بيانات النشر: Uppsala universitet, Institutionen för kvinnors och barns hälsa
Uppsala universitet, Klinisk kemi
Center of Perinatal Medicine and Health, Institute of Clinical Sciences, University of Gothenburg, Gothenburg, Sweden.
Clinical Epidemiology Division, Department of Medicine, Solna, Karolinska Institute, Stockholm, Sweden.
Department of Obstetrics and Gynecology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden.
Department of Women's Health, Division of Obstetrics, Karolinska University Hospital, Stockholm, Sweden.
Department of Clinical Sciences Lund, Obstetrics and Gynecology, Lund University and Skåne University Hospital, Lund/Malmö, Sweden.
Department of Clinical Science, Intervention and Technology - CLINTEC, Karolinska Institutet and Center for Fetal Medicine, Karolinska University Hospital, Stockholm, Sweden.
PerkinElmer Genomics, Stockholm, Sweden.
Department of Clinical Chemistry, Sahlgrenska University Hospital, Gothenburg, Sweden.
Wiley
نبذة مختصرة : 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+0 to 13+6 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: PlGFPerkinElmer = 0.553 (95% confidence interval [CI] 0.518-0.588) * PlGFRoche -1.112 (95% CI -2.773 to 0.550); r = 0.966, mean difference -24.6 (95% CI -26.4 to -22.8). PlGFPerkinElmer = 0.673 (95% CI 0.618-0.729) * PlGFThermoFisher -0.199 (95% CI -2.292 to 1.894); r = 0.945, mean difference -13.8 (95% CI -15.1 to -12.6). PlGFRoche = 1.809 (95% CI 1.694-1.923) * PlGFPerkinElmer +2.010 (95% CI -0.877 to 4.897); r = 0.966, mean difference 24.6 (95% CI 22.8-26.4). PlGFRoche = 1.237 (95% CI 1.113-1.361) * PlGFThermoFisher +0.840 (95% CI -3.684 to 5.363); r = 0.937, mean difference 10.8 (95% CI 9.4-12.1). PlGFThermoFisher = 1.485 (95% CI 1.363-1.607) * PlGFPerkinElmer +0.296 (95% CI -2.784 to 3.375); r = 0.945, mean difference 13.8 (95% CI 12.6-15.1). PlGFThermoFisher = 0.808 (95% CI 0.726-0.891) * PlGFRoche -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 ...
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