scholarly journals In Silico Assessment of Literature Insulin Bolus Calculation Methods Accounting for Glucose Rate of Change

2018 ◽  
Vol 13 (1) ◽  
pp. 103-110 ◽  
Author(s):  
Giacomo Cappon ◽  
Francesca Marturano ◽  
Martina Vettoretti ◽  
Andrea Facchinetti ◽  
Giovanni Sparacino
Keyword(s):  
Blood Glucose ◽  
In Silico ◽  
Blood Glucose Concentration ◽  
Risk Index ◽  
Glucose Monitoring ◽  
Rate Of Change ◽  
Self Monitoring ◽  
Single Meal ◽  
Ideal Framework

Background: The standard formula (SF) used in bolus calculators (BCs) determines meal insulin bolus using “static” measurement of blood glucose concentration (BG) obtained by self-monitoring of blood glucose (SMBG) fingerprick device. Some methods have been proposed to improve efficacy of SF using “dynamic” information provided by continuous glucose monitoring (CGM), and, in particular, glucose rate of change (ROC). This article compares, in silico and in an ideal framework limiting the exposition to possibly confounding factors (such as CGM noise), the performance of three popular techniques devised for such a scope, that is, the methods of Buckingham et al (BU), Scheiner (SC), and Pettus and Edelman (PE). Method: Using the UVa/Padova Type 1 diabetes simulator we generated data of 100 virtual subjects in noise-free, single-meal scenarios having different preprandial BG and ROC values. Meal insulin bolus was computed using SF, BU, SC, and PE. Performance was assessed with the blood glucose risk index (BGRI) on the 9 hours after meal. Results: On average, BU, SC, and PE improve BGRI compared to SF. When BG is rapidly decreasing, PE obtains the best performance. In the other ROC scenarios, none of the considered methods prevails in all the preprandial BG conditions tested. Conclusion: Our study showed that, at least in the considered ideal framework, none of the methods to correct SF according to ROC is globally better than the others. Critical analysis of the results also suggests that further investigations are needed to develop more effective formulas to account for ROC information in BCs.

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.

Methods for Insulin Bolus Adjustment Based on the Continuous Glucose Monitoring Trend Arrows in Type 1 Diabetes: Performance and Safety Assessment in an In Silico Clinical Trial

2021 ◽  
pp. 193229682110431
Author(s):  
Giulia Noaro ◽  
Giacomo Cappon ◽  
Giovanni Sparacino ◽  
Federico Boscari ◽  
Daniela Bruttomesso ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Continuous Glucose Monitoring ◽  
In Silico ◽  
Safety Assessment ◽  
Glucose Monitoring ◽  
Postprandial Glucose ◽  
Rate Of Change ◽  
The Other ◽  
Single Meal

Background: Providing real-time magnitude and direction of glucose rate-of-change (ROC) via trend arrows represents one of the major strengths of continuous glucose monitoring (CGM) sensors in managing type 1 diabetes (T1D). Several literature methods were proposed to adjust the standard formula (SF) used for insulin bolus calculation by accounting for glucose ROC, but each of them provides different suggestions, making it difficult to understand which should be applied in practice. This work aims at performing an extensive in-silico assessment of their performance and safety. Methods: The methods of Buckingham (BU), Scheiner (SC), Pettus/Edelman (PE), Klonoff/Kerr (KL), Aleppo/Laffel (AL), Ziegler (ZI), and Bruttomesso (BR) were evaluated using the UVa/Padova T1D simulator, in single-meal scenarios, where ROC and glucose at mealtime varied between [-2,+2] mg/dL/min and [80,200] mg/dL, respectively. Efficacy of postprandial glucose control was quantitatively assessed by time in, above and below range (TIR, TAR, and TBR, respectively). Results: For negative ROCs, all methods proved to increase TIR and decrease TAR and TBR vs SF, with KL, PE, and BR being the most effective. For positive ROCs, a general worsening of the performances is present, only BR improved the glycemic control when mealtime glucose was close to hypoglycemia, while SC resulted the safest in the other conditions. Conclusions: Insulin bolus adjustment methods are effective for negative ROCs, but they generally appear to overdose for positive ROCs, calling for safer strategies in such a scenario. These results can be useful in outlining guidelines to identify which adjustment to apply based on the mealtime condition.

scholarly journals A Neural-Network-Based Approach to Personalize Insulin Bolus Calculation Using Continuous Glucose Monitoring

2018 ◽  
Vol 12 (2) ◽  
pp. 265-272 ◽  
Author(s):  
Giacomo Cappon ◽  
Martina Vettoretti ◽  
Francesca Marturano ◽  
Andrea Facchinetti ◽  
Giovanni Sparacino
Keyword(s):  
Blood Glucose ◽  
Continuous Glucose Monitoring ◽  
Insulin Pump ◽  
Risk Index ◽  
Insulin Dose ◽  
Glucose Monitoring ◽  
Rate Of Change ◽  
Single Meal ◽  
Ratio Correction ◽  
Future Work

Background: In type 1 diabetes (T1D) therapy, the calculation of the meal insulin bolus is performed according to a standard formula (SF) exploiting carbohydrate intake, carbohydrate-to-insulin ratio, correction factor, insulin on board, and target glucose. Recently, some approaches were proposed to account for preprandial glucose rate of change (ROC) in the SF, including those by Scheiner and by Pettus and Edelman. Here, the aim is to develop a new approach, based on neural networks (NN), to optimize and personalize the bolus calculation using continuous glucose monitoring information and some easily accessible patient parameters. Method: The UVa/Padova T1D Simulator was used to simulate data of 100 virtual adults in a single-meal noise-free scenario with different conditions in terms of meal amount and preprandial blood glucose and ROC values. An NN was trained to learn the optimal insulin dose using the SF parameters, ROC, body weight, insulin pump basal infusion rate and insulin sensitivity as features. The performance of the NN for meal bolus calculation was assessed by blood glucose risk index (BGRI) and compared to the methods by Scheiner and by Pettus and Edelman. Results: The NN approach brings to a small but statistically significant ( P < .001) reduction of BGRI value, equal to 0.37, 0.23, and 0.20 versus SF, Scheiner, and Pettus and Edelman, respectively. Conclusion: This preliminary study showed the potentiality of using NNs for the personalization and optimization of the meal insulin bolus calculation. Future work will deal with more realistic scenarios including technological and physiological/behavioral sources of variability.

scholarly journals Long-term Cost-Effectiveness of Dexcom G6 Real-Time Continuous Glucose Monitoring Versus Self-Monitoring of Blood Glucose in Patients With Type 1 Diabetes in the U.K.

2020 ◽  
Author(s):  
Stéphane Roze ◽  
John Isitt ◽  
Jayne Smith-Palmer ◽  
Mehdi Javanbakht ◽  
Peter Lynch
Keyword(s):  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Cost Effectiveness ◽  
Real Time ◽  
Glucose Monitoring ◽  
Baseline Hba1c ◽  
Self Monitoring ◽  
Quality Adjusted Life

<b>Objective</b> <p>A long-term health economic analysis was performed to establish the cost-effectiveness of real-time continuous glucose monitoring (RT-CGM) (Dexcom G6) versus self-monitoring of blood glucose (SMBG) alone in UK-based patients with type 1 diabetes. </p> <p><b>Methods</b></p> <p>The analysis utilized the IQVIA CORE Diabetes Model. Clinical input data were sourced from the DIAMOND trial in adults with type 1 diabetes; simulations were performed separately in the overall population of patients with baseline HbA1c ≥7.5% (58 mmol/mol); and a secondary analysis was performed in patients with baseline HbA1c ≥8.5% (69 mmol/mol). The analysis was performed from the NHS healthcare payer perspective over a lifetime time horizon. </p> <p><b>Results</b></p> <p>In the overall population, G6 RT-CGM was associated with a mean incremental gain in quality-adjusted life expectancy of 1.49 quality-adjusted life years (QALYs) versus SMBG (mean [standard deviation; SD] 11.47 [2.04] QALYs versus 9.99 [1.84] QALYs). Total mean (SD) lifetime costs were also GBP 14,234 higher with RT-CGM (GBP 102,468 [35,681] versus GBP 88,234 [39,027]) resulting in an ICER of GBP 9,558 per QALY gained. Sensitivity analyses revealed that the findings were sensitive to changes in the quality of life benefit associated with reduced fear of hypoglycemia and avoidance of fingerstick testing as well as the HbA1c benefit associated with RT-CGM use. </p> <p><b>Conclusions</b></p> <p>For UK-based type 1 diabetes patients, the G6 RT-CGM device is associated with significant improvements in clinical outcomes and, over patient lifetimes, is a cost-effective disease management option relative to SMBG, based on a willingness-to-pay threshold of GBP 20,000 per QALY gained. </p>

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