scholarly journals The road from intermittently scanned glucose monitoring to hybrid closed-loop systems: Part A. Keys to success: subject profiles, choice of systems, education

2019 ◽  
Vol 10 ◽  
pp. 204201881986539 ◽  
Author(s):  
Francesca De Ridder ◽  
Marieke den Brinker ◽  
Christophe De Block
Keyword(s):  
Type 1 Diabetes ◽  
Closed Loop ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
Open Loop ◽  
Loop Control ◽  
The Road ◽  
Low Glucose ◽  
Functional Features

Managing type 1 diabetes (T1DM) is challenging and requires intensive glucose monitoring and titration of insulin in order to reduce the risk of complications. The use of continuous glucose monitoring (CGM) systems, either flash or intermittently scanned glucose monitoring (isCGM) or real-time (RT) CGM, has positively affected the management of type 1 diabetes with the potential to lower HbA1c, enhance time spent in range, reduce frequency and time spent in hypoglycemia and hyperglycemia, lower glycemic variability, and improve quality of life. In recent years, both CGM and pump technology have advanced, with improved functional features and integration, including low glucose suspend (LGS), predictive low glucose suspend (PLGS), and hybrid closed-loop (HCL) systems. In this review, we highlight the benefits and limitations of use of isCGM/RT-CGM for open-loop control and recent progress in closed-loop control systems. We also discuss different subject profiles for the different systems, and focus on educational aspects that are key to successful use of the systems.

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 Closed-Loop Artificial Pancreas Using Subcutaneous Glucose Sensing and Insulin Delivery and a Model Predictive Control Algorithm: The Virginia Experience

2009 ◽  
Vol 3 (5) ◽  
pp. 1031-1038 ◽  
Author(s):  
William L. Clarke ◽  
Stacey Anderson ◽  
Marc Breton ◽  
Stephen Patek ◽  
Laurissa Kashmer ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Predictive Control ◽  
Insulin Infusion ◽  
Closed Loop ◽  
Artificial Pancreas ◽  
Open Loop ◽  
Closed Loop Control ◽  
Open Loop Control ◽  
Loop Control

Background: Recent progress in the development of clinically accurate continuous glucose monitors (CGMs), automated continuous insulin infusion pumps, and control algorithms for calculating insulin doses from CGM data have enabled the development of prototypes of subcutaneous closed-loop systems for controlling blood glucose (BG) levels in type 1 diabetes. The use of a new personalized model predictive control (MPC) algorithm to determine insulin doses to achieve and maintain BG levels between 70 and 140 mg/dl overnight and to control postprandial BG levels is presented. Methods: Eight adults with type 1 diabetes were studied twice, once using their personal open-loop systems to control BG overnight and for 4 h following a standardized meal and once using a closed-loop system that utilizes the MPC algorithm to control BG overnight and for 4 h following a standardized meal. Average BG levels, percentage of time within BG target of 70–140 mg/dl, number of hypoglycemia episodes, and postprandial BG excursions during both study periods were compared. Results: With closed-loop control, once BG levels achieved the target range (70–140 mg/dl), they remained within that range throughout the night in seven of the eight subjects. One subject developed a BG level of 65 mg/dl, which was signaled by the CGM trend analysis, and the MPC algorithm directed the discontinuance of the insulin infusion. The number of overnight hypoglycemic events was significantly reduced ( p = .011) with closed-loop control. Postprandial BG excursions were similar during closed-loop and open-loop control Conclusion: Model predictive closed-loop control of BG levels can be achieved overnight and following a standardized breakfast meal. This “artificial pancreas” controls BG levels as effectively as patient-directed open-loop control following a morning meal but is significantly superior to open-loop control in preventing overnight hypoglycemia.

scholarly journals Hybrid Closed-Loop Insulin Delivery Systems for People With Type 1 Diabetes

2021 ◽  
Vol 1 (6) ◽  
Author(s):  
Health Technology Assessment Team
Keyword(s):  
Type 1 Diabetes ◽  
Closed Loop ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
Insulin Delivery ◽  
Monitoring Methods ◽  
Expert Review ◽  
Expert Review Panel ◽  
Time In Range

The CADTH Health Technology Expert Review Panel (HTERP) suggests that hybrid closed-loop insulin delivery (HCL) systems hold promise for the care of people with type 1 diabetes. HTERP considers that, at present, there are insufficient long-term data on clinically relevant and patient-important outcomes to recommend how extensive the role of HCL systems should be in care. HTERP recommends the collection of robust and comparative data for consideration of future reassessments that compare HCL systems to existing insulin delivery and glucose monitoring methods in terms of glycated hemoglobin (hemoglobin A1C); time-in-range; time above and below range; glycemic variability; quality of life; patient, parent or caregiver, and health care provider satisfaction; diabetes-related complications; discontinuation rates; and health system impact. Robust data are collected in well-designed comparative studies that are, among other considerations, of sufficient duration to ensure a clinically meaningful outcome assessment.

Glycemic Variability and Insulin Needs in Patients with Type 1 Diabetes Mellitus Supplemented with Vitamin D: A Pilot Study Using Continuous Glucose Monitoring System

2018 ◽  
Vol 14 (4) ◽  
pp. 395-403 ◽  
Author(s):  
Karem Mileo Felício ◽  
Ana Carolina Contente Braga de Souza ◽  
Joao Felicio Abrahao Neto ◽  
Franciane Trindade Cunha de Melo ◽  
Carolina Tavares Carvalho ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Vitamin D ◽  
Type 1 Diabetes ◽  
Pilot Study ◽  
Type 1 Diabetes Mellitus ◽  
Monitoring System ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Glycemic Variability

scholarly journals Teleassistance for Patients With Type 1 Diabetes During the COVID-19 Pandemic: Results of a Pilot Study (Preprint)

2020 ◽  
Author(s):  
Martina Parise ◽  
Linda Tartaglione ◽  
Antonio Cutruzzolà ◽  
Maria Ida Maiorino ◽  
Katherine Esposito ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Disease Management ◽  
Health Care Providers ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
Care Providers ◽  
Virtual Visits

BACKGROUND Telemedicine use in chronic disease management has markedly increased during health emergencies due to COVID-19. Diabetes and technologies supporting diabetes care, including glucose monitoring devices, software analyzing glucose data, and insulin delivering systems, would facilitate remote and structured disease management. Indeed, most of the currently available technologies to store and transfer web-based data to be shared with health care providers. OBJECTIVE During the COVID-19 pandemic, we provided our patients the opportunity to manage their diabetes remotely by implementing technology. Therefore, this study aimed to evaluate the effectiveness of 2 virtual visits on glycemic control parameters among patients with type 1 diabetes (T1D) during the lockdown period. METHODS This prospective observational study included T1D patients who completed 2 virtual visits during the lockdown period. The glucose outcomes that reflected the benefits of the virtual consultation were time in range (TIR), time above range, time below range, mean daily glucose, glucose management indicator (GMI), and glycemic variability. This metric was generated using specific computer programs that automatically upload data from the devices used to monitor blood or interstitial glucose levels. If needed, we changed the ongoing treatment at the first virtual visit. RESULTS Among 209 eligible patients with T1D, 166 completed 2 virtual visits, 35 failed to download glucose data, and 8 declined the visit. Among the patients not included in the study, we observed a significantly lower proportion of continuous glucose monitoring (CGM) and continuous subcutaneous insulin infusion (CSII) users (n=7/43, 16% vs n=155/166, 93.4% and n=9/43, 21% vs n=128/166, 77.1%, respectively; <i>P</i>&lt;.001) compared to patients who completed the study. TIR significantly increased from the first (62%, SD 18%) to the second (65%, SD 16%) virtual visit (<i>P</i>=.02); this increase was more marked among patients using the traditional meter (n=11; baseline TIR=55%, SD 17% and follow-up TIR=66%, SD 13%; <i>P</i>=.01) than among those using CGM, and in those with a baseline GMI of ≥7.5% (n=46; baseline TIR=45%, SD 15% and follow-up TIR=53%, SD 18%; <i>P</i>&lt;.001) than in those with a GMI of &lt;7.5% (n=120; baseline TIR=68%, SD 15% and follow-up TIR=69%, SD 15%; <i>P</i>=.98). The only variable independently associated with TIR was the change of ongoing therapy. The unstandardized beta coefficient (B) and 95% CI were 5 (95% CI 0.7-8.0) (<i>P</i>=.02). The type of glucose monitoring device and insulin delivery systems did not influence glucometric parameters. CONCLUSIONS These findings indicate that the structured virtual visits help maintain and improve glycemic control in situations where in-person visits are not feasible.

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