Use of the DIAS model to predict unrecognised hypoglycaemia in patients with insulin-dependent diabetes

Predicting the risk of kidney stone formation in the nephron by 'reverse engineering'

Although most kidney stones are found in the calyx, they are usually initiated upstream in the nephron by precipitation there of certain incipient mineral phases. The risk of kidney stone formation can thus be indicated by changes in the degree of saturation of these minerals in the nephron fluid. To this end, relevant concentration profiles in the fluid along the nephron have been calculated by starting with specified urine compositions and imposing constraints from the corresponding, much less variable, blood compositions. A model for supersaturation within ten sections of both long and short nephrons has accordingly been developed based on this 'reverse engineering' of the necessary substance concentrations coupled with chemical speciation distributions calculated by our Joint Expert Speciation System (JESS). This allows the likelihood of precipitation to be assessed based on Ostwald's 'Rule of Stages'. Differences between normal and stone-former profiles have been used to identify sections in the nephron where conditions seem most likely to induce heterogeneous nucleation.

Prediction of nocturnal hypoglycemia by an aggregation of previously known prediction approaches: proof of concept for clinical application

BACKGROUND AND OBJECTIVE

Nocturnal hypoglycemia (NH) is common in patients with insulin-treated diabetes. Despite the risk associated with NH, there are only a few methods aiming at the prediction of such events based on intermittent blood glucose monitoring data and none has been validated for clinical use. Here we propose a method of combining several predictors into a new one that will perform at the level of the best involved one, or even outperform all individual candidates.

METHODS

The idea of the method is to use a recently developed strategy for aggregating ranking algorithms. The method has been calibrated and tested on data extracted from clinical trials, performed in the European FP7-funded project DIAdvisor. Then we have tested the proposed approach on other datasets to show the portability of the method. This feature of the method allows its simple implementation in the form of a diabetic smartphone app.

RESULTS

On the considered datasets the proposed approach exhibits good performance in terms of sensitivity, specificity and predictive values. Moreover, the resulting predictor automatically performs at the level of the best involved method or even outperforms it.

CONCLUSION

We propose a strategy for a combination of NH predictors that leads to a method exhibiting a reliable performance and the potential for everyday use by any patient who performs self-monitoring of blood glucose.

Statistical hypoglycemia prediction

BACKGROUND

Hypoglycemia presents a significant risk for patients with insulin-dependent diabetes mellitus. We propose a predictive hypoglycemia detection algorithm that uses continuous glucose monitor (CGM) data with explicit certainty measures to enable early corrective action.

METHOD

The algorithm uses multiple statistical linear predictions with regression windows between 5 and 75 minutes and prediction horizons of 0 to 20 minutes. The regressions provide standard deviations, which are mapped to predictive error distributions using their averaged statistical correlation. These error distributions give confidence levels that the CGM reading will drop below a hypoglycemic threshold. An alarm is generated if the resultant probability of hypoglycemia from our predictions rises above an appropriate, user-settable value. This level trades off the positive predictive value against lead time and missed events.

RESULTS

The algorithm was evaluated using data from 26 inpatient admissions of Navigator(R) 1-minute readings obtained as part of a DirecNet study. CGM readings were postprocessed to remove dropouts and calibrate against finger stick measurements. With a confidence threshold set to provide alarms that correspond to hypoglycemic events 60% of the time, our results were (1) a 23-minute mean lead time, (2) false positives averaging a lowest blood glucose value of 97 mg/dl, and (3) no missed hypoglycemic events, as defined by CGM readings. Using linearly interpolated FreeStyle capillary glucose readings to define hypoglycemic events provided (1) the lead time was 17 minutes, (2) the lowest mean glucose with false alarms was 100 mg/dl, and (3) no hypoglycemic events were missed.

CONCLUSION

Statistical linear prediction gives significant lead time before hypoglycemic events with an explicit, tunable trade-off between longer lead times and fewer missed events versus fewer false alarms.

Impaired absorption and omission of insulin: a novel method of detection using the diabetes advisory system computer model

The Diabetes Advisory System (DIAS) is a decision-support program developed to assist insulin dose adjustment in type 1 diabetes. In this paper, we show how it might be used to identify impaired absorption or omission of insulin in patients with poorly controlled blood glucose. An evaluation of glucose results from four outpatients with persistent hyperglycemia is presented (age 19-48 years with type 1 diabetes for 13-18 years of duration, HbA1c 9.4-13.6%). Each had completed a 4-day record of blood glucose (BG, pre-meal and bedtime), dietary (carbohydrate) intake, and insulin doses (with injection sites). From these data, DIAS modeled a glucose profile (simulated glucose, SG) for the same period. Qualitative assessments were made of differences between BG and SG, and selective reduction or complete removal of insulin doses where BG >> SG. Large improvements in modeling were attributed to either impaired absorption or omission of insulin. Confirmation of these problems was sought from the patients by detailed consultation and physical examination. Impaired insulin absorption was suspected in two patients, both having significant injection site abnormalities. Insulin omission was suspected in the other two subjects. Both had normal injection sites, and one admitted to missing doses. Following retraining, data from three patients showed noticeable improvements in overall modeling as well as glucose control. Using DIAS in the evaluation of patients with type 1 diabetes may highlight previously unrecognized injection site abnormalities or insulin dose omission. This could assist rational optimization of insulin therapy in cases of persistently poor glucose control.

A model of the endogenous glucose balance incorporating the characteristics of glucose transporters

This paper describes the development and preliminary test of a model of the endogenous glucose balance that incorporates the characteristics of the glucose transporters GLUT1, GLUT3 and GLUT4. In the modeling process the model is parameterized with nine parameters that are subsequently estimated from data in the literature on the hepatic- and endogenous- balances at various combinations of blood glucose and insulin levels. The ability of the resulting endogenous balance to fit blood glucose measured from patients was tested on 20 patients. The fit obtained with this model compared favorably with the fit obtained with the endogenous balance currently incorporated in the DIAS system.

The promise of technology in the management of the patient with diabetes

The advent of technology has brought many improvements in the management of individual aspects of the care of the patient with diabetes. However, the best management requires communication between systems to enable the clinician to coordinate these various aspects. This article reviews examples of the application of technology to the individual aspects of care. It also discusses the problems and promise of technology to improve overall care management.

Dynamic updating in DIAS-NIDDM and DIAS causal probabilistic networks

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 CPN's 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.

Preliminary experience of the DIAS computer model in providing insulin dose advice to patients with insulin dependent diabetes

The Diabetes Advisory System (DIAS) is a model of human glucose metabolism which predicts hourly blood glucose concentrations and provides advice on insulin dose. Its ability to provide appropriate advice was assessed in 20 well-controlled IDDM patients (mean (SD) age 38 (11), duration 17 (9) years; HbA1 8.8 (0.9)%, reference range 5.4-7.6%). Patients recorded blood glucose measurements, insulin dose and food intake for 4 days. These data were used to generate insulin dose advice by both DIAS and a diabetes specialist nurse. Patients were then allocated to follow either DIAS or nurse advice for a further 4 days. There was no significant difference in mean recorded blood glucose values or frequency of reported hypoglycaemia between the DIAS and nurse groups either before or after insulin dose adjustment. The DIAS model, however, generated significantly lower insulin dose advice than the nurse (median (range)% change in insulin dose: DIAS group -13.3% (-25.0 to +11.6) versus nurse group 0% (-8.7 to +2.5), P < 0.05). We conclude that, in the patients studied, DIAS provided insulin dose advice which maintained good short term control of diabetes, despite significant reductions in dose in some cases.

Decision support systems in diabetes: a systems perspective

This paper examines, from a systems perspective, some of the major issues associated with the provision of computer-based decision support in the management of the diabetic patient. The importance of understanding the underlying dynamics is emphasised, as is the value of a systems approach to the specification, design and evaluation of decision support systems if they are to find clinical acceptance.

Using a double blind controlled clinical trial to evaluate the function of a Diabetes Advisory System: a feasible approach?

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 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.

DIAS--the diabetes advisory system: an outline of the system and the evaluation results obtained so far

The present paper gives a description of the Diabetes Advisory System (DIAS), and the evaluation results obtained so far. DIAS is a decision support system for the management of insulin dependent diabetes. The core of the system is a compartment model of the human carbohydrate metabolism implemented as a causal probabilistic network (CPN or Bayesian network), which gives it the ability to handle the uncertainty, for example, in blood glucose measurements or physiological variations in glucose metabolism. The evaluation results suggest that, at least in our hands, DIAS can generate advice that is safe and of a quality that is at least comparable to what is available from experienced clinicians.