比较使用三种用于预测早发性子痫前期的自动化胎盘生长因子免疫分析平台的数据得出的瑞典妊娠队列研究结果。
Comparing the results from a Swedish pregnancy cohort using data from three automated placental growth factor immunoassay platforms intended for first-trimester preeclampsia prediction.
机构信息
Department of Obstetrics and Gynecology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden.
Center of Perinatal Medicine and Health, Institute of Clinical Sciences, University of Gothenburg, Gothenburg, Sweden.
出版信息
Acta Obstet Gynecol Scand. 2023 Aug;102(8):1084-1091. doi: 10.1111/aogs.14615. Epub 2023 Jun 26.
INTRODUCTION
Risk evaluation for preeclampsia in early pregnancy allows identification of women at high risk. Prediction models for preeclampsia often include circulating concentrations of placental growth factor (PlGF); however, the models are usually limited to a specific PlGF method of analysis. The aim of this study was to compare three different PlGF methods of analysis in a Swedish cohort to assess their convergent validity and appropriateness for use in preeclampsia risk prediction models in the first trimester of pregnancy.
MATERIAL AND METHODS
First-trimester blood samples were collected in gestational week 11 to 13 from 150 pregnant women at Uppsala University Hospital during November 2018 until November 2020. These samples were analyzed using the different PlGF methods from Perkin Elmer, Roche Diagnostics, and Thermo Fisher Scientific.
RESULTS
There were strong correlations between the PlGF results obtained with the three methods, but the slopes of the correlations clearly differed from 1.0: PlGF = 0.553 (95% confidence interval [CI] 0.518-0.588) * PlGF -1.112 (95% CI -2.773 to 0.550); r = 0.966, mean difference -24.6 (95% CI -26.4 to -22.8). PlGF = 0.673 (95% CI 0.618-0.729) * PlGF -0.199 (95% CI -2.292 to 1.894); r = 0.945, mean difference -13.8 (95% CI -15.1 to -12.6). PlGF = 1.809 (95% CI 1.694-1.923) * PlGF +2.010 (95% CI -0.877 to 4.897); r = 0.966, mean difference 24.6 (95% CI 22.8-26.4). PlGF = 1.237 (95% CI 1.113-1.361) * PlGF +0.840 (95% CI -3.684 to 5.363); r = 0.937, mean difference 10.8 (95% CI 9.4-12.1). PlGF = 1.485 (95% CI 1.363-1.607) * PlGF +0.296 (95% CI -2.784 to 3.375); r = 0.945, mean difference 13.8 (95% CI 12.6-15.1). PlGF = 0.808 (95% CI 0.726-0.891) * PlGF -0.679 (95% CI -4.456 to 3.099); r = 0.937, mean difference -10.8 (95% CI -12.1 to -9.4).
CONCLUSION
The three PlGF methods have different calibrations. This is most likely due to the lack of an internationally accepted reference material for PlGF. Despite different calibrations, the Deming regression analysis indicated good agreement between the three methods, which suggests that results from one method may be converted to the others and hence used in first-trimester prediction models for preeclampsia.
简介
在孕早期对先兆子痫进行风险评估可以识别出高危妇女。先兆子痫预测模型通常包括胎盘生长因子（PlGF）的循环浓度；然而，这些模型通常仅限于特定的 PlGF 分析方法。本研究的目的是在瑞典队列中比较三种不同的 PlGF 分析方法，以评估它们在孕早期先兆子痫风险预测模型中的适用性和一致性。
材料和方法
2018 年 11 月至 2020 年 11 月，在乌普萨拉大学医院收集了 150 名孕妇在妊娠 11 至 13 周时的第一孕期血样。这些样本使用 Perkin Elmer、Roche Diagnostics 和 Thermo Fisher Scientific 的不同 PlGF 方法进行分析。
结果
三种方法得到的 PlGF 结果之间存在很强的相关性，但相关性的斜率明显不同于 1.0：PlGF=0.553（95%置信区间[CI]0.518-0.588）*PlGF-1.112（95%CI-2.773 至 0.550）；r=0.966，平均差异-24.6（95%CI-26.4 至-22.8）。PlGF=0.673（95%CI0.618-0.729）*PlGF-0.199（95%CI-2.292 至 1.894）；r=0.945，平均差异-13.8（95%CI-15.1 至-12.6）。PlGF=1.809（95%CI1.694-1.923）*PlGF+2.010（95%CI-0.877 至 4.897）；r=0.966，平均差异 24.6（95%CI22.8 至 26.4）。PlGF=1.237（95%CI1.113-1.361）*PlGF+0.840（95%CI-3.684 至 5.363）；r=0.937，平均差异 10.8（95%CI9.4 至 12.1）。PlGF=1.485（95%CI1.363-1.607）*PlGF+0.296（95%CI-2.784 至 3.375）；r=0.945，平均差异 13.8（95%CI12.6 至 15.1）。PlGF=0.808（95%CI0.726-0.891）*PlGF-0.679（95%CI-4.456 至 3.099）；r=0.937，平均差异-10.8（95%CI-12.1 至-9.4）。
结论
三种 PlGF 方法具有不同的校准。这很可能是由于缺乏国际公认的 PlGF 参考物质。尽管校准不同，但 Deming 回归分析表明这三种方法之间具有良好的一致性，这表明可以将一种方法的结果转换为其他方法的结果，从而用于孕早期先兆子痫的预测模型。
Zotero 插件