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1.  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.
PMCID: PMC3876369  PMID: 24124952
clinical study; closed-loop glucose control; model predictive control; type 1 diabetes mellitus
2.  An Early Warning System for Hypoglycemic/Hyperglycemic Events Based on Fusion of Adaptive Prediction Models 
Introduction
Early warning of future hypoglycemic and hyperglycemic events can improve the safety of type 1 diabetes mellitus (T1DM) patients. The aim of this study is to design and evaluate a hypoglycemia/hyperglycemia early warning system (EWS) for T1DM patients under sensor-augmented pump (SAP) therapy.
Methods
The EWS is based on the combination of data-driven online adaptive prediction models and a warning algorithm. Three modeling approaches have been investigated: (i) autoregressive (ARX) models, (ii) auto-regressive with an output correction module (cARX) models, and (iii) recurrent neural network (RNN) models. The warning algorithm performs postprocessing of the models′ outputs and issues alerts if upcoming hypoglycemic/hyperglycemic events are detected. Fusion of the cARX and RNN models, due to their complementary prediction performances, resulted in the hybrid autoregressive with an output correction module/recurrent neural network (cARN)-based EWS.
Results
The EWS was evaluated on 23 T1DM patients under SAP therapy. The ARX-based system achieved hypoglycemic (hyperglycemic) event prediction with median values of accuracy of 100.0% (100.0%), detection time of 10.0 (8.0) min, and daily false alarms of 0.7 (0.5). The respective values for the cARX-based system were 100.0% (100.0%), 17.5 (14.8) min, and 1.5 (1.3) and, for the RNN-based system, were 100.0% (92.0%), 8.4 (7.0) min, and 0.1 (0.2). The hybrid cARN-based EWS presented outperforming results with 100.0% (100.0%) prediction accuracy, detection 16.7 (14.7) min in advance, and 0.8 (0.8) daily false alarms.
Conclusion
Combined use of cARX and RNN models for the development of an EWS outperformed the single use of each model, achieving accurate and prompt event prediction with few false alarms, thus providing increased safety and comfort.
PMCID: PMC3869137  PMID: 23759402
adaptive models; diabetes; early warning system; glucose prediction
3.  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.
Conclusions
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.
PMCID: PMC3737645  PMID: 23567002
autocorrelation; blood glucose dynamics; statistical model building; stochastic differential equations; stochastic grey-box modeling; type 1 diabetes mellitus
4.  Psychosocial Factors and Adherence to Continuous Glucose Monitoring in Type 1 Diabetes 
PMCID: PMC3440174  PMID: 22920829
continuous glucose monitoring; focus group; insulin pump treatment; treatment adherence; type 1 diabetes
5.  Use of an Automated Bolus Calculator in MDI-Treated Type 1 Diabetes 
Diabetes Care  2012;35(5):984-990.
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.
doi:10.2337/dc11-2044
PMCID: PMC3329826  PMID: 22344610
6.  Routine Sensor-Augmented Pump Therapy in Type 1 Diabetes: The INTERPRET Study 
Abstract
Background
Sensor-augmented pump (SAP) therapy can improve glycemic control, compared with multiple daily insulin injections or with insulin pump therapy alone, without increasing the risk of hypoglycemia.
Subjects and Methods
A 12-month observational study in patients with type 1 diabetes treated with continuous subcutaneous insulin infusion (CSII), upon the introduction of continuous glucose monitoring (CGM), was conducted in 15 countries (in Europe and in Israel) to document the real-life use of SAP and assess which variables are associated with improvement in type 1 diabetes management.
Results
Data from 263 patients (38% male; mean age, 28.0±15.7 years [range, 1–69 years]; body mass index, 23.3±4.9 kg/m2; diabetes duration, 13.9±10.7 years; CSII duration, 2.6±3 years) were collected. Baseline mean glycated hemoglobin A1c (HbA1c) was 8.1±1.4%; 82% had suboptimal HbA1c (≥7%). The average sensor use for 12 months was 30% (range, 0–94%), and sensor use decreased with time (first 3 months, 37%; last 3 months, 27%). Factors associated with improvement in HbA1c after 12 months in patients with baseline HbA1c ≥7% were high baseline HbA1c (P<0.001), older age group (P<0.001), and more frequent sensor use (P=0.047). Significantly less hospitalization, increased treatment satisfaction, and reduced fear of hypoglycemia were reported after 12 months of SAP.
Conclusions
This is the largest and longest multicenter prospective observational study providing real-life data on SAP. These results are consistent with those of controlled trials showing the effectiveness of CGM in pump users.
doi:10.1089/dia.2012.0288
PMCID: PMC3696941  PMID: 23438304
7.  A prospective randomised cross-over study of the effect of insulin analogues and human insulin on the frequency of severe hypoglycaemia in patients with type 1 diabetes and recurrent hypoglycaemia (the HypoAna trial): study rationale and design 
Background
Severe hypoglycaemia still represents a significant problem in insulin-treated diabetes. Most patients do not experience severe hypoglycaemia often. However, 20% of patients with type 1 diabetes experience recurrent severe hypoglycaemia corresponding to at least two episodes per year. The effect of insulin analogues on glycaemic control has been documented in large trials, while their effect on the frequency of severe hypoglycaemia is less clear, especially in patients with recurrent severe hypoglycaemia. The HypoAna Trial is designed to investigate whether short-acting and long-acting insulin analogues in comparison with human insulin are superior in reducing the occurrence of severe hypoglycaemic episodes in patients with recurrent hypoglycaemia. This paper reports the study design of the HypoAna Trial.
Methods/design
The study is a Danish two-year investigator-initiated, prospective, randomised, open, blinded endpoint (PROBE), multicentre, cross-over trial investigating the effect of insulin analogues versus human insulin on the frequency of severe hypoglycaemia in subjects with type 1 diabetes. Patients are randomised to treatment with basal-bolus therapy with insulin detemir / insulin aspart or human NPH insulin / human regular insulin in random order. The major inclusion criterion is history of two or more episodes of severe hypoglycaemia in the preceding year.
Discussion
In contrast to almost all other studies in this field the HypoAna Trial includes only patients with major problems with hypoglycaemia. The HypoAna Trial will elucidate whether basal-bolus regimen with short-acting and long-acting insulin analogues in comparison with human insulin are superior in reducing occurrence of severe hypoglycaemic episodes in hypoglycaemia prone patients with type 1 diabetes. http://www.clinicaltrials.gov: NCT00346996.
doi:10.1186/1472-6823-12-10
PMCID: PMC3433358  PMID: 22727048
Type 1 diabetes; Severe hypoglycaemia; Human insulin; Insulin analogues; PROBE

Results 1-7 (7)