Grupo de Control Aplicado, Instituto LEICI (UNLP-CONICET), Facultad de Ingeniería, Universidad Nacional de La Plata, La Plata, Argentina.
Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina.
J Diabetes Sci Technol. 2023 Jul;17(4):1008-1015. doi: 10.1177/19322968221096162. Epub 2022 May 12.
The first two studies of an artificial pancreas (AP) system carried out in Latin America took place in 2016 (phase 1) and 2017 (phase 2). They evaluated a hybrid algorithm from the University of Virginia (UVA) and the automatic regulation of glucose (ARG) algorithm in an inpatient setting using an AP platform developed by the UVA. The ARG algorithm does not require carbohydrate (CHO) counting and does not deliver meal priming insulin boluses. Here, the first outpatient trial of the ARG algorithm using an own AP platform and doubling the duration of previous phases is presented.
Phase 3 involved the evaluation of the ARG algorithm in five adult participants (n = 5) during 72 hours of closed-loop (CL) and 72 hours of open-loop (OL) control in an outpatient setting. This trial was performed with an own AP and remote monitoring platform developed from open-source resources, called InsuMate. The meals tested ranged its CHO content from 38 to 120 g and included challenging meals like pasta. Also, the participants performed mild exercise (3-5 km walks) daily. The clinical trial is registered in ClinicalTrials.gov with identifier: NCT04793165.
The ARG algorithm showed an improvement in the time in hyperglycemia (52.2% [16.3%] OL vs 48.0% [15.4%] CL), time in range (46.9% [15.6%] OL vs 50.9% [14.4%] CL), and mean glucose (188.9 [25.5] mg/dl OL vs 186.2 [24.7] mg/dl CL) compared with the OL therapy. No severe hyperglycemia or hypoglycemia episodes occurred during the trial. The InsuMate platform achieved an average of more than 95% of the time in CL.
The results obtained demonstrated the feasibility of outpatient full CL regulation of glucose levels involving the ARG algorithm and the InsuMate platform.
在拉丁美洲进行的两项人工胰腺(AP)系统的初步研究分别于 2016 年(第 1 阶段)和 2017 年(第 2 阶段)进行。它们评估了弗吉尼亚大学(UVA)的混合算法和自动葡萄糖调节(ARG)算法在 UVA 开发的 AP 平台上的住院环境中的应用。ARG 算法不需要计算碳水化合物(CHO),也不提供餐时胰岛素推注。在此,介绍了首次使用自有 AP 平台并将前两个阶段的持续时间延长一倍的 ARG 算法的门诊试验。
第 3 阶段涉及在门诊环境中对 5 名成年参与者(n=5)进行 72 小时闭环(CL)和 72 小时开环(OL)控制的 ARG 算法评估。该试验使用自行开发的基于开源资源的 AP 和远程监控平台(称为 InsuMate)进行。所测试的餐食的 CHO 含量从 38 到 120 克不等,包括意大利面等具有挑战性的餐食。此外,参与者每天还进行轻度运动(3-5 公里步行)。该临床试验在 ClinicalTrials.gov 上注册,标识符为:NCT04793165。
ARG 算法在高血糖时间(52.2%[16.3%]OL 与 48.0%[15.4%]CL)、时间范围(46.9%[15.6%]OL 与 50.9%[14.4%]CL)和平均血糖(188.9[25.5]mg/dl OL 与 186.2[24.7]mg/dl CL)方面均显示出改善,与 OL 治疗相比。试验期间未发生严重高血糖或低血糖发作。InsuMate 平台在 CL 中的平均时间超过 95%。
结果表明,ARG 算法和 InsuMate 平台能够实现门诊患者血糖水平的全 CL 调节。