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

Abstract

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.

Individuals with Type 1 and Type 2 Diabetes Mellitus Trade Increased Hyperglycemia for Decreased Hypoglycemia When Glycemic Variability is not Improved

“…The gray contours illustrate how hypoglycemia risk varies with hyperglycemia risk if variability is held constant. The hypoglycemia and hyperglycemia metrics chosen here align with those reported by [ 4 ]. A similar plot can be generated with different measures for hypoglycemia risk, hyperglycemia risk, and glycemic variability without changing the underlying relationship.…”

“…While several measures of variability have been reported [ 7 ], variability here is defined as the difference between the median and 10th percentile of glucose values. A gamma distribution is obtained as per [ 4 ] for a range of glucose median and variability values. Dunn et al used data from the JDRF-CGM trial to generate the gamma distribution parameters for various combinations of median and variability values [ 8 ].…”

“…The role of glycemic variability in achieving better glucose control without having to trade-off one glycemic risk for another is an important topic. Dunn et al [ 4 ] developed a clinical report that presents all three metrics: hypoglycemia risk, hyperglycemia risk, and glycemic variability. The purpose of the report is to underscore the importance of addressing variability when both hypoglycemia and hyperglycemia are high.…”

“… Hyperglycemia risk: estimated HbA1c (eA1c). Glycemic variability: difference between the median and the 10th percentile [ 4 ]. …”

Role of free style Libre-Flash Glucose Monitoring: Glycemic control of Type-1 Diabetes

“…to achieve the median glucose goal without increasing the likelihood of low glucose). A stoplight chart 13 highlights every time interval as regards the three aforementioned information into green if normal, yellow if moderately abnormal and red if high abnormality detected (Fig. 1).…”