Comparison of the Clinical Information Provided by the FreeStyle Navigator Continuous Interstitial Glucose Monitor Versus Traditional Blood Glucose Readings

McGarraugh, Geoffrey; Clarke, William L.; Kovatchev, Boris

doi:10.1089/dia.2009.0136

Cited by 20 publications

(16 citation statements)

References 6 publications

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“…[4][5][6] The field is new and immature. The accuracy, precision, stability, reliability, and availability of CGM are improving 7,8 but not yet comparable to that of selfmonitoring of blood glucose (SMBG) and laboratory measurements. Methods for smoothing and preprocessing of signals from the glucose sensor may affect results obtained for measures of glycemic variability.…”

Section: Measurement Of Glycemic Variability Is Challengingmentioning

confidence: 99%

The Challenges of Measuring Glycemic Variability

Rodbard¹

2012

J Diabetes Sci Technol

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Abbreviations: (CGM) continuous glucose monitoring, (CV) coefficient of variation, (GRADE) glycemic risk assessment diabetes equation, (MAD) mean absolute difference, (MAG) mean absolute glucose rate of change, (MAGE) mean amplitude of glucose excursion, (MODD) mean of daily differences, (SD) standard deviation, (SEM) standard error of the mean, (SMBG) self-monitoring of blood glucose AbstractThis commentary reviews several of the challenges encountered when attempting to quantify glycemic variability and correlate it with risk of diabetes complications. These challenges include (1) immaturity of the field, including problems of data accuracy, precision, reliability, cost, and availability; (2) larger relative error in the estimates of glycemic variability than in the estimates of the mean glucose; (3) high correlation between glycemic variability and mean glucose level; (4) multiplicity of measures; (5) correlation of the multiple measures; (6) duplication or reinvention of methods; (7) confusion of measures of glycemic variability with measures of quality of glycemic control; (8) the problem of multiple comparisons when assessing relationships among multiple measures of variability and multiple clinical end points; and (9) differing needs for routine clinical practice and clinical research applications.

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Section: Measurement Of Glycemic Variability Is Challengingmentioning

confidence: 99%

The Challenges of Measuring Glycemic Variability

Rodbard¹

2012

J Diabetes Sci Technol

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“…The CG-EGA was used to compare the clinical utility of the information provided by CGM to the information provided by episodic self-monitoring (self-monitoring of BG [SMBG]), concluding that ''While SMBG produces more accurate instantaneous glucose values than CGM.the additional information about the direction and rate of glucose change increases the ability to make correct clinical decisions when compared to episodic SMBG tests.'' 30 In other words, judging CGM accuracy with the same metrics that are used for accuracy assessment of episodic SMBG underestimates the information carried by CGM data.…”

Section: Introductionmentioning

confidence: 99%

Assessing Sensor Accuracy for Non-Adjunct Use of Continuous Glucose Monitoring

Kovatchev

Patek

Ortiz

et al. 2015

Diabetes Technology & Therapeutics

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184

154

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Background: The level of continuous glucose monitoring (CGM) accuracy needed for insulin dosing using sensor values (i.e., the level of accuracy permitting non-adjunct CGM use) is a topic of ongoing debate. Assessment of this level in clinical experiments is virtually impossible because the magnitude of CGM errors cannot be manipulated and related prospectively to clinical outcomes. Materials and Methods: A combination of archival data (parallel CGM, insulin pump, self-monitoring of blood glucose [SMBG] records, and meals for 56 pump users with type 1 diabetes) and in silico experiments was used to ''replay'' real-life treatment scenarios and relate sensor error to glycemic outcomes. Nominal blood glucose (BG) traces were extracted using a mathematical model, yielding 2,082 BG segments each initiated by insulin bolus and confirmed by SMBG. These segments were replayed at seven sensor accuracy levels (mean absolute relative differences [MARDs] of 3-22%) testing six scenarios: insulin dosing using sensor values, threshold, and predictive alarms, each without or with considering CGM trend arrows. Results: In all six scenarios, the occurrence of hypoglycemia (frequency of BG levels £ 50 mg/dL and BG levels £ 39 mg/dL) increased with sensor error, displaying an abrupt slope change at MARD = 10%. Similarly, hyperglycemia (frequency of BG levels ‡ 250 mg/dL and BG levels ‡ 400 mg/dL) increased and displayed an abrupt slope change at MARD = 10%. When added to insulin dosing decisions, information from CGM trend arrows, threshold, and predictive alarms resulted in improvement in average glycemia by 1.86, 8.17, and 8.88 mg/dL, respectively. Conclusions: Using CGM for insulin dosing decisions is feasible below a certain level of sensor error, estimated in silico at MARD = 10%. In our experiments, further accuracy improvement did not contribute substantively to better glycemic outcomes.

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“…Most premarket analyses of CGM systems have been done by means of Clarke error grid analysis (CEGA) and assessment of correlation between CGM glucose values and reference blood glucose values collected during nonstandardized and/or nonspecified conditions. [5][6][7] The primary aim of this study was to assess the accuracy of the three widely used needle-type CGM systems in a way that includes both a highly standardized assessment within a clinical research center (CRC) as well as real-life usage at home. In addition, we assessed CGM accuracy within manufacturer-specified lifetime (MSL) and also beyond as it is possible to ''reactivate'' the CGM systems by tricking the CGM receiver into activating the old sensor as if a new sensor has been placed.…”

Section: Introductionmentioning

confidence: 99%

Accuracy and Reliability of Continuous Glucose Monitoring Systems: A Head-to-Head Comparison

Luijf

Mader

Doll

et al. 2013

Diabetes Technology & Therapeutics

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Objective: This study assessed the accuracy and reliability of three continuous glucose monitoring (CGM) systems. Research Design and Methods: We studied the Animas Ò (West Chester, PA) VibeÔ with Dexcom Ò (San Diego, CA) G4Ô version A sensor (G4A), the Abbott Diabetes Care (Alameda, CA) Freestyle Ò Navigator I (NAV), and the Medtronic (Northridge, CA) Paradigm Ò with EnliteÔ sensor (ENL) in 20 patients with type 1 diabetes mellitus. All systems were investigated both in a clinical research center (CRC) and at home. In the CRC, patients received a meal with a delayed and increased insulin dose to induce a postprandial glucose peak and nadir. Hereafter, randomization determined which two of the three systems would be worn at home until the end of functioning, attempting use beyond manufacturer-specified lifetime. Patients performed at least five reference finger sticks per day. An analysis of variance was performed on all data points ‡ 15 min apart. Results: Overall average mean absolute relative difference (MARD) (SD) measured at the CRC was 16.5% (14.3%) for NAV and 16.4% (15.6%) for ENL, outperforming G4A at 20.5% (18.2%) (P < 0.001). Overall MARD when assessed at home was 14.5% (16.7%) for NAV and 16.5 (18.8%) for G4A, outperforming ENL at 18.9% (23.6%) (P = 0.006). Median time until end of functioning was similar: 10.0 (1.0) days for G4A, 8.0 (3.5) days for NAV, and 8.0 (1.5) days for ENL (P = 0.119). Conclusions: In the CRC, G4A was less accurate than NAV and ENL sensors, which seemed comparable. However, at home, ENL was less accurate than NAV and G4A. Moreover, CGM systems often show sufficient accuracy to be used beyond manufacturer-specified lifetime.

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Comparison of the Clinical Information Provided by the FreeStyle Navigator Continuous Interstitial Glucose Monitor Versus Traditional Blood Glucose Readings

Cited by 20 publications

References 6 publications

The Challenges of Measuring Glycemic Variability

The Challenges of Measuring Glycemic Variability

Assessing Sensor Accuracy for Non-Adjunct Use of Continuous Glucose Monitoring

Accuracy and Reliability of Continuous Glucose Monitoring Systems: A Head-to-Head Comparison

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