Evidence map›Paper›PMID 30060692›Full record

ArticleJournal of diabetes science and technology2018

Control Limitations in Models of T1DM and the Robustness of Optimal Insulin Delivery.

Christopher Townsend, Maria M Seron, Graham C Goodwin, Bruce R King

Abstract read
In one paragraph

Article in Journal of diabetes science and technology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

4 authors.

Christopher Townsend1 Priority Research Centre for Complex Dynamic Systems and Control, School of Electrical Engineering and Computing, University of Newcastle, Callaghan, New South Wales, Australia.ORCID 0000-0001-6240-5036
Maria M Seron1 Priority Research Centre for Complex Dynamic Systems and Control, School of Electrical Engineering and Computing, University of Newcastle, Callaghan, New South Wales, Australia.
Graham C Goodwin1 Priority Research Centre for Complex Dynamic Systems and Control, School of Electrical Engineering and Computing, University of Newcastle, Callaghan, New South Wales, Australia.
Bruce R King2 Department of Paediatric Endocrinology and Diabetes, John Hunter Children's Hospital, Newcastle, New South Wales, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn insulin therapy, the blood glucose level is constrained from below by the hypoglycemic threshold, that is, the blood glucose level must remain above this threshold. It has been shown that this constraint fundamentally limits the ability to lower the maxima of the blood glucose level predicted by many mathematical models of glucose metabolism. However, it is desirable to minimize hyperglycemia as well. Hence, a desirable insulin input is one that minimizes the maximum glucose concentration while causing it to remain above the hypoglycemic, or higher, threshold. It has been shown that this input, which we call optimal, is characterized by glucose profiles for which either each maximum of the glucose concentration is followed by a minimum or each minimum is followed by a maximum.

methodsWe discuss the implication of this inherent control limitation for clinical practice and test, through simulation, the robustness of the optimal input to a number of different model and parameter uncertainties. We further develop guidelines on how to design an optimal insulin input that is robust to such uncertainties.

resultsThe optimal input is in general not robust to uncertainties. However, a number of strategies may be used to ensure the blood glucose level remains above the hypoglycemic threshold and the maximum blood glucose level achieved is less than that achieved by standard therapy.

conclusionsAn understanding of the limitations on the controllability of the blood glucose level is important for future treatment improvements and the development of artificial pancreas systems.

Indexed as

Models, BiologicalAlgorithmsDiabetes Mellitus, Type 1HumansHyperglycemiaHypoglycemic AgentsInsulinHypoglycemic AgentsInsulinanalytic modelsartificial pancreascontrol limitationsglucose controloptimal and robust controltype 1 diabetes

Identifiers

PMID30060692
PMCPMC6134626

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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.