Evidence map›Paper›PMID 31117804›Full record

ArticleJournal of diabetes science and technology2020

A Modular Safety System for an Insulin Dose Recommender: A Feasibility Study.

Chengyuan Liu, Parizad Avari, Yenny Leal, Marzena Wos, Kumuthine Sivasithamparam, Pantelis Georgiou, Monika Reddy, José Manuel Fernández-Real, Clare Martin, Mercedes Fernández-Balsells and 2 more

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Article in Journal of diabetes science and technology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

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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

12 authors.

Chengyuan LiuCentre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London, UK.ORCID 0000-0003-1891-4647
Parizad AvariDivision of Diabetes, Endocrinology and Metabolism, Department of Medicine, Faculty of Medicine Imperial College, London, UK.ORCID 0000-0001-9047-3589
Yenny LealInstitut d'Investigació Biomèdica de Girona Dr Josep Trueta, Girona, Spain.
Marzena WosInstitut d'Investigació Biomèdica de Girona Dr Josep Trueta, Girona, Spain.
Kumuthine SivasithamparamDivision of Diabetes, Endocrinology and Metabolism, Department of Medicine, Faculty of Medicine Imperial College, London, UK.
Pantelis GeorgiouCentre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London, UK.
Monika ReddyDivision of Diabetes, Endocrinology and Metabolism, Department of Medicine, Faculty of Medicine Imperial College, London, UK.
José Manuel Fernández-RealInstitut d'Investigació Biomèdica de Girona Dr Josep Trueta, Girona, Spain.
Clare MartinDepartment of Computing and Communication Technologies, Oxford Brookes University, Oxford, UK.
Mercedes Fernández-BalsellsInstitut d'Investigació Biomèdica de Girona Dr Josep Trueta, Girona, Spain.
Nick OliverDivision of Diabetes, Endocrinology and Metabolism, Department of Medicine, Faculty of Medicine Imperial College, London, UK.
Pau HerreroCentre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London, UK.ORCID 0000-0002-7088-5807

Funding

Department of Health
6 · The paper itself

Abstract

backgroundDelivering insulin in type 1 diabetes is a challenging, and potentially risky, activity; hence the importance of including safety measures as part of any insulin dosing or recommender system. This work presents and clinically evaluates a modular safety system that is part of an intelligent insulin dose recommender platform developed within the EU-funded PEPPER project.

methodsThe proposed safety system is composed of four modules which use a novel glucose forecasting algorithm. These modules are predictive glucose alerts and alarms; a predictive low-glucose basal insulin suspension module; an advanced rescue carbohydrate recommender for resolving hypoglycemia; and a personalized safety constraint applied to insulin recommendations. The technical feasibility of the proposed safety system was evaluated in a pilot study including eight adult subjects with type 1 diabetes on multiple daily injections over a duration of six weeks. Glycemic control and safety system functioning were compared between the two-weeks run-in period and the end point at eight weeks. A standard insulin bolus calculator was employed to recommend insulin doses.

resultsOverall, glycemic control improved over the evaluated period. In particular, percentage time in the hypoglycemia range (<3.0 mmol/l) significantly decreased from 0.82% (0.05-4.79) at run-in to 0.33% (0.00-0.93) at endpoint (

conclusionA safety system for an insulin dose recommender has been proven to be a viable solution to reduce the number of adverse events associated to glucose control in type 1 diabetes.

Indexed as

Insulin Infusion SystemsAdultBlood GlucoseBlood Glucose Self-MonitoringDiabetes Mellitus, Type 1Dose-Response Relationship, DrugFeasibility StudiesFemaleGlycemic ControlHumansHypoglycemic AgentsInsulinMaleMiddle AgedBlood GlucoseHypoglycemic AgentsInsulindecision supportinsulin deliveryrun-to-run controlsafetytype 1 diabetes

Identifiers

PMID31117804
PMCPMC7189144

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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.