Signe Schmidt

Use of an Automated Bolus Calculator in MDI-Treated Type 1 Diabetes: The BolusCal Study, a randomized controlled pilot study

OBJECTIVE To investigate the effect of flexible intensive insulin therapy (FIIT) and an automated bolus calculator (ABC) in a Danish type 1 diabetes population treated with multiple daily injections. Furthermore, to test the feasibility of teaching FIIT in a 3-h structured course. RESEARCH DESIGN AND METHODS The BolusCal Study was a 16-week randomized, controlled, open-label, three-arm parallel, clinical study of 51 adults with type 1 diabetes. Patients aged 18–65 years in poor metabolic control (HbA1c 8.0–10.5%) were randomized to the Control (n = 8), CarbCount (n = 21), or CarbCountABC (n = 22) arm. During a 3-h group teaching, the Control arm received FIIT education excluding carbohydrate counting. CarbCount patients were taught FIIT and how to count carbohydrates. CarbCountABC group teaching included FIIT and carbohydrate counting and patients were provided with an ABC. RESULTS At 16 weeks, the within-group change in HbA1c was −0.1% (95% CI −1.0 to 0.7%; P = 0.730) in the Control arm, −0.8% (−1.3 to −0.3%; P = 0.002) in the CarbCount arm, and −0.7% (−1.0 to −0.4%; P < 0.0001) in the CarbCountABC arm. The difference in change in HbA1c between CarbCount and CarbCountABC was insignificant. Adjusting for baseline HbA1c in a regression model, the relative change in HbA1c was −0.6% (−1.2 to 0.1%; P = 0.082) in CarbCount and −0.8% (−1.4 to −0.1%; P = 0.017) in CarbCountABC. Treatment satisfaction measured by the Diabetes Treatment Satisfaction Questionnaire (status version) improved in all study arms, but the improvement was significantly greater in CarbCountABC. CONCLUSIONS FIIT and carbohydrate counting were successfully taught in 3 h and improved metabolic control and treatment satisfaction. Concurrent use of an ABC improved treatment satisfaction further.

Model-Based Closed-Loop Glucose Control in Type 1 Diabetes: The DiaCon Experience

Background: To improve type 1 diabetes mellitus (T1DM) management, we developed a model predictive control (MPC) algorithm for closed-loop (CL) glucose control based on a linear second-order deterministic-stochastic model. The deterministic part of the model is specified by three patient-specific parameters: Insulin sensitivity factor, insulin action time, and basal insulin infusion rate. The stochastic part is identical for all patients but identified from data from a single patient. Results of the first clinical feasibility test of the algorithm are presented. Methods: We conducted two randomized crossover studies. Study 1 compared CL with open-loop (OL) control. Study 2 compared glucose control after CL initiation in the euglycemic (CL-Eu) and hyperglycemic (CL-Hyper) ranges, respectively. Patients were studied from 22:00–07:00 on two separate nights. Results: Each study included six T1DM patients (hemoglobin A1c 7.2% ± 0.4%). In study 1, hypoglycemic events (plasma glucose < 54 mg/dl) occurred on two OL and one CL nights. Average glucose from 22:00–07:00 was 90 mg/dl [74–146 mg/dl; median (interquartile range)] during OL and 108 mg/dl (101–128 mg/dl) during CL (determined by continuous glucose monitoring). However, median time spent in the range 70–144 mg/dl was 67.9% (3.0–73.3%) during OL and 80.8% (70.5–89.7%) during CL. In study 2, there was one episode of hypoglycemia with plasma glucose <54 mg/dl in a CL-Eu night. Mean glucose from 22:00–07:00 and time spent in the range 70–144 mg/dl were 121 mg/dl (117–133 mg/dl) and 69.0% (30.7–77.9%) in CL-Eu and 149 mg/dl (140–193 mg/dl) and 48.2% (34.9–72.5%) in CL-Hyper, respectively. Conclusions: This study suggests that our novel MPC algorithm can safely and effectively control glucose overnight, also when CL control is initiated during hyperglycemia.

Effects of Everyday Life Events on Glucose, Insulin, and Glucagon Dynamics in Continuous Subcutaneous Insulin Infusion–Treated Type 1 Diabetes: Collection of Clinical Data for Glucose Modeling

BACKGROUND In the development of glucose control algorithms, mathematical models of glucose metabolism are useful for conducting simulation studies and making real-time predictions upon which control calculations can be based. To obtain type 1 diabetes (T1D) data for the modeling of glucose metabolism, we designed and conducted a clinical study. METHODS Patients with insulin pump-treated T1D were recruited to perform everyday life events on two separate days. During the study, patients wore their insulin pumps and, in addition, a continuous glucose monitor and an activity monitor to estimate energy expenditure. The sequence of everyday life events was predetermined and included carbohydrate intake, insulin boluses, and bouts of exercise; the events were introduced, temporally separated, in different orders and in different quantities. Throughout the study day, 10-min plasma glucose measurements were taken, and samples for plasma insulin and glucagon analyses were obtained every 10 min for the first 30 min after an event and subsequently every 30 min. RESULTS We included 12 patients with T1D (75% female, 34.3±9.1 years old [mean±SD], hemoglobin A1c 6.7±0.4%). During the 24 study days we collected information-rich, high-quality data during fast and slow changes in plasma glucose following carbohydrate intake, exercise, and insulin boluses. CONCLUSIONS This study has generated T1D data suitable for glucose modeling, which will be used in the development of glucose control strategies. Furthermore, the study has given new physiologic insight into the metabolic effects of carbohydrate intake, insulin boluses, and exercise in continuous subcutaneous insulin infusion-treated patients with T1D.

Model Identification Using Stochastic Differential Equation Grey-Box Models in Diabetes

Background: The acceptance of virtual preclinical testing of control algorithms is growing and thus also the need for robust and reliable models. Models based on ordinary differential equations (ODEs) can rarely be validated with standard statistical tools. Stochastic differential equations (SDEs) offer the possibility of building models that can be validated statistically and that are capable of predicting not only a realistic trajectory, but also the uncertainty of the prediction. In an SDE, the prediction error is split into two noise terms. This separation ensures that the errors are uncorrelated and provides the possibility to pinpoint model deficiencies. Methods: An identifiable model of the glucoregulatory system in a type 1 diabetes mellitus (T1DM) patient is used as the basis for development of a stochastic-differential-equation-based grey-box model (SDE-GB). The parameters are estimated on clinical data from four T1DM patients. The optimal SDE-GB is determined from likelihood-ratio tests. Finally, parameter tracking is used to track the variation in the “time to peak of meal response” parameter. Results: We found that the transformation of the ODE model into an SDE-GB resulted in a significant improvement in the prediction and uncorrelated errors. Tracking of the “peak time of meal absorption” parameter showed that the absorption rate varied according to meal type. Conclusion: This study shows the potential of using SDE-GBs in diabetes modeling. Improved model predictions were obtained due to the separation of the prediction error. SDE-GBs offer a solid framework for using statistical tools for model validation and model development.

Overnight Control of Blood Glucose in People with Type 1 Diabetes

Abstract In this paper, we develop and test a Model Predictive Controller (MPC) for overnight stabilization of blood glucose in people with type 1 diabetes. The controller uses glucose measurements from a continuous glucose monitor (CGM) and its decisions are implemented by a continuous subcutaneous insulin infusion (CSII) pump. Based on a priori patient information, we propose a systematic method for computation of the model parameters in the MPC. Safety layers improve the controller robustness and reduce the risk of hypoglycemia. The controller is evaluated in silico on a cohort of 100 randomly generated patients with a representative inter-subject variability. This cohort is simulated overnight with realistic variations in the insulin sensitivities and needs. Finally, we provide results for the first tests of this controller in a real clinic.

Psychosocial Factors and Adherence to Continuous Glucose Monitoring in Type 1 Diabetes

Several studies have demonstrated beneficial effects of continuous glucose monitoring (CGM) in patients with type 1 diabetes mellitus (T1DM).1–3 In most of the studies, however, a subgroup of patients uses CGM less than recommended or discontinues use. To facilitate treatment adherence in this patient group, thorough understanding of motives for CGM termination is a prerequisite, but only little is known about mechanisms underlying patient adherence to CGM.4–6 Psychosocial factors may affect CGM use because of the constant efforts required of the user to benefit from CGM. To pursue this hypothesis, we invited 24 adults with T1DM from our clinic to take part in a focus group study. Twenty-five ± four months previously, patients had participated in the multicenter Eurythmics Trial2 and started using sensor-augmented pumps (SAPs) during (intervention group) or immediately after completion of the trial (control group). Sensor-augmented pump costs were covered by the Danish health care system. Sixteen patients agreed to participate in the focus group study (Table 1). Twelve were still using SAPs, while four had chosen to cease CGM and continue insulin pump treatment only. Three focus groups were formed based on self-reported CGM use: two groups of current CGM users (six persons in each) and one group of former CGM users (four persons). “CGM use” was defined as sensor use >60% of the time in the past three months. Table 1 Patient Characteristicsa The most notable observation during the focus group sessions was that personal ambitions for metabolic control clearly differed between former and current CGM users. Patients who had discontinued CGM utilization were aware that they could have better metabolic control using CGM; nevertheless, they chose not to. A former user stated: “I know that the sensor improves my hemoglobin A1c (HbA1c)… Intellectually I know that I should wear it. Why don’t I? Now that’s a good question.” Conversely, none of the current users were willing to stop CGM at the expense of a rise in HbA1c. “I want to live a little longer, and I want to be free of all the late complications, so in that respect I don’t think that I have much to negotiate with,” a current CGM user said. All participants praised the glucose curve on the pump display and the arrows indicating rises and falls, but a major complaint coming from both current and former SAP users was CGM inaccuracy. The expression “false security” was used, and two SAP users stated: “I don’t trust it 100% any longer.” “Neither do I. I use it mostly as a guideline.” Continuous glucose monitoring alarms were also vigorously discussed. In particular, alarms caused by inaccurate sensor readings were considered irrelevant interruptions, but also hypo- and hyperalarms that continued after the blood glucose had been corrected due to the inherent time lag of the sensor. Interestingly, former and current users reported different ways of coping with CGM alarms, and the current users seemed less disturbed by alarms. A SAP user explained: “I mute it. You can still follow [the glucose curve] on the display.” In contrast, a former user said: “These nightly alarms…I think you can turn them off, but I never figured out how.” A third important point was that body image played a crucial role for patients in all groups, and to some, it was the most limiting factor of CGM use. Based on this focus group study, we propose that pre-CGM counseling focusing on patient motivation, alarm coping strategies, and body image may increase CGM adherence.

Sensor-augmented pump therapy at 36 months.

BACKGROUND This follow-up study investigates the metabolic and psychosocial effects of sensor-augmented pump (SAP) therapy in adults with type 1 diabetes 36 months after therapy start. SUBJECTS AND METHODS We invited all 24 Danish adults with type 1 diabetes who had previously participated in the European multicenter randomized controlled Eurythmics Trial. Thirteen of the 24 patients started SAP therapy during the Eurythmics Trial; 11 patients were controls but started using SAP immediately after completion of the trial. In the current study, we estimated the effects of SAP 36 months after therapy start by change in glycated hemoglobin (HbA1c) and diabetes questionnaire scores (Diabetes Treatment Satisfactions Questionnaire [DTSQs], Problem Areas in Diabetes [PAID] questionnaire, and Hypoglycemia Fear Survey [HFS]). RESULTS At 36 months, 16 of the 24 patients were still using SAP, 14 of them > 70% of time. The HbA1c level decreased from 8.7% at therapy start to 7.3% at 36 months (P < 0.0001). Similar reductions in HbA1c were obtained regardless of whether SAP therapy was initiated during or after the Eurythmics Trial. DTSQs, PAID questionnaire, and HFS scores improved by 9.0 (P < 0.0001), -10.8 (P = 0.013), and -5.5 (P = 0.152), respectively, in the 16 SAP users. CONCLUSIONS This study documents persisting beneficial effects of SAP on HbA1c, treatment satisfaction, magnitude of diabetes-related problems, and fear of hypoglycemia 36 months after therapy start. The follow-up is considerably longer than in other published studies; still, the results are in line with the positive short-term outcomes of larger studies of SAP use.

An artificial pancreas for automated blood glucose control in patients with Type 1 diabetes

Automated glucose control in patients with Type 1 diabetes is much-coveted by patients, relatives and healthcare professionals. It is the expectation that a system for automated control, also know as an artificial pancreas, will improve glucose control, reduce the risk of diabetes complications and markedly improve patient quality of life. An artificial pancreas consists of portable devices for glucose sensing and insulin delivery which are controlled by an algorithm residing on a computer. The technology is still under development and currently no artificial pancreas is commercially available. This review gives an introduction to recent progress, challenges and future prospects within the field of artificial pancreas research.

Continuous glucose monitoring-enabled insulin-pump therapy in diabetic pregnancy

We describe the feasibility of continuous glucose monitoring (CGM)‐enabled insulin‐pump therapy during pregnancy in a woman with type 1 diabetes, who was treated with CGM‐enabled insulin‐pump therapy in her third pregnancy. During her first pregnancy, the woman was treated with multiple daily injections and baseline HbA1c was 8.9%. Due to pre‐eclampsia, the child was born preterm, and had neonatal hypoglycemia. In the planning of the second pregnancy, insulin‐pump therapy was initiated, resulting in an HbA1c of 6.8% in early pregnancy. Due to pre‐eclampsia, the second child was born preterm, but without neonatal morbidity. Before her third pregnancy, CGM‐enabled insulin‐pump therapy was introduced, and HbA1c was 6.4% in early pregnancy. The patient was satisfied with this therapy, pre‐eclampsia did not occur, and the child was born at term without neonatal morbidity. CGM‐enabled insulin‐pump therapy appears feasible in diabetic pregnancies.

Effects of subcutaneous, low-dose glucagon on insulin-induced mild hypoglycaemia in patients with insulin pump treated type 1 diabetes.

To investigate the dose–response relationship of subcutaneous (s.c.) glucagon administration on plasma glucose and on counter‐regulatory hormone responses during s.c. insulin‐induced mild hypoglycaemia in patients with type 1 diabetes treated with insulin pumps.