Insulin delivery devices for diabetes mellitus from open-loop to closed-loop

2021 ◽  
Author(s):  
Dafei Lian
Keyword(s):  
Diabetes Mellitus ◽  
Closed Loop ◽  
Insulin Delivery ◽  
Open Loop

scholarly journals A Wearable Closed-Loop Insulin Delivery System Based on Low-Power SoCs

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 612 ◽  
Author(s):  
Jesús Berián ◽  
Ignacio Bravo ◽  
Alfredo Gardel ◽  
José Luis Lázaro ◽  
Sergio Hernández
Keyword(s):  
Embedded System ◽  
Closed Loop ◽  
Ad Hoc ◽  
Glucose Sensor ◽  
Insulin Pump ◽  
Insulin Delivery ◽  
Open Loop ◽  
Number Of Patients ◽  
Do It Yourself ◽  
Conventional Manner

The number of patients living with diabetes has increased significantly in recent years due to several factors. Many of these patients are choosing to use insulin pumps for their treatment, artificial systems that administer their insulin and consist of a glucometer and an automatic insulin supply working in an open loop. Currently, only a few closed-loop insulin delivery devices are commercially available. The most widespread systems among patients are what have been called the “Do-It-Yourself Hybrid Closed-Loop systems.” These systems require the use of platforms with high computing power. In this paper, we will present a novel wearable system for insulin delivery that reduces the energy and computing consumption of the platform without affecting the computation requirements. Patients’ information is obtained from a commercial continuous glucose sensor and a commercial insulin pump operating in a conventional manner. An ad-hoc embedded system will connect with the pump and the sensor to collect the glucose data and process it. That connection is accomplished through a radiofrequency channel that provides a suitable system for the patient. Thus, this system does not require to be connected to any other processor, which increases the overall stability. Using parameters configured by the patient, the control system will make automatic adjustments in the basal insulin infusion thereby bringing the patient’s glycaemia to the target set by a doctor’s prescription. The results obtained will be satisfactory as long as the configured parameters faithfully match the specific characteristics of the patient. Results from the simulation of 30 virtual patients (10 adolescents, 10 adults, and 10 children), using a python implementation of the FDA-approved (Food and Drug Administration) UVa (University of Virginia)/Padova Simulator and a python implementation of the proposed algorithm, are presented.

scholarly journals Closed-Loop Artificial Pancreas Using Subcutaneous Glucose Sensing and Insulin Delivery and a Model Predictive Control Algorithm: Preliminary Studies in Padova and Montpellier

2009 ◽  
Vol 3 (5) ◽  
pp. 1014-1021 ◽  
Author(s):  
Daniela Bruttomesso ◽  
Anne Farret ◽  
Silvana Costa ◽  
Maria Cristina Marescotti ◽  
Monica Vettore ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Glucose Control ◽  
Control Algorithm ◽  
Closed Loop ◽  
Insulin Delivery ◽  
Glucose Sensing ◽  
Open Loop ◽  
Closed Loop Control ◽  
Loop Control

New effort has been made to develop closed-loop glucose control, using subcutaneous (SC) glucose sensing and continuous subcutaneous insulin infusion (CSII) from a pump, and a control algorithm. An approach based on a model predictive control (MPC) algorithm has been utilized during closed-loop control in type 1 diabetes patients. Here we describe the preliminary clinical experience with this approach. In Padova, two out of three subjects showed better performance with the closed-loop system compared to open loop. Altogether, mean overnight plasma glucose (PG) levels were 134 versus 111 mg/dl during open loop versus closed loop, respectively. The percentage of time spent at PG > 140 mg/dl was 45% versus 12%, while postbreakfast mean PG was 165 versus 156 mg/dl during open loop versus closed loop, respectively. Also, in Montpellier, two patients out of three showed a better glucose control during closed-loop trials. Avoidance of nocturnal hypoglycemic excursions was a clear benefit during algorithm-guided insulin delivery in all cases. This preliminary set of studies demonstrates that closed-loop control based entirely on SC glucose sensing and insulin delivery is feasible and can be applied to improve glucose control in patients with type 1 diabetes, although the algorithm needs to be further improved to achieve better glycemic control. Six type 1 diabetes patients (three in each of two clinical investigation centers in Padova and Montpellier), using CSII, aged 36 ± 8 and 48 ± 6 years, duration of diabetes 12 ± 8 and 29 ± 4 years, hemoglobin A1c 7.4% ± 0.1% and 7.3% ± 0.3%, body mass index 23.2 ± 0.3 and 28.4 ± 2.2 kg/m2, respectively, were studied on two occasions during 22 h overnight hospital admissions 2–4 weeks apart. A Freestyle Navigator® continuous glucose monitor and an OmniPod® insulin pump were applied in each trial. Admission 1 used open-loop control, while admission 2 employed closed-loop control using our MPC algorithm.

scholarly journals Control to Range for Diabetes: Functionality and Modular Architecture

2009 ◽  
Vol 3 (5) ◽  
pp. 1058-1065 ◽  
Author(s):  
Boris Kovatchev ◽  
Stephen Patek ◽  
Eyal Dassau ◽  
Francis J. Doyle ◽  
Lalo Magni ◽  
...  
Keyword(s):  
Closed Loop ◽  
Glucose Sensor ◽  
Insulin Pump ◽  
Insulin Delivery ◽  
Open Loop ◽  
Target Range ◽  
Closed Loop Control ◽  
Modular Architecture ◽  
Loop Control ◽  
Range Correction

Background: Closed-loop control of type 1 diabetes is receiving increasing attention due to advancement in glucose sensor and insulin pump technology. Here the function and structure of a class of control algorithms designed to exert control to range, defined as insulin treatment optimizing glycemia within a predefined target range by preventing extreme glucose fluctuations, are studied. Methods: The main contribution of the article is definition of a modular architecture for control to range. Emphasis is on system specifications rather than algorithmic realization. The key system architecture elements are two interacting modules: range correction module, which assesses the risk for incipient hyper- or hypoglycemia and adjusts insulin rate accordingly, and safety supervision module, which assesses the risk for hypoglycemia and attenuates or discontinues insulin delivery when necessary. The novel engineering concept of range correction module is that algorithm action is relative to a nominal open-loop strategy—a predefined combination of basal rate and boluses believed to be optimal under nominal conditions. Results: A proof of concept of the feasibility of our control-to-range strategy is illustrated by using a prototypal implementation tested in silico on patient use cases. These functional and architectural distinctions provide several advantages, including (i) significant insulin delivery corrections are only made if relevant risks are detected; (ii) drawbacks of integral action are avoided, e.g., undershoots with consequent hypoglycemic risks; (iii) a simple linear model is sufficient and complex algorithmic constraints are replaced by safety supervision; and (iv) the nominal profile provides straightforward individualization for each patient. Conclusions: We believe that the modular control-to-range system is the best approach to incremental development, regulatory approval, industrial deployment, and clinical acceptance of closed-loop control for diabetes.

scholarly journals Overnight Closed-Loop Insulin Delivery with Model Predictive Control: Assessment of Hypoglycemia and Hyperglycemia Risk Using Simulation Studies

2009 ◽  
Vol 3 (5) ◽  
pp. 1109-1120 ◽  
Author(s):  
Malgorzata E. Wilinska ◽  
Erwin S. Budiman ◽  
Marc B. Taub ◽  
Daniela Elleri ◽  
Janet M. Allen ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Insulin Infusion ◽  
Closed Loop ◽  
Insulin Delivery ◽  
Glucose Sensing ◽  
Open Loop ◽  
Calibration Error ◽  
Sensor Glucose ◽  
Young Subjects

Background: Hypoglycemia and hyperglycemia during closed-loop insulin delivery based on subcutaneous (SC) glucose sensing may arise due to (1) overdosing and underdosing of insulin by control algorithm and (2) difference between plasma glucose (PG) and sensor glucose, which may be transient (kinetics origin and sensor artifacts) or persistent (calibration error [CE]). Using in silico testing, we assessed hypoglycemia and hyperglycemia incidence during overnight closed loop. Additionally, a comparison was made against incidence observed experimentally during open-loop single-night in-clinic studies in young people with type 1 diabetes mellitus (T1DM) treated by continuous SC insulin infusion. Methods: Simulation environment comprising 18 virtual subjects with T1DM was used to simulate overnight closed-loop study with a model predictive control (MPC) algorithm. A 15 h experiment started at 17:00 and ended at 08:00 the next day. Closed loop commenced at 21:00 and continued for 11 h. At 18:00, protocol included meal (50 g carbohydrates) accompanied by prandial insulin. The MPC algorithm advised on insulin infusion every 15 min. Sensor glucose was obtained by combining model-calculated noise-free interstitial glucose with experimentally derived transient and persistent sensor artifacts associated with FreeStyle Navigator® (FSN). Transient artifacts were obtained from FSN sensor pairs worn by 58 subjects with T1DM over 194 nighttime periods. Persistent difference due to FSN CE was quantified from 585 FSN sensor insertions, yielding 1421 calibration sessions from 248 subjects with diabetes. Results: Episodes of severe (PG ≤ 36 mg/dl) and significant (PG ≤ 45 mg/dl) hypoglycemia and significant hyperglycemia (PG ≥ 300 mg/dl) were extracted from 18,000 simulated closed-loop nights. Severe hypoglycemia was not observed when FSN CE was less than 45%. Hypoglycemia and hyperglycemia incidence during open loop was assessed from 21 overnight studies in 17 young subjects with T1DM (8 males; 13.5 ± 3.6 years of age; body mass index 21.0 ± 4.0 kg/m2; duration diabetes 6.4 ± 4.1 years; hemoglobin A1c 8.5% ± 1.8%; mean ± standard deviation) participating in the Artificial Pancreas Project at Cambridge. Severe and significant hypoglycemia during simulated closed loop occurred 0.75 and 17.11 times per 100 person years compared to 1739 and 3479 times per 100 person years during experimental open loop, respectively. Significant hyperglycemia during closed loop and open loop occurred 75 and 15,654 times per 100 person years, respectively. Conclusions: The incidence of severe and significant hypoglycemia reduced 2300- and 200-fold, respectively, during simulated overnight closed loop with MPC compared to that observed during open-loop overnight clinical studies in young subjects with T1DM. Hyperglycemia was 200 times less likely. Overnight closed loop with the FSN and the MPC algorithm is expected to reduce substantially the risk of hypoglycemia and hyperglycemia.

Closing the loop

2011 ◽  
pp. 1869-1874
Author(s):  
Roman Hovorka
Keyword(s):  
Type 1 Diabetes ◽  
Real Time ◽  
Closed Loop ◽  
Insulin Delivery ◽  
Insulin Analogues ◽  
Open Loop ◽  
Subcutaneous Insulin ◽  
Closed Loop Systems ◽  
Acting Insulin

The standard therapy of type 1 diabetes is based on multiple daily injections of short- and long acting-insulin analogues accompanied by blood glucose self-monitoring. However, treatment goals identified by the Diabetes Control and Complications Trial are difficult to achieve due, at least in part, to a high risk of hypoglycaemia associated with many currents forms of intensive insulin therapy. Recent technological developments in real-time subcutaneous continuous glucose monitoring (CGM), combined with the continuous subcutaneous insulin infusion (CSII), could potentially reduce this risk. Since late 1990s at least five continuous or semicontinuous glucose monitors have received regulatory approval (1). CGM has been shown to improve glycaemic control in adults with type 1 diabetes, although apparent barriers to effectiveness in children and adolescents remain to be identified (see Chapter 13.4.9.1) (2). The availability of commercial CGM devices has reinvigorated research towards closed-loop systems (3-6), in which insulin is delivered according to real-time needs, as opposed to open-loop systems, which lack real-time responsiveness to changing glucose concentrations. Closed-loop insulin delivery, in which the insulin delivery is informed by the measured glucose concentrations has the potential gradually to revolutionize the management of type 1 diabetes by reducing or eliminating the risk of hypoglycaemia while achieving near-normal glucose levels. A closed-loop system, also called the ‘artificial pancreas’, comprises three components: a CGM device to measure real-time glucose concentration, a titrating algorithm to compute the amount of insulin needed, and an insulin pump delivering a rapid-acting insulin analogue (see Fig. 13.4.9.2.1). Only a few prototypes have been developed. Progress has been hindered by suboptimal accuracy and reliability of CGM devices, the relatively slow absorption of subcutaneously administered ‘rapid’-acting insulin analogues, and the lack of adequate control algorithms. So far, testing has been confined to the clinical setting. However, a concentrated effort promises an accelerated progress towards home testing of closed-loop systems. The research focus centres on systems utilizing subcutaneous glucose sensing and subcutaneous insulin delivery. This approach has the greatest potential for a near-future commercial exploitation, although other approaches utilizing intraperitoneal or intravenous sensing/delivery are, in principle, also feasible.

Diabetes Mellitus and Closed-Loop Insulin Delivery

2000 ◽  
Vol 17 (1) ◽  
pp. 455-496 ◽  
Author(s):  
Gary Adams ◽  
Jan Clark ◽  
Tarsem Sahota ◽  
Sangeeta Tanna ◽  
M. Joan Taylor
Keyword(s):  
Diabetes Mellitus ◽  
Closed Loop ◽  
Insulin Delivery

On closed-loop equilibrium strategies for mean-field stochastic linear quadratic problems

2020 ◽  
Vol 26 ◽  
pp. 41
Author(s):  
Tianxiao Wang
Keyword(s):  
Optimal Control ◽  
Closed Loop ◽  
Optimal Control Problems ◽  
Mean Field ◽  
Open Loop ◽  
Time Inconsistency ◽  
Control Problems ◽  
Linear Quadratic ◽  
Linear Quadratic Optimal Control ◽  
Equilibrium Strategies

This article is concerned with linear quadratic optimal control problems of mean-field stochastic differential equations (MF-SDE) with deterministic coefficients. To treat the time inconsistency of the optimal control problems, linear closed-loop equilibrium strategies are introduced and characterized by variational approach. Our developed methodology drops the delicate convergence procedures in Yong [Trans. Amer. Math. Soc. 369 (2017) 5467–5523]. When the MF-SDE reduces to SDE, our Riccati system coincides with the analogue in Yong [Trans. Amer. Math. Soc. 369 (2017) 5467–5523]. However, these two systems are in general different from each other due to the conditional mean-field terms in the MF-SDE. Eventually, the comparisons with pre-committed optimal strategies, open-loop equilibrium strategies are given in details.

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