A Personalized Week-to-Week Updating Algorithm to Improve Continuous Glucose Monitoring Performance

Abstract

Background: Continuous glucose monitoring (CGM) systems are increasingly becoming essential components in type 1 diabetes mellitus (T1DM) management. Current CGM technology requires frequent calibration to ensure accurate sensor performance. The accuracy of these systems is of great importance since medical decisions are made based on monitored glucose values and trends.

Methods: In this work, we introduce a calibration strategy that is augmented with a weekly updating feature. During the life cycle of the sensor, the calibration mechanism periodically estimates the parameters of a calibration model to fit self-monitoring blood glucose (SMBG) measurements. At the end of each week of use, an optimization problem that minimizes the sum of squared residuals between past reference and predicted blood glucose values is solved remotely to identify personalized calibration parameters. The newly identified parameters are used to initialize the calibration mechanism of the following week.

Results: The proposed method was evaluated using two sets of clinical data both consisting of 6 weeks of Dexcom G4 Platinum CGM data on 10 adults with T1DM (over 10 000 hours of CGM use), with seven SMBG data points per day measured by each subject in an unsupervised outpatient setting. Updating the calibration parameters using the history of calibration data indicated a positive trend of improving CGM performance.

Conclusions: Although not statistically significant, the updating framework showed a relative improvement of CGM accuracy compared to the non-updating, static calibration method. The use of information collected for longer periods is expected to improve the performance of the sensor over time.

Keywords: continuous glucose monitoring (CGM); glucose sensors; type 1 diabetes mellitus; weekly updating.

Conflict of interest statement

Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.

A schematic demonstrating the weekly operation of the proposed calibration strategy. The proposed calibration method is applied directly on the raw senor signal and therefore there is no learning from the original calibration.

Figure 2.

Calibration process. The calibration parameters denoted with θ are updated at T(i) time instants. The interval between parameter updates is usually 12 hours.

Figure 3.

Updating schemes. The blue filled circles denote the end of each week and the implementation of the weekly updating algorithm. The arrows show the history of data used in each scheme. Week 1, in all cases, is not considered in the process, and it is only used to obtain an initial estimate of the parameters.

Figure 4.

(Left) Contour plot zoomed in the area of interest (δ∈[0.0001,1]), indicating the range of parameters that the median of RMSE is minimized. (Right) Mean RMSE of the training set for different forgetting factors μ.

Figure 5.

Default calibration versus static calibration for adult no. 10 during week 5. The %RSS is computed to −8.6%.

Figure 6.

Improvements in percentage reduction of sum of squared residuals (%RSS) over successive weeks of use in terms of median (IQR) static calibration (blue line) versus the three updating schemes, normalized by residuals of the static calibration.

Figure 7.

Box-and-whisker plot representation of RMSE distribution over all subjects and all weeks of the training set computed for separate segments of the week for the default CGM, the static calibration and calibration augmented with Scheme 1.

Figure 8.

Box-and-whisker plot representation of RMSE distribution over all subjects and all weeks of the validation set computed for separate segments of the week for the default CGM, the static calibration and calibration augmented with Scheme 1.

Figure 9.

Box-and-whisker plot representation of RMSE distribution averaged over all subjects of the validation set and computed for each week for the default CGM, the static calibration and calibration augmented with Scheme 1.