Background and Objective

As continuous glucose monitoring (CGM) becomes common in research and clinical practice, there is a need to understand how CGM-based hypoglycemia relates to hypoglycemia episodes defined conventionally as patient reported hypoglycemia (PRH). Data show that CGM identify many episodes of low interstitial glucose (LIG) that are not experienced by patients, and so the aim of this study is to use different PRH simulations to optimize CGM parameters of threshold (h) and duration (d) to provide the best PRH detection performance.

Methods

The algorithm uses particle Markov chain Monte Carlo optimization to identify the optimal h and d which maximize an objective function for detecting PRH. We tested our algorithm by creating three different cases of PRH simulations.

Results

We added three types of simulated PRH events to 10 weeks of anonymized CGM data from 96 type 1 diabetes people to see if the algorithm can detect the optimal parameters set out in the simulations. In simulation 1, we changed the locations of PRHs with respect to LIG episodes in the CGM signal to simulate random optimal LIG parameters for every individual. In simulation 2, the PRHs are CGM glucose <3.9 mmol/L followed by at least 20 min of rise > 0.11 mmol/L/min. Simulation 3 is like simulation 2 but with glucose threshold of 3.0 mmol/L. The median [interquartile range] of deviation between the optimized (found by the algorithm) and the optimal (known) h and d are −0.07% [−0.4, 1.9] and −1.3% [−5.9, 6.8], respectively across the subjects for simulation 1. The mean [min max] of the optimized LIG parameters are h = 3.8 [3.7, 3.8] mmol/L and d = 12 [10, 14] min for simulation 2 and they are h = 3.0 [2.9, 3] mmol/L and d = 10 [8, 14] min for simulation 3 across a 10-fold cross validation.

Conclusions

This work demonstrates the feasibility of the algorithm to find the best-fit definition of CGM-based hypoglycemia for PRH detection. In a prospective clinical study collecting CGM and PRH, the current algorithm will be used to optimize the definition of hypoglycemia with respect to PRH with the ambition of using the resulted definition as a surrogate for PRH in clinical practice.

中文翻译：

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通过连续血糖监测实现低血糖的最佳定义

背景与目的

随着连续血糖监测 (CGM) 在研究和临床实践中变得普遍，有必要了解基于 CGM 的低血糖与传统定义为患者报告的低血糖 (PRH) 的低血糖发作之间的关系。数据显示 CGM 识别出许多患者未经历过的低间质葡萄糖 (LIG) 发作，因此本研究的目的是使用不同的 PRH 模拟来优化阈值 ( h ) 和持续时间 ( d ) 的CGM 参数，以提供最好的公屋检测性能。

方法

该算法使用粒子马尔可夫链蒙特卡罗优化来确定最优的 h 和 d，从而最大化检测 PRH 的目标函数。我们通过创建三个不同的公屋模拟案例来测试我们的算法。

结果

我们在来自 96 名 1 型糖尿病患者的 10 周匿名 CGM 数据中添加了三种类型的模拟 PRH 事件，以查看该算法是否可以检测到模拟中设定的最佳参数。在模拟 1 中，我们根据 CGM 信号中的 LIG 事件更改了 PRH 的位置，以模拟每个个体的随机最佳 LIG 参数。在模拟 2 中，PRH 是 CGM 葡萄糖 <3.9 mmol/L，然后至少上升 20 分钟 > 0.11 mmol/L/min。模拟 3 与模拟 2 相似，但葡萄糖阈值为 3.0 mmol/L。优化（由算法找到）和最佳（已知）h 和 d 之间偏差的中位数 [四分位距] 分别为 -0.07% [-0.4, 1.9] 和 -1.3% [-5.9, 6.8]模拟对象 1. 优化 LIG 参数的平均值 [min max] 为 h = 3.8 [3.7, 3.8] mmol/L 和 d = 12 [10, 14] min 用于模拟 2，它们为 h = 3.0 [2.9, 3] mmol/L 和 d = 10 [8, 14] 分钟，模拟 3 跨越 10 倍交叉验证。

结论

这项工作证明了该算法为 PRH 检测找到基于 CGM 的低血糖的最佳定义的可行性。在一项收集 CGM 和 PRH 的前瞻性临床研究中，当前算法将用于优化与 PRH 相关的低血糖定义，并希望将所得定义用作临床实践中 PRH 的替代指标。