RESUMEN
Objective:The results of the three lipid detection systems were compared to analyze their influence on risk stratification and clinical treatment in lipid management, especially the target goal cut-off point determination, and to find ways to reduce the impact on target goal determination of various lipid measurement system.Methods:A total of 196 serum samples with triglyceride TG <4.5 mmol/L were collected from people undergoing physical examinations and in-patients in the Second Xiangya Hospital of Central South University from August to October 2022. Triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) were directly detected with Hitachi-Woke (HW), Roche and Mindray detection systems, respectively. The non high-density lipoprotein cholesterol (non HDL-C) was calculated by formula (TC-HDL-C) and LDL-C (F-LDL-C) was calculated by Friedewald formula, and results from various methodology were compared. The coefficient of variation ( CV) of these six indicators derived from the three detection systems were calculated to evaluate the consistency of the obtained results from different venders. In addition, the Pearson correlation coefficient was analyzed to evaluate the correlation of each indicator among different systems. According to the Chinese Guidelines for Blood Lipid Management, samples were divided into groups with LDL-C levels of <1.4, 1.4-<1.8, 1.8-<2.6, 2.6-<3.4 and ≥3.4 mmol/L according to the recommended LDL-C levels for different risk stratification levels. The sample size and percentage of LDL-C test results from different systems in the same group were counted to evaluate the impact of LDL-C differences between systems on clinical decision-making of blood lipid management. The correction factor was calculated through two methods: (1) The average deviation of LDL-C between systems was estimated by EP9-A3 method; (2) Multiple linear stepwise regression was used to establish the regression model of LDL-C difference and related indexes between systems. The two correction factors were used to correct the deviation of LDL-C value obtained from various systems, and Chi-square test was used to compare the difference of LDL-C grouping consistency rate before and after correction. Result:The average CV values of TG, TC, LDL-C, F-LDL-C, HDL-C, and non HDL-C among the three detection systems were 4.84%, 1.92%, 11.96%, 3.81%, 5.82% and 2.61%, respectively. Correlation analysis showed that when comparing the three systems in pairs, except for LDL-C derived from HW and Roche′s, and Mindray and Roche′s LDL-C ( R 2=0.938 and 0.947), the R 2 of other indicators were all greater than 0.97. The consistency rates of the three systems on LDL-C and F-LDL-C were 51.0% (100/196) and 90.8% (178/196), respectively, according to the risk stratification standard values and the difference was statistically significant ( P<0.05). When comparing in pairs, the consistency rates of Roche and HW, Mindray and HW, Mindray and Roche system LDL-C grouping were 60.7% (119/196), 82.7% (162/196), and 54.1% (106/196), respectively. After adjusting for mean deviation, the group consistency rate of Roche and HW increased to 73.7%-79.4% ( P<0.05), and the group consistency rate of Roche and Mindray increased to 72.3%-79.0% ( P<0.05). After adjusting for difference regression model, the group consistency rate of Roche and HW increased to 82.5%-84.0%, and the group consistency rate of Roche and Mindray increased to 81.0%-89.2%. However, there was no significant change in the group consistency rate of Mindray and HW after adjusting for both correction methods ( P>0.05) .Conclusions:There are significant differences in LDL-C derived from different detection systems, and the consistency rate of grouping according to the lipid-lowering standard value is relatively low, which may affect clinical decision-making in lipid management. Adjusted by the correction factor, the consistency rate of grouping between Roche and HW, Roche and Mindray systems with large differences in LDL-C can be improved. Using the difference multiple linear regression model as a correction factor is superior to the average deviation.