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Carlsson Y, Sandström A, Bergman L, Conner P, Hansson S, Kublicka M, Görmüş U, Lindgren P, Oleröd G, Wikström AK, Larsson A. Comparing the results from a Swedish pregnancy cohort using data from three automated placental growth factor immunoassay platforms intended for first-trimester preeclampsia prediction. Acta Obstet Gynecol Scand 2023. [PMID: 37358242 PMCID: PMC10378007 DOI: 10.1111/aogs.14615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 05/31/2023] [Accepted: 06/06/2023] [Indexed: 06/27/2023]
Abstract
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 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.
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Affiliation(s)
- Ylva Carlsson
- 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
| | - Anna Sandström
- Clinical Epidemiology Division, Department of Medicine, Solna, Karolinska Institute, Stockholm, Sweden
- Department of Women's Health, Division of Obstetrics, Karolinska University Hospital, Stockholm, Sweden
| | - Lina Bergman
- Department of Obstetrics and Gynecology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden
- Department of Obstetrics and Gynecology, Institute of Clinical Sciences, University of Gothenburg, Gothenburg, Sweden
| | - Peter Conner
- Department of Women's Health, Division of Obstetrics, Karolinska University Hospital, Stockholm, Sweden
- Department of Women's and Children's Health, Karolinska Institute and Center for Fetal Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Stefan Hansson
- Department of Clinical Sciences Lund, Obstetrics and Gynecology, Lund University and Skåne University Hospital, Lund/Malmö, Sweden
| | - Marius Kublicka
- Department of Clinical Science, Intervention and Technology - CLINTEC, Karolinska Institutet and Center for Fetal Medicine, Karolinska University Hospital, Stockholm, Sweden
| | | | - Peter Lindgren
- Department of Clinical Science, Intervention and Technology - CLINTEC, Karolinska Institutet and Center for Fetal Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Göran Oleröd
- Department of Clinical Chemistry, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Anna-Karin Wikström
- Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
| | - Anders Larsson
- Department of Medical Sciences, Clinical Chemistry, Uppsala University, Uppsala, Sweden
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