Artificial Pancreas: Evaluating the ARG Algorithm Without Meal Announcement
- Autores
- Fushimi, Emilia; Colmegna, Patricio Hernán; De Battista, Hernán; Garelli, Fabricio; Sánchez Peña, Ricardo Salvador
- Año de publicación
- 2019
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Background: Either under standard basal-bolus treatment or hybrid closed-loop control, subjects with type 1 diabetes are required to count carbohydrates (CHOs). However, CHO counting is not only burdensome but also prone to errors. Recently, an artificial pancreas algorithm that does not require premeal insulin boluses—the so-called automatic regulation of glucose (ARG)—was introduced. In its first pilot clinical study, although the exact CHO counting was not required, subjects still needed to announce the meal time and classify the meal size. Method: An automatic switching signal generator (SSG) is proposed in this work to remove the manual mealtime announcement from the control strategy. The SSG is based on a Kalman filter and works with continuous glucose monitoring readings only. Results: The ARG algorithm with unannounced meals (ARGum) was tested in silico under the effect of different types of mixed meals and intrapatient variability, and contrasted with the ARG algorithm with announced meals (ARGam). Simulations reveal that, for slow-absorbing meals, the time in the euglycemic range, [70-180] mg/dL, increases using the unannounced strategy (ARGam: 78.1 [68.6-80.2]% (median [IQR]) and ARGum: 87.8 [84.5-90.6]%), while similar results were found with fastabsorbing meals (ARGam: 87.4 [86.0-88.9]% and ARGum: 87.6 [86.1-88.8]%). On the other hand, when intrapatient variability is considered, time in euglycemia is also comparable (ARGam: 81.4 [75.4-83.5]% and ARGum: 80.9 [77.0-85.1]%). Conclusion: In silico results indicate that it is feasible to perform an in vivo evaluation of the ARG algorithm with unannounced meals.
Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales
Consejo Nacional de Investigaciones Científicas y Técnicas - Materia
-
Ingeniería Electrónica
artificial pancreas
carbohydrate counting
sliding mode control
switched control - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/4.0/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/107443
Ver los metadatos del registro completo
id |
SEDICI_c0d8a004c56ed0c632607bdfcdfc9ecb |
---|---|
oai_identifier_str |
oai:sedici.unlp.edu.ar:10915/107443 |
network_acronym_str |
SEDICI |
repository_id_str |
1329 |
network_name_str |
SEDICI (UNLP) |
spelling |
Artificial Pancreas: Evaluating the ARG Algorithm Without Meal AnnouncementFushimi, EmiliaColmegna, Patricio HernánDe Battista, HernánGarelli, FabricioSánchez Peña, Ricardo SalvadorIngeniería Electrónicaartificial pancreascarbohydrate countingsliding mode controlswitched controlBackground: Either under standard basal-bolus treatment or hybrid closed-loop control, subjects with type 1 diabetes are required to count carbohydrates (CHOs). However, CHO counting is not only burdensome but also prone to errors. Recently, an artificial pancreas algorithm that does not require premeal insulin boluses—the so-called automatic regulation of glucose (ARG)—was introduced. In its first pilot clinical study, although the exact CHO counting was not required, subjects still needed to announce the meal time and classify the meal size. Method: An automatic switching signal generator (SSG) is proposed in this work to remove the manual mealtime announcement from the control strategy. The SSG is based on a Kalman filter and works with continuous glucose monitoring readings only. Results: The ARG algorithm with unannounced meals (ARGum) was tested in silico under the effect of different types of mixed meals and intrapatient variability, and contrasted with the ARG algorithm with announced meals (ARGam). Simulations reveal that, for slow-absorbing meals, the time in the euglycemic range, [70-180] mg/dL, increases using the unannounced strategy (ARGam: 78.1 [68.6-80.2]% (median [IQR]) and ARGum: 87.8 [84.5-90.6]%), while similar results were found with fastabsorbing meals (ARGam: 87.4 [86.0-88.9]% and ARGum: 87.6 [86.1-88.8]%). On the other hand, when intrapatient variability is considered, time in euglycemia is also comparable (ARGam: 81.4 [75.4-83.5]% and ARGum: 80.9 [77.0-85.1]%). Conclusion: In silico results indicate that it is feasible to perform an in vivo evaluation of the ARG algorithm with unannounced meals.Instituto de Investigaciones en Electrónica, Control y Procesamiento de SeñalesConsejo Nacional de Investigaciones Científicas y Técnicas2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf1035-1043http://sedici.unlp.edu.ar/handle/10915/107443enginfo:eu-repo/semantics/altIdentifier/url/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC6835180&blobtype=pdfinfo:eu-repo/semantics/altIdentifier/issn/1932-2968info:eu-repo/semantics/altIdentifier/pmid/31339059info:eu-repo/semantics/altIdentifier/doi/10.1177/1932296819864585info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:23:52Zoai:sedici.unlp.edu.ar:10915/107443Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:23:52.903SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Artificial Pancreas: Evaluating the ARG Algorithm Without Meal Announcement |
title |
Artificial Pancreas: Evaluating the ARG Algorithm Without Meal Announcement |
spellingShingle |
Artificial Pancreas: Evaluating the ARG Algorithm Without Meal Announcement Fushimi, Emilia Ingeniería Electrónica artificial pancreas carbohydrate counting sliding mode control switched control |
title_short |
Artificial Pancreas: Evaluating the ARG Algorithm Without Meal Announcement |
title_full |
Artificial Pancreas: Evaluating the ARG Algorithm Without Meal Announcement |
title_fullStr |
Artificial Pancreas: Evaluating the ARG Algorithm Without Meal Announcement |
title_full_unstemmed |
Artificial Pancreas: Evaluating the ARG Algorithm Without Meal Announcement |
title_sort |
Artificial Pancreas: Evaluating the ARG Algorithm Without Meal Announcement |
dc.creator.none.fl_str_mv |
Fushimi, Emilia Colmegna, Patricio Hernán De Battista, Hernán Garelli, Fabricio Sánchez Peña, Ricardo Salvador |
author |
Fushimi, Emilia |
author_facet |
Fushimi, Emilia Colmegna, Patricio Hernán De Battista, Hernán Garelli, Fabricio Sánchez Peña, Ricardo Salvador |
author_role |
author |
author2 |
Colmegna, Patricio Hernán De Battista, Hernán Garelli, Fabricio Sánchez Peña, Ricardo Salvador |
author2_role |
author author author author |
dc.subject.none.fl_str_mv |
Ingeniería Electrónica artificial pancreas carbohydrate counting sliding mode control switched control |
topic |
Ingeniería Electrónica artificial pancreas carbohydrate counting sliding mode control switched control |
dc.description.none.fl_txt_mv |
Background: Either under standard basal-bolus treatment or hybrid closed-loop control, subjects with type 1 diabetes are required to count carbohydrates (CHOs). However, CHO counting is not only burdensome but also prone to errors. Recently, an artificial pancreas algorithm that does not require premeal insulin boluses—the so-called automatic regulation of glucose (ARG)—was introduced. In its first pilot clinical study, although the exact CHO counting was not required, subjects still needed to announce the meal time and classify the meal size. Method: An automatic switching signal generator (SSG) is proposed in this work to remove the manual mealtime announcement from the control strategy. The SSG is based on a Kalman filter and works with continuous glucose monitoring readings only. Results: The ARG algorithm with unannounced meals (ARGum) was tested in silico under the effect of different types of mixed meals and intrapatient variability, and contrasted with the ARG algorithm with announced meals (ARGam). Simulations reveal that, for slow-absorbing meals, the time in the euglycemic range, [70-180] mg/dL, increases using the unannounced strategy (ARGam: 78.1 [68.6-80.2]% (median [IQR]) and ARGum: 87.8 [84.5-90.6]%), while similar results were found with fastabsorbing meals (ARGam: 87.4 [86.0-88.9]% and ARGum: 87.6 [86.1-88.8]%). On the other hand, when intrapatient variability is considered, time in euglycemia is also comparable (ARGam: 81.4 [75.4-83.5]% and ARGum: 80.9 [77.0-85.1]%). Conclusion: In silico results indicate that it is feasible to perform an in vivo evaluation of the ARG algorithm with unannounced meals. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales Consejo Nacional de Investigaciones Científicas y Técnicas |
description |
Background: Either under standard basal-bolus treatment or hybrid closed-loop control, subjects with type 1 diabetes are required to count carbohydrates (CHOs). However, CHO counting is not only burdensome but also prone to errors. Recently, an artificial pancreas algorithm that does not require premeal insulin boluses—the so-called automatic regulation of glucose (ARG)—was introduced. In its first pilot clinical study, although the exact CHO counting was not required, subjects still needed to announce the meal time and classify the meal size. Method: An automatic switching signal generator (SSG) is proposed in this work to remove the manual mealtime announcement from the control strategy. The SSG is based on a Kalman filter and works with continuous glucose monitoring readings only. Results: The ARG algorithm with unannounced meals (ARGum) was tested in silico under the effect of different types of mixed meals and intrapatient variability, and contrasted with the ARG algorithm with announced meals (ARGam). Simulations reveal that, for slow-absorbing meals, the time in the euglycemic range, [70-180] mg/dL, increases using the unannounced strategy (ARGam: 78.1 [68.6-80.2]% (median [IQR]) and ARGum: 87.8 [84.5-90.6]%), while similar results were found with fastabsorbing meals (ARGam: 87.4 [86.0-88.9]% and ARGum: 87.6 [86.1-88.8]%). On the other hand, when intrapatient variability is considered, time in euglycemia is also comparable (ARGam: 81.4 [75.4-83.5]% and ARGum: 80.9 [77.0-85.1]%). Conclusion: In silico results indicate that it is feasible to perform an in vivo evaluation of the ARG algorithm with unannounced meals. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Articulo http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://sedici.unlp.edu.ar/handle/10915/107443 |
url |
http://sedici.unlp.edu.ar/handle/10915/107443 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC6835180&blobtype=pdf info:eu-repo/semantics/altIdentifier/issn/1932-2968 info:eu-repo/semantics/altIdentifier/pmid/31339059 info:eu-repo/semantics/altIdentifier/doi/10.1177/1932296819864585 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
dc.format.none.fl_str_mv |
application/pdf 1035-1043 |
dc.source.none.fl_str_mv |
reponame:SEDICI (UNLP) instname:Universidad Nacional de La Plata instacron:UNLP |
reponame_str |
SEDICI (UNLP) |
collection |
SEDICI (UNLP) |
instname_str |
Universidad Nacional de La Plata |
instacron_str |
UNLP |
institution |
UNLP |
repository.name.fl_str_mv |
SEDICI (UNLP) - Universidad Nacional de La Plata |
repository.mail.fl_str_mv |
alira@sedici.unlp.edu.ar |
_version_ |
1844616115555139584 |
score |
13.070432 |