Laboratory for Analytical Chemistry, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium.
Clin Chem. 2010 Jun;56(6):921-9. doi: 10.1373/clinchem.2009.140228. Epub 2010 Apr 15.
Because total thyroid hormone testing is performed on many automated clinical chemistry instruments, the IFCC Scientific Division commissioned the Working Group for Standardization of Thyroid Function Tests to include total thyroxine (TT4) and total triiodothyronine (TT3) in its standardization efforts.
Existing SI-traceable reference measurement procedures (RMPs) were used to assign TT4 and TT3 values to 40 single-donor serum samples for subsequent use in a method comparison study with 11 TT4 and 12 TT3 immunoassays. Data from comparison of each immunoassay with the RMPs provided a basis for mathematical assay recalibration.
Seven TT4 assays had a mean bias within 10% of the RMP, but 2 deviated by an average of -12% and another 2 by +17%. All TT3 assays showed positive biases, 4 within and 8 outside 10%, up to 32%. Mathematical recalibration effectively eliminated assay-specific biases, but sample-related effects remained, particularly for TT3. Correlation coefficients with the RMPs ranged from 0.82 to 0.97 for TT4 and from 0.32 to 0.92 for TT3. The within-run and total imprecision ranges for TT4 were 1.4% to 9.1% and 3.0% to 9.4%, respectively, and for TT3 2.1% to 7.8% and 2.8% to 12.7%, respectively. Approximately one-half of the assays matched the internal QC targets within approximately 5%; however, we observed within-run drifts/shifts.
The study showed that of the assays we examined, only 4 TT4 but the majority of the TT3 assays needed establishment of calibration traceability to the existing RMPs. Most assays performed well, but some would benefit from improved precision, within-run stability, and between-run consistency.
由于许多自动化临床化学仪器都进行总甲状腺激素检测,国际临床化学联合会科学分部委托甲状腺功能检测标准化工作组将总甲状腺素(TT4)和总三碘甲状腺原氨酸(TT3)纳入其标准化工作中。
使用现有的 SI 可溯源参考测量程序(RMP)为 40 份单份供体血清样本赋值 TT4 和 TT3 值,随后将其用于与 11 种 TT4 和 12 种 TT3 免疫分析方法进行方法比较研究。比较每个免疫分析与 RMP 的数据为数学分析重新校准提供了基础。
7 种 TT4 分析的平均偏差在 RMP 的 10%范围内,但有 2 种的偏差平均为-12%,另外 2 种的偏差平均为+17%。所有 TT3 分析都显示出正偏差,其中 4 种在 10%以内,8 种在 10%以外,最高可达 32%。数学重新校准有效地消除了分析特异性偏差,但仍存在样本相关效应,尤其是 TT3。与 RMP 的相关系数范围分别为 TT4 的 0.82 至 0.97 和 TT3 的 0.32 至 0.92。TT4 的批内和总不精密度范围分别为 1.4%至 9.1%和 3.0%至 9.4%,TT3 的批内和总不精密度范围分别为 2.1%至 7.8%和 2.8%至 12.7%。大约一半的分析符合内部 QC 目标,偏差在 5%左右;然而,我们观察到批内漂移/偏移。
该研究表明,在所检查的分析中,只有 4 种 TT4 但大多数 TT3 分析需要建立与现有 RMP 的校准可溯源性。大多数分析性能良好,但有些分析可以从提高精密度、批内稳定性和批间一致性中受益。