Diabetes advisory system (DIAS) is a decision support system, which has been developed to provide advice on the amount of insulin injected by subjects with insulin-dependent diabetes mellitus (IDDM). ...DIAS employs a temporal causal probabilistic network (CPN) to implement a stochastic model of carbohydrate metabolism. The CPN network has recently been extended to provide also advice to subjects with noninsulin-dependent diabetes mellitus (NIDDM). However, due to increased complexity and size of the extended CPN the calculations became unfeasible. The CPN network was, therefore, simplified and a novel approach employed to generate conditional probability tables. The principles of dynamic CPNs were adopted and, in combination with the method of conditioning, learning, and forecasting, were implemented in a time- and memory-efficient way. An evaluation using experimental data was carried out to compare the original and revised DIAS implementations employing data collected by patients with IDDM, and to assess the a posteriori identifiability of model parameters in patients with NIDDM.
We explore the use of 15N,13Cleucine tracer to estimate whole-body fractional rates of a fast-turning-over protein pool employing synthetic data. The kinetics of 15N,13Cleucine tracer are simplified ...compared with those of traditional leucine tracers and benefit from irreversible transamination to 13Cα-ketoisocaproaic acid (KIC) resulting in a simplified model structure. A three-compartment model of 15N,13Cleucine kinetics was proposed and evaluated using data generated by a Reference Model (based on a model by Cobelli et al .). The results suggest that fractional turnover rates of a fast-turning-over protein pool can be estimated with a low but acceptable precision during a six-hour constant intravenous infusion of 15N,13Cleucine with frequent sampling of plasma tracer-to-tracee ratio (TTR) of 15N,13Cleucine. We conclude that 15N,13Cleucine may be useful for the measurement of protein kinetics and its full potential should be explored in clinical studies with compartmental data analysis.
This paper assesses the feasibility of using a double blind controlled clinical trial to evaluate the function of a decision support system by applying such a design to the evaluation of a Diabetes ...Advisory System (DIAS). DIAS is based on a model of the human carbohydrate metabolism and is designed as an interactive clinical tool, which can be used to predict the effects of changes in insulin dose or food intake on the blood glucose concentration in patients with insulin dependent diabetes. It can also be used to identify risk periods for hypoglycaemia, and to provide advice on insulin dose. The latter feature was evaluated in the present study. We believe double blind controlled clinical trials are prerequisites for clinical application of many decision support systems, and conclude that the present double blind controlled clinical trial is a suitable evaluation method for the function of DIAS.
A new method is described which allows the normalised unit impulse response (disposition kinetics) to be estimated from the response function (plasma concentration data). In conjunction with a ...suitable deconvolution technique, the method provides an estimate of the relative input function. The method assumes that the unit impulse response is described by a sum (≤ 2) of exponentials and that the terminal input rate follows a first-order process. Under these assumptions the method can be shown to provide an estimate of the normalised unit impulse response and an estimate of the relative input function which are theoretically identifiable. An example is given using synthetic data.
1 Metabolic Modelling Group,
Centre for Measurement and Information in Medicine, City
University, London EC1V 0HB; and
2 Department of Endocrinology,
St. Thomas' Hospital, London SE1 7EH, United ...Kingdom
Based on a mass-balance model, a surrogate
measure of the whole body leucine transport into and out of cells under
steady-state conditions was calculated as u/ TTR, where u is the
infusion rate of (stable label) leucine tracer and TTR is the
difference between the tracer-to-tracee ratio of extracellular and
intracellular leucine. The approach was evaluated in ten healthy
subjects 8 males and 2 females; age, 31 ± 9 (SD) yr; body mass
index, 24.0 ± 1.6 kg/m 2 who
received a primed (7.58 µmol/kg) constant intravenous infusion (7.58 µmol · kg 1 · h 1 )
of
L -1- 13 Cleucine
over 180 min (7 subjects) or 240 min (3 subjects). Five subjects were
studied on two occasions 1 wk apart to assess reproducibility. Blood
samples taken during the last 30 min of the leucine infusion were used
to determine plasma leucine concentration (129 ± 35 µmol/l), TTR
of leucine (9.0 ± 1.5%), and TTR of -ketoisocaproic acid (6.7 ± 0.8%). The latter TTR was taken as the measure of the free
intracellular leucine TTR. The whole body inward and outward transport
was 6.66 ± 3.82 µmol · kg 1 · min 1 ;
the rate of leucine appearance due to proteolysis was 1.93 ± 0.24 µmol · kg 1 · min 1 .
A positive linear relationship between the inward transport and plasma
leucine was observed ( P < 0.01),
indicating the presence of the mass effect of leucine on its own
transport. The transport was highly variable between subjects
(between-subject coefficient of variation 57%) but reproducible
(within-subject coefficient of variation 17%). We conclude that
reproducible estimates of whole body transport of leucine across the
cell membrane can be obtained under steady-state conditions with
existing experimental and analytical procedures.
mathematical model; stable-label isotope tracer; steady-state
conditions; reproducibility
A decision support system has been developed, Diabetes Insulin Advisory System for patients with non-insulin dependent diabetes mellitus (DIAS–NIDDM), assisting in the adjustment of insulin doses in ...insulin-treated subjects. DIAS–NIDDM uses a causal probabilistic network (CPN) model of carbohydrate metabolism to make stochastic predictions of blood glucose (BG) excursions. The CPN model is an extension of an existing model with an added component representing endogenous insulin secretion. A linear relationship between BG and insulin concentration due to BG stimulated insulin secretion is assumed. Model parameters (pancreatic sensitivity, insulin sensitivity, and time-to-peak of NPH insulin) are estimated by Bayesian probability updating from patient's specific data (food intake, insulin doses, BG measurements) recorded over a period of 4 days. The estimated parameters allow the system to be potentially used as a diagnostic tool to identify abnormalities of carbohydrate metabolism: impaired insulin secretion, insulin resistance and the severity of the impairments. DIAS–NIDDM was used to predict patient-specific BG profiles and advise on insulin doses during a pilot study in eight patients with NIDDM of whom five were treated with insulin. Compared to the administered insulin amount, daily insulin amount advised by DIAS–NIDDM was similar (within 4 U) in three patients, higher by 20% (19 U) in one patient and lower by 40% (18 U) and 50% (11 U) in two patients, respectively. The inter-day coefficient of variation of the daily insulin advice suggests that, at least according to DIAS–NIDDM criteria, day-to-day adjustment of insulin doses is necessary to maintain optimum control.
Tight glycaemic control has been shown to reduce mortality and morbidity in critically ill subjects. Using in silico computational approach, the objective of this study was to evaluate the effect of ...nutrition and the measurement error on glucose control. In silico simulation environment describing 21 synthetic subjects was used to simulate a 48 h clinical trial with an adaptive model predictive controller in the intensive care unit. Two types of nutritional protocols, simple and complex, and various levels of the measurement error (ME) were evaluated. The simple nutritional protocol resulted in more efficacious glucose control compared to that obtained with the complex nutritional protocol. A considerable deterioration was noted with the increasing level of the ME. Severe hypoglycaemia episodes (<2.8 mM) were observed with the ME> 10%. We conclude that nutritional protocol should be kept simple to facilitate efficacious glucose control with an adaptive model predictive controller. The measurement error of the glucose measuring device should be less or equal to 10%
