Prerequisites for establishing common reference intervals

可追溯性 标准化 计算机科学 参考值 质量(理念) 样品(材料) 统计 可靠性工程 参考数据 理论(学习稳定性) 参考模型 数据挖掘 数学 工程类 医学 哲学 化学 软件工程 认识论 色谱法 机器学习 内科学 操作系统
作者
Per Hyltoft Petersen,Pål Rustad
出处
期刊:Scandinavian Journal of Clinical & Laboratory Investigation [Informa]
卷期号:64 (4): 285-292 被引量:36
标识
DOI:10.1080/00365510410006298
摘要

AbstractEstablishment of common reference intervals for homogeneous populations within regions is based on the same basic principles as the IFCC recommendations for individual laboratories, but a few additional prerequisites are needed. Thus, the need for common standardization and traceability during production of the reference values and with the application of the common reference intervals in the laboratories becomes crucial. Furthermore, the external control system must be geared to the purpose, using matrix‐correct control materials with concentration values traceable to the same reference methods, and validation of results according to analytical quality specifications designed for the use of common reference intervals. Here, the standards may have a restrictive influence on the establishing of common reference intervals, with their demands for the use of the producers' traceability, instead of a relevant high‐quality reference preparation shared by all the participants. Two main strategies for measurements are analysis immediately after the sampling, and storage of samples until analysis in one or a few analytical runs. The former strategy needs constant standardization and stability of the performance in many laboratories and in several analytical runs, resulting in between‐run variation, whereas the latter precludes this between‐run variation, but makes demands on the stability of the components under storage. When a considerable number of laboratories decide to establish common reference intervals, it is possible to obtain large sample sizes of reference values, which reduces the confidence intervals around the reference limits. It also makes it possible to collect samples from many subgroups, such as racial groups and groups related to different environmental conditions, as well as the traditional groupings according to age and gender, pregnancy and use of oestrogens. If all these subgroups are large, e.g. n>500, the confidence limits will be small and criteria for partitioning can be applied. Choosing reference individuals is not easy, as definitions of health, as well as rule‐in and rule‐out criteria vary from one investigation to the other. Therefore, the strategy and the criteria must be thoroughly described. Arguments for establishing common reference intervals are not needed. On the contrary, lack of such common reference intervals should be explained.Common controlcommon standardizationhomogeneous populationspartitioning according to subgroupsstatistical evaluations
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