Farrell Christopher-John L, Nguyen Lan
Department of Clinical Chemistry, Laverty Pathology, North Ryde, NSW 2113, Australia.
Clin Biochem Rev. 2019 May;40(2):99-111. doi: 10.33176/AACB-19-00022.
Reference intervals are relied upon by clinicians when interpreting their patients' test results. Therefore, laboratorians directly contribute to patient care when they report accurate reference intervals. The traditional approach to establishing reference intervals is to perform a study on healthy volunteers. However, the practical aspects of the staff time and cost required to perform these studies make this approach difficult for clinical laboratories to routinely use. Indirect methods for deriving reference intervals, which utilise patient results stored in the laboratory's database, provide an alternative approach that is quick and inexpensive to perform. Additionally, because large amounts of patient data can be used, the approach can provide more detailed reference interval information when multiple partitions are required, such as with different age-groups. However, if the indirect approach is to be used to derive accurate reference intervals, several considerations need to be addressed. The laboratorian must assess whether the assay and patient population were stable over the study period, whether data 'clean-up' steps should be used prior to data analysis and, often, how the distribution of values from healthy individuals should be modelled. The assumptions and potential pitfalls of the particular indirect technique chosen for data analysis also need to be considered. A comprehensive understanding of all aspects of the indirect approach to establishing reference intervals allows the laboratorian to harness the power of the data stored in their laboratory database and ensure the reference intervals they report are accurate.
临床医生在解读患者的检测结果时会依赖参考区间。因此,检验人员在报告准确的参考区间时,直接对患者护理做出了贡献。建立参考区间的传统方法是对健康志愿者进行研究。然而,开展这些研究所需的人员时间和成本等实际问题,使得这种方法对于临床实验室来说难以常规使用。利用实验室数据库中存储的患者结果推导参考区间的间接方法,提供了一种快速且成本低廉的替代方法。此外,由于可以使用大量患者数据,当需要多个分区(如不同年龄组)时,该方法可以提供更详细的参考区间信息。然而,如果要使用间接方法来推导准确的参考区间,需要考虑几个因素。检验人员必须评估在研究期间检测方法和患者群体是否稳定,在数据分析之前是否应采用数据“清理”步骤,以及通常应如何对健康个体的值分布进行建模。还需要考虑为数据分析所选特定间接技术的假设和潜在陷阱。对建立参考区间的间接方法的所有方面有全面的了解,能使检验人员利用实验室数据库中存储的数据的力量,并确保他们报告的参考区间准确无误。