Literature DB >> 28459603

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

Paolo Rossetti1, Carmen Quirós2, Vanessa Moscardó3, Anna Comas4, Marga Giménez2, F Javier Ampudia-Blasco5, Fabián León4, Eslam Montaser3, Ignacio Conget2, Jorge Bondia3, Josep Vehí4.   

Abstract

BACKGROUND: Postprandial (PP) control remains a challenge for closed-loop (CL) systems. Few studies with inconsistent results have systematically investigated the PP period.
OBJECTIVE: To compare a new CL algorithm with current pump therapy (open loop [OL]) in the PP glucose control in type 1 diabetes (T1D) subjects.
METHODS: A crossover randomized study was performed in two centers. Twenty T1D subjects (F/M 13/7, age 40.7 ± 10.4 years, disease duration 22.6 ± 9.9 years, and A1c 7.8% ± 0.7%) underwent an 8-h mixed meal test on four occasions. In two (CL1/CL2), after meal announcement, a bolus was given followed by an algorithm-driven basal infusion based on continuous glucose monitoring (CGM). Alternatively, in OL1/OL2 conventional pump therapy was used. Main outcome measures were as follows: glucose variability, estimated with the coefficient of variation (CV) of the area under the curve (AUC) of plasma glucose (PG) and CGM values, and from the analysis of the glucose time series; mean, maximum (Cmax), and time to Cmax glucose concentrations and time in range (<70, 70-180, >180 mg/dL).
RESULTS: CVs of the glucose AUCs were low and similar in all studies (around 10%). However, CL achieved greater reproducibility and better PG control in the PP period: CL1 = CL2<OL1<OL2 (PGmean 123 ± 47 and 125 ± 44 vs. 152 ± 53 and 159 ± 54 mg/dL) and Cmax OL 217.1 ± 67.0 mg/dL versus CL 183.3 ± 63.9 mg/dL, P < 0.0001. Time-in-range was higher with CL versus OL (80% vs. 64%; P < 0.001). Neither the time below 70 mg/dL (CL 6.1% vs. OL 3.2%; P > 0.05) nor the need for oral glucose was significantly different (CL 40.0% vs. OL 22.5% of meals; P = 0.054).
CONCLUSIONS: This novel CL algorithm effectively and consistently controls PP glucose excursions without increasing hypoglycemia. Study registered at ClinicalTrials.gov : study number NCT02100488.

Entities:  

Keywords:  Artificial pancreas; Glucose control; Postprandial

Mesh:

Substances:

Year:  2017        PMID: 28459603     DOI: 10.1089/dia.2016.0443

Source DB:  PubMed          Journal:  Diabetes Technol Ther        ISSN: 1520-9156            Impact factor:   6.118


  10 in total

1.  Extensive Assessment of Blood Glucose Monitoring During Postprandial Period and Its Impact on Closed-Loop Performance.

Authors:  Lyvia Biagi; Arthur Hirata Bertachi; Ignacio Conget; Carmen Quirós; Marga Giménez; F Javier Ampudia-Blasco; Paolo Rossetti; Jorge Bondia; Josep Vehí
Journal:  J Diabetes Sci Technol       Date:  2017-06-21

2.  Incorporating Unannounced Meals and Exercise in Adaptive Learning of Personalized Models for Multivariable Artificial Pancreas Systems.

Authors:  Iman Hajizadeh; Mudassir Rashid; Kamuran Turksoy; Sediqeh Samadi; Jianyuan Feng; Mert Sevil; Nicole Hobbs; Caterina Lazaro; Zacharie Maloney; Elizabeth Littlejohn; Ali Cinar
Journal:  J Diabetes Sci Technol       Date:  2018-07-31

3.  Factors Beyond Carbohydrate to Consider When Determining Meantime Insulin Doses: Protein, Fat, Timing, and Technology.

Authors:  Alison B Evert
Journal:  Diabetes Spectr       Date:  2020-05

4.  Parallel Control of an Artificial Pancreas with Coordinated Insulin, Glucagon, and Rescue Carbohydrate Control Actions.

Authors:  Vanessa Moscardó; José Luis Díez; Jorge Bondia
Journal:  J Diabetes Sci Technol       Date:  2019-10-20

5.  Plasma-Insulin-Cognizant Adaptive Model Predictive Control for Artificial Pancreas Systems.

Authors:  Iman Hajizadeh; Mudassir Rashid; Ali Cinar
Journal:  J Process Control       Date:  2019-04-10       Impact factor: 3.666

6.  Adaptive and Personalized Plasma Insulin Concentration Estimation for Artificial Pancreas Systems.

Authors:  Iman Hajizadeh; Mudassir Rashid; Sediqeh Samadi; Jianyuan Feng; Mert Sevil; Nicole Hobbs; Caterina Lazaro; Zacharie Maloney; Rachel Brandt; Xia Yu; Kamuran Turksoy; Elizabeth Littlejohn; Eda Cengiz; Ali Cinar
Journal:  J Diabetes Sci Technol       Date:  2018-03-23

Review 7.  The challenges of achieving postprandial glucose control using closed-loop systems in patients with type 1 diabetes.

Authors:  Véronique Gingras; Nadine Taleb; Amélie Roy-Fleming; Laurent Legault; Rémi Rabasa-Lhoret
Journal:  Diabetes Obes Metab       Date:  2017-08-10       Impact factor: 6.577

8.  Modeling the Error of the Medtronic Paradigm Veo Enlite Glucose Sensor.

Authors:  Lyvia Biagi; Charrise M Ramkissoon; Andrea Facchinetti; Yenny Leal; Josep Vehi
Journal:  Sensors (Basel)       Date:  2017-06-12       Impact factor: 3.576

9.  Management of postprandial hyperglycaemia and weight gain in women with gestational diabetes mellitus using a novel telemonitoring system.

Authors:  Ebtisam A Al-Ofi; Hala H Mosli; Kholoud A Ghamri; Sarah M Ghazali
Journal:  J Int Med Res       Date:  2018-11-15       Impact factor: 1.671

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

Authors:  Arthur Bertachi; Lyvia Biagi; Aleix Beneyto; Josep Vehí
Journal:  J Healthc Eng       Date:  2020-01-11       Impact factor: 2.682

  10 in total

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