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In Silico Assessment of literature insulin bolus calculation methods accounting for glucose rate of change

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Cappon, Giacomo, Marturano, Francesca, Vettoretti, Martina, Facchinetti, Andrea, Sparacino, Giovanni·2019·Journal of Diabetes Science and Technology
DOI 10.1177/1932296818777524PMC PMC6313276
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Abstract

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.

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  • The standard formula is intrinsically suboptimal, and using it as a starting point to be corrected might prove to be an obstacle in the training process of the neural network.

    “Indeed, the SF is intrinsically suboptimal, and using it as a starting point to be corrected might prove to be an obstacle in the training process of NN [36].”

    From Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors (2020) · cites this paper as [36]

  • An in silico head-to-head comparison of empirical methods that adjust the standard formula according to glucose rate-of-change showed that their efficacy depends on the pre-meal scenario and no method globally outperforms the others.

    “However, an in silico head-to-head comparison of these methods has shown that their efficacy depends on the pre-meal scenario and that not one of these methodologies globally outperforms the others [36].”

    From Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors (2020) · cites this paper as [36]

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Authors
Cappon, G., Marturano, F., Vettoretti, M., Facchinetti, A., & Sparacino, G.
Affiliations
University of Padua (IT)
Corresponding author
Sparacino, Giovanni
ORCID
Venue
Journal of Diabetes Science and Technology
Publisher
SAGE Publications
Published
2018-05-31
Volume
13
Issue
1
Pages
103-110
ISSN
1932-2968 (ISSN-L)
DOI
10.1177/1932296818777524
PMC
PMC6313276
OpenAlex
W2806402769
Cited by
18
References
17
Citations by year
2026: 1 · 2025: 1 · 2024: 1 · 2023: 1 · 2022: 1 · 2021: 3 · 2020: 5 · 2019: 5
FWCI
2.58
Type
journal article
Language
en
Open access
Licence
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations
  • Journal of Diabetes Science and Technology (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • Padua Research Archive (University of Padova) (submittedVersion)
Topics
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
Keywords
continuous glucose monitoring, bolus calculator, type 1 diabetes, standard formula, preprandial blood glucose, virtual patients
MeSH terms
Blood Glucose, Chemistry, Pharmaceutical, Computer Simulation, Diabetes Mellitus, Type 1, Humans, Hypoglycemic Agents, Insulin, Insulin Infusion Systems, Blood Glucose Self-Monitoring, Postprandial Period

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