Closed-Loop Control with Unannounced Exercise for Adults with Type 1 Diabetes using the Ensemble Model Predictive Control.

This paper presents an individualized Ensemble Model Predictive Control (EnMPC) algorithm for blood glucose (BG) stabilization and hypoglycemia prevention in people with type 1 diabetes (T1D) who exercise regularly. The EnMPC formulation can be regarded as a simplified multi-stage MPC allowing for the consideration of N en scenarios gathered from the patient's recent behavior. The patient's physical activity behavior is characterized by an exercise-specific input signal derived from the deconvolution of the patient's continuous glucose monitor (CGM), accounting for known inputs such as meal, and insulin pump records. The EnMPC controller was tested in a cohort of in silico patients with representative inter-subject and intra-subject variability from the FDA-accepted UVA/Padova simulation platform. Results show a significant improvement on hypoglycemia prevention after 30 min of mild to moderate exercise in comparison to a similarly tuned baseline controller (rMPC); with a reduction in hypoglycemia occurrences (< 70 mg/dL), from 3.08% ± 3.55 with rMPC to 0.78% ± 2.04 with EnMPC (P < 0.05).

Feasibility of a New Approach to Initiate Insulin in Type 2 Diabetes.

BACKGROUND: Treatment inertia and prescription complexity are among reasons that people with type 2 diabetes (T2D) do not reach glycemic targets. This study investigated feasibility of a new approach to basal insulin initiation, where the dose needed to reach a glycemic target is estimated from two weeks of insulin and continuous glucose monitoring (CGM) data. METHODS: This was an exploratory single arm study with a maximum length of 84 days. Eight insulin naïve people with T2D, planning to initiate basal insulin, wore a CGM throughout the study period. A predetermined regime was followed for the first two weeks after which the end dose was estimated. The clinician decided whether to follow this advice and continued the titration until target was reached using a twice weekly stepwise titration algorithm. The primary outcome was the comparison between the estimated and the actual end doses. RESULTS: Median age of participants was 57 years (range: 50-77 years), duration of diabetes was 16 years (range: 5-29 years), and Bodi Mass Index (BMI) was 30.2 kg/m2 (range: 22.0-36.0 kg/m2). The median study end dose was 37 U (range: 20-123 U). The estimated end dose was smaller than or equal to the study end dose in all cases, with median error of 26.7% (range: 0.0%-75.8% underestimation). No self-monitoring of blood glucose values were below 70 mg/dL and no severe hypoglycemia occurred. CONCLUSION: While accuracy may be improved, it was found safe to predict the study end dose of insulin degludec from two weeks of data.

Modelling of fasting glucose-insulin dynamics from sparse data.

With the fast growth of diabetes prevalence, the disease is now considered an epidemic. Diabetes is characterized by elevated glucose levels, that may be treated with insulin. Tight control of glucose is essential for prevention of complications and patients' well-being. In this paper we model the fasting glucose-insulin dynamics in type 2 diabetes, aiming at controlling the glucose level. Relevant clinical data are typically sparse and have a sampling period much greater than the fast dynamics in the glucose-insulin dynamics in humans. We adapt a physiological model such that important slow non-linear dynamics are identifiable and test the resulting model on deterministic simulated data and sparse, slow sampled clinical data.

An Adaptive Nonlinear Basal-Bolus Calculator for Patients With Type 1 Diabetes.

BACKGROUND: Bolus calculators help patients with type 1 diabetes to mitigate the effect of meals on their blood glucose by administering a large amount of insulin at mealtime. Intraindividual changes in patients physiology and nonlinearity in insulin-glucose dynamics pose a challenge to the accuracy of such calculators. METHOD: We propose a method based on a continuous-discrete unscented Kalman filter to continuously track the postprandial glucose dynamics and the insulin sensitivity. We augment the Medtronic Virtual Patient (MVP) model to simulate noise-corrupted data from a continuous glucose monitor (CGM). The basal rate is determined by calculating the steady state of the model and is adjusted once a day before breakfast. The bolus size is determined by optimizing the postprandial glucose values based on an estimate of the insulin sensitivity and states, as well as the announced meal size. Following meal announcements, the meal compartment and the meal time constant are estimated, otherwise insulin sensitivity is estimated. RESULTS: We compare the performance of a conventional linear bolus calculator with the proposed bolus calculator. The proposed basal-bolus calculator significantly improves the time spent in glucose target ( P < .01) compared to the conventional bolus calculator. CONCLUSION: An adaptive nonlinear basal-bolus calculator can efficiently compensate for physiological changes. Further clinical studies will be needed to validate the results.

Comparison of three nonlinear filters for fault detection in continuous glucose monitors.

The purpose of this study is to compare the performance of three nonlinear filters in online drift detection of continuous glucose monitors. The nonlinear filters are the extended Kalman filter (EKF), the unscented Kalman filter (UKF), and the particle filter (PF). They are all based on a nonlinear model of the glucose-insulin dynamics in people with type 1 diabetes. Drift is modelled by a Gaussian random walk and is detected based on the statistical tests of the 90-min prediction residuals of the filters. The unscented Kalman filter had the highest average F score of 85.9%, and the smallest average detection delay of 84.1%, with the average detection sensitivity of 82.6%, and average specificity of 91.0%.

Stochastic activation among inspiratory cells in the pre-Bötzinger complex of the rat medulla revealed by Ca(2+) imaging.

In the pre-Bötzinger complex of the ventral medulla (preBötC), a variable pattern of inspiratory neuronal output and synchronous activation of inspiratory cells can be observed. However, it is not well known whether cellular activation patterns among inspiratory cells are variable or fixed. Here, we evaluated the activation sequence of inspiratory cells during individual rhythmic bursts using calcium imaging. Onset timing and peak timing of calcium fluctuations during rhythmic bursts in individual inspiratory cells were used to evaluate the activation sequence. The sequences of both timings changed stochastically in individual rhythmic bursts, although the sequences differed between the two timings even within the same rhythmic burst. The weak correlation between these two timings might indicate that the two parameters reflect different physiological events. Furthermore, a subset of inspiratory cells was found to initially activate in the sequences of successive rhythmic bursts. These results suggest that rhythmic activation of inspiratory cells occurs with a degree of loose regularity but is not invariable with respect to the sequence of either onset or peak timing.

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.

Pixel timing correction in time-lapsed calcium imaging using point scanning microscopy.

In point scanning imaging, data are acquired by sequentially scanning each pixel of a predetermined area. This way of scanning leads to time delays between pixels, especially for lower scanning speed or large scanned areas. Therefore, experiments are often performed at lower framerates in order to ensure a sufficient signal-to-noise ratio, even though framerates above 30 frames per second are technically feasible. For these framerates, we suggest that it becomes crucial to correct the time delay between image pixels prior to analyses. In this paper, we apply temporal interpolation (or pixel timing correction) for calcium imaging in two-photon microscopy as an example of fluorescence imaging. We present and compare three interpolation methods (linear, Lanczos and cubic B-spline). We test these methods on a simulated network of coupled bursting neurons at different framerates. In this network, we introduce a time delay to simulate a scanning by point scanning microscopy. We also assess these methods on actual microscopic calcium imaging movies recorded at usual framerates. Our numerical results suggest that point scanning microscopy imaging introduces statistically significant time delays between image pixels at low frequency. However, we demonstrate that pixel timing correction compensates for these time delays, regardless of the used interpolation method.

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.