Purpose

The aim of the current study is to develop a prediction model for glucose levels applicable for all patients admitted to the ICU with an expected ICU stay of at least 24 h. This model will be incorporated in a closed-loop glucose system to continuously and automatically control glucose values.

Methods

Data from a previous single-center randomized controlled study was used. All patients received a FreeStyle Navigator II subcutaneous CGM system from Abbott during their ICU stay.

The total dataset was randomly divided into a training set and a validation set. A glucose prediction model was developed based on historical glucose data. Accuracy of the prediction model was determined using the Mean Squared Difference (MSD), the Mean Absolute Difference (MAD) and a Clarke Error Grid (CEG).

Results

The dataset included 94 ICU patients with a total of 134,673 glucose measurements points that were used for modelling. MSD was 0.410 ± 0.495 for the model, the MAD was 5.19 ± 2.63 and in the CEG 99.8% of the data points were in the clinically acceptable regions.

Conclusion

In this study a glucose prediction model for ICU patients is developed. This study shows that it is possible to accurately predict a patient's glucose 30 min ahead based on historical glucose data. This is the first step in the development of a closed-loop glucose system.

中文翻译：

---

重症患者血糖预测模型的建立

目的

本研究的目的是建立一个适用于所有入住ICU且预期ICU停留时间至少为24 h的患者的血糖水平预测模型。该模型将并入闭环葡萄糖系统中，以连续自动地控制葡萄糖值。

方法

使用先前单中心随机对照研究的数据。所有患者在重症监护病房住院期间均接受了雅培公司的FreeStyle Navigator II皮下CGM系统治疗。

将总数据集随机分为训练集和验证集。基于历史葡萄糖数据开发了葡萄糖预测模型。使用均方差（MSD），平均绝对差（MAD）和克拉克误差网格（CEG）确定预测模型的准确性。

结果

该数据集包括94位ICU患者，总共有134,673个血糖测量点用于建模。该模型的MSD为0.410±0.495，MAD为5.19±2.63，CEG中99.8％的数据点在临床可接受的范围内。

结论

在这项研究中，开发了针对ICU患者的葡萄糖预测模型。这项研究表明，可以根据历史葡萄糖数据准确地预测患者30分钟之前的葡萄糖。这是开发闭环葡萄糖系统的第一步。