• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

预测血糖值:连续血糖监测的新时代。

Predicting Glucose Values: A New Era for Continuous Glucose Monitoring.

机构信息

Research Institute Diabetes Academy Mergentheim, Bad Mergentheim, Germany.

Diabetes Center Mergentheim, Bad Mergentheim, Germany.

出版信息

J Diabetes Sci Technol. 2024 Sep;18(5):1000-1003. doi: 10.1177/19322968241271925. Epub 2024 Aug 19.

DOI:10.1177/19322968241271925
PMID:39158996
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11418460/
Abstract

The last 25 years of CGM have been characterized above all by providing better and more accurate glucose values in real time and analyzing the measured glucose values. Trend arrows are the only way to look into the future, but they are often too imprecise for therapy adjustment. While AID systems provide algorithms to use glucose values for glucose control, this has not been possible with stand-alone CGM systems, which are most used by people with diabetes. By analyzing the measured values with algorithms, often supported by AI, this should be possible in the future. This provides the user with important information about the further course of the glucose level, such as during the night. Predictive approaches can be used by next-generation CGM systems. These systems can proactively prevent glucose events such as hypo- or hyperglycemia. With the Accu-Chek® SmartGuide Predict app, an integral part of a novel CGM system, and the Glucose Predict (GP) feature, people with diabetes have the first commercially available CGM system with predictive algorithms. It characterizes the CGM systems of the future, which not only analyze past values and current glucose values in the future, but also use these values to predict future glucose progression.

摘要

过去 25 年的 CGMS 主要特点是提供更好和更准确的实时血糖值,并分析测量的血糖值。趋势箭头是预测未来的唯一方法,但它们通常对治疗调整来说不够精确。虽然辅助胰岛素输注系统(AID)提供了使用血糖值进行血糖控制的算法,但独立的 CGMS 系统却无法做到这一点,而独立的 CGMS 系统是糖尿病患者最常使用的系统。通过使用算法(通常由人工智能支持)分析测量值,未来应该可以实现这一点。这为用户提供了关于血糖水平进一步发展的重要信息,例如在夜间。下一代 CGMS 系统可以使用预测方法。这些系统可以主动预防低血糖或高血糖等血糖事件。Accu-Chek® SmartGuide Predict 应用程序是新型 CGMS 系统的一个组成部分,它具有血糖预测(GP)功能,是首款具有预测算法的商业化 CGMS 系统。它是未来 CGMS 系统的特征,这些系统不仅分析过去的值和未来的当前血糖值,还使用这些值来预测未来的血糖进展。

相似文献

1
Predicting Glucose Values: A New Era for Continuous Glucose Monitoring.预测血糖值:连续血糖监测的新时代。
J Diabetes Sci Technol. 2024 Sep;18(5):1000-1003. doi: 10.1177/19322968241271925. Epub 2024 Aug 19.
2
Prescription of Controlled Substances: Benefits and Risks管制药品的处方:益处与风险
3
Integrated sensor-augmented pump therapy systems [the MiniMed® Paradigm™ Veo system and the Vibe™ and G4® PLATINUM CGM (continuous glucose monitoring) system] for managing blood glucose levels in type 1 diabetes: a systematic review and economic evaluation.用于管理1型糖尿病患者血糖水平的集成式传感器增强泵治疗系统[美敦力MiniMed® Paradigm™ Veo系统以及Vibe™和G4® PLATINUM连续血糖监测(CGM)系统]:一项系统综述与经济学评估
Health Technol Assess. 2016 Feb;20(17):v-xxxi, 1-251. doi: 10.3310/hta20170.
4
Continuous glucose monitoring systems for type 1 diabetes mellitus.1型糖尿病的连续血糖监测系统
Cochrane Database Syst Rev. 2012 Jan 18;1(1):CD008101. doi: 10.1002/14651858.CD008101.pub2.
5
Hybrid closed-loop systems for managing blood glucose levels in type 1 diabetes: a systematic review and economic modelling.用于管理1型糖尿病患者血糖水平的混合闭环系统:系统评价与经济建模
Health Technol Assess. 2024 Dec;28(80):1-190. doi: 10.3310/JYPL3536.
6
Methods for insulin delivery and glucose monitoring in diabetes: summary of a comparative effectiveness review.糖尿病胰岛素给药与血糖监测方法:一项比较有效性综述的总结
J Manag Care Pharm. 2012 Aug;18(6 Suppl):S1-17. doi: 10.18553/jmcp.2012.18.s6-A.1.
7
Nocturnal Hypoglycemia in the Era of Continuous Glucose Monitoring.实时动态血糖监测时代的夜间低血糖
J Diabetes Sci Technol. 2024 Sep;18(5):1052-1060. doi: 10.1177/19322968241267823. Epub 2024 Aug 19.
8
The Black Book of Psychotropic Dosing and Monitoring.《精神药物剂量与监测黑皮书》
Psychopharmacol Bull. 2024 Jul 8;54(3):8-59.
9
Preexisting Diabetes and Pregnancy: An Endocrine Society and European Society of Endocrinology Joint Clinical Practice Guideline.孕前糖尿病与妊娠:内分泌学会和欧洲内分泌学会联合临床实践指南
Eur J Endocrinol. 2025 Jun 30;193(1):G1-G48. doi: 10.1093/ejendo/lvaf116.
10
Idiopathic (Genetic) Generalized Epilepsy特发性(遗传性)全身性癫痫

引用本文的文献

1
Machine Learning Algorithms Based on Time Series Pre-Clustering for Nocturnal Glucose Prediction in People with Type 1 Diabetes.基于时间序列预聚类的机器学习算法用于1型糖尿病患者夜间血糖预测
Diagnostics (Basel). 2024 Oct 30;14(21):2427. doi: 10.3390/diagnostics14212427.

本文引用的文献

1
Clinical Usage and Potential Benefits of a Continuous Glucose Monitoring Predict App.连续血糖监测预测 APP 的临床应用及潜在获益
J Diabetes Sci Technol. 2024 Sep;18(5):1009-1013. doi: 10.1177/19322968241268353. Epub 2024 Aug 19.
2
Enhancing the Capabilities of Continuous Glucose Monitoring With a Predictive App.利用预测型 APP 提升连续血糖监测能力
J Diabetes Sci Technol. 2024 Sep;18(5):1014-1026. doi: 10.1177/19322968241267818. Epub 2024 Aug 19.
3
Concept and Implementation of a Novel Continuous Glucose Monitoring Solution With Glucose Predictions on Board.新型连续血糖监测解决方案的概念与实现,其中包含内置血糖预测功能。
J Diabetes Sci Technol. 2024 Sep;18(5):1004-1008. doi: 10.1177/19322968241269927. Epub 2024 Aug 19.
4
Nocturnal Hypoglycemia in the Era of Continuous Glucose Monitoring.实时动态血糖监测时代的夜间低血糖
J Diabetes Sci Technol. 2024 Sep;18(5):1052-1060. doi: 10.1177/19322968241267823. Epub 2024 Aug 19.
5
Performance of a Novel Continuous Glucose Monitoring Device in People With Diabetes.新型连续血糖监测仪在糖尿病患者中的性能表现。
J Diabetes Sci Technol. 2024 Sep;18(5):1044-1051. doi: 10.1177/19322968241267774. Epub 2024 Aug 19.
6
Characteristics of Nocturnal Hypoglycaemic Events and Their Impact on Glycaemia.夜间低血糖事件的特征及其对血糖的影响。
J Diabetes Sci Technol. 2024 Sep;18(5):1035-1043. doi: 10.1177/19322968241267765. Epub 2024 Aug 19.
7
Fear of Hypoglycemia and Diabetes Distress: Expected Reduction by Glucose Prediction.低血糖恐惧和糖尿病困扰:血糖预测可降低预期。
J Diabetes Sci Technol. 2024 Sep;18(5):1027-1034. doi: 10.1177/19322968241267886. Epub 2024 Aug 19.
8
Machine Learning Models for Blood Glucose Level Prediction in Patients With Diabetes Mellitus: Systematic Review and Network Meta-Analysis.糖尿病患者血糖水平预测的机器学习模型:系统评价与网络荟萃分析
JMIR Med Inform. 2023 Nov 20;11:e47833. doi: 10.2196/47833.
9
Continuous Glucose Monitoring Impact and Implications of Real-World Evidence: Past, Present, and Future.连续血糖监测:真实世界证据的影响和意义:过去、现在和未来。
Diabetes Technol Ther. 2023 Jun;25(S3):S5-S13. doi: 10.1089/dia.2023.0057.
10
Past, Present, and Future of Continuous Glucose Monitors.连续血糖监测仪的过去、现在与未来
Diabetes Technol Ther. 2023 Jun;25(S3):S1-S4. doi: 10.1089/dia.2023.0041.