the effect of switching Joe over to Ultralente in place of his previous NPH injections being simulated. Supposing, however, that Joe was tired of injecting himself three times a day and wanted to be put on a twice-daily injection regimen, like other patients he knows, Fig. 8b shows how the glycaemic effect of this could also be simulated, his previous split evening dose being combined into one injection of Actrapid and Ultralente at 5:30 p.m. As can be seen, this leads to a predicted hyperglycaemia during the afternoon, which would not be clinically ideal. If Joe was insistent, however, that he wanted to be put on a twice-daily injection regimen, Fig. 8c shows how it would be possible to use the computer to optimise Joe's entire insulin regimen. In Fig. 8c the system is shown increasing the Actrapid and Ultralente morning and afternoon doses in an attempt to improve Joe's overall glycaemic control. In this example the target blood glucose range has been set as 4-10 mmol/1 in order to reduce the risk of Joe going 'hypo'. The optimisation procedure is shown once it is complete in Fig. 8d, the final advice being to 'increase the 7:15 a.m. insulin injection by 5 units of Ultralente from 12 units to 17 units.' As can be seen, this advice results in a predicted near-normalisation of Joe's blood glucose profile. Fig. 9 demonstrates how the knowledge-based system (KBS) that has been implemented alongside the model could be used directly to provide advice to improve Joe's glycaemic control. Fig. 9a

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shows how the system generates a list of all problems in Joe's original blood glucose profile (data from Fig. 4a) and matches this up with a list of all possible solutions that might improve Joe's glycaemic control in the required period while not causing a deterioration in the blood glucose profile in any other period of the day. Such a display might be useful in an educational setting for mediCal or nursing students as a way of stimulating discussion about the different problems that individual patients might have in maintaining glycaemic control. Furthermore, it would then be possible for the students to consider the merits of the different solutions proposed by the computer, before going on to simulate the glycaemic effect of such insulin dosage adjustments using the model. Having generated this list of problems and solutions, the KBS then checks that the solutions for one problem do not contradict any of the other solutions recommended for other problems during the day. This problem-oriented display of recommendations is shown in Fig, 9b. Once again such a display might be useful for students as a way of stimulating discussion about the appropriateness (or otherwise) of rejecting (filtering) one piece of advice and/or accepting another. The KBS then presents the clinician with a final list of different insulin dosage adjustments and the therapeutic problems that they are intended to solve. This list of recommended control actions, with explanations, is shown in Fig. 9c. The clinician is now given the opportunity to simulate the glycaemic effect of any combination of these insulin dosage adjustments interactively using the model. In this particular case the physician has

Fig. 4. (a) Clinical and nutritional data collected by Joe Daniels while on intensive insulin therapy (injections three times per day). (b) Front end display used for accessing the model, parameter estimation 'in progress.' (c) Curve shows the result of a simulation after fitting has been performed (parameter estimation complete). Liver insulin sensitivity parameter (Sh) --- 0.4 and peripheral insulin sensitivity parameter (So) = 0.7. Fig. 5. (a) Demonstrates the effect of Joe taking his early morning insulin injection but missing breakfast. (b) Shows what would happen to Joe's blood glucose profile if he forgot his early morning insulin injection. Fig. 6. (a) Demonstrates the effect on Joe's blood glucose profile of increasing the carbohydrate content of lunch from 20 to 60 g in 10 g increments. (b) Shows insulin dosage optimisation 'in progress' following the addition of an Actrapid injection before lunch (at 12:30 p.m.). (c) Shows how the hyperglycaemia demonstrated in Fig. 6a could be corrected by a lunchtime injection of 8 units of Actrapid (as proposed by the insulin dosage optimisation algorithm).

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E.D. Lehmann et a l . / Comput. Methods Programs Biomed. 41 (1994) 183-203

chosen to 'decrease the dose of the morning Actrapid injection by 20%' and 'inject the morning Actrapid dose 20 rain later.' The 20% dose change and the 20 min time change recommended are default values set by the system, as the reasoning of the KBS is purely qualitative. The display shown in Fig. 9d allows a comparison of the old and new therapeutic regimens before simulating the glycaemic effect of the advice, which has decreased the 7:15 a.m. Actrapid dose from 4 units to 3 units and moved the injection to 7:35 a.m. In Fig. 10a the glycaemic effect of each of the KBS's individual suggestions (1-3 in Fig. 9c) has been simulated using the model. A value for the deviation from normoglycaemia (DFN) parameter has also been automatically calculated by the system for each simulation. As can be seen, all three suggestions lead to improvements in Joe's predicted blood glucose profile, the DFN values ranging from 0.4 to 0.5 mmol/l, compared with a baseline DFN value of 0.7 mmol/l (Fig. 4c). Fig. 10b shows the simulation of the two pieces of advice selected interactively by the clinician in Fig. 9c. As can be seen, this results in an improvement (increase) in the glycaemic profile before lunch, when the blood glucose level was previously low, and gives a DFN value on simulation of only 0.3 mmol/l. Furthermore, upon simulation of the two pieces of advice the system concludes that 'this patient's blood glucose profile does not require further optimisation,' i.e. the computer regards the default 20% dose and time changes as appropriate insulin therapy adjustments for this individual patient at this particular time. Supposing, however, that Joe had experienced a hypoglycaemic episode at 10:30 a.m. (rather than just having a low measured blood glucose level - -

193

3.2 mmol/l at 9:30 a.m.; Fig. 4a). Fig. 10c demonstrates how the advice from the KBS would be altered to take this new clinical information into consideration. In such a situation the KBS focuses solely on correcting the hypoglycaemic episode, as 'hypos' are such potentially life-threatening situations. Fig. 10d demonstrates the simulation of the three pieces of advice given in Fig. 10c: 'decreasing the dose of the morning Actrapid injection by 20%,' 'decreasing the dose of the morning NPH injection by 20%' and 'decreasing the dose of the bedtime NPH injection by 20%.' As can be seen, carrying out these three suggestions leads to a marked increase in Joe's blood glucose profile, with a predicted minimum of 5.8 mmol/1 at 11:15 a.m., a great improvement on his previous 'hypo'. Fig. 11 provides another example of how the model might be useful as an educational tool. Fig. l la shows a summary treatment plan for Joe Daniels 4 days after he came into hospital. As can be seen, Joe was by now on a three-times-daily insulin injection regimen of Actrapid and NPH with a split evening dose. The first injection was administered 30 min before breakfast, while the second injection was administered just before lunch, with the third injection taken 30 min before the afternoon snack. Fig. l i b shows the observed blood glucose readings recorded by Joe, these data being shown graphically in Fig. l lc. It is interesting to note that in this particular case, 4 days after the original parameter estimation was performed (Fig. 4b), a simulation based on Joe's original insulin sensitivity parameters (Sh = 0.4 and Sp--0.7) was still reasonably accurate, the root mean square deviation (rms fit) between observed and predicted blood glucose values being only 0.6 mmol/I.

Fig. 7. (a) Simulates the effect on Joe's blood glucose profile of going on the night shift. (b) Shows how the model could be used interactively by a clinician, in this case to adjust Joe's 7:15 p.m. insulin injection, increasing his NPH dose by 4 units and decreasing his Actrapid dose by 2 units. Fig. 8. (a) Demonstrates the glycaemic effect of changing one of Joe's insulin preparations from NPH to Ultralente. (b) Simulates the effect on Joe's blood glucose profile of combining his previous split evening dose into a single injection at 5:30 p.m. (c) Shows insulin dosage optimisation 'in progress' for the whole therapeutic regimen in an attempt to improve Joe's blood glucose profile with just two injections per day. Strategy is to 'increase insulin' because 'hyperglycaemia present. ' (d) Insulin dosage optimisation complete: suggestion is to 'increase 7:15 a.m. insulin injection by 5 units o f UItralente from 12 units to 17 units.'

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E.D. Lehmann et al. / Comput. Methods Programs Biomed. 41 (1994) 183-203

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E.D. Lehmann et al./ Comput. Methods Programs Biomed. 41 (1994) 183-203

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Fig. 12. (a) Demonstrates how a completely new insulin and dietary regimen could be planned, in this case involving a total daily carbohydrate intake of I 15 g and twice-daily Actrapid and Lente injections. (b) Simulates the hyperglycaemia that would result from Joe injecting his morning insulin too late. (c) Simulates the profound hypoglycaemia that would occur if Joe mixed up his insulin preparations, injecting 22 units of Actrapid and 6 units of Lente in place of his prescribed 6 units of Actrapid and 22 units of Lente. (d) Shows how the degree of hypoglycaemia would be markedly reduced by Joe taking a carbohydrate load at 8:30 a.m. once he had realised what he had done.

Fig. 1 ld demonstrates how the system might be used to highlight the various physiological processes underlying the glycaemic excursions shown in the simulation in Fig. 1 lc. In this example 'UPTAKE' is the grouped peripheral, central nervous system and red blood cell utilisation of glucose; 'NHGB' is the net hepatic glucose balance (the

production or utilisation of glucose by the liver); 'GLUCOSE ABSORPTION' is the systemic appearance of glucose from the gut; and 'RENAL EXCRETION' is the loss of glucose via the kidneys into the urine. As can be seen, Joe's plasma insulin level reached a predicted peak of 48.8 mU/l at 9:30 a.m., following the 8:00 a.m. injection of 7

E.D. Lehmann et al. /Comput. Methods Programs Biomed. 41 (1994) 183-203

units of Actrapid and 5 units of NPH. As the blood glucose level (10.0 mmol/1) was above the renal threshold of glucose (9.0 mmol/l), there was an appreciable degree of glycosuria, with a predicted loss of glucose into the urine at 9:30 a.m. of 7.0 mmol/h. It is possible that a display such as this, highlighting the different glucose fluxes within the model, may be particularly useful as a demonstration tool for teaching medical students about carbohydrate metabolism in type I diabetes mellitus. Fig. 12 provides another example of how the system might be useful as an educational tool, this time in planning a completely new insulin and dietary regimen. While changing Joe's therapeutic regimen, the same insulin sensitivity parameters are used as originally determined for Joe using the parameter estimation routine shown in Fig. 4b. In this particular case the new regimen involves a total daily carbohydrate intake of 115 g and twicedaily Actrapid and Lente injections (Fig. 12a). With this new therapeutic regimen the system can be used to simulate the hyperglycaemia that would result from Joe injecting his morning insulin dose too late, at 9:00 a.m. (Fig. 12b), while Fig. 12c demonstrates the profound hypoglycaemia that would result if Joe confused his Actrapid and Lente preparations before breakfast, injecting 22 units of Actrapid and 6 units of Lente in place of his prescribed 6 units of Actrapid and 22 units of Lente. Fig. 12d, however, shows how this marked hypoglycaemia could, at least in part, be corrected by Joe taking a glucose load once he had realised what he had done. Fig. 12d simulates the effect on Joe's blood glucose profile of the addition of 55 g of carbohydrate at 8:30 a.m. in an attempt to correct the profound hypoglycaemia shown in Fig. 12c. While the system still predicts that Joe will be at risk of a hypoglycaemic episode, the degree of hypoglycaemia is markedly reduced, with a predicted minimum of 2.8 mmol/l at 12:15 p.m., compared with 1.4 mmol/l at 11:00 a.m. (Fig. 12c). In general, the system functions reviewed here may have utility in an educational setting, either for demonstrating to patients how changes in their insulin or dietary regimen might affect their blood glucose profile or for medical and nursing students to help them experiment with different therapeutic

197

regimens, the computer coming up with suggestions that the students might then consider in more detail.

4. Modelling physical activity Effective management of insulin-dependent diabetic patients attempts to maintain a careful balance between insulin therapy, diet and physical activity. Generally the insulin regimen is the most frequently altered input variable, dietary intake usually being outlined in global terms by a dietitian (e.g. total daily carbohydrate intake). Exercise, however, is an important factor that needs to be taken into consideration. For example, it has long been known that exercise has a beneficial effect on blood glucose control in insulindependent diabetic patients with moderate fasting hyperglycaemia [9,16,19,31,37]. Non-diabetic individuals can exercise at any time. By comparison, diabetic patients must be restricted in this respect. Ideally diabetic patients should anticipate exercise, so that the insulin dose can be reduced. While clinicians have approached the management of exercise in type I diabetes mellitus in many different ways, a rigorously precise approach is, at present, still lacking [36]. For example, Schiffrin and Albisser [36] have developed a computer algorithm, based on earlier work [35], for use by patients who anticipate exercise. Such algorithms, however, because of their completely empirical nature, are unable to provide explanations or allow information to be accessed at a more detailed level by either clinicians or students. It is well recognised that documenting the amount of work performed during routine daily activities and/or during recreational sports can be difficult [38]. Notwithstanding such limitations in our ability to classify exercise levels, it might well be useful for a system such as AIDA to be able to simulate the glycaemic effect of exercise, if only for educational purposes. Acute exercise enhances the disposal of an oral glucose load [30,34] and decreases the daily insulin requirements of diabetic patients [12,19]. In fact hypoglycaemia is a well-documented complication of exercise in the insulin-dependent diabetic

198

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Studies in humans and animals have shown that exercise enhances the absorption of insulin from subcutaneous injection sites [8,9,18,32]. It follows that the resultant increase in circulating plasma insulin levels could exert a blood glucose lowering effect, either by inhibiting hepatic glucose production or by stimulating peripheral glucose uptake, or both. Such synergistic actions of insulin and exercise, enhancing glucose disposal in humans [9], are, however, beyond the scope of the simple model proposed here.

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patient [9,31]. Based on data from Benn et al. [3], in studies using exercise bicycles we have calculated the effect on peripheral (muscle) glucose uptake of a 60-min period of exercise at 35% '~/O2max. Fig. 13 demonstrates how it might be possible to model the effect of exercise on peripheral (muscle) glucose uptake in type I diabetic patients using these data. Within the obvious limitations of only being able to simulate the non-parameterisable glycaemic effect of one exercise level, Fig. 14 shows how these data might be applied within AIDA to demonstrate the reduction in Joe's blood glucose profile that might occur following a game of tennis after lunch.

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5. Discussion

The model presented here focuses on the adjustment of insulin and/or diet in the insulindependent (type I) diabetic patient. We do not propose that a set of differential equations with individually tailored parameters can be applied to model all patients in any conditions. However, as we have shown with Joe Daniels, such an approach does appear to work in a strictly defined domain for some patients. Being able to predict a patient's glycaemic profile with, say, 95% confidence to within a known error (say 4-1 mmol/l, based on prior large-scale validation studies) could provide a very powerful technique for patient and student education as well as for clinical simulation. We have previously collected clinical and nutritional data from six patients over a 5-day period and have compared the model's predictions with the patients' observed blood glucose measurements [2,23]. The root mean square deviation between observed and predicted blood glucose values ranged from 0.95 to 3.14 mmol/l, with a mean [q-S.D.] of 1.89 4- 0.62 mmol/1. Such an approach allows us to calculate a 95% range for our simulations (analogous to 95% confidence intervals for a measurement technique) and allows us to display such ranges on the computer screen. Fig. 15 shows an example of this for Joe Daniels 4 days after he came into hospital. Once the range shown has been validated against a larger number of patients, it would be possible to adapt the insulin dosage optimisation procedure described to ensure that not only the predicted glycaemic profile but also the 95% range around that prediction remained within the desired nor-

E.D. Lehmann et al./ Comput. Methods Programs Biomed. 41 (1994) 183-203

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moglycaemic limits. In this way it might be possible to adjust patients' insulin regimens in an attempt to avoid hypoglycaemic and hyperglycaemic episodes during the day, while still being cautious and ensuring that the established errors within the model were, at least in part, taken into consideration. We have previously described a clinical protocol that could be used for the retrospective medical validation of the AIDA model [29]. Such an approach would permit an accurate assessment to be made of the predictive accuracy of the system. Given that the model is not going to work with all patients, it would be extremely useful to be able to predict which patients the model might be used for and, conversely, for which patients the model would be highly unlikely to work. While it is clear that the estimation of insulin sensitivity parameters for an individual patient needs to be part of an on-going process [22], not least because the values of such parameters will change with time, it may well be useful to include a 'run-in' period into any protocol for the clinical or educational use of the model. Testing the model with the patient's own data, in effect as part of the parameter estimation routine, would permit an initial assessment to be made to see if the model might be used with data from that particular individual. Failure in this simple test could be defined above a certain cut-off value in terms of the root mean square deviation between observed and predicted blood glucose data during the 'run-in' period. While qualitative advice could be provided by the knowledge-based system (KBS) for patients for whom such parameter estimation could not be performed, it would not be possible for quantitative advice to be

199

generated by the insulin dosage optimisation procedure in its current form. In this respect the KBS provides patient-independent qualitative advice, based solely on the blood glucose measurements made by the patient. While a number of different alternative suggestions can be generated, the actual individualisation and quantification of that advice can only be achieved at present by the use of the model. Given this, an alternative computational approach would be required in order to provide quantitative therapeutic advice for patients for whom the model could not be used. Qualitative reasoning similar to that used by the knowledge-based system (KBS) for the generation of therapeutic advice could, we believe, be applied as an educational tool in order to guide the learning process about insulin dosage adjustment in diabetes. Fig. 16a shows one of the ways in which this might be possible, using data from Joe's baseline simulation shown in Fig. 4c. In this figure we see how it might be possible to interactively (using the cursor function) identify problems in Joe's observed blood glucose profile ('post-breakfast low blood glucose) and have the computer automatically link these graphically to the solutions suggested by the KBS ('decrease morning Actrapid dose,' 'inject morning Actrapid 20 min later' and 'decrease bedtime NPH dose'). Simulating the glycaemic effect of this advice in an educational setting would, we believe, provide positive reinforcement of the perceived problem with the patient's blood glucose profile and the effectiveness of the proposed solution(s). Another way in which knowledge-based reasoning might be applied in such an educational setting would be to examine the results of simulations from the model [6,27]. Blood glucose profiles can exhibit distinctive patterns, and the physician or student may well wish to obtain more detailed insight into how these patterns may have been produced. If a patient's blood glucose profile can be 'reproduced' by simulating the response of the model to the current therapy, then the simulated glycaemic profile would be amenable to knowledge-based analysis [6], whereby the simulated blood glucose profile could be decomposed into a series of 'events' during which the blood glucose curve could be

200

E.D. Lehmann et al./ Comput. Methods Programs Biomed. 41 (1994) 183-203

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Fig. 16. (a) Shows how it might be possible to identify interactively (using the cursor function) problems in Joe's observed blood glucose profile and have the computer automatically link these graphically to the solutions suggested by the knowledge-based system. (b) Causal relationships between glucose and insulin in an attempt to explain the predicted fall in blood glucose from I 1.3 mmol/I at 8:00 a.m. to 8.5 mmol/I at 10:45 a.m. for the data from Joe Daniels 4 days after he came into hospital (Fig. 11d).

described as being 'falling', 'rising' or 'fiat'. The events could then be interpreted in terms of causal relationships between glucose and insulin, as depicted in Fig. 16b for the data from Joe Daniels 4 days after he came into hospital (Fig. l ld).

We have previously suggested that an approach such as this might potentially be of use in assisting the KBS in generating its therapeutic advice, the final advice from the system being based on a much deeper knowledge about the effects such

E.D. Lehmann et al./ Comput. Methods Programs Biomed. 41 (1994) 183-203

changes might have on the individual patient's blood glucose profile [27]. Furthermore, such a system could serve as a very powerful educational tool, allowing student nurses or medical students to gain a much greater understanding of how changes in a patient's diet or insulin regimen might affect that individual's carbohydrate metabolism [27]. Anderson et al. [1] have recently reported a survey of some 400 nurse and dietitian 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 recordkeeping [1]. Anderson and co-workers [1] 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 who were 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' [1]. We believe that the system we have developed may have application as an educational tool separate from any potential role as an individual patient simulator, its educational utility being enhanced by the ability to identify problems in a patient's blood glucose profile and suggest remedies. One area in which the prototype might

201

be particularly useful would be as a demonstration tool for use by diabetic specialist nurses, one of their jobs in the UK being to transfer expertise and experience from the secondary or tertiary hospital setting to practice nurses who have relatively little specialist knowledge about insulin dosage adjustment in diabetes. Given this, there are plans for the educational use of the system to be evaluated in the Department of Endocrinology at St. Thomas's Hospital, London. Elsewhere in this issue Deutsch et al. [10] report how a blood glucose data interpretation module might be applied alongside AIDA to assist a physician or diabetic specialist nurse in the management of insulin-dependent diabetic patients. We believe that it should be possible, by combining this work with a rigorous validation protocol such as that outlined elsewhere [29], to perform clinical studies to formally assess the overall clinical utility of the AIDA system.

6. System availability A copy of the AIDA system used to perform the simulations shown here and a user guide are available for health-care professionals without charge by writing to Dr. E.D. Lehmann at the Division of Medicine, St. Thomas's Hospital, London SE1 7EH, UK. The AIDA software runs on IBM PC or compatible 80386/486 based machines and requires approximately 1lAMb of hard disk storage space.

7. Acknowledgements This work was supported by grants from the EEC AIM (Advanced Informatics in Medicine) Exploratory Action (EURODIABETA Project No. AI019), the Science and Engineering Research Council (SERC) and the Wellcome Trust. The loan of computer equipment from IBM (UK) Ltd. and IBM Europe is gratefully acknowledged. Joe F. Daniels is a pseudonym for a real patient.

8. References Ill R.M. Anderson, M.B. Donnelly and G.E. Hess, An assessment of computer use, knowledge, and attitudes of diabetes educators, Diabetes Educ. 18 (1992) 40-46.

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