[Utility of multivariate analysis in evaluating the results of health examination on the basis of intra-individual variations].

Y Koyama
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引用次数: 1

Abstract

Utility of multivariate analysis in evaluating the values of laboratory tests on the basis of intra-individual variations (IV) was studied. Periodic health examination on workers has many clinical laboratory tests and these values are generally evaluated in comparison with "normal ranges." Recently it has been ascertained that the individual normal ranges were narrower than the populational normal ranges. Although the values of an individual should be estimated with normal ranges of the individual, it is almost impossible to determine the normal ranges from only one or two health examinations conducted per annum at the place of work. The author in his study could easily determine IV with a method using Mahalanobis' distance on a basis of populational normal range. In this method, if many values of a person in the tests vary in several years, the distance becomes larger, and if a value in a test falls outside the normal limit, it also becomes larger. This method makes it possible to detect not only an "abnormal" value out of the normal range but also a large value out of IV. To examine the practical validity of the method, the data of 24 employees (males, 45 to 65 years old) from 3 periodic health examinations and those of 20 persons (males, 48 to 71 years old) from 3 to 6 medical checkups were analyzed with regard to 11 items. It was found that 33 out of 44 persons were abnormal in one or more items and that 10 abnormal persons had large IV. They would be missed in the judgement with the use of only "normal ranges." As the distances were computed by a portable computer with a basic program, the method can be easily used in a small place of work. It is considered that this new method is effective and valuable not only as the screening procedure but also in the evaluation of IV.

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[基于个体内变异的多变量分析在健康检查结果评价中的应用]。
研究了基于个体内变异(IV)的多变量分析在评价实验室检测值中的效用。对工人的定期健康检查有许多临床实验室测试,这些值通常与“正常范围”进行比较。最近已经确定,个体的正常范围比群体的正常范围窄。虽然个人的值应该以个人的正常范围来估计,但仅从每年在工作场所进行的一两次健康检查中几乎不可能确定正常范围。在本研究中,利用马氏距离在总体正态范围的基础上,可以很容易地确定IV值。在这种方法中,如果一个人的许多测试值在几年内发生变化,则距离变大,如果测试中的一个值超出正常限制,则距离也变大。该方法不仅可以检测出正常范围外的“异常”值,还可以检测出IV的大值。为了检验该方法的实际有效性,我们分析了24名员工(男性,45至65岁)3次定期健康检查的数据和20名员工(男性,48至71岁)3至6次健康检查的数据,涉及11个项目。结果发现,44人中有33人在一项或多项指标上异常,10人的IV值较大,如果只使用“正常范围”,会在判断中遗漏。由于该方法是在便携式计算机上用基本程序计算的,因此可以方便地在小的工作场所使用。认为该方法不仅是一种有效的筛选方法,而且是一种有价值的评价方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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