Evidence map›Paper›PMID 28688482›Full record

ArticleComputer methods and programs in biomedicine2017

Enhancing automatic closed-loop glucose control in type 1 diabetes with an adaptive meal bolus calculator - in silico evaluation under intra-day variability.

Pau Herrero, Jorge Bondia, Oloruntoba Adewuyi, Peter Pesl, Mohamed El-Sharkawy, Monika Reddy, Chris Toumazou, Nick Oliver, Pantelis Georgiou

Open access · bronzeAbstract read
In one paragraph

Article in Computer methods and programs in biomedicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
6.2field-weighted citation impact, top 3% of its field
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

19 citing papers in PubMed, 63 citations in OpenAlex.

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  17. Automatic Adaptation of Basal Insulin Using Sensor-Augmented Pump Therapy.Journal of diabetes science and technology · 2018
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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

9 authors at 4 institutions in 2 countries.

Pau HerreroCentre for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College London, London, United Kingdom. Electronic address: pherrero@imperial.ac.uk.
Jorge BondiaInstitut Universitari d'Automàtica i Informàtica Industrial, Universitat Politècnica de València, València, Spain.
Oloruntoba AdewuyiCentre for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College London, London, United Kingdom.
Peter PeslCentre for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College London, London, United Kingdom.
Mohamed El-SharkawyCentre for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College London, London, United Kingdom.
Monika ReddyCharing Cross Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom.
Chris ToumazouCentre for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College London, London, United Kingdom.
Nick OliverCharing Cross Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom.
Pantelis GeorgiouCentre for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College London, London, United Kingdom.
Imperial College London · GBImperial College Healthcare NHS Trust · GBNIHR Imperial Biomedical Research Centre · GBUniversitat Politècnica de València · ES

Funding

Medical Research Council MC_PC_12015Wellcome Trust 100921
6 · The paper itself

Abstract

BACKGROUND AND

objectiveCurrent prototypes of closed-loop systems for glucose control in type 1 diabetes mellitus, also referred to as artificial pancreas systems, require a pre-meal insulin bolus to compensate for delays in subcutaneous insulin absorption in order to avoid initial post-prandial hyperglycemia. Computing such a meal bolus is a challenging task due to the high intra-subject variability of insulin requirements. Most closed-loop systems compute this pre-meal insulin dose by a standard bolus calculation, as is commonly found in insulin pumps. However, the performance of these calculators is limited due to a lack of adaptiveness in front of dynamic changes in insulin requirements. Despite some initial attempts to include adaptation within these calculators, challenges remain.

methodsIn this paper we present a new technique to automatically adapt the meal-priming bolus within an artificial pancreas. The technique consists of using a novel adaptive bolus calculator based on Case-Based Reasoning and Run-To-Run control, within a closed-loop controller. Coordination between the adaptive bolus calculator and the controller was required to achieve the desired performance. For testing purposes, the clinically validated Imperial College Artificial Pancreas controller was employed. The proposed system was evaluated against itself but without bolus adaptation. The UVa-Padova T1DM v3.2 system was used to carry out a three-month in silico study on 11 adult and 11 adolescent virtual subjects taking into account inter-and intra-subject variability of insulin requirements and uncertainty on carbohydrate intake.

resultsOverall, the closed-loop controller enhanced by an adaptive bolus calculator improves glycemic control when compared to its non-adaptive counterpart. In particular, the following statistically significant improvements were found (non-adaptive vs. adaptive). Adults: mean glucose 142.2 ± 9.4vs. 131.8 ± 4.2mg/dl; percentage time in target [70, 180]mg/dl, 82.0 ± 7.0vs. 89.5 ± 4.2; percentage time above target 17.7 ± 7.0vs. 10.2 ± 4.1. Adolescents: mean glucose 158.2 ± 21.4vs. 140.5 ± 13.0mg/dl; percentage time in target, 65.9 ± 12.9vs. 77.5 ± 12.2; percentage time above target, 31.7 ± 13.1vs. 19.8 ± 10.2. Note that no increase in percentage time in hypoglycemia was observed.

conclusionUsing an adaptive meal bolus calculator within a closed-loop control system has the potential to improve glycemic control in type 1 diabetes when compared to its non-adaptive counterpart.

Indexed as

Insulin Infusion SystemsPancreas, ArtificialAlgorithmsBlood GlucoseBlood Glucose Self-MonitoringDiabetes Mellitus, Type 1HumansHypoglycemic AgentsInsulinBlood GlucoseHypoglycemic AgentsInsulinArtificial pancreasCase-based reasoningDiabetesRun-to-Run control

Identifiers

PMID28688482
PMCPMC6522376
OpenAlexW2621159547

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.