Evidence mapPaperPMID 34486426Full record

Trial reportJournal of diabetes science and technology2023

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.

Giulia Noaro, Giacomo Cappon, Giovanni Sparacino, Federico Boscari, Daniela Bruttomesso, Andrea Facchinetti

Abstract readClinical Trial
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Trial report in Journal of diabetes science and technology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Giulia NoaroDepartment of Information Engineering, University of Padova, Padova, Italy.ORCID 0000-0003-0001-6144
Giacomo CapponDepartment of Information Engineering, University of Padova, Padova, Italy.ORCID 0000-0003-4358-9268
Giovanni SparacinoDepartment of Information Engineering, University of Padova, Padova, Italy.ORCID 0000-0002-3248-1393
Federico BoscariDepartment of Medicine, University of Padova, Padova, Italy.
Daniela BruttomessoDepartment of Medicine, University of Padova, Padova, Italy.
Andrea FacchinettiDepartment of Information Engineering, University of Padova, Padova, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundProviding 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.

methodsThe 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).

resultsFor 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.

conclusionsInsulin 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.

Indexed as

Diabetes Mellitus, Type 1Blood GlucoseBlood Glucose Self-MonitoringHumansHypoglycemic AgentsInsulinInsulin Infusion SystemsInsulin, Regular, HumanBlood GlucoseHypoglycemic AgentsInsulinInsulin, Regular, Humancontinuous glucose monitoringin silico clinical trialinsulin dose adjustmenttrend arrowstype1 diabetes

Identifiers

PMID34486426
PMCPMC9846415

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.