Short-term prediction of future continuous glucose monitoring readings in type 1 diabetes: Development and validation of a neural network regression model,International Journal of Medical Informatics

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Short-term prediction of future continuous glucose monitoring readings in type 1 diabetes: Development and validation of a neural network regression model
International Journal of Medical Informatics ( IF 3.7 ) Pub Date : 2021-04-24 , DOI: 10.1016/j.ijmedinf.2021.104472
Simon Lebech Cichosz ₁ , Morten Hasselstrøm Jensen ₂ , Ole Hejlesen ₁

Affiliation

Background and objective

CGM systems are still subject to a time-delay, which especially during rapid changes causes clinically significant difference between the CGM and the actual BG level. This study had the aim of exploring the potential of developing and validating a model for prediction of future CGM measurements in order to overcome the time-delay.

Methods

An artificial neural network regression (NN) approach were used to predict CGM values with a lead-time of 15 min. The NN were trained and internally validated on 23 million minutes of CGM and externally validated on 2 million minutes of CGM. The validation included data from 278 type 1 diabetes patients using three different CGM sensors. The NN performance were compared with three alternative methods, linear extrapolation, spline extrapolation and last observation carried forward.

Results

The internal validation yielded a RMSE of 9.1 mg/dL, a MARD of 4.2 % and 99.9 % of predictions were in the A + B zone of the consensus error grid. The external validation yielded a RMSE of 5.9–11.3 mg/dL, a MARD of 3.2–5.4 % and 99.9–100 % of predictions were in the A + B zone of the consensus error grid. The NN performed better on all parameters compared to the two alternative methods.

Conclusions

We proposed and validated a NN glucose prediction model that is potential simple to use and implement. The model only needs input from a CGM system in order to facilitate glucose prediction with a lead time of 15 min. The approach yielded good results for both internal and external validation.

中文翻译：

1型糖尿病未来连续血糖监测读数的短期预测：神经网络回归模型的开发和验证

背景和目标

CGM系统仍会存在时间延迟，尤其是在快速变化期间，会导致CGM与实际BG水平在临床上产生显着差异。这项研究的目的是探索开发和验证用于预测未来CGM测量值的模型的潜力，以克服时间延迟。

方法

人工神经网络回归（NN）方法用于预测CGM值，前置时间为15分钟。对NN进行了2300万分钟的CGM培训和内部验证，并在200万分钟的CGM上进行了外部验证。验证包括使用三种不同的CGM传感器来自278名1型糖尿病患者的数据。将NN的性能与三种替代方法（线性外推，样条外推和最后观察得到的结果）进行了比较。

结果

内部验证得出的RMSE为9.1 mg / dL，4.2％的MARD和99.9％的预测在共有误差网格的A + B区域内。外部验证得出的均方根误差（RMSE）为5.9-11.3 mg / dL，3.2％的MARD和99.9-100％的预测均在共识误差网格的A + B区域内。与两种替代方法相比，NN在所有参数上的表现都更好。