S. Del Favero

Modular Closed-Loop Control of Diabetes

Modularity plays a key role in many engineering systems, allowing for plug-and-play integration of components, enhancing flexibility and adaptability, and facilitating standardization. In the control of diabetes, i.e., the so-called “artificial pancreas,” modularity allows for the step-wise introduction of (and regulatory approval for) algorithmic components, starting with subsystems for assured patient safety and followed by higher layer components that serve to modify the patients basal rate in real time. In this paper, we introduce a three-layer modular architecture for the control of diabetes, consisting in a sensor/pump interface module (IM), a continuous safety module (CSM), and a real-time control module (RTCM), which separates the functions of insulin recommendation (postmeal insulin for mitigating hyperglycemia) and safety (prevention of hypoglycemia). In addition, we provide details of instances of all three layers of the architecture: the APS© serving as the IM, the safety supervision module (SSM) serving as the CSM, and the range correction module (RCM) serving as the RTCM. We evaluate the performance of the integrated system via in silico preclinical trials, demonstrating 1) the ability of the SSM to reduce the incidence of hypoglycemia under nonideal operating conditions and 2) the ability of the RCM to reduce glycemic variability.

Multicenter outpatient dinner/overnight reduction of hypoglycemia and increased time of glucose in target with a wearable artificial pancreas using modular model predictive control in adults with type 1 diabetes.

To test in an outpatient setting the safety and efficacy of continuous subcutaneous insulin infusion (CSII) driven by a modular model predictive control (MMPC) algorithm informed by continuous glucose monitoring (CGM) measurement.

Psychological outcomes of evening and night closed-loop insulin delivery under free living conditions in people with Type 1 diabetes: a 2-month randomized crossover trial

To assess the impact on fear of hypoglycaemia and treatment satisfaction with an artificial pancreas system used for 2 consecutive months, as well as participant acceptance of the artificial pancreas system.

A Glucose-Specific Metric to Assess Predictors and Identify Models

In diabetes, the mean square error (MSE) metric is extensively used for assessing glucose prediction methods and identifying glucose models. One limitation of this metric is that, by equally treating errors in hypo-, eu-, and hyperglycemia, it is not able to weight the different clinical impact of errors in these three situations. In this paper, we propose a new cost function, which overcomes this limitation and can be used in place of MSE for several scopes, in particular for assessing the quality of glucose predictors and identifying glucose models. The new metric called glucose-specific MSE (gMSE) modifies MSE with a Clark error grid inspired penalty function, which penalizes overestimation in hypoglycemia and underestimation in hyperglycemia, i.e., the most harmful conditions on a clinical perspective. From a mathematical point of view, gMSE retains sensitivity of MSE and inherits some of its important mathematical features, in particular it has no local minima, simplifying the optimization. This makes it suitable for model identification purposes also. First, the goodness of it is demonstrated by means of three experiments, designed ad hoc to evidence its sensitivity to accuracy, precision, and distortion in glucose predictions. Second, a prediction assessment problem is presented, in which two real prediction profiles are compared. Results show that the MSE chooses the worst clinical situation, while gMSE correctly selects the situation with less clinical risk. Finally, we also demonstrate that models identified minimizing gMSE are more accurate in potentially harmful situations (hypo- and hyperglycemia) than those obtained by MSE.

Distributed sensor calibration and least-square parameter identification in WSNs using consensus algorithms

In this paper we study the problem of estimating the channel parameters for a generic wireless sensor network (WSN) in a completely distributed manner, using consensus algorithms. Specifically, we first propose a distributed strategy to minimize the effects of unknown constant offsets in the reading of the radio strength signal indicator (RSSI) due to uncalibrated sensors. Then we show how the computation of the optimal wireless channels parameters, which are the solution of a global least-square optimization problem, can be obtained with a consensus-based algorithm. The proposed algorithms are general algorithms for sensor calibration and distributed least-square parameter identification, and do not require any knowledge on the global topology of the network nor the total number of nodes. Finally, we apply these algorithms to experimental data collected from an indoor WSN.

Parental evaluation of a telemonitoring service for children with Type 1 Diabetes

Introduction In the past years, we developed a telemonitoring service for young patients affected by Type 1 Diabetes. The service provides data to the clinical staff and offers an important tool to the parents, that are able to oversee in real time their children. The aim of this work was to analyze the parents’ perceived usefulness of the service. Methods The service was tested by the parents of 31 children enrolled in a seven-day clinical trial during a summer camp. To study the parents’ perception we proposed and analyzed two questionnaires. A baseline questionnaire focused on the daily management and implications of their children’s diabetes, while a post-study one measured the perceived benefits of telemonitoring. Questionnaires also included free text comment spaces. Results Analysis of the baseline questionnaires underlined the parents’ suffering and fatigue: 51% of total responses showed a negative tendency and the mean value of the perceived quality of life was 64.13 in a 0–100 scale. In the post-study questionnaires about half of the parents believed in a possible improvement adopting telemonitoring. Moreover, the foreseen improvement in quality of life was significant, increasing from 64.13 to 78.39 (p-value = 0.0001). The analysis of free text comments highlighted an improvement in mood, and parents’ commitment was also proved by their willingness to pay for the service (median = 200 euro/year). Discussion A high number of parents appreciated the telemonitoring service and were confident that it could improve communication with physicians as well as the family’s own peace of mind.

Reduction of Hyper- and Hypoglycemia during Two Months with a Wearable Artifi cial Pancreas from Dinner to Breakfast in Patients with Type 1 Diabetes

929-P Patient Responses to Interim Data from Cardiovascular Outcomes Trials: Results from an Online Patient Survey MANU V. VENKAT, RICHARD S. WOOD, ADAM S. BROWN, PHIN YOUNGE, LISA S. ROTENSTEIN, KELLY L. CLOSE, San Francisco, CA, Boston, MA The disclosure of interim data from ongoing clinical trials is usually discouraged, as it can alter participant behavior and threaten trial integrity. Current U.S. regulatory guidance requires long-term cardiovascular outcomes trials (CVOTs) for new T2DM drugs, but allows interim data to be disclosed to support approval. The purpose of this study was to examine how such disclosure could influence enrollment dynamics in a CVOT. An online survey from the diabetes market research company dQ&A was distributed to a panel of adult T2DM patients. Of the 1,984 total respondents with T2DM, 1,542 reported a history of CVD and/or being told by their healthcare provider that they are at elevated CVD risk. This represents a patient subgroup that is targeted for enrollment in most diabetes CVOTs. In the survey, respondents were described a hypothetical CVOT. Next, they were randomized to receive scenarios in which evidence of either an increase or decrease in CVD incidence was disclosed during the trial. In both scenarios, the drug was approved. All participants selected one of four choices regarding their subsequent actions (see table below).