Intermittent closed-loop blood glucose control for people with type 1 diabetes on multiple daily injections

Background and objectives: Recent advances in Automated Insulin Delivery systems have been shown to dramatically improve glycaemic control and reduce the risk of hypoglycemia in people with type 1 diabetes. However, they are complex systems that require specific training and are not affordable for m...

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Detalles Bibliográficos
Autores: Estremera, Ernesto, Beneyto Tantiña, Aleix, Cabrera, Alvis, Contreras, Ivan, Vehí, Josep
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10256/22984
Acceso en línea:http://hdl.handle.net/10256/22984
Access Level:acceso abierto
Palabra clave:Diabetis
Diabetes
Glucèmia -- Control automàtic
Blood sugar -- Automatic control
Control predictiu
Predictive control
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oai_identifier_str oai:recercat.cat:10256/22984
network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv Intermittent closed-loop blood glucose control for people with type 1 diabetes on multiple daily injections
title Intermittent closed-loop blood glucose control for people with type 1 diabetes on multiple daily injections
spellingShingle Intermittent closed-loop blood glucose control for people with type 1 diabetes on multiple daily injections
Estremera, Ernesto
Diabetis
Diabetes
Glucèmia -- Control automàtic
Blood sugar -- Automatic control
Control predictiu
Predictive control
title_short Intermittent closed-loop blood glucose control for people with type 1 diabetes on multiple daily injections
title_full Intermittent closed-loop blood glucose control for people with type 1 diabetes on multiple daily injections
title_fullStr Intermittent closed-loop blood glucose control for people with type 1 diabetes on multiple daily injections
title_full_unstemmed Intermittent closed-loop blood glucose control for people with type 1 diabetes on multiple daily injections
title_sort Intermittent closed-loop blood glucose control for people with type 1 diabetes on multiple daily injections
dc.creator.none.fl_str_mv Estremera, Ernesto
Beneyto Tantiña, Aleix
Cabrera, Alvis
Contreras, Ivan
Vehí, Josep
author Estremera, Ernesto
author_facet Estremera, Ernesto
Beneyto Tantiña, Aleix
Cabrera, Alvis
Contreras, Ivan
Vehí, Josep
author_role author
author2 Beneyto Tantiña, Aleix
Cabrera, Alvis
Contreras, Ivan
Vehí, Josep
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Agencia Estatal de Investigación
dc.subject.none.fl_str_mv Diabetis
Diabetes
Glucèmia -- Control automàtic
Blood sugar -- Automatic control
Control predictiu
Predictive control
topic Diabetis
Diabetes
Glucèmia -- Control automàtic
Blood sugar -- Automatic control
Control predictiu
Predictive control
description Background and objectives: Recent advances in Automated Insulin Delivery systems have been shown to dramatically improve glycaemic control and reduce the risk of hypoglycemia in people with type 1 diabetes. However, they are complex systems that require specific training and are not affordable for most. Attempts to reduce the gap with closed-loop therapies using advanced dosing advisors have so far failed, mainly because they require too much human intervention. With the advent of smart insulin pens, one of the main constraints (having reliable bolus and meal information) disappears and new strategies can be employed. This is our starting hypothesis, which we have validated in a very demanding simulator. In this paper, we propose an intermittent closed-loop control system specifically intended for multiple daily injection therapy to bring the benefits of artificial pancreas to the application of multiple daily injections. Methods: The proposed control algorithm is based on model predictive control and integrates two patient-driven control actions. Correction insulin boluses are automatically computed and recommended to the patient to minimize the duration of hyperglycemia. Rescue carbohydrates are also triggered to avoid hypoglycemia episodes. The algorithm can adapt to different patient lifestyles with customizable triggering conditions, closing the gap between practicality and performance. The proposed algorithm is compared with conventional open-loop therapy, and its superiority is demonstrated through extensive in silico evaluations using realistic cohorts and scenarios. The evaluations were conducted in a cohort of 47 virtual patients. We also provide detailed explanations of the implementation, imposed constraints, triggering conditions, cost functions, and penalties for the algorithm. Results: The in-silico outcomes combining the proposed closed-loop strategy with slow-acting insulin analog injections at 09:00 h resulted in percentages of time in range (TIR) (70–180 mg/dL) of 69.5%, 70.6%, and 70.4% for glargine-100, glargine-300, and degludec-100, respectively, and injections at 20:00 h resulted in percentages of TIR of 70.5%, 70.3%, and 71.6%, respectively. In all the cases, the percentages of TIR were considerably higher than those obtained from the open-loop strategy, being only 50.7%, 53.9%, and 52.2% for daytime injection and 55.5%, 54.1%, and 56.9% for nighttime injection. Overall, the occurrence of hypoglycemia and hyperglycemia was notably reduced using our approach. Conclusions: Event-triggering model predictive control in the proposed algorithm is feasible and may meet clinical targets for people with type 1 diabetes
publishDate 2023
dc.date.none.fl_str_mv 2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
peer-reviewed
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10256/22984
http://hdl.handle.net/10256/22984
url http://hdl.handle.net/10256/22984
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.cmpb.2023.107568
info:eu-repo/semantics/altIdentifier/issn/0169-2607
info:eu-repo/semantics/altIdentifier/eissn/1872-7565
PID2019-107722RB-C22
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-107722RB-C22
dc.rights.none.fl_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv Computer Methods and Programs in Biomedicine, 2023, vol. 236, art. núm. 107568
Articles publicats (D-EEEiA)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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spelling Intermittent closed-loop blood glucose control for people with type 1 diabetes on multiple daily injectionsEstremera, ErnestoBeneyto Tantiña, AleixCabrera, AlvisContreras, IvanVehí, JosepDiabetisDiabetesGlucèmia -- Control automàticBlood sugar -- Automatic controlControl predictiuPredictive controlBackground and objectives: Recent advances in Automated Insulin Delivery systems have been shown to dramatically improve glycaemic control and reduce the risk of hypoglycemia in people with type 1 diabetes. However, they are complex systems that require specific training and are not affordable for most. Attempts to reduce the gap with closed-loop therapies using advanced dosing advisors have so far failed, mainly because they require too much human intervention. With the advent of smart insulin pens, one of the main constraints (having reliable bolus and meal information) disappears and new strategies can be employed. This is our starting hypothesis, which we have validated in a very demanding simulator. In this paper, we propose an intermittent closed-loop control system specifically intended for multiple daily injection therapy to bring the benefits of artificial pancreas to the application of multiple daily injections. Methods: The proposed control algorithm is based on model predictive control and integrates two patient-driven control actions. Correction insulin boluses are automatically computed and recommended to the patient to minimize the duration of hyperglycemia. Rescue carbohydrates are also triggered to avoid hypoglycemia episodes. The algorithm can adapt to different patient lifestyles with customizable triggering conditions, closing the gap between practicality and performance. The proposed algorithm is compared with conventional open-loop therapy, and its superiority is demonstrated through extensive in silico evaluations using realistic cohorts and scenarios. The evaluations were conducted in a cohort of 47 virtual patients. We also provide detailed explanations of the implementation, imposed constraints, triggering conditions, cost functions, and penalties for the algorithm. Results: The in-silico outcomes combining the proposed closed-loop strategy with slow-acting insulin analog injections at 09:00 h resulted in percentages of time in range (TIR) (70–180 mg/dL) of 69.5%, 70.6%, and 70.4% for glargine-100, glargine-300, and degludec-100, respectively, and injections at 20:00 h resulted in percentages of TIR of 70.5%, 70.3%, and 71.6%, respectively. In all the cases, the percentages of TIR were considerably higher than those obtained from the open-loop strategy, being only 50.7%, 53.9%, and 52.2% for daytime injection and 55.5%, 54.1%, and 56.9% for nighttime injection. Overall, the occurrence of hypoglycemia and hyperglycemia was notably reduced using our approach. Conclusions: Event-triggering model predictive control in the proposed algorithm is feasible and may meet clinical targets for people with type 1 diabetesThis work was partially supported by the Spanish Ministry of Science and Innovation under Grant number PID2019-107722RB-C22, by the Autonomous Government of Catalonia under Grant number 2017 SGR 1551, by the Spanish Ministry of Universities and European Union through the Next GenerationEU Plan (Margarita Salas), and by the program for researchers in training at the University of Girona (IFUdG2019)Open Access funding provided thanks to the CRUE-CSIC agreement with ElsevierElsevierAgencia Estatal de Investigación2023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionpeer-reviewedapplication/pdfhttp://hdl.handle.net/10256/22984http://hdl.handle.net/10256/22984Computer Methods and Programs in Biomedicine, 2023, vol. 236, art. núm. 107568Articles publicats (D-EEEiA)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)Inglésinfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.cmpb.2023.107568info:eu-repo/semantics/altIdentifier/issn/0169-2607info:eu-repo/semantics/altIdentifier/eissn/1872-7565PID2019-107722RB-C22info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-107722RB-C22Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10256/229842026-05-29T05:05:01Z
score 15,81155

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