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Acta Diabetologica

, Volume 55, Issue 8, pp 877–880 | Cite as

GlyCulator2: an update on a web application for calculation of glycemic variability indices

  • Konrad Pagacz
  • Konrad Stawiski
  • Agnieszka Szadkowska
  • Wojciech Mlynarski
  • Wojciech Fendler
Short Communication

Aims

Continuous glucose monitoring (CGM) was shown to be clinically useful in improving treatment of type 1 and type 2 diabetes [1]. The clinical benefits of CGM are numerous, but in essence, the additional insight into short-term glucose fluctuations may, if properly interpreted by the patient, limit the number of hypoglycemic episodes, reduce average blood glucose concentration and lower HbA1c levels [2]. However, apart from these indices, CGM enables the patients and their doctors to evaluate glycemic variability (GV) with unprecedented detail and make clinical judgments on the characteristics of the CGM changes quantified using multiple methodologies. The debate on the feasibility of using GV indices in clinical decision making process has been ongoing [3] and was hindered by the lack of clear guidelines for the manner of reporting and interpreting GV. Prompted by the publication of the International Consensus On Use of Continuous Glucose Monitoring [4], which specified the...

Keywords

Continuous glucose monitoring Glycemic variability Glycemic variability calculator 

Notes

Acknowledgements

Konrad Pagacz and Wojciech Fendler were supported by the First TEAM project funded by Smart Growth Operational Programme and the Foundation for Polish Science. Konrad Pagacz was supported by Medical University of Lodz's “Grants of UMED”.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.

Human and animal rights

This article does not contain any studies with human or animal subjects performed by any of the authors.

Informed consent

Informed consent for blinded data analysis was obtained from all patients undergoing CGM as part of their routine monitoring.

References

  1. 1.
    Rodbard D (2016) Continuous glucose monitoring: a review of successes, challenges, and opportunities. Diabetes Technol Ther 18(Suppl 2):S23–S213.  https://doi.org/10.1089/dia.2015.0417 CrossRefGoogle Scholar
  2. 2.
    Rodbard D (2017) Continuous glucose monitoring: a review of recent studies demonstrating improved glycemic outcomes. Diabetes Technol Ther 19:S25–S37.  https://doi.org/10.1089/dia.2017.0035 CrossRefPubMedGoogle Scholar
  3. 3.
    Hirsch IB (2015) Glycemic variability and diabetes complications: does it matter? Of course it does! Diabetes Care 38:1610–1614.  https://doi.org/10.2337/dc14-2898 CrossRefPubMedGoogle Scholar
  4. 4.
    Danne T, Nimri R, Battelino T et al (2017) International consensus on use of continuous glucose monitoring. Diabetes Care 40:1631–1640.  https://doi.org/10.2337/dc17-1600 CrossRefPubMedGoogle Scholar
  5. 5.
    Czerwoniuk D, Fendler W, Walenciak L, Mlynarski W (2011) GlyCulator: a glycemic variability calculation tool for continuous glucose monitoring data. J Diabetes Sci Technol 5:447–451.  https://doi.org/10.1177/193229681100500236 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag Italia S.r.l., part of Springer Nature 2018

Authors and Affiliations

  • Konrad Pagacz
    • 1
  • Konrad Stawiski
    • 1
  • Agnieszka Szadkowska
    • 2
  • Wojciech Mlynarski
    • 2
  • Wojciech Fendler
    • 1
    • 3
  1. 1.Department of Biostatistics and Translational MedicineMedical University of LodzLodzPoland
  2. 2.Department of Pediatrics, Oncology, Hematology and DiabetologyMedical University of LodzLodzPoland
  3. 3.Department of Radiation OncologyDana-Farber Cancer InstituteBostonUSA

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