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In-Silico Trials for Glucose Control in Hospitalized Patients with Type 2 Diabetes
Although various basal-bolus insulin therapy (BBIT) protocols have been used in the clinical environment, safer and more effective BBIT protocols are required for glucose control in hospitalized patients with type 2 diabetes (T2D). Modeling approaches could provide an evaluation environment for deve...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Korean Academy of Medical Sciences
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4729503/ https://www.ncbi.nlm.nih.gov/pubmed/26839477 http://dx.doi.org/10.3346/jkms.2016.31.2.231 |
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author | Choi, Karam Oh, Tae Jung Lee, Jung Chan Kim, Myungjoon Kim, Hee Chan Cho, Young Min Kim, Sungwan |
author_facet | Choi, Karam Oh, Tae Jung Lee, Jung Chan Kim, Myungjoon Kim, Hee Chan Cho, Young Min Kim, Sungwan |
author_sort | Choi, Karam |
collection | PubMed |
description | Although various basal-bolus insulin therapy (BBIT) protocols have been used in the clinical environment, safer and more effective BBIT protocols are required for glucose control in hospitalized patients with type 2 diabetes (T2D). Modeling approaches could provide an evaluation environment for developing the optimal BBIT protocol prior to clinical trials at low cost and without risk of danger. In this study, an in-silico model was proposed to evaluate subcutaneous BBIT protocols in hospitalized patients with T2D. The proposed model was validated by comparing the BBIT protocol and sliding-scale insulin therapy (SSIT) protocol. The model was utilized for in-silico trials to compare the protocols of adjusting basal-insulin dose (BBIT1) versus adjusting total-daily-insulin dose (BBIT2). The model was also used to evaluate two different initial total-daily-insulin doses for various levels of renal function. The BBIT outcomes were superior to those of SSIT, which is consistent with earlier studies. BBIT2 also outperformed BBIT1, producing a decreased daily mean glucose level and longer time-in-target-range. Moreover, with a standard dose, the overall daily mean glucose levels reached the target range faster than with a reduced-dose for all degrees of renal function. The in-silico studies demonstrated several significant findings, including that the adjustment of total-daily-insulin dose is more effective than changes to basal-insulin dose alone. This research represents a first step toward the eventual development of an advanced model for evaluating various BBIT protocols. |
format | Online Article Text |
id | pubmed-4729503 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | The Korean Academy of Medical Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-47295032016-02-02 In-Silico Trials for Glucose Control in Hospitalized Patients with Type 2 Diabetes Choi, Karam Oh, Tae Jung Lee, Jung Chan Kim, Myungjoon Kim, Hee Chan Cho, Young Min Kim, Sungwan J Korean Med Sci Original Article Although various basal-bolus insulin therapy (BBIT) protocols have been used in the clinical environment, safer and more effective BBIT protocols are required for glucose control in hospitalized patients with type 2 diabetes (T2D). Modeling approaches could provide an evaluation environment for developing the optimal BBIT protocol prior to clinical trials at low cost and without risk of danger. In this study, an in-silico model was proposed to evaluate subcutaneous BBIT protocols in hospitalized patients with T2D. The proposed model was validated by comparing the BBIT protocol and sliding-scale insulin therapy (SSIT) protocol. The model was utilized for in-silico trials to compare the protocols of adjusting basal-insulin dose (BBIT1) versus adjusting total-daily-insulin dose (BBIT2). The model was also used to evaluate two different initial total-daily-insulin doses for various levels of renal function. The BBIT outcomes were superior to those of SSIT, which is consistent with earlier studies. BBIT2 also outperformed BBIT1, producing a decreased daily mean glucose level and longer time-in-target-range. Moreover, with a standard dose, the overall daily mean glucose levels reached the target range faster than with a reduced-dose for all degrees of renal function. The in-silico studies demonstrated several significant findings, including that the adjustment of total-daily-insulin dose is more effective than changes to basal-insulin dose alone. This research represents a first step toward the eventual development of an advanced model for evaluating various BBIT protocols. The Korean Academy of Medical Sciences 2016-02 2016-01-26 /pmc/articles/PMC4729503/ /pubmed/26839477 http://dx.doi.org/10.3346/jkms.2016.31.2.231 Text en © 2016 The Korean Academy of Medical Sciences. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Choi, Karam Oh, Tae Jung Lee, Jung Chan Kim, Myungjoon Kim, Hee Chan Cho, Young Min Kim, Sungwan In-Silico Trials for Glucose Control in Hospitalized Patients with Type 2 Diabetes |
title | In-Silico Trials for Glucose Control in Hospitalized Patients with Type 2 Diabetes |
title_full | In-Silico Trials for Glucose Control in Hospitalized Patients with Type 2 Diabetes |
title_fullStr | In-Silico Trials for Glucose Control in Hospitalized Patients with Type 2 Diabetes |
title_full_unstemmed | In-Silico Trials for Glucose Control in Hospitalized Patients with Type 2 Diabetes |
title_short | In-Silico Trials for Glucose Control in Hospitalized Patients with Type 2 Diabetes |
title_sort | in-silico trials for glucose control in hospitalized patients with type 2 diabetes |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4729503/ https://www.ncbi.nlm.nih.gov/pubmed/26839477 http://dx.doi.org/10.3346/jkms.2016.31.2.231 |
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