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无进餐和活动通知的人工胰腺的多变量自适应闭环控制。

Multivariable adaptive closed-loop control of an artificial pancreas without meal and activity announcement.

机构信息

Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA.

出版信息

Diabetes Technol Ther. 2013 May;15(5):386-400. doi: 10.1089/dia.2012.0283. Epub 2013 Apr 1.

Abstract

BACKGROUND

Accurate closed-loop control is essential for developing artificial pancreas (AP) systems that adjust insulin infusion rates from insulin pumps. Glucose concentration information from continuous glucose monitoring (CGM) systems is the most important information for the control system. Additional physiological measurements can provide valuable information that can enhance the accuracy of the control system. Proportional-integral-derivative control and model predictive control have been popular in AP development. Their implementations to date rely on meal announcements (e.g., bolus insulin dose based on insulin:carbohydrate ratios) by the user. Adaptive control techniques provide a powerful alternative that do not necessitate any meal or activity announcements.

MATERIALS AND METHODS

Adaptive control systems based on the generalized predictive control framework are developed by extending the recursive modeling techniques. Physiological signals such as energy expenditure and galvanic skin response are used along with glucose measurements to generate a multiple-input-single-output model for predicting future glucose concentrations used by the controller. Insulin-on-board (IOB) is also estimated and used in control decisions. The controllers were tested with clinical studies that include seven cases with three different patients with type 1 diabetes for 32 or 60 h without any meal or activity announcements.

RESULTS

The adaptive control system kept glucose concentration in the normal preprandial and postprandial range (70-180 mg/dL) without any meal or activity announcements during the test period. After IOB estimation was added to the control system, mild hypoglycemic episodes were observed only in one of the four experiments. This was reflected in a plasma glucose value of 56 mg/dL (YSI 2300 STAT; Yellow Springs Instrument, Yellow Springs, OH) and a CGM value of 63 mg/dL).

CONCLUSIONS

Regulation of blood glucose concentration with an AP using adaptive control techniques was successful in clinical studies, even without any meal and physical activity announcement.

摘要

背景

准确的闭环控制对于开发能够调节胰岛素输注率的人工胰腺 (AP) 系统至关重要。连续血糖监测 (CGM) 系统提供的血糖浓度信息是控制系统最重要的信息。其他生理测量值可以提供有价值的信息,从而提高控制系统的准确性。比例积分微分控制和模型预测控制在 AP 开发中很受欢迎。迄今为止,它们的实现依赖于用户的餐食公告(例如,基于胰岛素:碳水化合物比值的胰岛素剂量)。自适应控制技术提供了一种强大的替代方案,不需要任何餐食或活动公告。

材料和方法

基于广义预测控制框架的自适应控制系统是通过扩展递归建模技术开发的。生理信号(如能量消耗和皮肤电反应)与血糖测量值一起用于生成用于控制器的预测未来血糖浓度的多输入单输出模型。胰岛素余量 (IOB) 也被估计并用于控制决策。该控制器在包括七名 1 型糖尿病患者的三项不同临床研究中进行了测试,测试时间为 32 或 60 小时,没有任何餐食或活动公告。

结果

自适应控制系统在测试期间无需任何餐食或活动公告即可将血糖浓度保持在正常的餐前和餐后范围内(70-180mg/dL)。在将 IOB 估计添加到控制系统后,仅在四个实验中的一个中观察到轻微的低血糖发作。这反映在血浆血糖值为 56mg/dL(YSI 2300 STAT;Yellow Springs Instrument,Yellow Springs,OH)和 CGM 值为 63mg/dL)。

结论

使用自适应控制技术的 AP 成功调节血糖浓度,即使没有任何餐食和体力活动公告。

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