Alarm Characterization for a Continuous Glucose Monitor That Replaces Traditional Blood Glucose Monitoring

McGarraugh, Geoffrey

doi:10.1177/193229681000400107

Cited by 11 publications

(3 citation statements)

References 4 publications

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“…Because glucose monitor values within ±20% of reference are generally viewed as clinically accurate, 6,8 detection of glucose levels 20% higher than the alarm setting and false alarms 20% lower than the alarm setting were also reported. 9 The time window for hyperglycemic detection was also 30 min from the time reference glucose rose above the alarm setting.…”

Section: Analysis Methodsmentioning

confidence: 99%

FreeStyle Navigator Continuous Glucose Monitoring System with TRUstart Algorithm, a 1-Hour Warm-up Time

McGarraugh

Brazg

Weinstein

2011

J Diabetes Sci Technol

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Section: Analysis Methodsmentioning

confidence: 99%

FreeStyle Navigator Continuous Glucose Monitoring System with TRUstart Algorithm, a 1-Hour Warm-up Time

McGarraugh

Brazg

Weinstein

2011

J Diabetes Sci Technol

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“…Another area where trend accuracy is particularly important is hypoglycemia alarms, which often inherently use trends to predict the onset of hypoglycemia. 7,[13][14][15][16] In this case, poor trend accuracy can result in a high rate of false alarms, or worse, missed hypoglycemic events.…”

Section: Introductionmentioning

confidence: 99%

Continuous Glucose Monitoring and Trend Accuracy

Signal

Gottlieb

Compte

et al. 2014

J Diabetes Sci Technol

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Continuous glucose monitoring (CGM) devices are being increasingly used to monitor glycemia in people with diabetes. One advantage with CGM is the ability to monitor the trend of sensor glucose (SG) over time. However, there are few metrics available for assessing the trend accuracy of CGM devices. The aim of this study was to develop an easy to interpret tool for assessing trend accuracy of CGM data. SG data from CGM were compared to hourly blood glucose (BG) measurements and trend accuracy was quantified using the dot product. Trend accuracy results are displayed on the Trend Compass, which depicts trend accuracy as a function of BG. A trend performance table and Trend Index (TI) metric are also proposed. The Trend Compass was tested using simulated CGM data with varying levels of error and variability, as well as real clinical CGM data. The results show that the Trend Compass is an effective tool for differentiating good trend accuracy from poor trend accuracy, independent of glycemic variability. Furthermore, the real clinical data show that the Trend Compass assesses trend accuracy independent of point bias error. Finally, the importance of assessing trend accuracy as a function of BG level is highlighted in a case example of low and falling BG data, with corresponding rising SG data. This study developed a simple to use tool for quantifying trend accuracy. The resulting trend accuracy is easily interpreted on the Trend Compass plot, and if required, performance table and TI metric.

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“…Because the current CGMs are somewhat noisy (mean absolute deviation of 2.6-22.6 mg/dL [0.14-1.26 mmol/L] below 70 mg/dL [3.9 mmol/L]), 14 false-positive alarms may be frequent and can lead to alarm fatigue with audible alarms. 15 This method of action requires an algorithm tuned for a short warning time with a low false-positive rate.…”

mentioning

confidence: 99%

Clinically Relevant Hypoglycemia Prediction Metrics for Event Mitigation

Harvey

Dassau²,

Zisser³

et al. 2012

Diabetes Technology & Therapeutics

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Background: The purpose of this study was to develop a method to compare hypoglycemia prediction algorithms and choose parameter settings for different applications, such as triggering insulin pump suspension or alerting for rescue carbohydrate treatment. Materials and Methods: Hypoglycemia prediction algorithms with different parameter settings were implemented on an ambulatory dataset containing 490 days from 30 subjects with type 1 diabetes mellitus using the DexcomÔ (San Diego, CA) SEVENÔ continuous glucose monitoring system. The performance was evaluated using a proposed set of metrics representing the true-positive ratio, false-positive rate, and distribution of warning times. A prospective, in silico study was performed to show the effect of using different parameter settings to prevent or rescue from hypoglycemia. Results: The retrospective study results suggest the parameter settings for different methods of hypoglycemia mitigation. When rescue carbohydrates are used, a high true-positive ratio, a minimal false-positive rate, and alarms with short warning time are desired. These objectives were met with a 30-min prediction horizon and two successive flags required to alarm: 78% of events were detected with 3.0 false alarms/day and 66% probability of alarms occurring within 30 min of the event. This parameter setting selection was confirmed in silico: treating with rescue carbohydrates reduced the duration of hypoglycemia from 14.9% to 0.5%. However, for a different method, such as pump suspension, this parameter setting only reduced hypoglycemia to 8.7%, as can be expected by the low probability of alarming more than 30 min ahead. Conclusions: The proposed metrics allow direct comparison of hypoglycemia prediction algorithms and selection of parameter settings for different types of hypoglycemia mitigation, as shown in the prospective in silico study in which hypoglycemia was alerted or treated with rescue carbohydrates.

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Alarm Characterization for a Continuous Glucose Monitor That Replaces Traditional Blood Glucose Monitoring

Abstract: Background:

Cited by 11 publications

References 4 publications

FreeStyle Navigator Continuous Glucose Monitoring System with TRUstart Algorithm, a 1-Hour Warm-up Time

FreeStyle Navigator Continuous Glucose Monitoring System with TRUstart Algorithm, a 1-Hour Warm-up Time

Continuous Glucose Monitoring and Trend Accuracy

Clinically Relevant Hypoglycemia Prediction Metrics for Event Mitigation

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