Quantification of the Variability of Continuous Glucose Monitoring Data

Aboufadel, Edward; Castellano, Robert; Olson, Derek

doi:10.3390/a4010016

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…Computerized treatment algorithms using CGM data have been developed in an effort to use the abundant information in a clinically meaningful manner. 15,[28][29][30][31][32] In a 2-month study of 22 insulin-dependent subjects using daily capture of selfmonitoring of blood glucose and a predictive glucose model, Albisser et reported a reduction in rates of hypoglycemia by 9-fold and insulin therapy by −9 U/day. Computerized glucose programs have also been used for educational purposes to allow patients to gain insight into the effect of insulin dosage adjustments, diet changes, and exercise on glucose levels.…”

Section: Discussionmentioning

confidence: 99%

Development of the Likelihood of Low Glucose (LLG) Algorithm for Evaluating Risk of Hypoglycemia

Dunn

Hayter

Doniger

et al. 2014

J Diabetes Sci Technol

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The objective was to develop an analysis methodology for generating diabetes therapy decision guidance using continuous glucose (CG) data. The novel Likelihood of Low Glucose (LLG) methodology, which exploits the relationship between glucose median, glucose variability, and hypoglycemia risk, is mathematically based and can be implemented in computer software. Using JDRF Continuous Glucose Monitoring Clinical Trial data, CG values for all participants were divided into 4-week periods starting at the first available sensor reading. The safety and sensitivity performance regarding hypoglycemia guidance "stoplights" were compared between the LLG method and one based on 10th percentile (P10) values. Examining 13 932 hypoglycemia guidance outputs, the safety performance of the LLG method ranged from 0.5% to 5.4% incorrect "green" indicators, compared with 0.9% to 6.0% for P10 value of 110 mg/dL. Guidance with lower P10 values yielded higher rates of incorrect indicators, such as 11.7% to 38% at 80 mg/dL. When evaluated only for periods of higher glucose (median above 155 mg/dL), the safety performance of the LLG method was superior to the P10 method. Sensitivity performance of correct "red" indicators of the LLG method had an in sample rate of 88.3% and an out of sample rate of 59.6%, comparable with the P10 method up to about 80 mg/dL. To aid in therapeutic decision making, we developed an algorithm-supported report that graphically highlights low glucose risk and increased variability. When tested with clinical data, the proposed method demonstrated equivalent or superior safety and sensitivity performance.

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Section: Discussionmentioning

confidence: 99%

Development of the Likelihood of Low Glucose (LLG) Algorithm for Evaluating Risk of Hypoglycemia

Dunn

Hayter

Doniger

et al. 2014

J Diabetes Sci Technol

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“…The growth of diabetic patients increased the use of continuous glucose monitoring devices (CGM), which are becoming the new method of continuous monitoring. They provide real-time information about the glucose level, which is updated every five minutes [6].…”

Section: Introductionmentioning

confidence: 99%

Glucose Data Classification for Diabetic Patient Monitoring

Rghioui¹,

Lloret

Parra

et al. 2019

Applied Sciences

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Living longer and healthier is the wish of all patients. Therefore, to design effective solutions for this objective, the concept of Big Data in the health field can be integrated. Our work proposes a patient monitoring system based on Internet of Things (IoT) and a diagnostic prediction tool for diabetic patients. This system provides real-time blood glucose readings and information on blood glucose levels. It monitors blood glucose levels at regular intervals. The proposed system aims to prevent high blood sugar and significant glucose fluctuations. The system provides a precise result. The collected and stored data will be classified by using several classification algorithms to predict glucose levels in diabetic patients. The main advantage of this system is that the blood glucose level is reported instantly; it can be lowered or increased.

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