Prediction of postprandial blood glucose under uncertainty and intra-patient variability in type 1 diabetes: a comparative study of three interval models.

The behavior of three insulin action and glucose kinetics models was assessed for an insulin therapy regime in the presence of patient variability. For this purpose, postprandial glucose in patients with type 1 diabetes was predicted by considering intra- and inter-patient variability using modal interval analysis. Equations to achieve optimal prediction are presented for models 1, 2 and 3, which are of increasing complexity. The model parameters were adjusted to reflect the "same" patient in the presence of variability. The glucose response envelope for model 1, the simplest insulin-glucose model assessed, included the responses of the other two models when a good fit of the model parameters was achieved. Thus, under variability, simple glucose-insulin models may be sufficient to describe patient dynamics in most situations.

Insulin dosage optimization based on prediction of postprandial glucose excursions under uncertain parameters and food intake.

Considering the difficulty in selecting correct insulin doses and the problem of hyper- and hypoglycemia episodes in type 1 diabetes, dosage-aid systems are very useful for these patients. A model-based approach to this problem must unavoidably consider uncertainty sources such as large intra-patient variability and food intake. In the present study, postprandial glucose is predicted considering this uncertain information using modal interval analysis. This approach calculates a safer prediction of possible hyper- and hypoglycemia episodes induced by insulin therapy for an individual patient's parameters and integrates this information into a dosage-aid system. Predictions of a patient's postprandial glucose at 5-h intervals are used to predict the risk for a given therapy. Then the insulin dose and injection-to-meal time with the lowest risk are calculated. The method has been validated for three different scenarios corresponding to preprandial glucose values of 100, 180 and 250mg/dl.

Comparison of interval and Monte Carlo simulation for the prediction of postprandial glucose under uncertainty in type 1 diabetes mellitus.

In this paper, the problem of tackling uncertainty in the prediction of postprandial blood glucose is analyzed. Two simulation approaches, Monte Carlo and interval models, are studied and compared. Interval simulation is carried out using modal interval analysis. Simulation of a glucoregulatory model with uncertainty in insulin sensitivities, glucose absorption and food intake is carried out using both methods. Interval simulation is superior in predicting all severe and mild hyper- and hypoglycemia episodes. Furthermore, much less computational time is required for interval simulation than for Monte Carlo simulation.

Calculation of the best basal-bolus combination for postprandial glucose control in insulin pump therapy.

Intensive insulin therapy in type 1 diabetes is based on the well-established practice of adjusting basal and bolus insulin independently. Basal insulin delivery is designed to optimize glucose concentrations between meals and overnight, while bolus insulin delivery is designed to optimize postprandial glucose concentrations. However, this strategy shows some limitations in the postprandial glucose control, especially for meals with high carbohydrate content. Strategies based on coordinating basal and bolus insulin in the postprandial period help in overcoming these limitations. An algorithm, based on mathematically guaranteed techniques (interval analysis), is presented in this paper. It determines, given the current glycemic state of the patient and the meal to be ingested, a basal-bolus combination that will yield a tight postprandial glycemic control according to the International Diabetes Federation guidelines. For a given meal, the algorithm reveals which bolus administration mode will enable a good postprandial performance: standard, square-wave, dual-wave, or temporal basal decrement. The algorithm is validated through an in silico study using the 30 subjects in the educational version of the Food and Drug Administration accepted University of Virginia simulator.

Prediction of glucose excursions under uncertain parameters and food intake in intensive insulin therapy for type 1 diabetes mellitus.

Considering the difficulty in the insulin dosage selection and the problem of hyper-and hypoglycaemia episodes in type 1 diabetes, dosage-aid systems appear as tremendously helpful for these patients. A model-based approach to this problem must unavoidably consider uncertainty sources such as the large intra-patient variability and food intake. This work addresses the prediction of glycaemia for a given insulin therapy face to parametric and input uncertainty, by means of modal interval analysis. As result, a band containing all possible glucose excursions suffered by the patient for the given uncertainty is obtained. From it, a safer prediction of possible hyper-and hypoglycaemia episodes can be calculated.