Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes
Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes
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DOI：
10.1088/0967-3334/25/4/010
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发表时间：
2004-08-01
影响因子：
3.2
通讯作者：
Wilinska, ME
中科院分区：
文献类型：
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作者：
Hovorka, R;Canonico, V;Wilinska, ME
已经开发出一种非线性模型预测控制器，用于在禁食期间（例如过夜禁食期间）维持 1 型糖尿病受试者的血糖正常。该控制器采用隔室模型，该模型代表葡萄糖调节系统，并包括代表皮下注射的短效胰岛素赖脯胰岛素的吸收和肠道吸收的子模型。控制器使用贝叶斯参数估计来确定时变模型参数。移动目标轨迹有助于缓慢、受控地使升高的血糖水平正常化，并更快地使低血糖值正常化。该模型的预测能力已使用来自 15 项 1 型糖尿病受试者临床实验的数据进行了评估。实验采用静脉内葡萄糖采样（每15分钟一次）和通过胰岛素泵皮下注射赖脯胰岛素（也修改为每15分钟一次）。该模型给出的葡萄糖预测值的均方误差与预测范围成比例相关，值为每 15 分钟 0.2 mmol L-1。使用 Clarke 误差网格分析对基于模型的血糖预测的临床效用进行评估，对于长达 60 分钟的血糖预测，95% 的值位于 A 区，其余 5% 的值位于 B 区（n = 1674）。总之，自适应非线性模型预测控制有望用于控制 1 型糖尿病受试者禁食期间的血糖浓度。
A nonlinear model predictive controller has been developed to maintain normoglycemia in subjects with type 1 diabetes during fasting conditions such as during overnight fast. The controller employs a compartment model, which represents the glucoregulatory system and includes submodels representing absorption of subcutaneously administered short-acting insulin Lispro and gut absorption. The controller uses Bayesian parameter estimation to determine time-varying model parameters. Moving target trajectory facilitates slow, controlled normalization of elevated glucose levels and faster normalization of low glucose values. The predictive capabilities of the model have been evaluated using data from 15 clinical experiments in subjects with type 1 diabetes. The experiments employed intravenous glucose sampling (every 15 min) and subcutaneous infusion of insulin Lispro by insulin pump (modified also every 15 min). The model gave glucose predictions with a mean square error proportionally related to the prediction horizon with the value of 0.2 mmol L-1 per 15 min. The assessment of clinical utility of model-based glucose predictions using Clarke error grid analysis gave 95% of values in zone A and the remaining 5% of values in zone B for glucose predictions up to 60 min (n = 1674). In conclusion, adaptive nonlinear model predictive control is promising for the control of glucose concentration during fasting conditions in subjects with type 1 diabetes.