Jens Ulrik Poulsen

How Much Do Forgotten Insulin Injections Matter to Hemoglobin A1c in People with Diabetes? a Simulation Study

Background: Forgotten or omitted insulin injections are an important contributing factor to poor glycemic control in people with type 1 diabetes. This study uses mathematical modeling and examines the impact on hemoglobin A1c (HbA1c) levels if insulin injections are forgotten. The simulation concerns people with type 1 diabetes on intensive insulin therapy. Methods: Five sets of blood glucose profiles with and without a forgotten injection were obtained. The difference to HbA1c was calculated using an HbA1c estimator on the profiles and was multiplied by the frequency of forgotten events. A frequency of 2.1 forgotten injections per week was found in the literature. Results: Calculations showed that forgetting 2.1 meal-related injections per week would lead to an increase in HbA1c of at least 0.3–0.4% points, and similarly 0.2–0.3% points related to forgotten injections of the long-acting insulin. In case of even more pronounced nonadherence (e.g., if 39% of all injections are forgotten) there is a possible increase of HbA1c of 1.8% points. Conclusions: The magnitude of the possible improvement in HbA1c agrees well with other studies in the relation between adherence and HbA1c levels. The estimated numbers suggest that missing injections are an important reason for suboptimal treatment.

A diabetes management system empowering patients to reach optimised glucose control: From monitor to advisor

The DIAdvisor™ is an EC/FP7 funded project aiming at the development of a Blood Glucose prediction device which uses easily available information to optimise the therapy of patients with diabetes.

QT interval prolongation during rapid fall in blood glucose in type I diabetes

Prolongation of QT interval on the ECG has been shown to be possibly associated with hypoglycaemia. In this study we investigated QT prolongation in episodes of single bolus induced hypoglycaemia in ten subjects with known type 1 diabetes mellitus. A mean QTc prolongation from baseline of 27(SD 19) ms (p<0.001) was measured 15 minutes after the injection of insulin. At this point the mean blood glucose was 7.2(SD 3.1) mmol/L. At the nadir of blood glucose the mean QTc prolongation from baseline was 25 (SD 22) ms (p<0.001). The study suggests that changes in the QTc in diabetics may occur not only as a result of low blood glucose per se but maybe also during rapid fall in blood glucose. The finding could be explained by pathophysiological changes in diabetes.

A Study of Trained Clinicians’ Blood Glucose Predictions Based on Diaries of People with Type 1 Diabetes

OBJECTIVES How accurate can trained clinicians predict blood glucose concentrations? Good clinical treatment is, among other things, related to understanding the factors influencing blood glucose level. We analyze trained clinicians prediction accuracy in comparison with selected computer-implemented prediction algorithms and models. METHODS We have in this study included diaries of 12 people with type 1 diabetes. This test group consists of seven males and five females, ages 24 to 60, HbA1c 6.0 to 8.9 and a BMI between 20 and 28 kg/m2. Eight experienced clinicians tried to predict the blood glucose measurements based on minimum three days of diary history. Selected prediction algorithms and models were used for comparison. The reason we focus on type 1 diabetes is that it has the most critical insulin requirement, so accurate prediction can be more critical than for type 2. RESULTS An accuracy of 28.5% and an error of 26.7% were found from predictions made by the clinicians. A physiological model and an artificial intelligence model showed higher accuracy of 32.2% and 34.2% in comparison with the clinicians (p<0.05). A simple predictor algorithm based on the mean blood glucose history showed significant (p<0.05) lower total root mean square error compared to predictions made by the clinicians. CONCLUSION To predict blood glucose level from diaries has shown to be profoundly difficult even for experienced clinicians in comparison with predictions from computer algorithms and models. This suggests that computer-based systems incorporating predicting algorithms and models are likely to contribute positively to the day-to-day treatment of people with diabetes.

Meta-Learning Based Blood Glucose Predictor for Diabetic Smartphone App

The obvious and highly accepted convenience of smartphone apps will, already in the nearest future, bring new opportunities for diabetes therapy management. In particular, it is expected that smartphones will be able to read, store, and display the blood glucose concentration from the continuous glucose monitoring systems. Using our knowledge and experience gained in the framework of the large-scale European Union FP7 funded project “DIAdvisor: personal glucose predictive diabetes advisor” (2008–2012), we explore a possibility to develop a novel smartphone app for diabetes patients that provides estimations of the future blood glucose concentration from current and past blood glucose readings. In addition to reliable clinical accuracy, a prediction algorithm implemented in such an app should satisfy multiple requirements, such as easily and quickly implementable on any mobile operating system, portability from individual to individual without readjustment or retraining procedure, and a low battery usage feature. In this study, we present a description of the prediction algorithm, developed in the course of the DIAdvisor project, and its version on Android OS that meets the above-mentioned requirements. Additionally, we compare the clinical accuracy of the algorithm with the state of the art in terms of the “gold standard” metric, Clarke error grid analysis, and the recently introduced metric, prediction error grid analysis.