从常规实验室数据中简单估计参考区间

IF 0.1 Q4 OTORHINOLARYNGOLOGY
Georg F. Hoffmann, R. Lichtinghagen, W. Wosniok
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引用次数: 20

摘要

摘要:根据IFCC和其他组织的建议,医学实验室应建立或至少调整自己的参考区间,以确保它们反映各自方法和患者群体的特点。然而,在实践中,这一假设很难实现。因此,DGKL的两个工作组(“AG Richtwerte”和“AG Bioinformatik”)开发了从常规实验室数据估计参考区间的方法。在这里,我们描述了一个可视化过程,它可以在没有任何编程知识的情况下在Excel表上执行。使用NORM,根据标准正态分布的分位数(所谓的QQ图)绘制患者值。Excel的INV函数。如果被检查的人口中主要包含大致为正态分布值的非患病人群,则相应的点形成一条直线。这些值通常是对数正态分布;在这种情况下,对原始值进行对数变换后可以检测到直线。与假设的理论分布不匹配的值偏离了线性形状,可以很容易地识别和消除。利用简化后的数据集,计算均值和标准差,估计参考区间(μ±2σ)。该方法对模拟数据和实际数据都能得到合理的结果。随着与模型不匹配的结果越来越多,它往往会低估标准偏差。在所有情况下,如果QQ图不产生实质性的线性部分,则所提出的方法不适用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Simple estimation of reference intervals from routine laboratory data
Abstract: According to the recommendations of the IFCC and other organizations, medical laboratories should establish or at least adapt their own reference intervals, to make sure that they reflect the peculiar characteristics of the respective methods and patient collectives. In practice, however, this postulate is hard to fulfill. Therefore, two task forces of the DGKL (“AG Richtwerte” and “AG Bioinformatik”) have developed methods for the estimation of reference intervals from routine laboratory data. Here we describe a visual procedure, which can be performed on an Excel sheet without any programming knowledge. Patient values are plotted against the quantiles of the standard normal distribution (so-called QQ plot) using the NORM. INV function of Excel. If the examined population contains mainly non-diseased persons with approximately normally distributed values, the respective dots form a straight line. Very often the values are rather lognormally distributed; in this case the straight line can be detected after logarithmic transformation of the original values. Values, which do not match with the assumed theoretical distribution, deviate from the linear shape and can easily be identified and eliminated. Using the reduced data set, the mean value and standard deviation are calculated and the reference interval (μ±2σ) is estimated. The method yields plausible results with simulated and real data. With the increasing number of results, which do not match with the model, it tends to underestimate the standard deviation. In all cases, where the QQ plot does not yield a substantial linear part, the proposed method is not applicable.
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来源期刊
CiteScore
0.80
自引率
0.00%
发文量
1
审稿时长
>12 weeks
期刊介绍: Information not localized
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