Performance comparison of the medtronic sof-sensor and enlite glucose sensors in inpatient studies of individuals with type 1 diabetes.

OBJECTIVE Knowledge of the accuracy of continuous glucose monitoring (CGM) devices is important for its use as a management tool for individuals with diabetes and for its use to assess outcomes in clinical studies. Using data from several inpatient studies, we compared the accuracy of two sensors, the Medtronic Enlite™ using MiniMed Paradigm(®) Veo™ calibration and the Sof-Sensor(®) glucose sensor using Guardian(®) REAL-Time CGM calibration (all from Medtronic Diabetes, Northridge, CA). SUBJECTS AND METHODS Nocturnal data were analyzed from eight inpatient studies in which both CGM and reference glucose measurements were available. The analyses included 1,666 CGM-reference paired glucose values for the Enlite in 54 participants over 69 nights and 3,627 paired values for the Sof-Sensor in 66 participants over 91 nights. RESULTS The Enlite sensor tended to report glucose levels lower than the reference over the entire range of glucose values, whereas the Sof-Sensor values tended to be higher than reference values in the hypoglycemic range and lower than reference values in the hyperglycemic range. The overall median sensor-reference difference was -15 mg/dL for the Enlite and -1 mg/dL for the Sof-Sensor (P<0.001). The median relative absolute difference was 15% for the Enlite versus 12% for the Sof-Sensor (P=0.06); 66% of Enlite values and 73% of Sof-Sensor values met International Organization for Standardization criteria. CONCLUSIONS We found that the Enlite tended to be biased low over the entire glucose range, whereas the Sof-Sensor showed the more typical sensor pattern of being biased high in the hypoglycemic range and biased low in the hyperglycemic range.

[1]  F. El-Khatib,et al.  Blood Glucose Control in Type 1 Diabetes With a Bihormonal Bionic Endocrine Pancreas , 2012, Diabetes Care.

[2]  D. B. Keenan,et al.  Accuracy of a New Real-Time Continuous Glucose Monitoring Algorithm , 2010, Journal of diabetes science and technology.

[3]  Darrell M. Wilson,et al.  Inpatient Studies of a Kalman-Filter-Based Predictive Pump Shutoff Algorithm , 2012, Journal of diabetes science and technology.

[4]  R. Beck,et al.  Use of continuous glucose monitoring as an outcome measure in clinical trials. , 2012, Diabetes technology & therapeutics.

[5]  Howard Zisser,et al.  Improved quality of glycemic control and reduced glycemic variability with use of continuous glucose monitoring. , 2009, Diabetes technology & therapeutics.

[6]  Howard Zisser,et al.  Accuracy of the Enlite 6-day glucose sensor with guardian and Veo calibration algorithms. , 2012, Diabetes technology & therapeutics.

[7]  Ahmad Haidar,et al.  Assessing performance of closed-loop insulin delivery systems by continuous glucose monitoring: drawbacks and way forward. , 2013, Diabetes technology & therapeutics.

[8]  Robert G. Sutherlin,et al.  A Bihormonal Closed-Loop Artificial Pancreas for Type 1 Diabetes , 2010, Science Translational Medicine.

[9]  G. Steil,et al.  Use of Subcutaneous Interstitial Fluid Glucose to Estimate Blood Glucose: Revisiting Delay and Sensor Offset , 2010, Journal of diabetes science and technology.

[10]  J. Mastrototaro,et al.  The accuracy and efficacy of real-time continuous glucose monitoring sensor in patients with type 1 diabetes. , 2008, Diabetes technology & therapeutics.

[11]  Michael O'Grady,et al.  Continuous glucose monitoring and intensive treatment of type 1 diabetes. , 2008, The New England journal of medicine.

[12]  Janet M. Allen,et al.  Manual closed-loop insulin delivery in children and adolescents with type 1 diabetes: a phase 2 randomised crossover trial , 2010, The Lancet.

[13]  Darrell M. Wilson,et al.  Inpatient studies of a Kalman-filter-based predictive pump shutoff algorithm , 2012 .