首页> 外文期刊>Clinical chemistry and laboratory medicine: CCLM >Technical and clinical evaluation of the VITROS? Immunodiagnostic Products 25-OH Vitamin D Total Assay - Comparison with marketed automated immunoassays and a liquid chromatographytandem mass spectrometry method
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Technical and clinical evaluation of the VITROS? Immunodiagnostic Products 25-OH Vitamin D Total Assay - Comparison with marketed automated immunoassays and a liquid chromatographytandem mass spectrometry method

机译:VITROS的技术和临床评估?免疫诊断产品25-OH维生素D总含量测定-与市售自动免疫测定和液相色谱串联质谱法的比较

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Background: The study was conducted to evaluate the technical and clinical performance of the VITROS? Immunodiagnostic Products 25-OH Vitamin D Total Assay, and compare it with the performance of five marketed automated assays and a liquid chromatography/mass spectrometry reference method (LC-MS/MS). Methods: Three hundred patient serum samples were used to compare the correlation of the VITROS? 25-OH Vitamin D Total Assay with both the other immunoassays and the LC-MS/MS method, using Passing-Bablok regression and Bland-Altman analyses. Concordance of the diagnosis of vitamin D status was calculated to test the agreement between the different assays. In addition, samples containing vitamin D2 were used to test the assay's ability to detect the D2 form of the vitamin. Results and conclusions: These results from the VITROS? 25-OH Vitamin D Total Assay generally correlated well with those from most of the marketed immunoassays. Cross-reactivity of the D2 form was calculated as being close to 100%. Additionally, we found substantial variability in performance amongst the various assays, which suggests the need for optimisation and recalibration of commercial methods.
机译:背景:该研究旨在评估VITROS?的技术和临床性能。免疫诊断产品25-OH维生素D总检测法,并将其与五种市售自动检测法和液相色谱/质谱参考方法(LC-MS / MS)的性能进行比较。方法:使用300例患者血清样本比较VITROS?的相关性。使用Passing-Bablok回归和Bland-Altman分析法,采用其他免疫测定法和LC-MS / MS方法进行25-OH维生素D总测定。计算维生素D状态诊断的一致性,以测试不同测定之间的一致性。此外,使用含维生素D2的样品来测试检测D2形式维生素的能力。结果与结论:这些结果来自VITROS?通常,25-OH维生素D总检测与大多数市售免疫检测的相关性很好。计算出D2形式的交叉反应性接近100%。此外,我们发现各种分析之间在性能上存在很大差异,这表明需要对商业方法进行优化和重新校准。

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