在线识别胰岛素敏感性对危重患者血糖控制的影响

IF 0.8 Q4 ENGINEERING, BIOMEDICAL
Sha Wu, E. Furutani, Tomonori Sugawara, Takehiko Asaga, G. Shirakami
{"title":"在线识别胰岛素敏感性对危重患者血糖控制的影响","authors":"Sha Wu, E. Furutani, Tomonori Sugawara, Takehiko Asaga, G. Shirakami","doi":"10.14326/abe.9.43","DOIUrl":null,"url":null,"abstract":"Hyperglycemia is common in critically ill patients and leads to various severe complications and even death. Keeping blood glucose within the range of 80–110 mg / dL (4.4–6.1 mmol / L) has been shown to re-duce mortality and morbidity in intensive care units (ICU). Many studies on BG control systems for ICU patients have been reported. However, it is not easy to maintain blood glucose within the desired range because of the time variability of insulin sensitivity in critically ill patients. In this study, to improve the prediction accuracy of blood glucose level in patients, we modified a glycometabolism model developed in our previous study, by identifying parameter values from clinical ICU data. Then, we modified insulin sensitivity online identification algorithm to avoid a sudden change in insulin sensitivity during online identification that updates insulin sensitivity value at intervals of 30 min. Finally, since hypoglycemia prevention as important, we de-signed a glycemic control system using nonlinear model predictive control based on the modified model and the online identification algorithm of insulin sensitivity. The new glycemic control system achieved 71% of blood glucose measurements within the range of 80–110 mg / dL and 1.5% of measurements below 80 mg / dL, which indicated effectiveness and safety.","PeriodicalId":54017,"journal":{"name":"Advanced Biomedical Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.8000,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Glycemic Control for Critically Ill Patients with Online Identification of Insulin Sensitivity\",\"authors\":\"Sha Wu, E. Furutani, Tomonori Sugawara, Takehiko Asaga, G. Shirakami\",\"doi\":\"10.14326/abe.9.43\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Hyperglycemia is common in critically ill patients and leads to various severe complications and even death. Keeping blood glucose within the range of 80–110 mg / dL (4.4–6.1 mmol / L) has been shown to re-duce mortality and morbidity in intensive care units (ICU). Many studies on BG control systems for ICU patients have been reported. However, it is not easy to maintain blood glucose within the desired range because of the time variability of insulin sensitivity in critically ill patients. In this study, to improve the prediction accuracy of blood glucose level in patients, we modified a glycometabolism model developed in our previous study, by identifying parameter values from clinical ICU data. Then, we modified insulin sensitivity online identification algorithm to avoid a sudden change in insulin sensitivity during online identification that updates insulin sensitivity value at intervals of 30 min. Finally, since hypoglycemia prevention as important, we de-signed a glycemic control system using nonlinear model predictive control based on the modified model and the online identification algorithm of insulin sensitivity. The new glycemic control system achieved 71% of blood glucose measurements within the range of 80–110 mg / dL and 1.5% of measurements below 80 mg / dL, which indicated effectiveness and safety.\",\"PeriodicalId\":54017,\"journal\":{\"name\":\"Advanced Biomedical Engineering\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":0.8000,\"publicationDate\":\"2020-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advanced Biomedical Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.14326/abe.9.43\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced Biomedical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14326/abe.9.43","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 1

摘要

高血糖症在危重病人中很常见,可导致各种严重并发症,甚至死亡。将血糖控制在80-110毫克/分升(4.4-6.1毫摩尔/升)范围内已被证明可降低重症监护病房(ICU)的死亡率和发病率。许多关于ICU患者BG控制系统的研究已被报道。然而,由于危重患者胰岛素敏感性的时变,将血糖维持在理想的范围内并不容易。在本研究中,为了提高患者血糖水平的预测精度,我们修改了我们之前研究中建立的糖代谢模型,从临床ICU数据中识别参数值。然后,我们改进了胰岛素敏感性在线识别算法,以避免在线识别过程中胰岛素敏感性的突然变化,每隔30分钟更新一次胰岛素敏感性值。最后,由于低血糖预防同样重要,我们基于改进的模型和胰岛素敏感性在线识别算法设计了一个使用非线性模型预测控制的血糖控制系统。新的血糖控制系统在80 - 110 mg / dL范围内实现了71%的血糖测量,在80 mg / dL以下实现了1.5%的血糖测量,表明了有效性和安全性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Glycemic Control for Critically Ill Patients with Online Identification of Insulin Sensitivity
Hyperglycemia is common in critically ill patients and leads to various severe complications and even death. Keeping blood glucose within the range of 80–110 mg / dL (4.4–6.1 mmol / L) has been shown to re-duce mortality and morbidity in intensive care units (ICU). Many studies on BG control systems for ICU patients have been reported. However, it is not easy to maintain blood glucose within the desired range because of the time variability of insulin sensitivity in critically ill patients. In this study, to improve the prediction accuracy of blood glucose level in patients, we modified a glycometabolism model developed in our previous study, by identifying parameter values from clinical ICU data. Then, we modified insulin sensitivity online identification algorithm to avoid a sudden change in insulin sensitivity during online identification that updates insulin sensitivity value at intervals of 30 min. Finally, since hypoglycemia prevention as important, we de-signed a glycemic control system using nonlinear model predictive control based on the modified model and the online identification algorithm of insulin sensitivity. The new glycemic control system achieved 71% of blood glucose measurements within the range of 80–110 mg / dL and 1.5% of measurements below 80 mg / dL, which indicated effectiveness and safety.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Advanced Biomedical Engineering
Advanced Biomedical Engineering ENGINEERING, BIOMEDICAL-
CiteScore
1.40
自引率
10.00%
发文量
15
审稿时长
15 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信