Diabetes Research Institute, Mills-Peninsula Medical Center, San Mateo, CA, USA.
Florida State University College of Nursing, Tallahassee, FL, USA.
J Diabetes Sci Technol. 2023 Sep;17(5):1226-1242. doi: 10.1177/19322968221085273. Epub 2022 Mar 29.
A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data.
We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation.
The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals.
The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.
连续血糖监测(CGM)轨迹的血糖质量综合指标对于辅助基本的 CGM 数据临床解读可能是有用的。
我们收集了 225 名接受胰岛素治疗的糖尿病成人 14 天的 CGM 轨迹数据。采用平衡不完全区组设计,330 名对 CGM 分析和解释具有丰富经验的临床医生对血糖质量从好到差对 CGM 轨迹进行排名。我们使用主成分分析和多元回归来建立一个基于动态血糖图谱中七个标准指标的模型,根据该模型预测临床医生的排名:低血糖和严重低血糖;高血糖和严重高血糖;血糖控制达标时间;平均血糖;和变异系数。
分析表明,临床医生的排名取决于两个因素,一个与低血糖有关,对非常低血糖的权重比对低血糖的权重更大,另一个与高血糖有关,对非常高血糖的权重比对高血糖的权重更大。这两个因素应该单独计算和显示,但也可以合并为一个单独的血糖风险指数(GRI),与血糖质量的整体临床医生排名密切相关(r = 0.95)。GRI 可以在 GRI 网格上以图形方式显示,低血糖分量在水平轴上,高血糖分量在垂直轴上。对角线将图形分为五个区域(五分位数),对应于整体血糖质量从最佳(0 到 20 百分位)到最差(81 到 100 百分位)。GRI 网格使用户能够跟踪个体随时间的连续变化,并比较个体组。
GRI 是血糖质量的一个单一数字总结。它的低血糖和高血糖分量提供了可操作的分数和图形显示(GRI 网格),临床医生和研究人员可以使用它来确定处方和研究治疗的血糖效应。