Glycemic Control Indices and Their Aggregation in the Prediction of Nocturnal Hypoglycemia From Intermittent Blood Glucose Measurements

Abstract

Background: Despite the risk associated with nocturnal hypoglycemia (NH) there are only a few methods aiming at the prediction of such events based on intermittent blood glucose monitoring data. One of the first methods that potentially can be used for NH prediction is based on the low blood glucose index (LBGI) and suggested, for example, in Accu-Chek® Connect as a hypoglycemia risk indicator. On the other hand, nowadays there are other glucose control indices (GCI), which could be used for NH prediction in the same spirit as LBGI. In the present study we propose a general approach of combining NH predictors constructed from different GCI. Methods: The approach is based on a recently developed strategy for aggregating ranking algorithms in machine learning. NH predictors have been calibrated and tested on data extracted from clinical trials, performed in EU FP7-funded project DIAdvisor. Then, to show a portability of the method we have tested it on another dataset that was received from EU Horizon 2020-funded project AMMODIT. Results: We exemplify the proposed approach by aggregating NH predictors that have been constructed based on 4 GCI associated with hypoglycemia. Even though these predictors have been preliminary optimized to exhibit better performance on the considered dataset, our aggregation approach allows a further performance improvement. On the dataset, where a portability of the proposed approach has been demonstrated, the aggregating predictor has exhibited the following performance: sensitivity 77%, specificity 83.4%, positive predictive value 80.2%, negative predictive value 80.6%, which is higher than conventionally considered as acceptable. Conclusion: The proposed approach shows potential to be used in telemedicine systems for NH prediction.

Prediction of Nocturnal Hypoglycemia in Adults with Type 1 Diabetes under Multiple Daily Injections Using Continuous Glucose Monitoring and Physical Activity Monitor

“…An advantage of the approach presented in this work is the inclusion of features related with physical activity as input for the prediction models. It has already been shown that daytime physical activity is strongly related with NH [38][39][40]; however, previous investigations intended for NH prediction did not consider such information [28,41]. In our proposal, physical activity data were collected by the wristband.…”

A Minimal Model Approach for Analyzing Continuous Glucose Monitoring in Type 2 Diabetes

“…A substantial body of literature exists trying to explain it in different contexts such as in juvenile diabetes (Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group et al, 2011 ), or for type I diabetes patients (Kim et al, 2011 ), and in assessing its impact on productivity (Brod et al, 2011 ). Notably, Sampath et al ( 2016 ) have recently proposed a machine learning algorithm that combines different glycemic indices to successfully predict occurrences of nocturnal hypoglycemic incidents. In order to model hypoglycemic events more carefully the model should probably be extended to include glucagon dynamics as well; these questions will be explored further in future work.…”

Machine Learning Techniques for Hypoglycemia Prediction: Trends and Challenges

“…Most of these models are time series forecasting models and involve BG data that have certain timestamps associated with the actual BG values. Nocturnal hypoglycemia prediction was targeted in studies such as Kriukova et al [ 44 ], Vu et al [ 47 ], Sampath et al [ 69 ], and Tkachenko et al [ 49 ]. Seo et al [ 43 ] proposed the prediction of postprandial hypoglycemia by training ML models with BG data while Jung et al [ 54 ] predicted day-time hypoglycemia using similar data.…”