Statistical Tools to Analyze Continuous Glucose Monitor Data
Statistical Tools to Analyze Continuous Glucose Monitor Data
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DOI：
10.1089/dia.2008.0138
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发表时间：
2009-04-01
影响因子：
5.4
通讯作者：
Kovatchev, Boris
中科院分区：
文献类型：
--
作者：
Clarke, William;Kovatchev, Boris
动态葡萄糖监测仪（CGM）生成的数据流既复杂又庞大。这些数据的分析需要了解这项技术所涉及的物理，生物化学和数学特性。本文介绍了几种与CGM数据分析相关的方法，同时考虑到连续监测数据流的具体情况。这些方法包括：（1）评估CGM的数值和临床准确性。我们区分两种类型的准确性度量-数值和临床每个有两个亚型测量点和趋势的准确性。趋势准确性的增加，e。例如，在一个实施例中，CGM反映血糖（BG）变化的速率和方向的能力是CGM所独有的，因为这些新设备不仅能够间歇地捕获BG，而且能够及时地捕获BG。(2)解释CGM数据的统计方法。认识到大多数分析的基本单位是个体的葡萄糖迹线的重要性，即，每个人的一系列带时间戳的血糖数据。我们讨论了风险评估的使用，以及通过葡萄糖和风险轨迹和庞加莱图的数据的图形表示，并在组水平上通过控制变异网格分析。总之，审查特定的CGM数据系列的分析方法，以及一些新技术。这些方法应有助于从复杂和大量的CGM时间序列中提取信息并对其进行解释。
Continuous glucose monitors (CGMs) generate data streams that are both complex and voluminous. The analyses of these data require an understanding of the physical, biochemical, and mathematical properties involved in this technology. This article describes several methods that are pertinent to the analysis of CGM data, taking into account the specifics of the continuous monitoring data streams. These methods include: (1) evaluating the numerical and clinical accuracy of CGM. We distinguish two types of accuracy metrics-numerical and clinical each having two subtypes measuring point and trend accuracy. The addition of trend accuracy, e. g., the ability of CGM to reflect the rate and direction of blood glucose (BG) change, is unique to CGM as these new devices are capable of capturing BG not only episodically, but also as a process in time. (2) Statistical approaches for interpreting CGM data. The importance of recognizing that the basic unit for most analyses is the glucose trace of an individual, i.e., a time-stamped series of glycemic data for each person, is stressed. We discuss the use of risk assessment, as well as graphical representation of the data of a person via glucose and risk traces and Poincare plots, and at a group level via Control Variability-Grid Analysis. In summary, a review of methods specific to the analysis of CGM data series is presented, together with some new techniques. These methods should facilitate the extraction of information from, and the interpretation of, complex and voluminous CGM time series.