scholarly journals A “Slide Rule” to Adjust Insulin Dose Using Trend Arrows in Adults with Type 1 Diabetes: Test in Silico and in Real Life

2021 ◽  
Author(s):  
Daniela Bruttomesso ◽  
Federico Boscari ◽  
Giuseppe Lepore ◽  
Giulia Noaro ◽  
Giacomo Cappon ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
In Silico ◽  
Insulin Dose ◽  
Real Life ◽  
Slide Rule

scholarly journals The UVA/Padova Type 1 Diabetes Simulator Goes From Single Meal to Single Day

2018 ◽  
Vol 12 (2) ◽  
pp. 273-281 ◽  
Author(s):  
Roberto Visentin ◽  
Enrique Campos-Náñez ◽  
Michele Schiavon ◽  
Dayu Lv ◽  
Martina Vettoretti ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
In Silico ◽  
Artificial Pancreas ◽  
Real Life ◽  
Glucose Monitoring ◽  
Glucose Variability ◽  
Self Monitoring ◽  
Inhaled Insulin ◽  
Single Meal

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.

scholarly journals Combined Algorithm-Based Adaptations of Insulin Dose and Carbohydrate Intake During Exercise in Children With Type 1 Diabetes: Results From the CAR2DIAB Study

2021 ◽  
Vol 12 ◽  
Author(s):  
Philippe Antoine Lysy ◽  
Hélène Absil ◽  
Emy Gasser ◽  
Hasnae Boughaleb ◽  
Thierry Barrea ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Insulin Dose ◽  
Real Life ◽  
Severe Hypoglycemia ◽  
Carbohydrate Intake ◽  
Cardiac Evaluation ◽  
Maximum Effort ◽  
Combined Algorithm ◽  
Fast Acting

ObjectivesTo evaluate the evolution of subcutaneous glucose during two sessions of monitored aerobic exercise in children or adolescents with type 1 diabetes after adaptation of insulin doses and carbohydrate intake according to a combined algorithm.MethodsTwelve patients with type 1 diabetes (15.1 ± 2 years; diabetes duration: 9.5 ± 3.1 years) performed two series of exercise sessions after cardiac evaluation. The first series (TE#1) consisted in a monitored exercise of moderate to vigorous intensity coupled with a bout of maximum effort. The second series of exercises (TE#2) was carried out in real life during exercises categorized and monitored by connected watches. TE#2 sessions were performed after adaptation of insulin doses and fast-acting carbohydrates according to decision algorithms.ResultsPatients did not experience episodes of severe hypoglycemia, symptomatic hyperglycemia, or hyperglycemia associated with ketosis. Analysis of CGM data (15 h) during TE#2 sessions revealed an overall improvement in glycemic average [± standard deviation] (104 ± 14 mg/dl vs. 122 ± 17 mg/dl during TE#1; p < 0.001), associated with a decrease in proportion of hyperglycemia in periods ranging from 4 h to 15 h after performing the exercises. The proportion of hypoglycemia was not changed, except during the TE#2 +4–8 h period, where a significant increase in hypoglycemia <60 mg/dl was observed (25% vs. 6.2%; p = 0.04), yet without concurrent complications.ConclusionIn our pediatric series, the application of algorithmic adaptations of insulin doses and carbohydrate intake has globally improved glycemic control during 15 h after real-time exercises performed by children and adolescents with type 1 diabetes.

scholarly journals Basal Insulin Dose in Adults with Type 1 Diabetes Mellitus on Insulin Pumps in Real-Life Clinical Practice: A Single-Center Experience

2018 ◽  
Vol 2018 ◽  
pp. 1-5 ◽  
Author(s):  
Bartłomiej Matejko ◽  
Aneta Kukułka ◽  
Beata Kieć-Wilk ◽  
Agnieszka Stąpór ◽  
Tomasz Klupa ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Basal Insulin ◽  
Hba1c Level ◽  
Metabolic Diseases ◽  
Insulin Dose ◽  
Real Life ◽  
Pump Therapy ◽  
Single Center Experience

Introduction. Basal insulin (BI) infusion in pump therapy of type 1 diabetes (T1DM) mimics physiological secretion during the night and between meals. The recommended percentage of the total BI to daily insulin dose (termed the %BI) ranges between 30 and 50%. We analyzed whether this recommendation was followed in adults with T1DM from a university center, and whether BI doses were linked with glycemic control. Materials and Methods. We included 260 consecutive patients with T1DM (159 women and 101 men) treated with continuous subcutaneous insulin infusion at the Department of Metabolic Diseases, Krakow, Poland. Data were downloaded from patients’ pumps and collected from medical records. We analyzed the settings of BI and the association of %BI with HbA1c level. Linear regression was performed. Results. The mean age of T1DM individuals was 26.6 ± 8.2 years, BMI was 23.1 ± 3.0 kg/m2, T1DM duration was 13.3 ± 6.4 years, and HbA1c level was 7.4%. There were 69.6% (n=181) of T1DM patients with %BI in the recommended range. The T1DM duration and HbA1c level of patients with a %BI <30% (n=23) was 9.5 years and 6.4%, respectively; for a %BI of 30–50%, it was 13.2 years and 7.4%; and for a %BI >50% (n=56), it was 15.8 years and 7.8% (p<0.001 for both three-group comparisons). Multiple regression identified %BI among independent predictors of the HbA1c level. Conclusion. In this real-life analysis, the recommendations concerning %BI dosing were not followed by almost one-third of adult T1DM patients. Low %BI was associated with better glycemic control; however, this requires further confirmation.

scholarly journals Insulin Basal Dose Is Associated With Better Metabolic Control in Type 1 Diabetes Children and Adolescents

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. A660-A660
Author(s):  
Abril Arellano-Llamas ◽  
Luz Elena Mejía-Carmona ◽  
Alicia Rojas-Zacarias ◽  
Oscar Ochoa-Romero ◽  
Irene Díaz-Rodríguez
Keyword(s):  
Type 1 Diabetes ◽  
Metabolic Control ◽  
Basal Insulin ◽  
Glycated Hemoglobin ◽  
Insulin Dose ◽  
Real Life ◽  
Total Daily Dose ◽  
Daily Dose ◽  
Basal Bolus

Abstract Basal insulin dose in type 1 diabetes has been established empirically, since 2011 all guidelines suggest insulin basal dose less than 50% of total insulin dose in the pediatric population. However, in real life, basal dose indication has not changed in all patients in the basal-bolus treatment scheme. Objective: To measure how the physician indicates in real-life basal insulin dose in pediatric patients with type 1 diabetes in the basal-bolus scheme, and correlate this dose with metabolic control measured by glycated hemoglobin. Methods. This was a retrospective study, subjects include pediatric T1D (2 to 16 years, non-obese, using insulin more than 0.3 UI/Kg/d), more than 1 year of diagnostic, none of them in ketoacidosis, attended during 2019. The protocol was revised and accepted in the institution. Data were analyzed with Kruskal-Wallis, U Mann Withney, Pearson correlation test. Results: There were 141 subjects, male (51%), median age 13.3 years (3.6-15.9), median evolution time since diagnosis 8 years (1-14), pre-pubertal (Tanner stage 1, 22%), total daily dose 1.02 UI/Kg/d (0.3-2.19 UI/Kg/d). Basal insulin was glargine 50.4%, and NPH 49.6%, prandial insulin was lispro 66.7%, and regular human 29.8%. Children using 50% or less basal insulin of total insulin dose was 40.4%. The basal dose was 38% of total insulin dose in children less than 6 years, and 59% in children older than 6 years. (p=0.033). Glycated hemoglobin was less than 7.5% in 12.8%. The persons with glycated hemoglobin less than 7.5% used less basal insulin 0.38 u/kg/d, than those with higher glycated hemoglobin 0.57 U/kg/d (p=0.02) with no impact in total insulin dose (0.86 vs 1.05 UI/Kg/d, p=0.129). The correlation of the percentage of insulin basal dose and glycated hemoglobin was 0.279, p=0.001, meaning, more basal insulin, worse diabetes control. Conclusion: Lower basal insulin dose percentage from total daily dose is associated with better metabolic control in children treated with the basal-bolus scheme. There is high clinical inertia in the indication of basal insulin in older children.

scholarly journals Classification of Postprandial Glycemic Status with Application to Insulin Dosing in Type 1 Diabetes—An In Silico Proof-of-Concept

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3168 ◽  
Author(s):  
Cappon ◽  
Facchinetti. ◽  
Sparacino. ◽  
Georgiou ◽  
Herrero
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Real Time ◽  
In Silico ◽  
Blood Glucose Concentration ◽  
Insulin Dose ◽  
Target Range ◽  
Glycemic Status ◽  
Meal Time

In the daily management of type 1 diabetes (T1D), determining the correct insulin dose to be injected at meal-time is fundamental to achieve optimal glycemic control. Wearable sensors, such as continuous glucose monitoring (CGM) devices, are instrumental to achieve this purpose. In this paper, we show how CGM data, together with commonly recorded inputs (carbohydrate intake and bolus insulin), can be used to develop an algorithm that allows classifying, at meal-time, the post-prandial glycemic status (i.e., blood glucose concentration being too low, too high, or within target range). Such an outcome can then be used to improve the efficacy of insulin therapy by reducing or increasing the corresponding meal bolus dose. A state-of-the-art T1D simulation environment, including intraday variability and a behavioral model, was used to generate a rich in silico dataset corresponding to 100 subjects over a two-month scenario. Then, an extreme gradient-boosted tree (XGB) algorithm was employed to classify the post-prandial glycemic status. Finally, we demonstrate how the XGB algorithm outcome can be exploited to improve glycemic control in T1D through real-time adjustment of the meal insulin bolus. The proposed XGB algorithm obtained good accuracy at classifying post-prandial glycemic status (AUROC = 0.84 [0.78, 0.87]). Consequently, when used to adjust, in real-time, meal insulin boluses obtained with a bolus calculator, the proposed approach improves glycemic control when compared to the baseline bolus calculator. In particular, percentage time in target [70, 180] mg/dL was improved from 61.98 (± 13.89) to 67.00 (± 11.54; p < 0.01) without increasing hypoglycemia.

scholarly journals Replay Simulations with Personalized Metabolic Model for Treatment Design and Evaluation in Type 1 Diabetes

2020 ◽  
pp. 193229682097319
Author(s):  
Jonathan Hughes ◽  
Thibault Gautier ◽  
Patricio Colmegna ◽  
Chiara Fabris ◽  
Marc D Breton
Keyword(s):  
Type 1 Diabetes ◽  
In Silico ◽  
Real Life ◽  
Treatment Optimization ◽  
Method Performance ◽  
In Silico Studies ◽  
Wide Range ◽  
Subject Specific ◽  
Model Personalization

Background: The capacity to replay data collected in real life by people with type 1 diabetes mellitus (T1DM) would lead to individualized (vs population) assessment of treatment strategies to control blood glucose and possibly true personalization. Patek et al introduced such a technique, relying on regularized deconvolution of a population glucose homeostasis model to estimate a residual additive signal and reproduce the experimental data; therefore, allowing the subject-specific replay of what-if scenarios by altering the model inputs (eg, insulin). This early method was shown to have a limited domain of validity. We propose and test in silico a similar approach and extend the method applicability. Methods: A subject-specific model personalization of insulin sensitivity and meal-absorption parameters is performed. The University of Virginia (UVa)/Padova T1DM simulator is used to generate experimental scenarios and test the ability of the methodology to accurately reproduce changes in glucose concentration to alteration in meal and insulin inputs. Method performance is assessed by comparing true (UVa/Padova simulator) and replayed glucose traces, using the mean absolute relative difference (MARD) and the Clarke error grid analysis (CEGA). Results: Model personalization led to a 9.08 and 6.07 decrease in MARD over a prior published method of replaying altered insulin scenarios for basal and bolus changes, respectively. Replay simulations achieved high accuracy, with MARD <10% and more than 95% of readings falling in the CEGA A-B zones for a wide range of interventions. Conclusions: In silico studies demonstrate that the proposed method for replay simulation is numerically and clinically valid over broad changes in scenario inputs, indicating possible use in treatment optimization.

scholarly journals Glycaemic Control in Children and Adolescents With Type 1 Diabetes Following a Single Telehealth Visit:What Have We Learned From the COVID-19 Lockdown?

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. A341-A342
Author(s):  
Marianna Rachmiel ◽  
Yael Lebenthal ◽  
Kineret Mazor-Aronovitch ◽  
Avivit Brener ◽  
Noah Levek ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Insulin Dose ◽  
Real Life ◽  
Single Parent ◽  
Clinic Visit ◽  
Considerable Proportion ◽  
Continuous Glucose Monitor ◽  
Life Study ◽  
Single Parent Household

Abstract Aims: Children with chronic diseases were unable to receive their usual care during COVID-19 lockdown. We assessed the feasibility and impact of telehealth visits on the time-in-range (TIR) of pediatric individuals with type 1 diabetes (T1D). Methods: An observational multicenter real-life study. Patients scheduled for an in-clinic visit during the lockdown were offered to participate in a telehealth visit. Sociodemographic, clinical, continuous glucose monitor and pump data were recorded 2 weeks prior and 2 weeks after telehealth visit. The primary endpoint was change in relative-TIR, i.e change in TIR divided by the percent of possible change (∆TIR/(100-TIRbefore)*100). Results: The study group comprised 195 individuals with T1D (47.7% males), mean±SD age 14.6±5.3 years, diabetes duration 6.0±4.6 years. Telehealth was accomplished with 121 patients and their parents (62.0%); 74 (38.0%) did not transfer complete data. Mean TIR was significantly higher for the two-week period after the telehealth visit than for the two-week period prior the visit (62.9±16.0, p&lt;0.001 vs. 59.0±17.2); the improvement in relative-TIR was 5.7±26.1%. Initial higher mean glucose level, lower TIR, less time spent at &lt;54 mg/dl range, longer time spent at 180–250 mg/dl range, higher daily insulin dose and single parent household were associated with improved relative-TIR. Multiple regression logistic analysis demonstrated only initial lower TIR and single-parent household were significant, odds ratio: -0.506, (95%CI -0.99,-0.023), p=0.04 and 13.82, (95%CI 0.621, 27.016), p=0.04, respectively. Conclusions: Pediatric patients with T1D benefited from a telehealth visit during COVID-19. This modality and its benefit should be employed, and used in the future as well. However, this modality is not yet suitable for a considerable proportion of patients.

1066-P: Improvement in Time-in-Range (TIR) with Real-Life Use of Hybrid Closed-Loop System in Patients with Type 1 Diabetes (T1D)

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1066-P
Author(s):  
HALIS K. AKTURK ◽  
DOMINIQUE A. GIORDANO ◽  
HAL JOSEPH ◽  
SATISH K. GARG ◽  
JANET K. SNELL-BERGEON
Keyword(s):  
Type 1 Diabetes ◽  
Closed Loop ◽  
Real Life ◽  
Loop System ◽  
Closed Loop System ◽  
Time In Range
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