利用间歇扫描连续血糖测量法对糖尿病患者进行实时和远程监控的人类使用概念。

IF 2.9 4区 医学 Q3 ENGINEERING, BIOMEDICAL BioMedical Engineering OnLine Pub Date : 2024-02-28 DOI:10.1186/s12938-024-01217-z
Jhon E Goez-Mora, Natalia Arbeláez-Córdoba, Norman Balcazar-Morales, Pablo S Rivadeneira
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引用次数: 0

摘要

背景:FreeStyle Libre(FSL)传感器等闪存葡萄糖监测系统在监测糖尿病患者血糖水平方面越来越受欢迎。这种传感器可与标示外转换的实时连续血糖监测仪(c-rtCGM)和特设计算机/智能手机接口配对,对糖尿病受试者进行远程实时监测,并可进行趋势分析和生成警报:本研究评估了 FSL 传感器与开发的 c-rtCGM 系统之间的准确性和一致性。由于实时监测是其主要特点,因此在试验过程中每隔 5 分钟对系统的连接性进行一次评估:方法:使用 FSL 传感器和 c-rtCGM 收集了 16 只 1 型糖尿病大鼠一周的血糖数据。在用链脲佐菌素诱发 1 型糖尿病之前的第一天采集基线血样。一旦确认为糖尿病大鼠,就植入 FSL 和 c-rtCGM,为了提高两种监测设备之间的数据匹配度,c-rtCGM 根据 FSL 血糖仪的读数进行了校准。通过 2 × 3^3 的因子设计和二阶回归,找到了传感器原始数据线性模型转换的基准值。通过绝对相对差值中值(Median ARD)、量程平均时间、Parkes 共识误差网格分析(EGA)和非参数方法的布兰-阿尔特曼分析,对准确性、一致性和连通性进行了评估:与 FSL 传感器相比,c-rtCGM 的总体中位正差值为 6.58%,在不进行校准的情况下,93.06% 的结果位于 A 区。当校准频率从每 50 小时一次变为每 1 小时一次时,总体中值 ARD 分别从 6.68% 降至 2.41%。连通性评估显示,计算机界面每 5 分钟可成功接收 95% 的数据:研究结果表明,使用开发的 c-rtCGM 系统对糖尿病患者进行实时和远程监测是可行和可靠的。根据 FSL 读数进行校准可提高界面显示数据的准确性。
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A concept for human use of real-time and remote monitoring of diabetic subjects using intermittent scanned continuous glucose measurement.

Background: Flash glucose monitoring systems like the FreeStyle Libre (FSL) sensor have gained popularity for monitoring glucose levels in people with diabetes mellitus. This sensor can be paired with an off-label converted real-time continuous glucose monitor (c-rtCGM) plus an ad hoc computer/smartphone interface for remote real-time monitoring of diabetic subjects, allowing for trend analysis and alarm generation.

Objectives: This work evaluates the accuracy and agreement between the FSL sensor and the developed c-rtCGM system. As real-time monitoring is the main feature, the system's connectivity was assessed at 5-min intervals during the trials.

Methods: One week of glucose data were collected from 16 type 1 diabetic rats using the FSL sensor and the c-rtCGM. Baseline blood samples were taken the first day before inducing type 1 diabetes with streptozotocin. Once confirmed diabetic rats, FSL and c-rtCGM, were implanted, and to improve data matching between the two monitoring devices, the c-rtCGM was calibrated to the FSL glucometer readings. A factorial design 2 × 3^3 and a second-order regression was used to find the base values of the linear model transformation of the raw data obtained from the sensor. Accuracy, agreement, and connectivity were assessed by median absolute relative difference (Median ARD), range averaging times, Parkes consensus error grid analysis (EGA), and Bland-Altman analysis with a non-parametric approach.

Results: Compared to the FSL sensor, the c-rtCGM had an overall Median ARD of 6.58%, with 93.06% of results in zone A when calibration was not carried out. When calibration frequency changed from every 50 h to 1 h, the overall Median ARD improved from 6.68% to 2.41%, respectively. The connectivity evaluation showed that 95% of data was successfully received every 5 min by the computer interface.

Conclusions and clinical importance: The results demonstrate the feasibility and reliability of real-time and remote subjects with diabetes monitoring using the developed c-rtCGM system. Performing calibrations relative to the FSL readings increases the accuracy of the data displayed at the interface.

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来源期刊
BioMedical Engineering OnLine
BioMedical Engineering OnLine 工程技术-工程:生物医学
CiteScore
6.70
自引率
2.60%
发文量
79
审稿时长
1 months
期刊介绍: BioMedical Engineering OnLine is an open access, peer-reviewed journal that is dedicated to publishing research in all areas of biomedical engineering. BioMedical Engineering OnLine is aimed at readers and authors throughout the world, with an interest in using tools of the physical and data sciences and techniques in engineering to understand and solve problems in the biological and medical sciences. Topical areas include, but are not limited to: Bioinformatics- Bioinstrumentation- Biomechanics- Biomedical Devices & Instrumentation- Biomedical Signal Processing- Healthcare Information Systems- Human Dynamics- Neural Engineering- Rehabilitation Engineering- Biomaterials- Biomedical Imaging & Image Processing- BioMEMS and On-Chip Devices- Bio-Micro/Nano Technologies- Biomolecular Engineering- Biosensors- Cardiovascular Systems Engineering- Cellular Engineering- Clinical Engineering- Computational Biology- Drug Delivery Technologies- Modeling Methodologies- Nanomaterials and Nanotechnology in Biomedicine- Respiratory Systems Engineering- Robotics in Medicine- Systems and Synthetic Biology- Systems Biology- Telemedicine/Smartphone Applications in Medicine- Therapeutic Systems, Devices and Technologies- Tissue Engineering
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