Model of glucose sensor error components: identification and assessment for new Dexcom G4 generation devices
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.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Cites
Continuous glucose monitoring and intensive treatment of type 1 diabetes
(2008) · New England Journal of Medicine · doi:10.1056/nejmoa0805017 · Cited by 1,559 · see citing works
The UVA/PADOVA Type 1 Diabetes Simulator: New Features
Man, C. D., Micheletto, F., Lv, D., Breton, M., Kovatchev, B., & Cobelli, C. (2014) · Journal of Diabetes Science and Technology · doi:10.1177/1932296813514502 · Cited by 791 · cited 1× · see citing works
“, in the UVA/Padova T1D simulator [10], in order to simulate longer reliable scenarios.”
BackgroundSelf-citationArtificial Pancreas: Past, Present, Future
Cobelli, C., Renard, E., & Kovatchev, B. (2011) · Diabetes · doi:10.2337/db11-0654 · Cited by 600 · see citing works
Self-citationHovorka, R., Allen, J. M., Elleri, D., Chassin, L. J., Harris, J. R., Xing, D., Kollman, C., Hovorka, T., Larsen, A. M. F., Nodale, M., et al. (2010) · The Lancet · doi:10.1016/s0140-6736(09)61998-x · Cited by 448 · see citing works
Nocturnal Glucose Control with an Artificial Pancreas at a Diabetes Camp
Phillip, M., Battelino, T., Atlas, E., Kordonouri, O., Bratina, N., Miller, S., Biester, T., Stefanija, M. A., Muller, I., Nimri, R., et al. (2013) · New England Journal of Medicine · doi:10.1056/nejmoa1206881 · Cited by 431 · see citing works
Rebrin, K., Steil, G. M., van Antwerp, W. P., & Mastrototaro, J. J. (1999) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.1999.277.3.e561 · Cited by 367 · see citing works
Symmetrization of the Blood Glucose Measurement Scale and Its Applications
Kovatchev, B. P., Cox, D. J., Gonder-Frederick, L. A., & Clarke, W. L. (1997) · Diabetes Care · doi:10.2337/diacare.20.11.1655 · Cited by 258 · see citing works
Statistical Tools to Analyze Continuous Glucose Monitor Data
Clarke, W. L., & Kovatchev, B. P. (2009) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2008.0138 · Cited by 248 · see citing works
Comparison of the Numerical and Clinical Accuracy of Four Continuous Glucose Monitors
Kovatchev, B. P., Anderson, S. M., Heinemann, L., & Clarke, W. L. (2008) · Diabetes Care · doi:10.2337/dc07-2401 · Cited by 236 · see citing works
Service, F. J. (2013) · Diabetes · doi:10.2337/db12-1396 · Cited by 222 · see citing works
Time Lag of Glucose From Intravascular to Interstitial Compartment in Humans
Basu, A., Dube, S., Slama, M. Q., Errazuriz, I., Amezcua, J. C., Kudva, Y. C., Peyser, T. A., Carter, R. E., Cobelli, C., & Basu, R. (2013) · Diabetes · doi:10.2337/db13-1132 · Cited by 208 · see citing works
Self-citationContinuous Glucose Monitoring: Real-Time Algorithms for Calibration, Filtering, and Alarms
Bequette, B. W. (2010) · Journal of Diabetes Science and Technology · doi:10.1177/193229681000400222 · Cited by 193 · see citing works
Blood Glucose Control in Type 1 Diabetes With a Bihormonal Bionic Endocrine Pancreas
Russell, S. J., El-Khatib, F. H., Nathan, D. M., Magyar, K. L., Jiang, J., & Damiano, E. R. (2012) · Diabetes Care · doi:10.2337/dc12-0071 · Cited by 176 · see citing works
Analysis, Modeling, and Simulation of the Accuracy of Continuous Glucose Sensors
Breton, M. D., & Kovatchev, B. P. (2008) · Journal of Diabetes Science and Technology · doi:10.1177/193229680800200517 · Cited by 173 · see citing works
The Chemistry of Commercial Continuous Glucose Monitors
McGarraugh, G. V. (2009) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2008.0133 · Cited by 144 · see citing works
Adaptive Nonlinear System Identification
Ogunfunmi, T. (2007) · Signals and communication technology · doi:10.1007/978-0-387-68630-1 · Cited by 139 · see citing works
Christiansen, M. P., Bailey, T. S., Watkins, E., Liljenquist, D. R., Price, D. A., Nakamura, K., Boock, R. J., & Peyser, T. A. (2013) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2013.0077 · Cited by 136 · see citing works
Modeling the glucose sensor error
Facchinetti, A., Del Favero, S., Sparacino, G., Castle, J. R., Ward, W. K., & Cobelli, C. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2284023 · Cited by 130 · see citing works
Self-citationPilot Studies of Wearable Outpatient Artificial Pancreas in Type 1 Diabetes
Cobelli, C., Renard, É., Kovatchev, B. P., Keith-Hynes, P., Brahim, N. B., Place, J., Favero, S. D., Breton, M. D., Farret, A., Bruttomesso, D., et al. (2012) · Diabetes Care · doi:10.2337/dc12-0660 · Cited by 119 · see citing works
Self-citationA Comparative Effectiveness Analysis of Three Continuous Glucose Monitors
Damiano, E. R., El-Khatib, F. H., Zheng, H., Nathan, D. M., & Russell, S. J. (2012) · Diabetes Care · doi:10.2337/dc12-0070 · Cited by 109 · see citing works
Artificial Pancreas: Model Predictive Control Design from Clinical Experience
Toffanin, C., Messori, M., Palma, F. D., Nicolao, G. D., Cobelli, C., & Magni, L. (2013) · Journal of Diabetes Science and Technology · doi:10.1177/193229681300700607 · Cited by 106 · see citing works
Self-citation“Smart” Continuous Glucose Monitoring Sensors: On-Line Signal Processing Issues
Sparacino, G., Facchinetti, A., & Cobelli, C. (2010) · Sensors · doi:10.3390/s100706751 · Cited by 95 · see citing works
Self-citationCharacterisation of the iterative integral parameter identification method
Docherty, P., Chase, J., & David, T. (2012) · Medical and Biological Engineering and Computing · doi:10.1007/s11517-011-0851-y · Cited by 90 · see citing works
Bailey, T. S., Zisser, H., & Chang, A. (2009) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2009.0075 · Cited by 71 · see citing works
Joubert, M., & Reznik, Y. (2012) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2011.12.010 · Cited by 70 · see citing works
Analysis of Time Lags and Other Sources of Error of the DexCom SEVEN Continuous Glucose Monitor
Kamath, A., Mahalingam, A., & Brauker, J. H. (2009) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2009.0060 · Cited by 69 · see citing works
Glycemic Variability: Measurement and Utility in Clinical Medicine and Research—One Viewpoint
Rodbard, D. (2011) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2011.0104 · Cited by 54 · see citing works
Lane, J. E., Shivers, J. P., & Zisser, H. (2013) · Current Opinion in Endocrinology Diabetes and Obesity · doi:10.1097/med.0b013e32835edb9d · Cited by 50 · see citing works
Bamashmus, M. A., Gunaid, A. A., & Khandekar, R. B. (2009) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2007.0299 · Cited by 48 · see citing works
Zecchin, C., Facchinetti, A., Sparacino, G., & Cobelli, C. (2013) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2012.0208 · Cited by 47 · see citing works
Self-citationDexcom G4AP: An Advanced Continuous Glucose Monitor for the Artificial Pancreas
Garcia, A., Rack-Gomer, A. L., Bhavaraju, N. C., Hampapuram, H., Kamath, A., Peyser, T. A., Facchinetti, A., Zecchin, C., Sparacino, G., & Cobelli, C. (2013) · Journal of Diabetes Science and Technology · doi:10.1177/193229681300700604 · Cited by 47 · see citing works
Self-citationDaskalaki, E., Nørgaard, K., Züger, T., Prountzou, A., Diem, P., & Mougiakakou, S. G. (2013) · Journal of Diabetes Science and Technology · doi:10.1177/193229681300700314 · Cited by 46 · see citing works
Multi-objective blood glucose control for type 1 diabetes
Dua, P., Doyle, F. J., & Pistikopoulos, E. N. (2009) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-009-0453-0 · Cited by 39 · see citing works
A Dynamic Risk Measure from Continuous Glucose Monitoring Data
Guerra, S., Sparacino, G., Facchinetti, A., Schiavon, M., Man, C. D., & Cobelli, C. (2011) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2011.0006 · Cited by 37 · see citing works
Self-citationLunn, D. J., Wei, C., & Hovorka, R. (2011) · Statistics in Medicine · doi:10.1002/sim.4254 · Cited by 34 · see citing works
An Overview of Continuous Glucose Monitoring Systems
Cox, M. A. (2009) · Journal of Pediatric Health Care · doi:10.1016/j.pedhc.2009.06.002 · Cited by 25 · see citing works
Sanz, A. J. L., Rossetti, P., Ampudia‐Blasco, F. J., Vehı́, J., & Bondía, J. (2013) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2012.12.003 · Cited by 9 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
The UVA/Padova Type 1 Diabetes Simulator Goes From Single Meal to Single Day
Visentin, R., Campos-Náñez, E., Schiavon, M., Lv, D., Vettoretti, M., Breton, M., Kovatchev, B. P., Dalla Man, C., & Cobelli, C. (2018) · Journal of Diabetes Science and Technology · doi:10.1177/1932296818757747 · Cited by 280 · see citing works
Self-citationContinuous glucose monitoring sensors: past, present and future algorithmic challenges
Facchinetti, A. (2016) · Sensors · doi:10.3390/s16122093 · Cited by 176 · see citing works
Self-citationHeinemann, L., Schoemaker, M., Schmelzeisen-Redecker, G., Hinzmann, R., Kassab, A., Freckmann, G., Reiterer, F., & Del Re, L. (2020) · Journal of Diabetes Science and Technology · doi:10.1177/1932296819855670 · Cited by 170 · see citing works
Resalat, N., El Youssef, J., Tyler, N., Castle, J., & Jacobs, P. G. (2019) · PLOS ONE · doi:10.1371/journal.pone.0217301 · Cited by 78 · see citing works
Cappon, G., Vettoretti, M., Marturano, F., Facchinetti, A., & Sparacino, G. (2018) · Journal of Diabetes Science and Technology · doi:10.1177/1932296818759558 · Cited by 62 · see citing works
Self-citationMachine-Learning Based Model to Improve Insulin Bolus Calculation in Type 1 Diabetes Therapy
Noaro, G., Cappon, G., Vettoretti, M., Sparacino, G., Favero, S. D., & Facchinetti, A. (2021) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2020.3004031 · Cited by 57 · see citing works
Self-citationLimits to the Evaluation of the Accuracy of Continuous Glucose Monitoring Systems by Clinical Trials
Schrangl, P., Reiterer, F., Heinemann, L., Freckmann, G., & Del Re, L. (2018) · Biosensors · doi:10.3390/bios8020050 · Cited by 46 · see citing works
Camerlingo, N., Vettoretti, M., Del Favero, S., Cappon, G., Sparacino, G., & Facchinetti, A. (2019) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2019.0139 · Cited by 22 · see citing works
Self-citationSchiavon, M., Acciaroli, G., Vettoretti, M., Giaretta, A., & Visentin, R. (2018) · 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8512257 · Cited by 5 · see citing works
- Authors
- Facchinetti, A., Del Favero, S., Sparacino, G., & Cobelli, C.
- Affiliations
- University of Padua (IT)
- Corresponding author
- Cobelli, Claudio
- Venue
- Medical & Biological Engineering & Computing
- Published
- 2014-11-23
- Volume
- 53
- Issue
- 12
- Pages
- 1259-1269
- ISSN
- 0140-0118 (ISSN-L)
- 1741-0444
- OpenAlex
- W2054083908
- Semantic Scholar
- 7f7de2342e21e009f1409540233e27dd5133d5a9
- Cited by
- 80
- References
- 37
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 6 · 2023: 12 · 2022: 4 · 2021: 4 · 2020: 6 · 2019: 11 · 2018: 11 · 2017: 13 · 2016: 5 · 2015: 4
- FWCI
- 5.90
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Medical & Biological Engineering & Computing (publishedVersion)
- PubMed (publishedVersion)
- Padua Research Archive (University of Padova) (submittedVersion)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electrochemical sensors and biosensors
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Hyperglycemia and glycemic control in critically ill and hospitalized patients
- Keywords
- continuous glucose monitoring, sensor calibration errors, mean absolute relative difference, artificial pancreas, in silico testing, diabetes mellitus
- MeSH terms
- Adult, Algorithms, Blood Glucose, Calibration, Diabetes Mellitus, Female, Humans, Kinetics, Male, Middle Aged, Models, Theoretical, Blood Glucose Self-Monitoring, Equipment Failure Analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
