Comparing the results from a Swedish pregnancy cohort using data from three automated placental growth factor immunoassay platforms intended for first‐trimester preeclampsia prediction
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 a...
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-08-01
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| Series: | Acta Obstetricia et Gynecologica Scandinavica |
| Subjects: | |
| Online Access: | https://doi.org/10.1111/aogs.14615 |
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| Summary: | 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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| ISSN: | 0001-6349 1600-0412 |