Application of Glycemic Variability Percentage: Implications for Continuous Glucose Monitor Utilization and Analysis of Artificial Pancreas Data

García, Arturo Muñiz; Balo, Andrew; Buckingham, Bruce A.; Hirsch, Irl B.; Peyser, T. A.

doi:10.1089/dia.2017.0188

Cited by 6 publications

(12 citation statements)

References 15 publications

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“…The studies using Multistate Entropy [22][23][24][25][26] appear to be consistent with the results of Strange et al, 15 Fico et al, 21 Hirsch, 58 Peyser et al, 19 and Garcia et al 20 There is a need for additional studies to confirm and expand these findings.…”

Section: Time Series Analysis Of Glucose Monitoring Datasupporting

confidence: 78%

“…Dozens of metrics for variability have been proposed. [1][2][3][4][5][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] Table 1 displays some of the more important and frequently used metrics.…”

Section: Multiple Metrics For Glycemic Variabilitymentioning

confidence: 99%

“…This is qualitatively consistent with the reports by Strange and Miller. 15 Garcia et al 20 showed differences in GVP for people with T1D using a bihormonal (''bionic'') closed-loop system compared with subjects using open loop control: the closed loop resulted in a decrease in SD and MAGE but an increase in GVP. Presumably, this discrepancy is due to the presence of small, high-frequency fluctuations or oscillations that contribute to GVP, but not to the overall SD or MAGE and other measures of ''macro'' variability.…”

Section: Time Series Analysis Of Glucose Monitoring Datamentioning

confidence: 99%

“…Cobelli implied that development of methods such as GVP to characterize GV was unnecessary 59 and restated his preference for the methods such as a frequency distribution for Rate of Change of glucose (DG/Dt), the Poincaré plot, and Variability Grid Analysis. 5,59 Ironically, GVP 19,20,58 was able to detect the several kinds of rapid oscillations as identified by traditional methods of time series analysis, 14,15,21 and was used successfully to make novel observations of clinical and pathophysiological significance. 19,20,58 Multiscale entropy and complexity analysis.…”

Section: Time Series Analysis Of Glucose Monitoring Datamentioning

confidence: 99%

“…5,59 Ironically, GVP 19,20,58 was able to detect the several kinds of rapid oscillations as identified by traditional methods of time series analysis, 14,15,21 and was used successfully to make novel observations of clinical and pathophysiological significance. 19,20,58 Multiscale entropy and complexity analysis. Chen et al and Costa et al [22][23][24] used different measures of ''complexity'' and multiscale entropy (MSE) to demonstrate loss of complexity of glucose time series in people with progressively longer duration of diabetes.…”

Section: Time Series Analysis Of Glucose Monitoring Datamentioning

confidence: 99%

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Glucose Variability: A Review of Clinical Applications and Research Developments

Rodbard¹

2018

Diabetes Technology & Therapeutics

120

113

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Glycemic variability (GV) is a major consideration when evaluating quality of glycemic control. GV increases progressively from prediabetes through advanced T2D and is still higher in T1D. GV is correlated with risk of hypoglycemia. The most popular metrics for GV are the %Coefficient of Variation (%CV) and standard deviation (SD). The %CV is correlated with risk of hypoglycemia. Graphical display of glucose by date, time of day, and day of the week, and display of simplified glucose distributions showing % of time in several ranges, provide clinically useful indicators of GV. SD is highly correlated with most other measures of GV, including interquartile range, mean amplitude of glycemic excursion, mean of daily differences, and average daily risk range. Some metrics are sensitive to the frequency, periodicity, and complexity of glycemic fluctuations, including Fourier analysis, periodograms, frequency spectrum, multiscale entropy (MSE), and Glucose Variability Percentage (GVP). Fourier analysis indicates progressive changes from normal subjects to children and adults with T1D, and from prediabetes to T2D. The GVP identifies novel characteristics for children, adolescents, and adults with type 1 diabetes and for adults with type 2. GVP also demonstrated small rapid glycemic fluctuations in people with T1D when using a dual-hormone closed-loop control. MSE demonstrated systematic changes from normal subjects to people with T2D at various stages of duration, intensity of therapy, and quality of glycemic control. We describe new metrics to characterize postprandial excursions, day-to-day stability of glucose patterns, and systematic changes of patterns by day of the week. Metrics for GV should be interpreted in terms of percentiles and z-scores relative to identified reference populations. There is a need for large accessible databases for reference populations to provide a basis for automated interpretation of GV and other features of continuous glucose monitoring records.

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Section: Time Series Analysis Of Glucose Monitoring Datasupporting

confidence: 78%

Section: Multiple Metrics For Glycemic Variabilitymentioning

confidence: 99%

Section: Time Series Analysis Of Glucose Monitoring Datamentioning

confidence: 99%

Section: Time Series Analysis Of Glucose Monitoring Datamentioning

confidence: 99%

Section: Time Series Analysis Of Glucose Monitoring Datamentioning

confidence: 99%

See 3 more Smart Citations

Glucose Variability: A Review of Clinical Applications and Research Developments

Rodbard¹

2018

Diabetes Technology & Therapeutics

120

113

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Artificial Pancreas as an Effective and Safe Alternative in Patients with Type 1 Diabetes Mellitus: A Systematic Review and Meta-Analysis

et al. 2018

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IntroductionInsulin injection is the main treatment in patients with type 1 diabetes mellitus (T1DM). Even though continuous glucose monitoring has significantly improved the conditions of these patients, limitations still exist. To further enhance glucose control in patients with T1DM, an artificial pancreas has been developed. We aimed to systematically compare artificial pancreas with its control group during a 24-h basis in patients with T1DM.MethodsElectronic databases were carefully searched for English publications comparing artificial pancreas with its control group. Overall daytime and nighttime glucose parameters were considered as the endpoints. Data were evaluated by means of weighted mean differences (WMDs) and 95% confidence intervals (CIs) generated by RevMan 5.3 software.ResultsA total number of 354 patients were included. Artificial pancreas significantly maintained a better mean concentration of glucose (WMD − 1.03, 95% CI − 1.32 to − 0.75; P = 0.00001). Time spent in the hypoglycemic phase was also significantly lower (WMD − 1.23, 95% CI − 1.56 to − 0.91; P = 0.00001). Daily insulin requirement also significantly favored artificial pancreas (WMD − 3.43, 95% CI − 4.27 to − 2.59; P = 0.00001). Time spent outside the euglycemic phase and hyperglycemia phase (glucose > 10.0 mmol/L) also significantly favored artificial pancreas. Also, the numbers of hypoglycemic events were not significantly different.ConclusionArtificial pancreas might be considered an effective and safe alternative to be used during a 24-h basis in patients with T1DM.

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Glycemic Variability Percentage: A Novel Method for Assessing Glycemic Variability from Continuous Glucose Monitor Data

Peyser

Balo

Buckingham

et al. 2018

Diabetes Technology & Therapeutics

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Background: High levels of glycemic variability are still observed in most patients with diabetes with severe insulin deficiency. Glycemic variability may be an important risk factor for acute and chronic complications. Despite its clinical importance, there is no consensus on the optimum method for characterizing glycemic variability.Method: We developed a simple new metric, the glycemic variability percentage (GVP), to assess glycemic variability by analyzing the length of the continuous glucose monitoring (CGM) temporal trace normalized to the duration under evaluation. The GVP is similar to other recently proposed glycemic variability metrics, the distance traveled, and the mean absolute glucose (MAG) change. We compared results from distance traveled, MAG, GVP, standard deviation (SD), and coefficient of variation (CV) applied to simulated CGM traces accentuating the difference between amplitude and frequency of oscillations. The GVP metric was also applied to data from clinical studies for the Dexcom G4 Platinum CGM in subjects without diabetes, with type 2 diabetes, and with type 1 diabetes (adults, adolescents, and children).Results: In contrast to other metrics, such as CV and SD, the distance traveled, MAG, and GVP all captured both the amplitude and frequency of glucose oscillations. The GVP metric was also able to differentiate between diabetic and nondiabetic subjects and between subjects with diabetes with low, moderate, and high glycemic variability based on interquartile analysis.Conclusion: A new metric for the assessment of glycemic variability has been shown to capture glycemic variability due to fluctuations in both the amplitude and frequency of glucose given by CGM data.

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Application of Glycemic Variability Percentage: Implications for Continuous Glucose Monitor Utilization and Analysis of Artificial Pancreas Data

Cited by 6 publications

References 15 publications

Glucose Variability: A Review of Clinical Applications and Research Developments

Glucose Variability: A Review of Clinical Applications and Research Developments

Artificial Pancreas as an Effective and Safe Alternative in Patients with Type 1 Diabetes Mellitus: A Systematic Review and Meta-Analysis

Glycemic Variability Percentage: A Novel Method for Assessing Glycemic Variability from Continuous Glucose Monitor Data

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