Scimita Ventures Pty Ltd, Sydney, NSW, Australia.
Canterbury Hospital, Sydney, NSW, Australia.
J Diabetes Sci Technol. 2023 Mar;17(2):336-344. doi: 10.1177/19322968211054110. Epub 2021 Oct 29.
Frequent blood glucose level (BGL) monitoring is essential for effective diabetes management. Poor compliance is common due to the painful finger pricking or subcutaneous lancet implantation required from existing technologies. There are currently no commercially available non-invasive devices that can effectively measure BGL. In this real-world study, a prototype non-invasive continuous glucose monitoring system (NI-CGM) developed as a wearable ring was used to collect bioimpedance data. The aim was to develop a mathematical model that could use these bioimpedance data to estimate BGL in real time.
The prototype NI-CGM was worn by 14 adult participants with type 2 diabetes for 14 days in an observational clinical study. Bioimpedance data were collected alongside paired BGL measurements taken with a Food and Drug Administration (FDA)-approved self-monitoring blood glucose (SMBG) meter and an FDA-approved CGM. The SMBG meter data were used to improve CGM accuracy, and CGM data to develop the mathematical model.
A gradient boosted model was developed using a randomized 80-20 training-test split of data. The estimated BGL from the model had a Mean Absolute Relative Difference (MARD) of 17.9%, with the Parkes error grid (PEG) analysis showing 99% of values in clinically acceptable zones A and B.
This study demonstrated the reliability of the prototype NI-CGM at collecting bioimpedance data in a real-world scenario. These data were used to train a model that could successfully estimate BGL with a promising MARD and clinically relevant PEG result. These results will enable continued development of the prototype NI-CGM as a wearable ring.
频繁的血糖水平(BGL)监测对于有效的糖尿病管理至关重要。由于现有技术需要进行痛苦的手指刺破或皮下采血针植入,因此患者的依从性通常较差。目前尚无可有效测量 BGL 的商业上可用的非侵入性设备。在这项真实世界的研究中,使用一种可穿戴式指环原型开发了一种非侵入式连续血糖监测系统(NI-CGM)来收集生物阻抗数据。目的是开发一种数学模型,该模型可以使用这些生物阻抗数据实时估计 BGL。
14 名 2 型糖尿病成年参与者在一项观察性临床研究中佩戴原型 NI-CGM 长达 14 天。同时收集生物阻抗数据以及使用经食品和药物管理局(FDA)批准的自我监测血糖(SMBG)计和经 FDA 批准的 CGM 进行的配对 BGL 测量值。SMBG 计数据用于提高 CGM 准确性,CGM 数据用于开发数学模型。
使用经过随机 80-20 训练-测试数据分割的梯度提升模型进行开发。模型估计的 BGL 的平均绝对相对差异(MARD)为 17.9%,Parkes 误差网格(PEG)分析显示 99%的数值在临床可接受的 A 和 B 区。
这项研究证明了原型 NI-CGM 在真实场景中收集生物阻抗数据的可靠性。这些数据被用于训练模型,该模型可以成功地以有希望的 MARD 和具有临床意义的 PEG 结果来估计 BGL。这些结果将使原型 NI-CGM 作为可穿戴指环继续开发。