The development of a glucose prediction model in critically ill patients

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. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

The aim of the study was to develop a glucose prediction model applicable for all ICU patients with an expected stay of at least 24 hours. The model, based on historical glucose data, showed promising accuracy in predicting glucose levels 30 minutes ahead, marking a significant step in the development of a closed-loop glucose system.