PDB46 - CLINICAL AND ECONOMIC BURDEN OF TYPE 1 DIABETES IN REAL-LIFE SETTING IN SPAIN

Background: A new version of the UVA/Padova Type 1 Diabetes (T1D) Simulator is presented which provides a more realistic testing scenario. The upgrades to the previous simulator, which was accepted by the Food and Drug Administration in 2013, are described. Method: Intraday variability of insulin sensitivity (SI) has been modeled, based on clinical T1D data, accounting for both intra- and intersubject variability of daily SI. Thus, time-varying distributions of both subject’s basal insulin infusion and insulin-to-carbohydrate ratio were calculated and made available to the user. A model of “dawn” phenomenon based on clinical T1D data has been also included. Moreover, the model of subcutaneous insulin delivery has been updated with a recently developed model of commercially available fast-acting insulin analogs. Models of both intradermal and inhaled insulin pharmacokinetics have been included. Finally, new models of error affecting continuous glucose monitoring and self-monitoring of blood glucose devices have been added. Results: One hundred in silico adults, adolescent, and children have been generated according to the above modifications. The new simulator reproduces the intraday glucose variability observed in clinical data, also describing the nocturnal glucose increase, and the simulated insulin profiles reflect real life data. Conclusions: The new modifications introduced in the T1D simulator allow to extend its domain of validity from “single-meal” to “single-day” scenarios, thus enabling a more realistic framework for in silico testing of advanced diabetes technologies including glucose sensors, new insulin molecules and artificial pancreas.

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Frequency and predictors of confirmed hypoglycaemia in type 1 and insulin-treated type 2 diabetes mellitus patients in a real-life setting: Results from the DIALOG study

Diabetes & Metabolism ◽

10.1016/j.diabet.2014.10.007 ◽

2015 ◽

Vol 41 (2) ◽

pp. 116-125 ◽

Cited By ~ 69

Author(s):

B. Cariou ◽

P. Fontaine ◽

E. Eschwege ◽

M. Lièvre ◽

D. Gouet ◽

...

Keyword(s):

Diabetes Mellitus ◽

Type 2 Diabetes ◽

Type 2 Diabetes Mellitus ◽

Real Life ◽

Real Life Setting

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Diabeloop DBLG1 Closed-Loop System Enables Patients With Type 1 Diabetes to Significantly Improve Their Glycemic Control in Real-Life Situations Without Serious Adverse Events: 6-Month Follow-up

10.2337/figshare.13583129 ◽

2021 ◽

Author(s):

Coralie Amadou ◽

Sylvia Franc ◽

Pierre-Yves Benhamou ◽

Sandrine Lablanche ◽

Erik Huneker ◽

...

Keyword(s):

Type 1 Diabetes ◽

Glycemic Control ◽

Adverse Events ◽

Closed Loop ◽

Artificial Pancreas ◽

Real Life ◽

Serious Adverse Events ◽

Life Conditions ◽

Time In Range

OBJECTIVE To analyze safety and efficacy of the DBLG1 hybrid closed-loop artificial pancreas system in patients with Type 1 Diabetes in real life conditions. METHODS Following a one-week run-in period with usual pump, 25 patients were provided with the commercial DBLG1 system. We present the results of Time-in-Range and HbA1c over a 6-month period. RESULTS The mean (SD;range) age of patients was 43 years (13.8; 25-72). At baseline, mean HbA1c and TIR 70-180mg/dL were respectively 7.9% (0.93; 5.6- 8.5) [63mmol/mol (10; 38-69)] and 53% (16.4;21-85). One patient stopped using the system after 2 months. At 6-month, mean HbA1c decreased to 7.1% [54mmol/mol] (p<0.001) and TIR 70-180mg/dL increased to 69.7% (p<0.0001). TIR<70mg/dL decreased from 2.4 to 1.3% (p=0.03). TIR<54mg/dL decreased from 0.32 to 0.24% (p=0.42). No serious adverse event was reported during the study. CONCLUSION The DBLG1 System confirms its ability to significantly improve glycemic control in real life conditions, without serious adverse events.

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T1D Hypoglycemia Prediction Algorithms: A Systematic Review (Preprint)

10.2196/preprints.34699 ◽

2021 ◽

Author(s):

Stella Tsichlaki ◽

Lefteris Koumakis ◽

Manolis Tsiknakis

Keyword(s):

Type 1 Diabetes ◽

Blood Glucose ◽

Digital Libraries ◽

Predictive Models ◽

Real Life ◽

Blood Glucose Monitoring ◽

Detection Techniques ◽

Glucose Levels ◽

Wide Range

BACKGROUND Diabetes is a chronic condition that necessitates regular monitoring and self-management of the patient's blood glucose levels. People with type 1 diabetes (T1D) can live a productive life if they receive proper diabetes care. Nonetheless, a loose glycemic control might increase the risk of developing hypoglycemia. This incident can occur due to a variety of causes, such as taking additional doses of insulin, skipping meals, or over-exercising. Mainly, the symptoms of hypoglycemia range from mild dysphoria to more severe conditions, if not detected in a timely manner. OBJECTIVE In this review, we report on innovative detection techniques and tactics for identifying and preventing hypoglycemic episodes, focusing on type 1 diabetes. METHODS A systematic literature search following the PRISMA guidelines was performed focusing on the “PUBMED”, “Google Scholar”, “IEEE Xplore” and “ACM” digital libraries to find articles about technologies related to hypoglycemia detection in type 1 diabetes patients. RESULTS The presented approaches have been utilized or devised to enhance blood glucose monitoring and boost its efficacy to forecast future glucose levels, which could aid the prediction of future episodes of hypoglycemia. We detected nineteen predictive models for hypoglycemia, specifically on type 1 diabetes, utilizing a wide range of algorithmic methodologies, spanning from statistics (10%) to machine learning (52%) and deep learning (38%). The algorithms employed most are the kalman filtering and classification models (SVM, KNN, random forests). The performance of the predictive models was found overall to be satisfactory, reaching accuracies between 70% and 99% which proves that such technologies are capable to facilitate the prediction of T1D hypoglycemia. CONCLUSIONS It is evident that CGM can improve the glucose control in diabetes but predictive models for hypo- and hyper- glycemia using only mainstream noninvasive sensors such as wristbands and smartwatches are foreseen to be the next step for mHealth in T1D. Prospective studies are required to demonstrate the value of such models in real-life mHealth interventions.

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