Evidence map›Paper›PMID 29848104›Full record

ArticleJournal of diabetes science and technology2019

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

Giacomo Cappon, Francesca Marturano, Martina Vettoretti, Andrea Facchinetti, Giovanni Sparacino

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Trial
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  5. Machine learning for initial insulin estimation in hospitalized patients.Journal of the American Medical Informatics Association : JAMIA · 2021
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  6. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Giacomo Cappon1 Department of Information Engineering, University of Padova, Padova, Italy.ORCID 0000-0003-4358-9268
Francesca Marturano1 Department of Information Engineering, University of Padova, Padova, Italy.
Martina Vettoretti1 Department of Information Engineering, University of Padova, Padova, Italy.
Andrea Facchinetti1 Department of Information Engineering, University of Padova, Padova, Italy.
Giovanni Sparacino1 Department of Information Engineering, University of Padova, Padova, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Blood GlucoseBlood Glucose Self-MonitoringChemistry, PharmaceuticalComputer SimulationDiabetes Mellitus, Type 1HumansHypoglycemic AgentsInsulinInsulin Infusion SystemsPostprandial PeriodBlood GlucoseHypoglycemic AgentsInsulinbolus calculatorcarbohydrate-to-insulin rationonadjunctive userate of changetype 1 diabetes

Identifiers

PMID29848104
PMCPMC6313276

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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 OpenQuestion graph.