- Email: [email protected]
Insulin dosage adjustment in diabetes
Insulin dosage adjustment E.D. Lehmann
in diabetes
and T. Deutsch*
Diabetic Research Laboratory, Medical Unit (4NW), Department of Endocrinology and Chemical Pathology, United Medical and Dental Schools, St. Thomas’ Hospital, Lambeth Palace Road, London SE1 7EH, UK, *Computer Centre, Semmelweis University of Medicine, Budapest, Hungary and Centre for Measurement and Information in Medicine, Department of Systems Science, City University, London EClV OHB, UK
ABSTRACT A prototype computer system has been developed to provide advice on the day-to-day adjustment of carbohydrate intake and insulin regimen in the insulin-treated diabetic patient. The system also produces a 24-hour simulation of the patient’s blood glucose profile based on these aa$.stments. Advice is generated by a qualitative knowledge-based system which suggests what the next step in improving glycaemic control might be for a given patient, e.g. ‘decrease evening medium-acting insulin by two units’. Simulations are provided by a non-linear model which consists of a onecompartment glucose model linked to a model with plasma and ‘active’ insulin compartments. A description of the integrated system isprovidedand its operation illustrated by clinical case studiesfrom insulin-treated diabeticpatients. Keywords:Knowledge-based system, insulin advisory therapy, diabetes mellitus, dynamic simulation
INTRODUCTION Diabetes mellitus is one of the major chronic diseases in Western society and as such constitutes a major medical challenge. It is a disorder which results from under production or reduced action of the hormone insulin and is characterized by high blood glucose levels. It is a lifelong condition which has a variety of debilitating and life-threatening complications. These and peripheral include retinopath , nephropathy neuropathy as we1l as the more acute problems of hypoglycaemic and hyperglycaemic coma’. Diabetes is also the commonest cause of blindness in people under the age of 65 in the UK and has been reported to account for 46% of lower limb amputations carried out by the National Health Service2. In terms of patient care it is the management of the disease which dominates, since once diagnosed insulin-dependent diabetic patients require insulin therapy for the rest of their lives. If the insulin regimen is chosen appropriately then the blood glucose profile can be well controlled provided that appropriate attention is paid to both lifestyle and diet planning. This, however, requires a level of clinical expertise which, although found in specialized diabetic clinics, is not always available in other sectors of the health service3. 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. These include knowledge-based systems (KBS) to advise on patient management in out-patient clinics”, computer algorithms for insulin dosage adjustment5m7, approaches drawing on optimal and adaptive contro18*g and the use of mathematical Correspondence
and reprint requests to: E.D. Lehmann.
0 1992 Butterworth-Heinemann 0141-5425/92/030243-07
models as a means of predicting or simulating patient blood glucose levels l”-12. Our approach for making the required expertise for the management of insulin-treated diabetic patients more widely available is different in that it is based on an integrated methodology that uses ‘period orientated’ knowledge-based reasoning to analyse blood glucose rofiles and hypoglycaemic episodes at different cK aracteristic time points during the day. Qualitative insulin algebra and quantitative modelling have been introduced to predict changes in the patient’s blood glucose profile brought about by alterations in the insulin and dietary regimen. This work also incorporates the dynamics of glucoseinsulin interactions in a manner which reflects their clinical importance as a way of providing advice to the referring clinician. In this paper we will describe a rototype computer system that links a rule-base B expert system with a physiological model of glucose-insulin interaction in type 1 diabetes to provide an integrated decision support environment for assisting a physician or diabetic specialist nurse in the management of insulin-treated diabetic patients. SYSTEM
OVERVIEW
We are interested in established insulin-treated diabetic patients who require restabilization of their blood glucose profile. In such patients, blood glucose levels are monitored at home several times a day and these measurements, along with the registered occurrences of hypoglycaemic episodes, represent the observations upon which the insulin regimen and/or diet plan can be adjusted in order to achieve an improved degree of glycaemic control.
for BES J. Biomed. Eng. 1992, Vol. 14, May
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Insulin dosage adjustment in diabetes: E.D. Lehmann and i? Deutsch
University*4. Data from these sources can be combined to produce a ‘modal day’ current patient profile which represents a ‘snapshot’ of the patient’s current metabolic status with respect to insulin-treated diabetes. The averaging process used to generate this current patient refile requires that the patient have an a proximate Py constant carbohydrate intake and a fixe B insulin regimen over the ‘modal day’ period.
Patient
9’ Diabetic specialist nurse
Dietitian Data
collection
[&ET1
Qualitative and quantitative
models
The current version of the KBS requires the patient to T r Clinical data
m
I
Nutritional Data
+ data
e3
processing
_
I ‘Modal
day’
generation
/ f
Clycaemic
Figure
1
\
/
Structure of the integrated
+ Current patient profile \
\
system
The prototype developed to achieve this is a PCbased computer system which consists of three main components: a data recessing (DP) module, a qualitative knowledge- Flased system (KBS) and a uantitative simulator module. These modules, as s own in Figure 7, can be used to collect, analyse and R store clinical and nutritional patient data. On the basis of these data the system can provide suggestions expected to produce an improvement in the patient’s carbohydrate metabolism and predict glycaemic excursions following adjustments in the current treatment regimen.
)ata processing module The data processing module serves as the control module for the whole system. It accepts data from a number of different sources and processes it before making it available to the KBS and simulator modules. The DP module accepts time and date stamped clinical data such as blood glucose measurements, insulin injection doses and special event markers from CAMIT, a diabetes data management system developed by Boehringer-Mannheim13. This is in itself a three- art system which incorporates a Reflolux II/M portab Pe blood glucose meter, a portable electronic log book and a PC-based data management software ackage. The electronic log book can be connecte x via an RS-232 link to an IBM PC or compatible computer and also to the Reflolux blood glucose meter. In this wa , direct electronic transfer of data can take place. TL e data processing module also accepts nutritional data, such as the carbohydrate content of meals from MIcRoDIET, a nutritional analysis and dietary planning system developed at Salford
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J. Biomed. Eng. 1992, Vol. 14, May
make blood glucose measurements at characteristic times of the day, before the three main meals and before going to bed as it uses ‘period orientated’ ualitative reasoning in which the glucose balances in % e four main daily periods (breakfast-lunch, lunchsupper-bedtime and bedtime-breakfast) supper, serve as guides for selecting appropriate control actions. Post- randial observations are optional, but when availab Pe, are used to refine suggestions made solely on the basis of pre-prandial readings. Thera eutic objectives are formulated in terms of reasona 1 le ranges for the blood glucose level at each mealrelated characteristic time. In addition, there is a basic requirement that no hypoglycaemic episodes should occur at any time during the day. When hypoglycaemic episodes do occur, their timing determines the adjustments in the current regimen. In such cases the blood glucose observations are ignored as they may have arisen from rebound hyperglycaemia and as such may not reflect the patient’s ‘true’ response to the current treatment regimen. Depending on the location of the hypoglycaemic episodes relative to the adjacent meals, three types of episodes (shortly-before-a-meal, betweenmeals and shortly-after-a-meal type hypoglycaemic episodes) can be distinguished. Each of these has specific implications for the type of control actions used to eliminate them. The quality of glycaemic control is characterized by a set of ‘distance-from-the-target’ parameters calculated for each main daily period. This parameter reflects the absolute blood glucose value at the end of a given period as well as the change in the blood glucose value (glucose balance) over that time. As such it constitutes a marker, sensitive to hyperglycaemia, resulting from inappropriate control in the previous periods as well as from the inadequate local insulin effect in the current period. The period with the highest absolute value of this parameter constitutes the main target period for the control action to be administered. The meal plan and insulin dose are the controlling variables which are adjusted to ensure normoglycaemia. The necessary adjustments are based on the dynamics of the overall insulin action and the kinetics of the systemic appearance of glucose after food ingestion. The dynamic effect of insulin on blood glucose is assumed to follow a trapezoidal attern characterized by the onset, length of maxima P effect and total duration of action. The effect of food ingestion on the rise in glycaemia is assumed to depend on the carbohydrate content of the meal. Data defining the pharrnacodynamic effects of these control variables which represent the core of the
Insulin dosage adjustment in diabetes: E.D. Lehmann and T. Deutsch
system’s knowledge base are stored as PROLOG clauses. Possible control actions related to the insulin regimen include modifying the time or dose of the insulin injection. When adjusting the meal plan it is possible to modify either the carbohydrate content or the time of the meal. The knowledge base contains rules which define appropriate, indicated and contra-indicated control actions depending on the kind of problem in the ‘diagnosed’ in the different daily glucose suppl periods. This B iagnosis can be directly deduced from the value of the ‘distance-from-the-target’ parameter calculated for a given period. A highly positive value, for example, means an excess in the glucose supply in the period in question. Negative values and the presence of a hypoglycaemic e isode clearly indicate a shortage in glucose availabi Pity in the given time period. An example of an indicated control action is given below in PROLOG:
7 Insulin
absorption
indicated (decrease-dose NPH (before supper)) if problem (shortage- lucose-supply (bedtime-breakfast 7) This means that a decrease in the dose of NPH injected before supper is indicated if a persistent roblem exists with a shortage in the glucose su ply lzetween bedtime and breakfast. The knowledge Kase has been described in more detail elsewhere by Deutsch et al. 15,16. As a result of the reasoning process, the KBS comes up with all possible alternative control actions which are expected to improve glycaemic control in at least one daily period while not causing deterioration in the control of any other period. The advice provided, however, is purely qualitative. As such, another ualitative approach is needed to convert these suggestions into quantitative advice an 1 also to predict the blood glucose responses expected to be produced by the different proposed control actions. This role is fulfilled by the physiological model, which has been described separately in this journal issue in a paper by Lehmann and Deutsch (J Biomed Eng 1992; 14: 235-42). The physiological model is designed for patient and medical staff education about insulin-dependent diabetes mellitus, as well as possibly as a tool for individual patient simulation. As such, the purpose of the model is to simulate steady state glycaemic and plasma insulin responses to a given insulin therap and dietary regimen. For this quantitative approac K there are no constraints on the timing or frequency of meals or insulin injections that can be accepted by the system. Figure 2 demonstrates the compartmental structure of the physiological model, which consists of a one-compartment glucose model linked to a model with plasma and ‘active’ insulin compartments. In this model the abso tion of glucose from the gut and insulin following su r! cutaneous depot are both assumed to be patient independent which allows the plasma and ‘active ’ insulin profiles for any insulin injection as well as the glucose absorption profiles for any meal to be computed separately. This means that 12 out of the 17 physiological parameters in the model can be assumed to be patient independent.
In$hn
lnjeclions
Figure 2
Compartmental structure of the physiological model
The remaining 5 parameters, which are patient specific, are utilized for modelling interactions between the glucose and insulin parts of the model. These patient specific parameters include the individual’s weight, renal threshold of glucose and creatinine clearance rate, which can all be assessed in the routine clinical setting. The values for the two other parameters, sh and Sp, which modulate the glycaemic effect of insulin in the liver and periphery are automatically determined by the system using a parameter estimation routine.
Computer implementation The system components
have been linked together as shown in Figure 1. The integrated system allows the effects of qualitative advice from the knowledgebased system (KBS) to be simulated using the physiological model, potential1 allowing quantification of the advice from the KB H. Furthermore, as the KBS can provide a number of different su estions for any one given current patient profile, the Yink with the model provides an opportunity to identify the ‘optimal’ suggestion for an individual patient. The current version of the proto pe runs under DOS on an IBM PC or compatible. X e data processing module and the physiological model have been implemented in TURBO PASCAL while the KBS is written in MicroPRoLoG using an APES expert system shell. CLINICAL
EXAMPLES
Table 7 gives clinical and nutritional data collected by a 60 kg, female, insulin-dependent diabetic patient over a two-day eriod using a CAMIT portable electronic log I3. This B ata is shown gr a p hicall x in Figure 3a where the distribution of the 10 g carbo ydrate bread equivalents that the patient consumed can be seen in the lower anel along with the four-times-daily actrapid an tf NPH injection regimen that the patient was on. The upper panel shows the observed blood
J. Biomed. Eng. 1992, Vol. 14, May
245
Insulin dosage adjuctnent in diabetes: E.D. Lehmann and T, Deutxh Table 1 Clinical insulin regimen
data for a 60 kg female,
insulin-dependent
diabetic
patients
before
(Fipure 3a) and after (Figure 3~) changes
Value (Day 1)
Value
07:30 lo:oo 12:oo
30 g 20 g 50 g
30 g 20 g 50 g
Afternoon snack Supper Bedtime snack Actrapid injection Actrapid injection
16:00 19:oo 22:oo 07:30 12:oo
NPH injection Actrapid injection NPH injection
12:oo 19:oo 22:oo
2Q 40 20 6 4 4 3 6
20 40 20 5 4 6 3 6
Blood Blood Blood Blood Blood Blood Blood Blood
06:OO 07:oo 09:30 11:30 13:30 17:oo 20:oo 22:00
6.8 mm01 litre-’ 8.7mmollitre-’ 4.1 mm01 litre-’ 9.0 mm01 litie-’ 8.6 mm01 litre-’ 13.6 mmol litre-’ 12.9 mm01 litie-’ 9.4 mmol like-’
Clinical
Time
Data
Breakfast Mid-morning Lunch
glucose glucose glucose glucose glucose glucose glucose glucose
*Changes
made to insulin regime
Patient:
;
snack
Work
Blood
20 :
1
Hospital
glucose
Code
Change 2)
No change
g g g units* units units* units units
- 1 unit +2 units
7.4 mm01 litre-‘* 8.3 mmol litre-” 10.6 mmol litre-‘*
New observed values
9.1 mm01 litre-‘* 9.9 mm01 litrem’* 13.4 mmollitre-‘*
Number:
020201
Weight: 60.0 kg Liver: 0.50 Periphery: 1.00 _, Fit: 1 .2 mmol litre
level
r
Insulin
g g g units units units units units
to her
by clinician
2 Day
Package
Pay
were made
and carbohydrate intake + plasma insulin level
Plasma insulin_, 24.7 microlJ ml
8 NPHImonotard L1 Carbohydrate
3
6
9
12 Time
Patient:
Work
Package
2 Day
15
18
21
Code
Number:
24
(h) 2
Hospital
020202
Weight:
60.0
21
24
kg
~~~~~
0 0
3
6
9
12 Time
15
b
0
3
6
9
12 Time
15
18
21
24
(h)
[h)
Figure 3 a, Clinical data for a 60 kg, female, insulin-dependent diabetic patient. Results of a 24-h simulation, after parameter estimation has been performed, are shown superimposed. Upper panel: observed (0) and predicted blood glucose levels. Lower panel: carbohydrate intake with predicted plasma insulin curve. b, Simulations of the steady state blood glucose profiles compared with the data for Day 1 of the study for each of the four pieces of advice generated by the KBS. c, Simulation of changes made to the patient’s insulin regimen (Day 2) and use of the system as an educational tool illustrating the effect on the patient’s steady state blood glucose profile of taking her early morning injection but missing breakfast. d, Advice from the KBS as to how the patient’s blood glucose profile for Day 2 of the study could be improved (Table 7)
d
246
18
J. Biomed.
Eng. 1992, Vol. 14, May
Insulin dosage adjustment in diabetes: ED. Lehmann and T. Deutsch
glucose readings recorded by the patient using the electronic log. Superimposed on these gra hs are the results of a 24-h simulation, as predicte B by the hysiological model, after parameter estimation has lzeen performed. The lower panel shows the simulated plasma insulin curve for the patient’s current insulin regimen while the u per panel displays the blood glucose profile pre B icted for the patient’s current carbohydrate and insulin intake. The overall root mean square (rms) deviation between observed and predicted blood glucose values obtained following fitting was 1.2 mmol like-’ for hepatic and peripheral insulin sensitivities of & = 0.5 and S, = 1.0. This same data were made available to the knowledge-based system (KBS), which rovided suggestions as to how the patient’s blood gPucose profile could be improved. As the reasoning of the knowledge base is purely qualitative, it was not possible for the KBS to distinguish between the advice. This was achieved, however, by using the physiological model to predict the glycaemic effect of each of the suggestions. The quality of glycaemic control produced in each case was then assessed in order to identify the optimum piece of advice for the individual patient. This has been done as shown in Figure36, for each of the four pieces of advice generated. The quality of glycaemic control has been determined in terms of the rms deviation between the predicted blood glucose profile and a pre-defined acce table normoglycaemic range (4- 10 mm01 1itie-‘). l!l e rms values obtained for each of the four simulations were 2.5, 2.6, 1.1 and 0.3 mmol litre-i respectively. As such, suggestion number (4) from the KBS appears to be the atient. If we compare this best for that individual advice with the data co1 Pected by the patient for the second da of the study (Table 7) we see that the patient’s d actor suggested a change in the insulin regimen on the second day; decreasing the morning actrapid dose by 1 unit and increasing the lunchtime NPH dose by 2 units. The insulin dosage adjustment advice from the system to ‘increase the before lunch NPH dose by 2 units’ as shown in Figure 36 (No 4) therefore seems quite appropriate. Using the previously determined hepatic and peripheral insulin sensitivity parameters (& = 0.5 and .!$ = 1.0) a 24-h simulation was performed for the second day of the study. Figure 3c shows the observed blood glucose measurements for that day with the predicted curve superimposed. The rms deviation between observed and predicted values was 0.7 mm01 litre-’ . As can be seen from this figure, using the previously determined parameter values for this patient it has still been possible to apply the model to produce a reasonable simulation 24 h later. Figure 3c also demonstrates another way in which the system can be used, in this case for educational purposes. In this example we have simulated the effect on the patient’s blood glucose profile of taking her early morning injection but missing breakfast. The hypoglycaemia which would result is predicted to reach a nadir of 2.9mmol litre-’ at 10:00 h. The data for Day 2 of the study was also given to the KBS which came up with a number of different ways to improve the patient’s blood glucose profile (Figure3d). Once again the glycaemic effect of each of
Table 2 Changes in the patient’s treatment regimen during the 12day study period Day 0 I 2 4 9 10 11 12
Comments Patient had been on 30 g carbohydrate/day for the preceding 3 days Weight = 83 kg. Parameter estimation performed: St, = 0.1, s, = 0.3 Diet changed to 70 g carbohydrate/day Weight = 79.3 kg Patient experienced ‘hypos’ during the day; model could not assess fit pm NPH dose decreased by 5 units to try and reduce incidence of hypos am NPH dose decreased by 5 units to try and reduce incidence of hypos Weight = 75.2 kg. Diet changed back to 30 g carbohydrate/day
these was simulated and the rms deviation between predicted blood glucose levels and the normoglycaemic range assessed. In this case the rms values for each of the five pieces of advice were 1.7, 1.6, 1 .O, 1.3 and 1.8 mmol litre-’ respectively. Unfortunately, details of the actual changes made by the patient were not available for comparison. However, the data for Day 2 was made available to a clinician who inde endently gave advice as to how the patient’s b Pood glucose profile could be improved. The clinician’s suggestion was to add 2 units of NPH before breakfast and lunch and 1 unit of actrapid before supper. This advice ties in quite closely with the three pieces of advice from the KBS (2,3 and 4) which on simulation gave the lowest values for the rms deviation. The determination of patient specific insulin sensitivity parameters is a key requirement for the use of the model with individual patient data. In order to investigate how frequently these parameters need to be assessed, we collected blood glucose data from a atient 22-year-old, female, insulin-treated diabetic over a 12-day period. Table2 summarizes the c anges in the patient’s treatment regimen which took place during this time. As can be seen the patient was
R
0
I
I
I
I
I
2
4
6
8
10
1 12
Day (number)
Figure 4 RMS deviation between observed and predicted blood glucose values as a function of time from parameter estimation
J. Biomed. Eng. 1992, Vol. 14,
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iusulin dosage adjustment in diabetes: E.D. Lehmann and T. Deutsch
overweight and had been placed by a dietitian on a restricted diet to lose weight. Parameter estimation was erformed on Day 1 of the study. Subsequently a num g er of changes were made to the patient’s dietary and insulin regimen as documented in Table 2. The effects of these changes were simulated using the patient specific model ammeter values (Sr, = 0.1 and S, = 0.3) previously B etermined for Day 1 of the study. Figure 4 shows how the rms deviation between observed and predicted blood glucose values varied over the course of the study when simulating the changes which took place in the patient’s treatment regimen. Re-estimation of the parameter values for Day 8 of the study resulted in an improvement in the rms deviation between observed and predicted blood glucose values, from 2.6mmol litre-’ to 1.9 mm01 1it&.
DISCUSSION A proto e computer system has been developed ‘YRs a qualitative rule-based expert system which lin with a quantitative physiological model to provide advice and allow simulations to be made of the dayto-day adjustment of carbohydrate intake and insulin regimen in the insulin-treated diabetic patient. This integrated approach fulfils an important part of the total information management and decision support requirement for the management of the diabetic patient. The first case study demonstrates how the integrated a preach, linking the KBS with the physiological mo B el, can work quite well for an individual patient. However, it is apparent from the second case study that insulin sensitivity parameters estimated for one set of patient data on one day may not necessarily be accurate several days later. As can be seen from Figure 4, 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. For example, in this particular case, 6 days after the original fitting was performed the rrns deviation between observed and redicted data was nearly double that on Day 1 of tR e study. The figures do not, however, show that on Day 9 of the study the patient experienced a number of h oglycaemic episodes which the prototype wasn’t a ZPe to model or predict, although we do not know if the patient was sub’ected to some kind of stress or missed a meal whit L the system wasn’t told about. Hosker et al. I7 have reported that patients’ insulin sensitivity can vary quite appreciably day-by-day. Furthermore, we have found with our model that reestimation of patient specific parameters can lead to a significant improvement in the fit obtained following simulation. As such it is clear that the estimation of insulin sensitivity parameters for an individual patient needs to be part of an on-going process. The KBS component of the rototype has already undergone a preliminary me 1 ical validation. This focused on the verification by clinicians of the rules contained in the knowledge base18 and the comparison of the qualitative advice from the KBS, for 12
248 J. Biomed. Eng. 1992, Vol. 14, May
patient cases, with that of an independent diabetologistlg and the patients’ own doctors”. This work demonstrated that the KBS was able to match the advice of the patients’ own doctors in over 70% of cases studied’g. However, when the computer’s advice was compared with that of an independent diabetologist, a small bias towards increasing the insulin dose was observed2r. Considerable differences, however, were observed between the advice of doctors who saw the patients and the independent diabetologist who only saw recorded home monitoring blood glucose data. Other work has separately compared the advice from the KBS with that of a panel of diabetologists for seven clinical scenarios from the postgraduate teaching literature22. The advice from the KBS matched that of at least one of the diabetologists in over 60% of the cases studied, although once again considerable differences in advice were observed between diabetologistsz2. This prelimina validation work has hi hlighted the fact that the XI owledge base lacks ru Kes about insulin regimen changes. It has become clear that such knowledge is needed if the computer is to match the variety and flexibility of the advice given by clinicians. This work has also highli hted some interesting facts about the advice provi B ed by clinicians. Quite often their suggestions are qualitative advising an ‘increase’ or ‘decrease’ in a patient’s insulin therapy. However, they are also generally able to suggest the approximate extent of the proposed change; for example, whether the patient’s insulin should be adjusted by 2 units or 10 units. This cannot be achieved by the KBS on its own and this is where the use of the simulator module becomes necessary. In our paper describing the physiological model (J Biomed Eng 1992; 14: 235-42) we demonstrated how the system could be used for insulin-dosage o timization. For this, some sim le optimization a Pgorithms have been im lemente : . A much more powerful a preach woul s be to allow the KBS to direct insu Pin-dosage optimization b determining the periods d uring the day which iI ad the worst glycaemic control and identifying the insulin injections which exert the greatest effect during these periods. Having determined the direction of the change in the insulin regimen required (‘increase’ or ‘decrease’ dose) it would then be possible to use the model to optimize the insulin regimen for an individual patient in a more intelligent and flexible way than is possible at present. medical validation of the A preliminary knowled e-based system has been performed. Future work wil B undertake a more major clinical evaluation of both the KBS and the model. In particular, further testing of the model is required to determine whether it is suitable for individual patient parameterization which is a key requirement for clinical use. However, separate from its potential use as a patient simulator the model clearly has a role as an educational tool and may be particularly useful in a clinical setting for demonstrating to patients the effects of proposed changes in their therapy. Connected with this there are plans to develop the front end of the system further to allow more rapid user interaction to take place with the model; the intention being to provide a
Insulindosageadjustmentin drdhmtes: E.D. Lehmannand T Dcubth
dozen or so clinical studies on-line permitting the model to be used, in the first instance by medical students, for learning about insulin dosage adjustment in diabetes.
ACKNOWLEDGEMENTS work was supported by grants from the EEC AIM (Advanced Informatics in Medicine) Exploratory Action (EURODIABETA Project No. A1019), the Wellcome Trust and the Scientific and Engineering Research Council (SERC). The loan of computer equipment from IBM (UK) Ltd and IBM Europe is gratefully acknowledged.
This
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Carson ER, Cob& C, Finkelstein L. MuttulmeticatModeiling of Metabolic 471d Endocnhe Systems. New York: Wiley, 1983. Cobelli C, Ruggeri A. Evaluation of portal/peripheral route and of algorithms for insulin delivery in the closedloop control of glucose in diabetes. A modelling study. Im t-BME 1983; 30: 93-103. Braun D, Heinrichs HR. Monitoring of metabolic control using the Camit system: practical experience. Diabetes News 1989; 10: 6-10. Bassham S, Fletcher LR Spantan RHJ. Dietary analysis with the aid of a microcomputer. J Micro A#1 1984; 7: 279-89. Deutsch T, Carson ER, Harvey FE, Lehmann ED, Sonksen PH, Tamas G, Whitney G, Williams CD. Computer-assisted diabetic management: a complex approach. Camp Meth Prvg Biomed 1990; 32: 195-214. Deutsch T, Lehmann ED, Carson ER, Sonksen PH. Rules and models for insulin dosage adjustment. Diab Nutr Metab 1991; 4 (Suppl. 1): 159-62. Hosker JP, Matthews DR, Rudenski AS, Burnett MA, Darling P, Brown EG, Turner RC. Continuous infusion of glucose with model assessment: measurement of insulin resistance and P-cell function in man. Diabetv~gia i985; 28: 401-11. Roudsari AV, Lehmann ED, Leicester HJ, Carey S, Berm JJ, Deutsch T, Carson ER. The medical verification of a knowledge-based model for treating insulin-dependent diabetes. In: Computer Modelling. Amsterdam: NorthHolland, 1991, 161-67. Lehmann ED, Deutsch T, Roudsari AV, Carson ER, Benn JJ, Sonksen PH. A metabolic prototype to aid in the management of insulin-treated diabetic patienb. Dial N&Me&b 1991; 4 (Suppl. 1): 163-67. Lehmann ED, Deutsch T, Raudsari AV, Carson ER, Sonksen PH. A computer system tD aid in the trealmenl of diabetic patients. In: Computer Modelling. Amsterdam: North-Holland, 199 1,90- 100. Andreassen S, Bauersachs R, Benn J, Carson E, Gomez E, Hovorka R, Lehmann E, Nahgang P, de1 Pozo F, Roudsari A, Schneider J. Report on developed prototypes integrating KBS and other methodologies for insulin therapy advisory systems. EURODIABETA Technical Report to the EEC Advanced Informatics in Medicine Exploratory Action, 15, EEC-AIM, Brussels, 1990. Lehmann ED, Roudsari AV, Deutsch T, Carson ER, 5enn JJ, Sonksen PH. Clinical assessment of a computer system for insulin dosage adjustment. In: Adlassnig K-P, Grabner G, Bengtsson S, Hansen R, eds, Lecture Notes in Mcdrcal Znformatics.Berlin: Springer-Verlag, 1991; 45: 376-81.
J. Biomed. Eng. 1992, Vol. 14, May
249
in diabetes
and T. Deutsch*
Diabetic Research Laboratory, Medical Unit (4NW), Department of Endocrinology and Chemical Pathology, United Medical and Dental Schools, St. Thomas’ Hospital, Lambeth Palace Road, London SE1 7EH, UK, *Computer Centre, Semmelweis University of Medicine, Budapest, Hungary and Centre for Measurement and Information in Medicine, Department of Systems Science, City University, London EClV OHB, UK
ABSTRACT A prototype computer system has been developed to provide advice on the day-to-day adjustment of carbohydrate intake and insulin regimen in the insulin-treated diabetic patient. The system also produces a 24-hour simulation of the patient’s blood glucose profile based on these aa$.stments. Advice is generated by a qualitative knowledge-based system which suggests what the next step in improving glycaemic control might be for a given patient, e.g. ‘decrease evening medium-acting insulin by two units’. Simulations are provided by a non-linear model which consists of a onecompartment glucose model linked to a model with plasma and ‘active’ insulin compartments. A description of the integrated system isprovidedand its operation illustrated by clinical case studiesfrom insulin-treated diabeticpatients. Keywords:Knowledge-based system, insulin advisory therapy, diabetes mellitus, dynamic simulation
INTRODUCTION Diabetes mellitus is one of the major chronic diseases in Western society and as such constitutes a major medical challenge. It is a disorder which results from under production or reduced action of the hormone insulin and is characterized by high blood glucose levels. It is a lifelong condition which has a variety of debilitating and life-threatening complications. These and peripheral include retinopath , nephropathy neuropathy as we1l as the more acute problems of hypoglycaemic and hyperglycaemic coma’. Diabetes is also the commonest cause of blindness in people under the age of 65 in the UK and has been reported to account for 46% of lower limb amputations carried out by the National Health Service2. In terms of patient care it is the management of the disease which dominates, since once diagnosed insulin-dependent diabetic patients require insulin therapy for the rest of their lives. If the insulin regimen is chosen appropriately then the blood glucose profile can be well controlled provided that appropriate attention is paid to both lifestyle and diet planning. This, however, requires a level of clinical expertise which, although found in specialized diabetic clinics, is not always available in other sectors of the health service3. 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. These include knowledge-based systems (KBS) to advise on patient management in out-patient clinics”, computer algorithms for insulin dosage adjustment5m7, approaches drawing on optimal and adaptive contro18*g and the use of mathematical Correspondence
and reprint requests to: E.D. Lehmann.
0 1992 Butterworth-Heinemann 0141-5425/92/030243-07
models as a means of predicting or simulating patient blood glucose levels l”-12. Our approach for making the required expertise for the management of insulin-treated diabetic patients more widely available is different in that it is based on an integrated methodology that uses ‘period orientated’ knowledge-based reasoning to analyse blood glucose rofiles and hypoglycaemic episodes at different cK aracteristic time points during the day. Qualitative insulin algebra and quantitative modelling have been introduced to predict changes in the patient’s blood glucose profile brought about by alterations in the insulin and dietary regimen. This work also incorporates the dynamics of glucoseinsulin interactions in a manner which reflects their clinical importance as a way of providing advice to the referring clinician. In this paper we will describe a rototype computer system that links a rule-base B expert system with a physiological model of glucose-insulin interaction in type 1 diabetes to provide an integrated decision support environment for assisting a physician or diabetic specialist nurse in the management of insulin-treated diabetic patients. SYSTEM
OVERVIEW
We are interested in established insulin-treated diabetic patients who require restabilization of their blood glucose profile. In such patients, blood glucose levels are monitored at home several times a day and these measurements, along with the registered occurrences of hypoglycaemic episodes, represent the observations upon which the insulin regimen and/or diet plan can be adjusted in order to achieve an improved degree of glycaemic control.
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Insulin dosage adjustment in diabetes: E.D. Lehmann and i? Deutsch
University*4. Data from these sources can be combined to produce a ‘modal day’ current patient profile which represents a ‘snapshot’ of the patient’s current metabolic status with respect to insulin-treated diabetes. The averaging process used to generate this current patient refile requires that the patient have an a proximate Py constant carbohydrate intake and a fixe B insulin regimen over the ‘modal day’ period.
Patient
9’ Diabetic specialist nurse
Dietitian Data
collection
[&ET1
Qualitative and quantitative
models
The current version of the KBS requires the patient to T r Clinical data
m
I
Nutritional Data
+ data
e3
processing
_
I ‘Modal
day’
generation
/ f
Clycaemic
Figure
1
\
/
Structure of the integrated
+ Current patient profile \
\
system
The prototype developed to achieve this is a PCbased computer system which consists of three main components: a data recessing (DP) module, a qualitative knowledge- Flased system (KBS) and a uantitative simulator module. These modules, as s own in Figure 7, can be used to collect, analyse and R store clinical and nutritional patient data. On the basis of these data the system can provide suggestions expected to produce an improvement in the patient’s carbohydrate metabolism and predict glycaemic excursions following adjustments in the current treatment regimen.
)ata processing module The data processing module serves as the control module for the whole system. It accepts data from a number of different sources and processes it before making it available to the KBS and simulator modules. The DP module accepts time and date stamped clinical data such as blood glucose measurements, insulin injection doses and special event markers from CAMIT, a diabetes data management system developed by Boehringer-Mannheim13. This is in itself a three- art system which incorporates a Reflolux II/M portab Pe blood glucose meter, a portable electronic log book and a PC-based data management software ackage. The electronic log book can be connecte x via an RS-232 link to an IBM PC or compatible computer and also to the Reflolux blood glucose meter. In this wa , direct electronic transfer of data can take place. TL e data processing module also accepts nutritional data, such as the carbohydrate content of meals from MIcRoDIET, a nutritional analysis and dietary planning system developed at Salford
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make blood glucose measurements at characteristic times of the day, before the three main meals and before going to bed as it uses ‘period orientated’ ualitative reasoning in which the glucose balances in % e four main daily periods (breakfast-lunch, lunchsupper-bedtime and bedtime-breakfast) supper, serve as guides for selecting appropriate control actions. Post- randial observations are optional, but when availab Pe, are used to refine suggestions made solely on the basis of pre-prandial readings. Thera eutic objectives are formulated in terms of reasona 1 le ranges for the blood glucose level at each mealrelated characteristic time. In addition, there is a basic requirement that no hypoglycaemic episodes should occur at any time during the day. When hypoglycaemic episodes do occur, their timing determines the adjustments in the current regimen. In such cases the blood glucose observations are ignored as they may have arisen from rebound hyperglycaemia and as such may not reflect the patient’s ‘true’ response to the current treatment regimen. Depending on the location of the hypoglycaemic episodes relative to the adjacent meals, three types of episodes (shortly-before-a-meal, betweenmeals and shortly-after-a-meal type hypoglycaemic episodes) can be distinguished. Each of these has specific implications for the type of control actions used to eliminate them. The quality of glycaemic control is characterized by a set of ‘distance-from-the-target’ parameters calculated for each main daily period. This parameter reflects the absolute blood glucose value at the end of a given period as well as the change in the blood glucose value (glucose balance) over that time. As such it constitutes a marker, sensitive to hyperglycaemia, resulting from inappropriate control in the previous periods as well as from the inadequate local insulin effect in the current period. The period with the highest absolute value of this parameter constitutes the main target period for the control action to be administered. The meal plan and insulin dose are the controlling variables which are adjusted to ensure normoglycaemia. The necessary adjustments are based on the dynamics of the overall insulin action and the kinetics of the systemic appearance of glucose after food ingestion. The dynamic effect of insulin on blood glucose is assumed to follow a trapezoidal attern characterized by the onset, length of maxima P effect and total duration of action. The effect of food ingestion on the rise in glycaemia is assumed to depend on the carbohydrate content of the meal. Data defining the pharrnacodynamic effects of these control variables which represent the core of the
Insulin dosage adjustment in diabetes: E.D. Lehmann and T. Deutsch
system’s knowledge base are stored as PROLOG clauses. Possible control actions related to the insulin regimen include modifying the time or dose of the insulin injection. When adjusting the meal plan it is possible to modify either the carbohydrate content or the time of the meal. The knowledge base contains rules which define appropriate, indicated and contra-indicated control actions depending on the kind of problem in the ‘diagnosed’ in the different daily glucose suppl periods. This B iagnosis can be directly deduced from the value of the ‘distance-from-the-target’ parameter calculated for a given period. A highly positive value, for example, means an excess in the glucose supply in the period in question. Negative values and the presence of a hypoglycaemic e isode clearly indicate a shortage in glucose availabi Pity in the given time period. An example of an indicated control action is given below in PROLOG:
7 Insulin
absorption
indicated (decrease-dose NPH (before supper)) if problem (shortage- lucose-supply (bedtime-breakfast 7) This means that a decrease in the dose of NPH injected before supper is indicated if a persistent roblem exists with a shortage in the glucose su ply lzetween bedtime and breakfast. The knowledge Kase has been described in more detail elsewhere by Deutsch et al. 15,16. As a result of the reasoning process, the KBS comes up with all possible alternative control actions which are expected to improve glycaemic control in at least one daily period while not causing deterioration in the control of any other period. The advice provided, however, is purely qualitative. As such, another ualitative approach is needed to convert these suggestions into quantitative advice an 1 also to predict the blood glucose responses expected to be produced by the different proposed control actions. This role is fulfilled by the physiological model, which has been described separately in this journal issue in a paper by Lehmann and Deutsch (J Biomed Eng 1992; 14: 235-42). The physiological model is designed for patient and medical staff education about insulin-dependent diabetes mellitus, as well as possibly as a tool for individual patient simulation. As such, the purpose of the model is to simulate steady state glycaemic and plasma insulin responses to a given insulin therap and dietary regimen. For this quantitative approac K there are no constraints on the timing or frequency of meals or insulin injections that can be accepted by the system. Figure 2 demonstrates the compartmental structure of the physiological model, which consists of a one-compartment glucose model linked to a model with plasma and ‘active’ insulin compartments. In this model the abso tion of glucose from the gut and insulin following su r! cutaneous depot are both assumed to be patient independent which allows the plasma and ‘active ’ insulin profiles for any insulin injection as well as the glucose absorption profiles for any meal to be computed separately. This means that 12 out of the 17 physiological parameters in the model can be assumed to be patient independent.
In$hn
lnjeclions
Figure 2
Compartmental structure of the physiological model
The remaining 5 parameters, which are patient specific, are utilized for modelling interactions between the glucose and insulin parts of the model. These patient specific parameters include the individual’s weight, renal threshold of glucose and creatinine clearance rate, which can all be assessed in the routine clinical setting. The values for the two other parameters, sh and Sp, which modulate the glycaemic effect of insulin in the liver and periphery are automatically determined by the system using a parameter estimation routine.
Computer implementation The system components
have been linked together as shown in Figure 1. The integrated system allows the effects of qualitative advice from the knowledgebased system (KBS) to be simulated using the physiological model, potential1 allowing quantification of the advice from the KB H. Furthermore, as the KBS can provide a number of different su estions for any one given current patient profile, the Yink with the model provides an opportunity to identify the ‘optimal’ suggestion for an individual patient. The current version of the proto pe runs under DOS on an IBM PC or compatible. X e data processing module and the physiological model have been implemented in TURBO PASCAL while the KBS is written in MicroPRoLoG using an APES expert system shell. CLINICAL
EXAMPLES
Table 7 gives clinical and nutritional data collected by a 60 kg, female, insulin-dependent diabetic patient over a two-day eriod using a CAMIT portable electronic log I3. This B ata is shown gr a p hicall x in Figure 3a where the distribution of the 10 g carbo ydrate bread equivalents that the patient consumed can be seen in the lower anel along with the four-times-daily actrapid an tf NPH injection regimen that the patient was on. The upper panel shows the observed blood
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Insulin dosage adjuctnent in diabetes: E.D. Lehmann and T, Deutxh Table 1 Clinical insulin regimen
data for a 60 kg female,
insulin-dependent
diabetic
patients
before
(Fipure 3a) and after (Figure 3~) changes
Value (Day 1)
Value
07:30 lo:oo 12:oo
30 g 20 g 50 g
30 g 20 g 50 g
Afternoon snack Supper Bedtime snack Actrapid injection Actrapid injection
16:00 19:oo 22:oo 07:30 12:oo
NPH injection Actrapid injection NPH injection
12:oo 19:oo 22:oo
2Q 40 20 6 4 4 3 6
20 40 20 5 4 6 3 6
Blood Blood Blood Blood Blood Blood Blood Blood
06:OO 07:oo 09:30 11:30 13:30 17:oo 20:oo 22:00
6.8 mm01 litre-’ 8.7mmollitre-’ 4.1 mm01 litre-’ 9.0 mm01 litie-’ 8.6 mm01 litre-’ 13.6 mmol litre-’ 12.9 mm01 litie-’ 9.4 mmol like-’
Clinical
Time
Data
Breakfast Mid-morning Lunch
glucose glucose glucose glucose glucose glucose glucose glucose
*Changes
made to insulin regime
Patient:
;
snack
Work
Blood
20 :
1
Hospital
glucose
Code
Change 2)
No change
g g g units* units units* units units
- 1 unit +2 units
7.4 mm01 litre-‘* 8.3 mmol litre-” 10.6 mmol litre-‘*
New observed values
9.1 mm01 litre-‘* 9.9 mm01 litrem’* 13.4 mmollitre-‘*
Number:
020201
Weight: 60.0 kg Liver: 0.50 Periphery: 1.00 _, Fit: 1 .2 mmol litre
level
r
Insulin
g g g units units units units units
to her
by clinician
2 Day
Package
Pay
were made
and carbohydrate intake + plasma insulin level
Plasma insulin_, 24.7 microlJ ml
8 NPHImonotard L1 Carbohydrate
3
6
9
12 Time
Patient:
Work
Package
2 Day
15
18
21
Code
Number:
24
(h) 2
Hospital
020202
Weight:
60.0
21
24
kg
~~~~~
0 0
3
6
9
12 Time
15
b
0
3
6
9
12 Time
15
18
21
24
(h)
[h)
Figure 3 a, Clinical data for a 60 kg, female, insulin-dependent diabetic patient. Results of a 24-h simulation, after parameter estimation has been performed, are shown superimposed. Upper panel: observed (0) and predicted blood glucose levels. Lower panel: carbohydrate intake with predicted plasma insulin curve. b, Simulations of the steady state blood glucose profiles compared with the data for Day 1 of the study for each of the four pieces of advice generated by the KBS. c, Simulation of changes made to the patient’s insulin regimen (Day 2) and use of the system as an educational tool illustrating the effect on the patient’s steady state blood glucose profile of taking her early morning injection but missing breakfast. d, Advice from the KBS as to how the patient’s blood glucose profile for Day 2 of the study could be improved (Table 7)
d
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Insulin dosage adjustment in diabetes: ED. Lehmann and T. Deutsch
glucose readings recorded by the patient using the electronic log. Superimposed on these gra hs are the results of a 24-h simulation, as predicte B by the hysiological model, after parameter estimation has lzeen performed. The lower panel shows the simulated plasma insulin curve for the patient’s current insulin regimen while the u per panel displays the blood glucose profile pre B icted for the patient’s current carbohydrate and insulin intake. The overall root mean square (rms) deviation between observed and predicted blood glucose values obtained following fitting was 1.2 mmol like-’ for hepatic and peripheral insulin sensitivities of & = 0.5 and S, = 1.0. This same data were made available to the knowledge-based system (KBS), which rovided suggestions as to how the patient’s blood gPucose profile could be improved. As the reasoning of the knowledge base is purely qualitative, it was not possible for the KBS to distinguish between the advice. This was achieved, however, by using the physiological model to predict the glycaemic effect of each of the suggestions. The quality of glycaemic control produced in each case was then assessed in order to identify the optimum piece of advice for the individual patient. This has been done as shown in Figure36, for each of the four pieces of advice generated. The quality of glycaemic control has been determined in terms of the rms deviation between the predicted blood glucose profile and a pre-defined acce table normoglycaemic range (4- 10 mm01 1itie-‘). l!l e rms values obtained for each of the four simulations were 2.5, 2.6, 1.1 and 0.3 mmol litre-i respectively. As such, suggestion number (4) from the KBS appears to be the atient. If we compare this best for that individual advice with the data co1 Pected by the patient for the second da of the study (Table 7) we see that the patient’s d actor suggested a change in the insulin regimen on the second day; decreasing the morning actrapid dose by 1 unit and increasing the lunchtime NPH dose by 2 units. The insulin dosage adjustment advice from the system to ‘increase the before lunch NPH dose by 2 units’ as shown in Figure 36 (No 4) therefore seems quite appropriate. Using the previously determined hepatic and peripheral insulin sensitivity parameters (& = 0.5 and .!$ = 1.0) a 24-h simulation was performed for the second day of the study. Figure 3c shows the observed blood glucose measurements for that day with the predicted curve superimposed. The rms deviation between observed and predicted values was 0.7 mm01 litre-’ . As can be seen from this figure, using the previously determined parameter values for this patient it has still been possible to apply the model to produce a reasonable simulation 24 h later. Figure 3c also demonstrates another way in which the system can be used, in this case for educational purposes. In this example we have simulated the effect on the patient’s blood glucose profile of taking her early morning injection but missing breakfast. The hypoglycaemia which would result is predicted to reach a nadir of 2.9mmol litre-’ at 10:00 h. The data for Day 2 of the study was also given to the KBS which came up with a number of different ways to improve the patient’s blood glucose profile (Figure3d). Once again the glycaemic effect of each of
Table 2 Changes in the patient’s treatment regimen during the 12day study period Day 0 I 2 4 9 10 11 12
Comments Patient had been on 30 g carbohydrate/day for the preceding 3 days Weight = 83 kg. Parameter estimation performed: St, = 0.1, s, = 0.3 Diet changed to 70 g carbohydrate/day Weight = 79.3 kg Patient experienced ‘hypos’ during the day; model could not assess fit pm NPH dose decreased by 5 units to try and reduce incidence of hypos am NPH dose decreased by 5 units to try and reduce incidence of hypos Weight = 75.2 kg. Diet changed back to 30 g carbohydrate/day
these was simulated and the rms deviation between predicted blood glucose levels and the normoglycaemic range assessed. In this case the rms values for each of the five pieces of advice were 1.7, 1.6, 1 .O, 1.3 and 1.8 mmol litre-’ respectively. Unfortunately, details of the actual changes made by the patient were not available for comparison. However, the data for Day 2 was made available to a clinician who inde endently gave advice as to how the patient’s b Pood glucose profile could be improved. The clinician’s suggestion was to add 2 units of NPH before breakfast and lunch and 1 unit of actrapid before supper. This advice ties in quite closely with the three pieces of advice from the KBS (2,3 and 4) which on simulation gave the lowest values for the rms deviation. The determination of patient specific insulin sensitivity parameters is a key requirement for the use of the model with individual patient data. In order to investigate how frequently these parameters need to be assessed, we collected blood glucose data from a atient 22-year-old, female, insulin-treated diabetic over a 12-day period. Table2 summarizes the c anges in the patient’s treatment regimen which took place during this time. As can be seen the patient was
R
0
I
I
I
I
I
2
4
6
8
10
1 12
Day (number)
Figure 4 RMS deviation between observed and predicted blood glucose values as a function of time from parameter estimation
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iusulin dosage adjustment in diabetes: E.D. Lehmann and T. Deutsch
overweight and had been placed by a dietitian on a restricted diet to lose weight. Parameter estimation was erformed on Day 1 of the study. Subsequently a num g er of changes were made to the patient’s dietary and insulin regimen as documented in Table 2. The effects of these changes were simulated using the patient specific model ammeter values (Sr, = 0.1 and S, = 0.3) previously B etermined for Day 1 of the study. Figure 4 shows how the rms deviation between observed and predicted blood glucose values varied over the course of the study when simulating the changes which took place in the patient’s treatment regimen. Re-estimation of the parameter values for Day 8 of the study resulted in an improvement in the rms deviation between observed and predicted blood glucose values, from 2.6mmol litre-’ to 1.9 mm01 1it&.
DISCUSSION A proto e computer system has been developed ‘YRs a qualitative rule-based expert system which lin with a quantitative physiological model to provide advice and allow simulations to be made of the dayto-day adjustment of carbohydrate intake and insulin regimen in the insulin-treated diabetic patient. This integrated approach fulfils an important part of the total information management and decision support requirement for the management of the diabetic patient. The first case study demonstrates how the integrated a preach, linking the KBS with the physiological mo B el, can work quite well for an individual patient. However, it is apparent from the second case study that insulin sensitivity parameters estimated for one set of patient data on one day may not necessarily be accurate several days later. As can be seen from Figure 4, 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. For example, in this particular case, 6 days after the original fitting was performed the rrns deviation between observed and redicted data was nearly double that on Day 1 of tR e study. The figures do not, however, show that on Day 9 of the study the patient experienced a number of h oglycaemic episodes which the prototype wasn’t a ZPe to model or predict, although we do not know if the patient was sub’ected to some kind of stress or missed a meal whit L the system wasn’t told about. Hosker et al. I7 have reported that patients’ insulin sensitivity can vary quite appreciably day-by-day. Furthermore, we have found with our model that reestimation of patient specific parameters can lead to a significant improvement in the fit obtained following simulation. As such it is clear that the estimation of insulin sensitivity parameters for an individual patient needs to be part of an on-going process. The KBS component of the rototype has already undergone a preliminary me 1 ical validation. This focused on the verification by clinicians of the rules contained in the knowledge base18 and the comparison of the qualitative advice from the KBS, for 12
248 J. Biomed. Eng. 1992, Vol. 14, May
patient cases, with that of an independent diabetologistlg and the patients’ own doctors”. This work demonstrated that the KBS was able to match the advice of the patients’ own doctors in over 70% of cases studied’g. However, when the computer’s advice was compared with that of an independent diabetologist, a small bias towards increasing the insulin dose was observed2r. Considerable differences, however, were observed between the advice of doctors who saw the patients and the independent diabetologist who only saw recorded home monitoring blood glucose data. Other work has separately compared the advice from the KBS with that of a panel of diabetologists for seven clinical scenarios from the postgraduate teaching literature22. The advice from the KBS matched that of at least one of the diabetologists in over 60% of the cases studied, although once again considerable differences in advice were observed between diabetologistsz2. This prelimina validation work has hi hlighted the fact that the XI owledge base lacks ru Kes about insulin regimen changes. It has become clear that such knowledge is needed if the computer is to match the variety and flexibility of the advice given by clinicians. This work has also highli hted some interesting facts about the advice provi B ed by clinicians. Quite often their suggestions are qualitative advising an ‘increase’ or ‘decrease’ in a patient’s insulin therapy. However, they are also generally able to suggest the approximate extent of the proposed change; for example, whether the patient’s insulin should be adjusted by 2 units or 10 units. This cannot be achieved by the KBS on its own and this is where the use of the simulator module becomes necessary. In our paper describing the physiological model (J Biomed Eng 1992; 14: 235-42) we demonstrated how the system could be used for insulin-dosage o timization. For this, some sim le optimization a Pgorithms have been im lemente : . A much more powerful a preach woul s be to allow the KBS to direct insu Pin-dosage optimization b determining the periods d uring the day which iI ad the worst glycaemic control and identifying the insulin injections which exert the greatest effect during these periods. Having determined the direction of the change in the insulin regimen required (‘increase’ or ‘decrease’ dose) it would then be possible to use the model to optimize the insulin regimen for an individual patient in a more intelligent and flexible way than is possible at present. medical validation of the A preliminary knowled e-based system has been performed. Future work wil B undertake a more major clinical evaluation of both the KBS and the model. In particular, further testing of the model is required to determine whether it is suitable for individual patient parameterization which is a key requirement for clinical use. However, separate from its potential use as a patient simulator the model clearly has a role as an educational tool and may be particularly useful in a clinical setting for demonstrating to patients the effects of proposed changes in their therapy. Connected with this there are plans to develop the front end of the system further to allow more rapid user interaction to take place with the model; the intention being to provide a
Insulindosageadjustmentin drdhmtes: E.D. Lehmannand T Dcubth
dozen or so clinical studies on-line permitting the model to be used, in the first instance by medical students, for learning about insulin dosage adjustment in diabetes.
ACKNOWLEDGEMENTS work was supported by grants from the EEC AIM (Advanced Informatics in Medicine) Exploratory Action (EURODIABETA Project No. A1019), the Wellcome Trust and the Scientific and Engineering Research Council (SERC). The loan of computer equipment from IBM (UK) Ltd and IBM Europe is gratefully acknowledged.
This
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Carson ER, Cob& C, Finkelstein L. MuttulmeticatModeiling of Metabolic 471d Endocnhe Systems. New York: Wiley, 1983. Cobelli C, Ruggeri A. Evaluation of portal/peripheral route and of algorithms for insulin delivery in the closedloop control of glucose in diabetes. A modelling study. Im t-BME 1983; 30: 93-103. Braun D, Heinrichs HR. Monitoring of metabolic control using the Camit system: practical experience. Diabetes News 1989; 10: 6-10. Bassham S, Fletcher LR Spantan RHJ. Dietary analysis with the aid of a microcomputer. J Micro A#1 1984; 7: 279-89. Deutsch T, Carson ER, Harvey FE, Lehmann ED, Sonksen PH, Tamas G, Whitney G, Williams CD. Computer-assisted diabetic management: a complex approach. Camp Meth Prvg Biomed 1990; 32: 195-214. Deutsch T, Lehmann ED, Carson ER, Sonksen PH. Rules and models for insulin dosage adjustment. Diab Nutr Metab 1991; 4 (Suppl. 1): 159-62. Hosker JP, Matthews DR, Rudenski AS, Burnett MA, Darling P, Brown EG, Turner RC. Continuous infusion of glucose with model assessment: measurement of insulin resistance and P-cell function in man. Diabetv~gia i985; 28: 401-11. Roudsari AV, Lehmann ED, Leicester HJ, Carey S, Berm JJ, Deutsch T, Carson ER. The medical verification of a knowledge-based model for treating insulin-dependent diabetes. In: Computer Modelling. Amsterdam: NorthHolland, 1991, 161-67. Lehmann ED, Deutsch T, Roudsari AV, Carson ER, Benn JJ, Sonksen PH. A metabolic prototype to aid in the management of insulin-treated diabetic patienb. Dial N&Me&b 1991; 4 (Suppl. 1): 163-67. Lehmann ED, Deutsch T, Raudsari AV, Carson ER, Sonksen PH. A computer system tD aid in the trealmenl of diabetic patients. In: Computer Modelling. Amsterdam: North-Holland, 199 1,90- 100. Andreassen S, Bauersachs R, Benn J, Carson E, Gomez E, Hovorka R, Lehmann E, Nahgang P, de1 Pozo F, Roudsari A, Schneider J. Report on developed prototypes integrating KBS and other methodologies for insulin therapy advisory systems. EURODIABETA Technical Report to the EEC Advanced Informatics in Medicine Exploratory Action, 15, EEC-AIM, Brussels, 1990. Lehmann ED, Roudsari AV, Deutsch T, Carson ER, 5enn JJ, Sonksen PH. Clinical assessment of a computer system for insulin dosage adjustment. In: Adlassnig K-P, Grabner G, Bengtsson S, Hansen R, eds, Lecture Notes in Mcdrcal Znformatics.Berlin: Springer-Verlag, 1991; 45: 376-81.
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