QT Measurement and Heart Rate Correction during Hypoglycemia: Is There a Bias?

Introduction. Several studies show that hypoglycemia causes QT interval prolongation. The aim of this study was to investigate the effect of QT measurement methodology, heart rate correction, and insulin types during hypoglycemia. Methods. Ten adult subjects with type 1 diabetes had hypoglycemia induced by intravenous injection of two insulin types in a cross-over design. QT measurements were done using the slope-intersect (SI) and manual annotation (MA) methods. Heart rate correction was done using Bazett's (QTcB) and Fridericia's (QTcF) formulas. Results. The SI method showed significant prolongation at hypoglycemia for QTcB (42(6) ms; P < .001) and QTcF (35(6) ms; P < .001). The MA method showed prolongation at hypoglycemia for QTcB (7(2) ms, P < .05) but not QTcF. No difference in ECG variables between the types of insulin was observed. Discussion. The method for measuring the QT interval has a significant impact on the prolongation of QT during hypoglycemia. Heart rate correction may also influence the QT during hypoglycemia while the type of insulin is insignificant. Prolongation of QTc in this study did not reach pathologic values suggesting that QTc prolongation cannot fully explain the dead-in-bed syndrome.

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.

Modeling the effect of blood glucose and physical exercise on plasma adrenaline in people with type 1 diabetes.

BACKGROUND: Adrenaline is often studied in people with type 1 diabetes during hypoglycemic episodes. Adrenaline is difficult and costly to measure, and therefore a pharmacokinetic model of adrenaline can be a supportive tool that adds information and saves measurements resources. METHODS: We have developed a compartment model of adrenaline secretion and elimination. It is based on input on physical exercise, blood glucose level, and optional infused adrenaline. The model parameters are identified using least square regression on published data of adrenaline kinetics measured in a number of different clinical studies. RESULTS: Simulation of published adrenaline measurements shows agreement with data of adrenaline infusion (R(2) = 0.9), exercise (R(2) = 0.97), and hypoglycemic episodes (R(2) = 0.93-0.97). The identified function describing adrenaline secretion during hypoglycemia shows an exponential increase for a blood glucose decreasing below 3.5 mmol/L and an approaching maximum around 1 mmol/L. Exercise intensity increasing above 50% of maximal oxygen uptake maximum causes approximately exponential increase in adrenaline secretion. CONCLUSION: The model is a simple tool that can be used to simulate and predict adrenaline concentrations in situations of hypoglycemia, physical exercise, and adrenaline infusion. In conclusion, the developed model, although simple, seems to be useful for simulating adrenaline dynamics in situations with hypoglycemic episodes, physical exercise, or infusion.

The impact of non-model-related variability on blood glucose prediction.

BACKGROUND: Physiological models are frequently used to predict blood glucose values from insulin and meal data of people with diabetes. Obviously, errors in the input data used result in prediction errors. A more complex problem is that no model may include all factors influencing the blood glucose level in any given situation. We have analyzed the influence of five parameters on prediction accuracy with respect to the time horizon. METHODS: A physiological model, consisting of an insulin model, a meal model, and a glucose metabolism model in combination with a Monte Carlo simulation, was used for this investigation. It was used to examine the change in blood glucose following the intake of carbohydrate and insulin. The intra-individual variability, which was studied, included pharmacokinetic variability of insulin aspart and estimation error of carbohydrate intake, as well as the accuracy of blood glucose meters and insulin pens. RESULTS: Simulations showed how the coefficient of variance for the different model compartments changes over time. For average people with diabetes the inaccuracies of blood glucose meters and carbohydrate estimates contribute to more than half of the variance. CONCLUSION: We showed how blood glucose prediction is severely affected by the inaccuracy in the input variables. Metabolic fluctuations, causing variability in insulin dynamics, also display important effects, but these are difficult to change. The inaccuracy of carbohydrate counting and the use of blood glucose meters appear to be the two main sources of error, which can be reduced through better patient education.