Clinical Laboratory, Chongqing Emergency Medical Center, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing 400014, China.
Physical Examination Center, Chongqing Emergency Medical Center, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing 400014, China.
Clin Biochem. 2023 Mar;113:9-16. doi: 10.1016/j.clinbiochem.2022.12.016. Epub 2022 Dec 29.
Reference intervals (RIs) are vital for interpreting laboratory biomarkers and enabling clinical decision-making. Among various RI-estimation methods, we explored the application value of Hoffmann, Bhattacharya, nonparametric test, and Q-Q plot methods for estimating the RI of urea, creatinine, and uric acid (UA).
This cross-sectional study collected patient data recorded between January 2020 and April 2022 at the Chongqing University Central Hospital Laboratory Information System. The RIs of urea, creatinine, and UA levels were established using the Hoffmann, Bhattacharya, nonparametric, and Q-Q plot methods, and RI differences with different computational methods were verified using the reference change value (RCV%) of biological variability.
We included 16,474 and 123,570 patients in the physical examination and clinical groups, respectively. In the clinical group, differences in the RI upper limit of analytes with the four methods (excluding the Q-Q plot method) were within the permissible RCV% range; only the nonparametric test produced an RI of urea with the lower limit within the permissible RCV% range. In the physical examination group, the relative RI differences among the four methods (excluding the lower limit of RI obtained using the Q-Q plot) were all within the acceptable RCV% range; the relative deviation of the RI of UA with the four methods was within the acceptable RCV% range (excluding the lower RI limit obtained using the Q-Q plot and nonparametric test).
The Hoffmann and Bhattacharya methods may provide reliable RIs for indirect estimations of urea, creatinine, and UA based on laboratory datasets.
参考区间(RIs)对于解释实验室生物标志物和支持临床决策至关重要。在各种 RI 估计方法中,我们探索了 Hoffmann、Bhattacharya、非参数检验和 Q-Q 图方法在估计尿素、肌酐和尿酸(UA)RI 中的应用价值。
本横断面研究收集了 2020 年 1 月至 2022 年 4 月期间在重庆大学附属中心医院实验室信息系统中记录的患者数据。使用 Hoffmann、Bhattacharya、非参数和 Q-Q 图方法建立了尿素、肌酐和 UA 水平的 RI,并用生物变异的参考变化值(RCV%)验证了不同计算方法的 RI 差异。
我们纳入了体检组和临床组各 16474 名和 123570 名患者。在临床组中,四种方法(不包括 Q-Q 图方法)的分析物 RI 上限差异在可接受的 RCV%范围内;只有非参数检验产生的尿素 RI 下限在可接受的 RCV%范围内。在体检组中,四种方法(不包括 Q-Q 图获得的 RI 下限)的相对 RI 差异均在可接受的 RCV%范围内;四种方法的 UA RI 相对偏差在可接受的 RCV%范围内(不包括 Q-Q 图和非参数检验获得的较低 RI 下限)。
Hoffmann 和 Bhattacharya 方法可能为基于实验室数据集的尿素、肌酐和 UA 的间接估计提供可靠的 RI。