In Silico Analysis of an Exercise-Safe Artificial Pancreas With Multistage Model Predictive Control and Insulin Safety System.
In Silico Analysis of an Exercise-Safe Artificial Pancreas With Multistage Model Predictive Control and Insulin Safety System.
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DOI：
10.1177/1932296819879084
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发表时间：
2019-11-01
影响因子：
5
通讯作者：
Breton, Marc D
中科院分区：
文献类型：
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作者：
Garcia-Tirado, Jose;Colmegna, Patricio;Breton, Marc D
背景技术背景：对于1型糖尿病（T1 D）患者来说，维持血糖平衡可能具有挑战性，因为许多因素（例如，长度、类型、持续时间、体内胰岛素、压力和训练）都会影响由身体活动引发的代谢变化，可能导致低血糖和高血糖。因此，尽管注意到健康益处，许多T1 D患者并不像他们的健康同龄人那样锻炼。虽然技术进步已经改善了运动期间和运动后立即的血糖控制，但它仍然是人工胰腺（AP）系统的关键限制之一，主要是因为在运动开始时停止胰岛素可能不足以防止即将发生的运动诱导的低血糖。一种混合AP算法，特定的运动行为识别和预期动作被设计为在中等强度运动期间和之后预防低血糖事件。我们的方法依赖于一些关键的创新，即，活动通知餐前推注计算器，个性化的运动模式识别，以及多级模型预测控制（MS-MPC）策略，可以在反应和预期模式之间转换。在来自最新FDA认可的UVA/Padova代谢模拟器的100名计算机模拟受试者中评价了该AP设计，模拟门诊临床试验环境。结果与基线控制器，定期MPC（rMPC），也包括用于comparison purposes.RESULTS：在硅片实验表明，建议的MS-MPC策略显着减少了运动相关的低血糖事件（8比68）的数量。结论：预期模式的单激素AP控制器的胰岛素给药降低了低血糖的发生在中等强度的运动。
BACKGROUND: Maintaining glycemic equilibrium can be challenging for people living with type 1 diabetes (T1D) as many factors (eg, length, type, duration, insulin on board, stress, and training) will impact the metabolic changes triggered by physical activity potentially leading to both hypoglycemia and hyperglycemia. Therefore, and despite the noted health benefits, many individuals with T1D do not exercise as much as their healthy peers. While technology advances have improved glucose control during and immediately after exercise, it remains one of the key limitations of artificial pancreas (AP) systems, largely because stopping insulin at the onset of exercise may not be enough to prevent impending, exercise-induced hypoglycemia.METHODS: A hybrid AP algorithm with subject-specific exercise behavior recognition and anticipatory action is designed to prevent hypoglycemic events during and after moderate-intensity exercise. Our approach relies on a number of key innovations, namely, an activity informed premeal bolus calculator, personalized exercise pattern recognition, and a multistage model predictive control (MS-MPC) strategy that can transition between reactive and anticipatory modes. This AP design was evaluated on 100 in silico subjects from the most up-to-date FDA-accepted UVA/Padova metabolic simulator, emulating an outpatient clinical trial setting. Results with a baseline controller, a regular MPC (rMPC), are also included for comparison purposes.RESULTS: In silico experiments reveal that the proposed MS-MPC strategy markedly reduces the number of exercise-related hypoglycemic events (8 vs 68).CONCLUSION: An anticipatory mode for insulin administration of a monohormonal AP controller reduces the occurrence of hypoglycemia during moderate-intensity exercise.