Improving glucose control with advanced technology designed for high risk patients with type 1 diabetes

摘要 本提案的目的是优化设计和评估一个健壮的人工胰腺(R-AP)系统 用于HbA1C大于8%的未控制的1型糖尿病(T1D)患者，并比较HbA1C 这些患者的预后与使用持续血糖监测的决策支持系统相关 (CGM)和每日多次注射(MDI)治疗。尽管高危患者可能获益最多 从AP技术的使用来看，他们往往代表不足或被排除在临床试验之外。这有 一直是因为这些AP系统的故障风险增加，而这些AP系统的设计不是为了处理 不一致的膳食报告、可变活动水平和输液器故障。针对高风险的AP系统 患者需要被设计为获得最大利益，包括降低急性和慢性疾病的风险 并发症。高危患者使用AP的一个主要障碍是这些患者可能 不太符合系统的使用指南，包括错过用餐通知、不频繁的传感器 校准和长时间的输液器磨损导致输液器故障。在这笔赠款中，我们将整合新的 OHSU单激素AP的风险缓解功能，使陷入 上述类别。我们提出了自动检测遗漏餐食的新算法 公告、错过的校准和混合使用模式的稳健处理。虽然AP系统可能 作为改善血糖控制的最佳选择，许多T1D患者更喜欢MDI疗法。决策支持 OHSU开发的DailyDose决策支持系统等系统可用于改善血糖 对偏爱MDI治疗的患者进行对照。DailyDose决策支持系统是为CGM设计的 强化MDI疗法。它支持按需计算胰岛素剂量，使胰岛素剂量自动化 基于模式识别的调整，并使用机器学习方法提醒患者 DailyDose系统的好处是它 是一个简单的系统，不需要使用胰岛素泵，这对一些患者来说可能是一个挑战 对于未得到控制的1型糖尿病，泵治疗更密集，需要更换输液器。它是 在这一高危人群中，未知患者的需求、生活质量和血糖控制是否是最好的 使用AP系统或决策支持工具解决问题，或者如果两种处理方法都合适。我们设计了 一项为期3个月的临床研究，比较AP和决策支持干预期间的血糖结果。 风险T1D队列(HbA1C 8-10.5%)，目的是证明在临床上显著降低 糖化血红蛋白。我们的假设是AP和决策支持疗法都会降低HbA1C 但AP将提供比DailyDose更多的好处。

Summary The objective of this proposal is to optimize the design and evaluate a robust artificial pancreas (R-AP) system for use in patients with uncontrolled type 1 diabetes (T1D) with HbA1C greater than 8% and compare HbA1C outcomes in these patients relative to a decision support system that utilizes continuous glucose monitoring (CGM) and multiple daily injection (MDI) therapy. Although high risk patients have possibly the most to gain from usage of AP technology, they are oftentimes under-represented or excluded from clinical trials. This has been because of the increased risk of failure of these AP systems that were not designed to handle inconsistent reporting of meals, variable activity level, and infusion set failures. An AP system for high risk patients needs to be designed to achieve maximal benefit, including reducing the risk of acute and chronic complications. A major obstacle for enabling the AP for usage by high-risk patients is that these patients may be less compliant with use guidelines for the system including missed meal announcements, infrequent sensor calibrations, and prolonged infusion set wear leading to infusion set failures. In this grant, we will integrate new risk-mitigation features into the OHSU single-hormone AP to enable usage by high-risk patients that fall into the categories described above. We present new algorithms for automating the detection of missed meal announcements, missed calibrations, and robust handling of hybrid usage mode. While AP systems may be an optimal choice for improving glycemic control, many people with T1D prefer MDI therapy. Decision support systems such as the DailyDose decision support system developed at OHSU can be used to improve glycemic control for patients who prefer MDI therapy. The DailyDose decision support system is designed for CGM augmented MDI therapy. It enables on-demand calculation of insulin doses, automates insulin dose adjustments based on pattern recognition, and uses machine learning approaches to alert the patients to events such as predicted hypoglycemia and missed meal doses.The benefit of the DailyDose system is that it is a simple system and does not require use of an insulin pump, which may be a challenge for some patients with uncontrolled type 1 diabetes as pump therapy is more intensive and requires infusion set changes. It is unknown in this high risk group of people whether patient needs, quality of life, and glycemic control are best addressed with an AP system or decision support tool or if both treatments are appropriate. We have designed a 3-month clinical study to compare glycemic outcomes during AP vs. decision support interventions in a high- risk T1D cohort (HbA1C 8-10.5%), with the aim of demonstrating a significant clinically relevant reduction in HbA1C. Our hypothesis is that both AP and decision support therapies will decrease HbA1C relative to baseline but that the AP will provide further benefit over DailyDose.