Improving the Robustness of the Glycemic Variability Percentage Metric to Sensor Dropouts in Continuous Glucose Monitor Data

  • Michael MayoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11432)


Continuous glucose monitors generate significant volumes of high frequency blood glucose data. Analysis of this data by a physician may entail the calculation of various glycemic variability metrics. In this paper, we consider the problem of metric robustness to sensor dropouts. We show that the standard metrics for glycemic variability are unreliable with missing data. A more recent metric, glycemic variability percentage, is shown to consistently underestimate glycemic variability as the amount of missing data increases. We therefore propose a new algorithm based on random sampling combined with linear regression to correct this underestimation, and show that the metric’s accuracy is significantly increased with our correction.


  1. 1.
    Bruen, D., Delaney, C., Florea, L., Diamond, D.: Glucose sensing for diabetes monitoring: recent developments. Sensors 17(8), 1866 (2017)CrossRefGoogle Scholar
  2. 2.
    Buckingham, B., et al.: Effectiveness of early intensive therapy on \(\beta \)-cell preservation in type 1 diabetes. Diabetes Care 36, 4030 (2013)CrossRefGoogle Scholar
  3. 3.
    Cox, D.J., Gonder-Frederick, L., Ritterband, L., Clarke, W., Kovatchev, B.P.: Prediction of severe hypoglycemia. Diabetes Care 30(6), 1370–1373 (2007). Scholar
  4. 4.
    Danne, T., et al.: International consensus on use of continuous glucose monitoring. Diabetes Care 40(12), 1631–1640 (2017). Scholar
  5. 5.
    Fabris, C., Patek, S.D., Breton, M.D.: Are risk indices derived from CGM interchangeable with SMBG-based indices? J. Diabetes Sci. Technol. 10(1), 50–59 (2016). Scholar
  6. 6.
    Hirsch, I.B., Balo, A.K., Sayer, K., Garcia, A., Buckingham, B.A., Peyser, T.A.: A simple composite metric for the assessment of glycemic status from continuous glucose monitoring data: implications for clinical practice and the artificial pancreas. Diabetes Technol. Ther. 19(S3), S-38–S-48 (2017). Scholar
  7. 7.
    Kovatchev, B.P., Cox, D.J., Gonder-Frederick, L.A., Clarke, W.: Symmetrization of the blood glucose measurement scale and its applications. Diabetes Care 20(11), 1655–1658 (1997)CrossRefGoogle Scholar
  8. 8.
    Kovatchev, B.P., Straume, M., Cox, D.J., Farhy, L.S.: Risk analysis of blood glucose data: a quantitative approach to optimizing the control of insulin dependent diabetes. J. Theor. Med. 3(1), 1–10 (2000). Scholar
  9. 9.
    Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar
  10. 10.
    Peyser, T.A., Balo, A.K., Buckingham, B.A., Hirsch, I.B., Garcia, A.: Glycemic variability percentage: a novel method for assessing glycemic variability from continuous glucose monitor data. Diabetes Technol. Ther. 20(1), 6–16 (2018). Scholar
  11. 11.
    The Nightscout Foundation: About the Nightscout Data Commons on Open Humans (2014).

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of WaikatoHamiltonNew Zealand

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