Closed-Loop Control of Postprandial Glycemia Using an Insulin-on-Board Limitation Through Continuous Action on Glucose Target

Rossetti, Paolo; Quirós, Carmen; Moscardó, Vanessa; Comas, Anna; Giménez, Marga; Ampudia‐Blasco, Francisco Javier; Leon, F. Martinez; Montaser, Eslam; Conget, Ignacio; Bondia, Jorge; Vehı́, Josep

doi:10.1089/dia.2016.0443

Cited by 46 publications

(23 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, as stated above, acquiring such a dataset is difficult and very expensive. As previously mentioned and pointed out by Rossetti et al [20], the dataset used in this paper was not acquired to derive CGM error model, but to compare CL and OL treatment during postprandial period. However, the dataset was suitable (even if not optimal) for the derivation of ENL sensor error into its different components and for comparing the resultant model with the previously decomposed error of other CGM sensors described in [7,8].…”

Section: Discussionmentioning

confidence: 99%

“…This was a randomized, prospective, one-way, repeated measures (four periods, two sequences) crossover study in subjects with T1D under continuous subcutaneous insulin infusion (CSII) [19,20]. Subjects underwent an 8-hour standardized mixed meal test (60 g carbohydrate, CH) on 4 occasions; on 2 occasions (CL1 and CL2), after a meal-announcement an augmented bolus was given, followed by manual adjustments of the basal rate every 15 min obtained via a CL controller; and on the other two occasions (OL1 and OL2), conventional CSII was used and boluses were based on the individual insulin-to-carbohydrate (I:CH) ratios.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Modeling the Error of the Medtronic Paradigm Veo Enlite Glucose Sensor

Biagi

Ramkissoon

Facchinetti

et al. 2017

Sensors

View full text Add to dashboard Cite

Continuous glucose monitors (CGMs) are prone to inaccuracy due to time lags, sensor drift, calibration errors, and measurement noise. The aim of this study is to derive the model of the error of the second generation Medtronic Paradigm Veo Enlite (ENL) sensor and compare it with the Dexcom SEVEN PLUS (7P), G4 PLATINUM (G4P), and advanced G4 for Artificial Pancreas studies (G4AP) systems. An enhanced methodology to a previously employed technique was utilized to dissect the sensor error into several components. The dataset used included 37 inpatient sessions in 10 subjects with type 1 diabetes (T1D), in which CGMs were worn in parallel and blood glucose (BG) samples were analyzed every 15 ± 5 min Calibration error and sensor drift of the ENL sensor was best described by a linear relationship related to the gain and offset. The mean time lag estimated by the model is 9.4 ± 6.5 min. The overall average mean absolute relative difference (MARD) of the ENL sensor was 11.68 ± 5.07% Calibration error had the highest contribution to total error in the ENL sensor. This was also reported in the 7P, G4P, and G4AP. The model of the ENL sensor error will be useful to test the in silico performance of CGM-based applications, i.e., the artificial pancreas, employing this kind of sensor.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Modeling the Error of the Medtronic Paradigm Veo Enlite Glucose Sensor

Biagi

Ramkissoon

Facchinetti

et al. 2017

Sensors

View full text Add to dashboard Cite

show abstract

“…CLS generally maintains glucose levels in target for 70% to 75% of the time with the remaining time spent outside target mostly due to post‐meal hyperglycaemia. A strategy using sliding mode reference conditioning (SMRC) combined with hybrid CLS could improve postprandial glucose control but larger outpatient studies remain needed . Again, an important limitation of a hybrid CLS is the persistent need for CHO counting.…”

Section: Closed‐loop Systemsmentioning

confidence: 99%

“…A strategy using sliding mode reference conditioning (SMRC) combined with hybrid CLS could improve postprandial glucose control but larger outpatient studies remain needed. 32 Again, an important limitation of a hybrid CLS is the persistent need for CHO counting.…”

Section: Hybrid Clsmentioning

confidence: 99%

The challenges of achieving postprandial glucose control using closed‐loop systems in patients with type 1 diabetes

Gingras

Taleb

Roy‐Fleming

et al. 2017

Diabetes Obesity Metabolism

View full text Add to dashboard Cite

For patients with type 1 diabetes, closed-loop delivery systems (CLS) combining an insulin pump, a glucose sensor and a dosing algorithm allowing a dynamic hormonal infusion have been shown to improve glucose control when compared with conventional therapy. Yet, reducing glucose excursion and simplification of prandial insulin doses remain a challenge. The objective of this literature review is to examine current meal-time strategies in the context of automated delivery systems in adults and children with type 1 diabetes. Current challenges and considerations for post-meal glucose control will also be discussed. Despite promising results with meal detection, the fully automated CLS has yet failed to provide comparable glucose control to CLS with carbohydrate-matched bolus in the post-meal period. The latter strategy has been efficient in controlling post-meal glucose using different algorithms and in various settings, but at the cost of a meal carbohydrate counting burden for patients. Further improvements in meal detection algorithms or simplified meal-priming boluses may represent interesting avenues. The greatest challenges remain in regards to the pharmacokinetic and dynamic profiles of available rapid insulins as well as sensor accuracy and lag-time. New and upcoming faster acting insulins could provide important benefits. Multi-hormone CLS (eg, dual-hormone combining insulin with glucagon or pramlintide) and adjunctive therapy (eg, GLP-1 and SGLT2 inhibitors) also represent promising options. Meal glucose control with the artificial pancreas remains an important challenge for which the optimal strategy is still to be determined.

show abstract

“…e Spanish Consortium on Artificial Pancreas and Diabetes Technology has been working over the last decade on the development of a new artificial pancreas system. In the first clinical trial, to evaluate the performance of the PD controller with the SAFE layer, an individualized constraint IOB was designed to control postprandial BG levels after the consumption of a meal with 60 grams of CHO [26]. e closed-loop controller achieved better outcomes compared with the open-loop therapy, reducing significantly the time spent in hyperglycemia without increase the risk of hypoglycemia.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Rule-Based Algorithm to Tune Insulin-on-Board Constraints for a Hybrid Artificial Pancreas System

Bertachi

Biagi

Beneyto

et al. 2020

Journal of Healthcare Engineering

Self Cite

View full text Add to dashboard Cite

The artificial pancreas (AP) is a system intended to control blood glucose levels through automated insulin infusion, reducing the burden of subjects with type 1 diabetes to manage their condition. To increase patients’ safety, some systems limit the allowed amount of insulin active in the body, known as insulin-on-board (IOB). The safety auxiliary feedback element (SAFE) layer has been designed previously to avoid overreaction of the controller and thus avoiding hypoglycemia. In this work, a new method, so-called “dynamic rule-based algorithm,” is presented in order to adjust the limits of IOB in real time. The algorithm is an extension of a previously designed method which aimed to adjust the limits of IOB for a meal with 60 grams of carbohydrates (CHO). The proposed method is intended to be applied on hybrid AP systems during 24 h operation. It has been designed by combining two different strategies to set IOB limits for different situations: (1) fasting periods and (2) postprandial periods, regardless of the size of the meal. The UVa/Padova simulator is considered to assess the performance of the method, considering challenging scenarios. In silico results showed that the method is able to reduce the time spent in hypoglycemic range, improving patients’ safety, which reveals the feasibility of the approach to be included in different control algorithms.

show abstract

Closed-Loop Control of Postprandial Glycemia Using an Insulin-on-Board Limitation Through Continuous Action on Glucose Target

Abstract: This novel CL algorithm effectively and consistently controls PP glucose excursions without increasing hypoglycemia. Study registered at ClinicalTrials.gov : study number NCT02100488.

Cited by 46 publications

References 22 publications

Modeling the Error of the Medtronic Paradigm Veo Enlite Glucose Sensor

Modeling the Error of the Medtronic Paradigm Veo Enlite Glucose Sensor

The challenges of achieving postprandial glucose control using closed‐loop systems in patients with type 1 diabetes

Dynamic Rule-Based Algorithm to Tune Insulin-on-Board Constraints for a Hybrid Artificial Pancreas System

Contact Info

Product

Resources

About