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Data Mining Approaches to Reference Interval Studies
Clinical Chemistry ( IF 7.1 ) Pub Date : 2021-08-17 , DOI: 10.1093/clinchem/hvab137
Amrom E Obstfeld 1 , Khushbu Patel 1 , James C Boyd 2 , Julia Drees 3 , Daniel T Holmes 4 , John P A Ioannidis 5 , Arjun K Manrai 6
Affiliation  

Both laboratories and in vitro diagnostic manufacturers alike struggle with performing the adequate studies required for producing high quality reference intervals (RIs). The most common approach for RI determination is a priori (direct) sampling of healthy individuals for each RI partition based on age, sex, and preanalytical factors such as diurnal, postural, and postprandial variations. With this approach, healthy reference individuals are selected using specific, well-defined criteria to resemble the patient population being evaluated. According to Clinical Laboratory Standards Institute (CLSI) EP28-A3c, best practice for establishing RIs is to obtain measurements in >120 healthy individuals per each RI partition. However, this is a time-consuming, expensive, and impractical endeavor for a single laboratory.

中文翻译:

参考区间研究的数据挖掘方法

实验室和体外诊断制造商都在努力进行产生高质量参考区间 (RI) 所需的充分研究。确定 RI 的最常见方法是根据年龄、性别和分析前因素(如昼夜、姿势和餐后变化)对每个 RI 分区的健康个体进行先验(直接)抽样。通过这种方法,使用特定的、明确定义的标准选择健康的参考个体,以类似于被评估的患者群体。根据临床实验室标准协会 (CLSI) EP28-A3c,建立 RI 的最佳实践是在每个 RI 分区中获得 >120 个健康个体的测量值。然而,对于单个实验室来说,这是一项耗时、昂贵且不切实际的工作。
更新日期:2021-09-02
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