Skip to main content

Advertisement

SpringerLink
Log in

Model of glucose sensor error components: identification and assessment for new Dexcom G4 generation devices

  • Special Issue - Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

It is clinically well-established that minimally invasive subcutaneous continuous glucose monitoring (CGM) sensors can significantly improve diabetes treatment. However, CGM readings are still not as reliable as those provided by standard fingerprick blood glucose (BG) meters. In addition to unavoidable random measurement noise, other components of sensor error are distortions due to the blood-to-interstitial glucose kinetics and systematic under-/overestimations associated with the sensor calibration process. A quantitative assessment of these components, and the ability to simulate them with precision, is of paramount importance in the design of CGM-based applications, e.g., the artificial pancreas (AP), and in their in silico testing. In the present paper, we identify and assess a model of sensor error of for two sensors, i.e., the G4 Platinum (G4P) and the advanced G4 for artificial pancreas studies (G4AP), both belonging to the recently presented “fourth” generation of Dexcom CGM sensors but different in their data processing. Results are also compared with those obtained by a sensor belonging to the previous, “third,” generation by the same manufacturer, the SEVEN Plus (7P). For each sensor, the error model is derived from 12-h CGM recordings of two sensors used simultaneously and BG samples collected in parallel every 15 ± 5 min. Thanks to technological innovations, G4P outperforms 7P, with average mean absolute relative difference (MARD) of 11.1 versus 14.2 %, respectively, and lowering of about 30 % the error of each component. Thanks to the more sophisticated data processing algorithms, G4AP resulted more reliable than G4P, with a MARD of 10.0 %, and a further decrease to 20 % of the error due to blood-to-interstitial glucose kinetics.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Bailey T, Zisser H, Chang A (2009) New features and performance of a next-generation SEVEN-day continuous glucose monitoring system with short lag time. Diabetes Technol Ther 11(12):749–755

    Article  CAS  PubMed  Google Scholar 

  2. Basu A, Dube S, Slama M, Errazuriz I, Amezcua JC, Kudva YC, Peyser T, Carter RE, Cobelli C, Basu R (2013) Time lag of glucose from intravascular to interstitial compartment in humans. Diabetes 62(12):4083–4087

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  3. Bequette BW (2010) Continuous glucose monitoring: real-time algorithms for calibration, filtering, and alarms. J Diabetes Sci Technol 4(2):404–418

    Article  PubMed Central  PubMed  Google Scholar 

  4. Breton M, Kovatchev B (2008) Analysis, modeling, and simulation of the accuracy of continuous glucose sensors. J Diabetes Sci Technol 2(5):853–862

    Article  PubMed Central  PubMed  Google Scholar 

  5. Christiansen M, Bailey T, Watkins E, Liljenquist D, Price D, Nakamura K, Boock R, Peyser T (2013) A new-generation continuous glucose monitoring system: improved accuracy and reliability compared with a previous-generation system. Diabetes Technol Ther 15(10):881–888

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  6. Clarke W, Kovatchev B (2009) Statistical tools to analyze continuous glucose monitor data. Diabetes Technol Ther 11(Suppl 1):45–54

    Google Scholar 

  7. Cobelli C, Renard E, Kovatchev B (2011) Artificial pancreas: past, present, future. Diabetes 60(11):2672–2682

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  8. Cobelli C, Renard E, Kovatchev BP, Keith-Hynes P, Ben Brahim N, Place J, Del Favero S, Breton M, Farret A, Bruttomesso D, Dassau E, Zisser H, Doyle FJ, Patek SD, Avogaro A (2012) Pilot studies of wearable outpatient artificial pancreas in type 1 diabetes. Diabetes Care 35(9):e65–e67

    Article  PubMed Central  PubMed  Google Scholar 

  9. Cox M (2009) An overview of continuous glucose monitoring systems. J Pediatr Health Care 23(5):344–347

    Article  PubMed  Google Scholar 

  10. Dalla Man C, Micheletto F, Lv D, Breton M, Kovatchev B, Cobelli C (2014) The UVA/Padova type 1 diabetes simulator: new features. J Diabetes Sci Technol 8(1):26–34

    Article  Google Scholar 

  11. Damiano ER, El-Khatib FH, Zheng H, Nathan DM, Russell SJ (2013) A comparative effectiveness analysis of three continuous glucose monitors. Diabetes Care 36(2):251–259

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  12. Daskalaki E, Norgaard K, Zuger T, Prountzou A, Diem P, Mougiakakou S (2013) An early warning system for hypoglycemic/hyperglycemic events based on fusion of adaptive prediction models. J Diabetes Sci Technol 7(3):689–698

    Article  PubMed Central  PubMed  Google Scholar 

  13. Docherty PD, Chase JG, David T (2012) Characterisation of the iterative integral parameter identification method. Med Biol Eng Comput 50(2):127–134

    Article  PubMed  Google Scholar 

  14. Dua P, Doyle FJ, Pistikopoulos EN (2009) Multi-objective blood glucose control for type 1 diabetes. Med Biol Eng Comput 47(3):343–352

    Article  PubMed  Google Scholar 

  15. Facchinetti A, Del Favero S, Sparacino G, Castle J, Ward W, Cobelli C (2014) Modeling the glucose sensor error. IEEE Trans Biomed Eng 61(3):620–629

    Article  PubMed  Google Scholar 

  16. Garcia A, Rack-Gomer AL, Bhavaraju NC, Hampapuram H, Kamath A, Peyser T, Facchinetti A, Zecchin C, Sparacino G, Cobelli C (2013) Dexcom G4AP: an advanced continuous glucose monitor for the artificial pancreas. J Diabetes Sci Technol 7(6):1436–1445

    PubMed Central  PubMed  Google Scholar 

  17. Guerra S, Sparacino G, Facchinetti A, Schiavon M, Man CD, Cobelli C (2011) A dynamic risk measure from continuous glucose monitoring data. Diabetes Technol Ther 13(8):843–852

    Article  PubMed  Google Scholar 

  18. Hovorka R, Allen JM, Elleri D, Chassin LJ, Harris J, Xing D, Kollman C, Hovorka T, Larsen AM, Nodale M, De Palma A, Wilinska ME, Acerini CL, Dunger DB (2010) Manual closed-loop insulin delivery in children and adolescents with type 1 diabetes: a phase 2 randomised crossover trial. Lancet 375(9716):743–751

    Article  CAS  PubMed  Google Scholar 

  19. Joubert M, Reznik Y (2012) Personal continuous glucose monitoring (CGM) in diabetes management: review of the literature and implementation for practical use. Diabetes Res Clin Pract 96(3):294–305

  20. Kamath A, Mahalingam A, Brauker J (2009) Analysis of time lags and other sources of error of the DexCom SEVEN continuous glucose monitor. Diabetes Technol Ther 11(11):689–695

    Article  CAS  PubMed  Google Scholar 

  21. Kovatchev B, Anderson S, Heinemann L, Clarke W (2008) Comparison of the numerical and clinical accuracy of four continuous glucose monitors. Diabetes Care 31(6):1160–1164

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  22. Kovatchev BP, Cox DJ, Gonder-Frederick LA, Clarke W (1997) Symmetrization of the blood glucose measurement scale and its applications. Diabetes Care 20(11):1655–1658

    Article  CAS  PubMed  Google Scholar 

  23. Laguna AJ, Rossetti P, Ampudia-Blasco FJ, Vehí J, Bondia J (2014) Postprandial performance of Dexcom® SEVEN® PLUS and Medtronic® Paradigm® Veo™: modeling and statistical analysis. Biomed Signal Process Control 10:322–331

    Article  Google Scholar 

  24. Lane JE, Shivers JP, Zisser H (2013) Continuous glucose monitors: current status and future developments. Curr Opin Endocrinol Diabetes Obes 20(2):106–111

    Article  CAS  PubMed  Google Scholar 

  25. Lunn DJ, Wei C, Hovorka R (2011) Fitting dynamic models with forcing functions: application to continuous glucose monitoring in insulin therapy. Stat Med 30(18):2234–2250

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  26. McGarraugh G (2009) The chemistry of commercial continuous glucose monitors. Diabetes Technol Ther 11(Suppl 1):17–24

    Google Scholar 

  27. Ogunfunmi T (2007) Adaptive nonlinear system identification : the Volterra and Wiener model approaches. Springer, New York

    Book  Google Scholar 

  28. Phillip M, Battelino T, Atlas E, Kordonouri O, Bratina N, Miller S, Biester T, Stefanija MA, Muller I, Nimri R, Danne T (2013) Nocturnal glucose control with an artificial pancreas at a diabetes camp. N Engl J Med 368(9):824–833

    Article  CAS  PubMed  Google Scholar 

  29. Rebrin K, Steil GM, van Antwerp WP, Mastrototaro JJ (1999) Subcutaneous glucose predicts plasma glucose independent of insulin: implications for continuous monitoring. Am J Physiol 277(3 Pt 1):E561–E571

    CAS  PubMed  Google Scholar 

  30. Rodbard D (2011) Glycemic variability: measurement and utility in clinical medicine and research—one viewpoint. Diabetes Technol Ther 13(11):1077–1080

    Article  PubMed  Google Scholar 

  31. Russell SJ, El-Khatib FH, Nathan DM, Magyar KL, Jiang J, Damiano ER (2012) Blood glucose control in type 1 diabetes with a bihormonal bionic endocrine pancreas. Diabetes Care 35(11):2148–2155

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  32. Service FJ (2013) Glucose variability. Diabetes 62(5):1398–1404

    Article  PubMed Central  PubMed  Google Scholar 

  33. Sparacino G, Facchinetti A, Cobelli C (2010) “Smart” continuous glucose monitoring sensors: on-line signal processing issues. Sensors (Basel) 10(7):6751–6772

    Article  Google Scholar 

  34. Tamborlane WV, Beck RW, Bode BW, Buckingham B, Chase HP, Clemons R, Fiallo-Scharer R, Fox LA, Gilliam LK, Hirsch IB, Huang ES, Kollman C, Kowalski AJ, Laffel L, Lawrence JM, Lee J, Mauras N, O’Grady M, Ruedy KJ, Tansey M, Tsalikian E, Weinzimer S, Wilson DM, Wolpert H, Wysocki T, Xing D, Chase HP, Fiallo-Scharer R, Messer L, Gage V, Burdick P, Laffel L, Milaszewski K, Pratt K, Bismuth E, Keady J, Lawlor M, Buckingham B, Wilson DM, Block J, Benassi K, Tsalikian E, Tansey M, Kucera D, Coffey J, Cabbage J, Wolpert H, Shetty G, Atakov-Castillo A, Giusti J, O’Donnell S, Ghiloni S, Hirsch IB, Gilliam LK, Fitzpatrick K, Khakpour D, Wysocki T, Fox LA, Mauras N, Englert K, Permuy J, Bode BW, O’Neil K, Tolbert L, Lawrence JM, Clemons R, Maeva M, Sattler B, Weinzimer S, Tamborlane WV, Ives B, Bosson-Heenan J, Beck RW, Ruedy KJ, Kollman C, Xing D, Jackson J, Steffes M, Bucksa JM, Nowicki ML, Van Hale C, Makky V, O’Grady M, Huang E, Basu A, Meltzer DO, Zhao L, Lee J, Kowalski AJ, Laffel L, Tamborlane WV, Beck RW, Kowalski AJ, Ruedy KJ, Weinstock RS, Anderson BJ, Kruger D, LaVange L, Rodriguez H (2008) Continuous glucose monitoring and intensive treatment of type 1 diabetes. N Engl J Med 359:1464–1476

    Article  PubMed  Google Scholar 

  35. Toffanin C, Messori M, Di Palma F, De Nicolao G, Cobelli C, Magni L (2013) Artificial pancreas: model predictive control design from clinical experience. J Diabetes Sci Technol 7(6):1470–1483

    PubMed Central  PubMed  Google Scholar 

  36. Zecchin C, Facchinetti A, Sparacino G, Cobelli C (2013) Reduction of number and duration of hypoglycemic events by glucose prediction methods: a proof-of-concept in silico study. Diabetes Technol Ther 15(1):66–77

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Engineering, University of Padova, Via G.Gradenigo 6/B, 35131, Padua, Italy

    Andrea Facchinetti, Simone Del Favero, Giovanni Sparacino & Claudio Cobelli

Authors
  1. Andrea Facchinetti
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Simone Del Favero
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Giovanni Sparacino
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Claudio Cobelli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Claudio Cobelli.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Facchinetti, A., Del Favero, S., Sparacino, G. et al. Model of glucose sensor error components: identification and assessment for new Dexcom G4 generation devices. Med Biol Eng Comput 53, 1259–1269 (2015). https://doi.org/10.1007/s11517-014-1226-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-014-1226-y

Keywords

Search

Navigation