Performance evaluation of automated magnetic beads extraction method for the measurement of catecholamine metabolites analyzed by liquid chromatography tandem mass spectrometry / 中华检验医学杂志

ABSTRACT

Objective:To evaluate the performance of magnetic beads extraction method (MGE) for the measurement of catecholamine metabolites by liquid chromatography tandem mass spectrometry.Methods:This is a methodological evaluation study. The linearity, limit of quantitation, recovery, precision, and matrix effect of catecholamine metabolites 3-methoxyepinephrine (MN), 3-methoxynorepinephrine (NMN) and 3-methoxytyramine (3-MT) extracted by MGE method were evaluated according to CLSI C62-A. Consensus of method development and validation of liquid chromatography-tandem mass spectrometry in clinical laboratories and other guidelines, 132 clinical residual plasma samples were collected and extracted by automated MGE and traditional solid phase extraction (SPE) method to compare the harmonization of the two extraction methods.Results:The linearity of MN, NMN and 3-MT extracted by automated MGE was>0.99, and the LOQ for MN, NMN and 3-MT were 0.033 5 nmol/L, 0.054 7 nmol/L and 0.011 0 nmol/L, respectively. The repeatability of MN, NMN and 3-MT were 1.3%-5.1%, 2.2%-5.6% and 1.7%-7.1%, respectively. The total imprecision in the laboratory were 1.5%-8.2%, 2.2%-7.7%, 2.1%-11.2%. Although the absolute recovery is low, the average relative recoveries of MN, NMN and 3-MT were 91.5%-108.5%, 92.0%-108.6%, and 89.3%-104.1%, respectively, and the percentage deviation from the expected concentration was within 15%. After isotope internal standard correction, the relative matrix effect is close to 100%, which can compensate for the potential matrix effect. The results of MGE and SPE of MN, NMN and 3-MT showeda good correlation (correlation coefficient r>0.99). The average relative deviations of MN, NMN and 3-MT were 0.2%, -1.4% and 1.0%, respectively. Conclusion:The automatic MGE method hasa good performance in extracting catecholamine metabolites, and is expected to be used in high-throughput analysis of samples in clinical in the future.