Home blood glucose prediction: clinical feasibility and validation in islet cell transplantation candidates

Abstract

Aims/hypothesis: Diabetic subjects do home monitoring to substantiate their success (or failure) in meeting blood glucose targets set by their providers. To succeed, patients require decision support, which, until now, has not included knowledge of future blood glucose levels or of hypoglycaemia. To remedy this, we devised a glucose prediction engine. This study validates its predictions. Methods: The prediction engine is a computer program that accesses a central database in which daily records of self-monitored blood glucose data and life-style parameters are stored. New data are captured by an interactive voice response server on-line 24 h a day, 7 days a week. Study subjects included 24 patients with debilitating hypoglycaemia (unawareness), which qualified them for islet cell transplantation. Comparison of each prediction with the actually observed data was done using a Clarke Error Grid (CEG). Patients and providers were blinded as to the predictions. Results: Prior to transplantation, a total of 31,878 blood glucose levels were reported by the study subjects. Some 31,353 blood glucose predictions were made by the engine on a total of 8,733 days-used. Of these, 79.4% were in the clinically acceptable Zones of the CEG. Of 728 observed episodes of hypoglycaemia, 384 were predicted. After transplantation, a total of 45,529 glucose measurements were reported on a total of 12,906 days-used. Some 42,316 glucose predictions were made, of which 97.5% were in the acceptable CEG Zones A and B. Successful transplantation eliminated hypoglycaemia, improved glycaemic control, lowered HbA 1 c and freed 10 of 24 patients from daily insulin therapy. Conclusions/ interpretation: It is clinically feasible to generate valid predictions of future blood glucose levels. Prediction accuracy is related to glycaemic stability. Risk of hypoglycaemia can be predicted. Such knowledge may be useful in self-management.

The Impact of Non–Model-Related Variability on Blood Glucose Prediction

“…1,[3][4][5]28 The resulting prediction errors are known to be influenced by uncontrollable factors such as measurement errors or unknown physiological fluctuations. With the model setup in this paper we have evaluated the influence of factors on prediction error in order to obtain a theoretical prediction limit.…”

“…The first-order elimination constant 15 0.016 p 1 Effect of glucose on its own utilization 13 0 p 2 Rate of insulin action 13 2.5 ϫ 10 Ϫ2 (min Ϫ1 ) p 3 Transendothelial insulin transport 13 13 ϫ 10 Ϫ6 (min Ϫ2 /U/mL) V G Distribution volume of glucose 13 …”

“…In several studies the percentage of data points in Zone A has been used as a measure of the prediction accuracy. 3,[22][23][24][25][26] The Zone A percentage represents the percentage of BG estimates deviating no more than 20% from the true value. SD was found for normal and optimal self-treatment at a 2-h horizon.…”

“…In recent years, several groups have set out to establish automatic BG prediction algorithms and models useful for use with automatic insulin controllers 1,2 or decision support systems. [3][4][5] Studies have led to clinical implementation of some short-term controllers. However, accurate BG prediction systems based on diary data are still considered too inaccurate to be used in clinical practice.…”

Towards Personalization of Diabetes Therapy Using Computerized Decision Support and Machine Learning: Some Open Problems and Challenges

“…But also patients without CGM which are not so intensively monitored could benefit from the prediction of future blood glucose levels. In [38][39][40] the authors devised an engine that predicts the expected blood glucose level at the next meal and the pending risks of hypoglycemia. They performed a study for safety and efficacy of using predicted data in dosing decision support for routine patient care.…”