Are Risk Indices Derived From CGM Interchangeable With SMBG-Based Indices?
Are Risk Indices Derived From CGM Interchangeable With SMBG-Based Indices?
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DOI：
10.1177/1932296815599177
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发表时间：
2015-08-14
影响因子：
5
通讯作者：
Breton, Marc D
中科院分区：
文献类型：
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作者：
Fabris, Chiara;Patek, Stephen D;Breton, Marc D
背景：多年来，人们一直通过在稀疏的自我监测血糖（SMBG）读数上计算众所周知的低血糖指数（LBGI）和高血糖指数（HBGI）来评估低血糖和高血糖的风险。这些指标已被证明可以预测未来的血糖事件，并且已经定义了用于对患者状态进行分类的临床相关截止值，但它们在连续血糖监测（CGM）曲线中的应用尚未得到验证。本文的目的是探讨基于 CGM 和基于 SMBG 的 LBGI/HBGI 之间的关系，并为在 CGM 时间序列上计算这些指数时提供遵循的指南。 方法：使用 SMBG 和 CGM 系统对 28 名 1 型糖尿病 (T1DM) 受试者的日常生活条件进行长达 4 周的监测。考虑线性和非线性模型来描述基于 SMBG 和 CGM 数据评估的风险指数之间的关系。 结果：从 CGM 获得的 LBGI 值与基于 SMBG 的值并不紧密匹配，尤其是在低风险范围内明显低估，并且线性变换最适合将基于 CGM 的 LBGI 与基于 SMBG 的 LBGI 匹配。对于 HBGI，具有单一斜率和无截距的线性模型是可靠的，这表明无需校正即可从 CGM 时间序列计算该指数。结论：已提出适应 CGM 信号特征的 LBGI 和 HBGI 的替代版本，能够扩展为 SMBG 数据获得的结果，并使用先前定义的临床相关截止值来快速对患者的血糖状况进行分类。 病人。
BACKGROUND: The risk of hypo- and hyperglycemia has been assessed for years by computing the well-known low blood glucose index (LBGI) and high blood glucose index (HBGI) on sparse self-monitoring blood glucose (SMBG) readings. These metrics have been shown to be predictive of future glycemic events and clinically relevant cutoff values to classify the state of a patient have been defined, but their application to continuous glucose monitoring (CGM) profiles has not been validated yet. The aim of this article is to explore the relationship between CGM-based and SMBG-based LBGI/HBGI, and provide a guideline to follow when these indices are computed on CGM time series.METHODS: Twenty-eight subjects with type 1 diabetes mellitus (T1DM) were monitored in daily-life conditions for up to 4 weeks with both SMBG and CGM systems. Linear and nonlinear models were considered to describe the relationship between risk indices evaluated on SMBG and CGM data.RESULTS: LBGI values obtained from CGM did not match closely SMBG-based values, with clear underestimation especially in the low risk range, and a linear transformation performed best to match CGM-based LBGI to SMBG-based LBGI. For HBGI, a linear model with unitary slope and no intercept was reliable, suggesting that no correction is needed to compute this index from CGM time series.CONCLUSIONS: Alternate versions of LBGI and HBGI adapted to the characteristics of CGM signals have been proposed that enable extending results obtained for SMBG data and using clinically relevant cutoff values previously defined to promptly classify the glycemic condition of a patient.