ArticleMedical & biological engineering & computing2015
Model of glucose sensor error components: identification and assessment for new Dexcom G4 generation devices.
Article in Medical & biological engineering & computing, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 79 citations in OpenAlex.
- Quantitative estimation of disposition index from postprandial glucose data across the spectrum of glucose tolerance.American journal of physiology. Endocrinology and metabolism · 2025Article
- Short- and Long-Term Effects on Glucose Control of Nonadherence to Insulin Therapy in People With Type 2 Diabetes AnJournal of diabetes science and technology · 2024Article
- Bayesian denoising algorithm dealing with colored, non-stationary noise in continuous glucose monitoring timeseries.Frontiers in bioengineering and biotechnology · 2023Article
- Titration of Long-Acting Insulin Using Continuous Glucose Monitoring and Smart Insulin Pens in Type 1 Diabetes: A Model-Based Carbohydrate-Free Approach.Frontiers in endocrinology · 2021Article
- Alterations in Glycemic Variability, Vascular Health, and Oxidative Stress following a 12-Week Aerobic Exercise Intervention-A Pilot Study.International journal of exercise science · 2021Article
- Accuracy of the Dexcom G6 Glucose Sensor during Aerobic, Resistance, and Interval Exercise in Adults with Type 1 Diabetes.Biosensors · 2020Article
- Benefits and Limitations of MARD as a Performance Parameter for Continuous Glucose Monitoring in the Interstitial Space.Journal of diabetes science and technology · 2020Article
- Development of an Error Model for a Factory-Calibrated Continuous Glucose Monitoring Sensor with 10-Day Lifetime.Sensors (Basel, Switzerland) · 2019Article
- Artificial Pancreas Systems and Physical Activity in Patients with Type 1 Diabetes: Challenges, Adopted Approaches, and Future Perspectives.Journal of diabetes science and technology · 2019Review
- Classification of Postprandial Glycemic Status with Application to Insulin Dosing in Type 1 Diabetes-An In Silico Proof-of-Concept.Sensors (Basel, Switzerland) · 2019Article
- A statistical virtual patient population for the glucoregulatory system in type 1 diabetes with integrated exercise model.PloS one · 2019Article
- Modeling of Diabetes and Its Clinical Impact.Journal of diabetes science and technology · 2018Review
- Limits to the Evaluation of the Accuracy of Continuous Glucose Monitoring Systems by Clinical Trials.Biosensors · 2018Article
- A Neural-Network-Based Approach to Personalize Insulin Bolus Calculation Using Continuous Glucose Monitoring.Journal of diabetes science and technology · 2018Article
- The UVA/Padova Type 1 Diabetes Simulator Goes From Single Meal to Single Day.Journal of diabetes science and technology · 2018Article
- Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them.Biomedical engineering online · 2018Review
- The challenges of achieving postprandial glucose control using closed-loop systems in patients with type 1 diabetes.Diabetes, obesity & metabolism · 2018Review
- Continuous Glucose Monitoring Measures Can Be Used for Glycemic Control in the ICU: An In-Silico Study.Journal of diabetes science and technology · 2018Article
- Comparison of Continuous Glucose Monitoring between Dexcom G4 Platinum and HD-XG Systems in Nonhuman Primates (Macaca Fascicularis).Scientific reports · 2017Article
- Continuous glucose monitoring in the ICU: clinical considerations and consensus.Critical care (London, England) · 2017Review
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.