Abstract

Reference intervals (RIs) are the cornerstone for evaluation of test results in clinical practice and are invaluable in judging patient health and making clinical decisions. Establishing RIs based on clinical laboratory data is a branch of real-world data mining research. Compared to the traditional direct method, this indirect approach is highly practical, widely applicable, and low-cost. Improving the accuracy of RIs requires not only the collection of sufficient data and the use of correct statistical methods, but also proper stratification of heterogeneous subpopulations. This includes the establishment of age-specific RIs and taking into account other characteristics of reference individuals. Although there are many studies on establishing RIs by indirect methods, it is still very difficult for laboratories to select appropriate statistical methods due to the lack of formal guidelines. This review describes the application of real-world data and an approach for establishing indirect reference intervals (iRIs). We summarize the processes for establishing iRIs using real-world data and analyze the principle and applicable scope of the indirect method model in detail. Moreover, we compare different methods for constructing growth curves to establish age-specific RIs, in hopes of providing laboratories with a reference for establishing specific iRIs and giving new insight into clinical laboratory RI research. (201 words)

摘要

参考区间（RI）是临床实践中评估测试结果的基石，对于判断患者健康状况和做出临床决策具有无价的价值。基于临床实验室数据建立RI是现实世界数据挖掘研究的一个分支。与传统的直接法相比，这种间接法实用性强、适用范围广、成本低。提高RIs的准确性不仅需要收集足够的数据和使用正确的统计方法，还需要对异质亚群进行适当的分层。这包括建立特定年龄的 RI 并考虑参考个体的其他特征。尽管通过间接方法建立RI的研究很多，但由于缺乏正式的指南，实验室选择合适的统计方法仍然非常困难。这篇综述描述了现实世界数据的应用以及建立间接参考区间（iRI）的方法。我们总结了利用真实数据建立iRI的过程，并详细分析了间接方法模型的原理和适用范围。此外，我们还比较了不同构建生长曲线的方法来建立特定年龄的RI，希望为实验室建立特定的iRI提供参考，为临床实验室RI研究提供新的见解。（201字）