Clinical Laboratories, Biochemistry Department, Vall d'Hebron University Hospital, Barcelona, Spain.
Department of Clinical Chemistry and Laboratory Medicine, Leiden University Medical Centre, Leiden, The Netherlands.
PLoS One. 2022 May 19;17(5):e0268522. doi: 10.1371/journal.pone.0268522. eCollection 2022.
The aim of this study was to determine reference intervals in an outpatient population from Vall d'Hebron laboratory using an indirect approach previously described in a Dutch population (NUMBER project). We used anonymized test results from individuals visiting general practitioners and analysed during 2018. Analytical quality was assured by EQA performance, daily average monitoring and by assessing longitudinal accuracy between 2018 and 2020 (using trueness verifiers from Dutch EQA). Per test, outliers by biochemically related tests were excluded, data were transformed to a normal distribution (if necessary) and means and standard deviations were calculated, stratified by age and sex. In addition, the reference limit estimator method was also used to calculate reference intervals using the same dataset. Finally, for standardized tests reference intervals obtained were compared with the published NUMBER results. Reference intervals were calculated using data from 509,408 clinical requests. For biochemical tests following a normal distribution, similar reference intervals were found between Vall d'Hebron and the Dutch study. For creatinine and urea, reference intervals increased with age in both populations. The upper limits of Gamma-glutamyl transferase were markedly higher in the Dutch study compared to Vall d'Hebron results. Creatine kinase and uric acid reference intervals were higher in both populations compared to conventional reference intervals. Medical test results following a normal distribution showed comparable and consistent reference intervals between studies. Therefore a simple indirect method is a feasible and cost-efficient approach for calculating reference intervals. Yet, for generating standardized calculated reference intervals that are traceable to higher order materials and methods, efforts should also focus on test standardization and bias assessment using commutable trueness verifiers.
本研究的目的是使用先前在荷兰人群（NUMBER 项目）中描述的间接方法，确定 Vall d'Hebron 实验室门诊人群的参考区间。我们使用了在 2018 年期间访问全科医生并进行分析的个体的匿名测试结果。通过外部质量评估表现、日常平均监测以及评估 2018 年至 2020 年期间的纵向准确性（使用来自荷兰外部质量评估的准确度验证器）来确保分析质量。对于每个测试，通过与生化相关的测试排除离群值，对数据进行正态分布转换（如果需要），并按年龄和性别分层计算平均值和标准差。此外，还使用参考限估计器方法使用相同的数据集计算参考区间。最后，将获得的标准化测试参考区间与已发表的 NUMBER 结果进行比较。使用来自 509,408 项临床请求的数据计算参考区间。对于遵循正态分布的生化测试，在 Vall d'Hebron 和荷兰研究之间发现了相似的参考区间。对于肌酐和尿素，在两个人群中，参考区间随年龄增加而增加。在荷兰研究中，γ-谷氨酰转移酶的上限明显高于 Vall d'Hebron 结果。肌酸激酶和尿酸参考区间在两个人群中均高于传统参考区间。遵循正态分布的医学测试结果显示，研究之间的参考区间具有可比性和一致性。因此，对于计算参考区间，简单的间接方法是可行且具有成本效益的方法。然而，为了生成可溯源至更高阶材料和方法的标准化计算参考区间，还应努力关注使用可互换准确度验证器进行测试标准化和偏差评估。
