ArticleJournal of diabetes science and technology2019
In Silico Assessment of Literature Insulin Bolus Calculation Methods Accounting for Glucose Rate of Change.
Article in Journal of diabetes science and technology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- 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.Journal of diabetes science and technology · 2023Trial
- Safety and Feasibility Evaluation of Step Count Informed Meal Boluses in Type 1 Diabetes: A Pilot Study.Journal of diabetes science and technology · 2022Trial
- Trial
- Randomised Controlled Multicentre Trial to Investigate the Effectiveness of a Mobile Artificial Intelligence Solution for Diabetes Adapted Care: The MELISSA Trial Protocol.Diabetes, obesity & metabolism · 2026Article
- Machine learning for initial insulin estimation in hospitalized patients.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors.Sensors (Basel, Switzerland) · 2020Review
- Development of an Error Model for a Factory-Calibrated Continuous Glucose Monitoring Sensor with 10-Day Lifetime.Sensors (Basel, Switzerland) · 2019Article
- Continuous Glucose Monitoring Sensors for Diabetes Management: A Review of Technologies and Applications.Diabetes & metabolism journal · 2019Review
- Classification of Postprandial Glycemic Status with Application to Insulin Dosing in Type 1 Diabetes-An In Silico Proof-of-Concept.Sensors (Basel, Switzerland) · 2019Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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).
methodUsing 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.
resultsOn 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.
conclusionOur 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.
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