Prediction of pre-eclampsia and its subtypes in high-risk cohort: hyperglycosylated human chorionic gonadotropin in multivariate models

Background The proportion of hyperglycosylated human chorionic gonadotropin (hCG-h) to total human chorionic gonadotropin (%hCG-h) during the first trimester is a promising biomarker for prediction of early-onset pre-eclampsia. We wanted to evaluate the performance of clinical risk factors, mean art...
Ausführliche Beschreibung

Background The proportion of hyperglycosylated human chorionic gonadotropin (hCG-h) to total human chorionic gonadotropin (%hCG-h) during the first trimester is a promising biomarker for prediction of early-onset pre-eclampsia. We wanted to evaluate the performance of clinical risk factors, mean arterial pressure (MAP), %hCG-h, hCGβ, pregnancy-associated plasma protein A (PAPP-A), placental growth factor (PlGF) and mean pulsatility index of the uterine artery (Uta-PI) in the first trimester in predicting pre-eclampsia (PE) and its subtypes early-onset, late-onset, severe and non-severe PE in a high-risk cohort. Methods We studied a subcohort of 257 high-risk women in the prospectively collected Prediction and Prevention of Pre-eclampsia and Intrauterine Growth Restriction (PREDO) cohort. Multivariate logistic regression was used to construct the prediction models. The first model included background variables and MAP. Additionally, biomarkers were included in the second model and mean Uta-PI was included in the third model. All variables that improved the model fit were included at each step. The area under the curve (AUC) was determined for all models. Results We found that lower levels of serum PlGF concentration were associated with early-onset PE, whereas lower %hCG-h was associated with the late-onset PE. Serum PlGF was lower and hCGβ higher in severe PE, while %hCG-h and serum PAPP-A were lower in non-severe PE. By using multivariate regression analyses the best prediction for all PE was achieved with the third model: AUC was 0.66, and sensitivity 36% at 90% specificity. Third model also gave the highest prediction accuracy for late-onset, severe and non-severe PE: AUC 0.66 with 32% sensitivity, AUC 0.65, 24% sensitivity and AUC 0.60, 22% sensitivity at 90% specificity, respectively. The best prediction for early-onset PE was achieved using the second model: AUC 0.68 and 20% sensitivity at 90% specificity. Conclusions Although the multivariate models did not meet the requirements to be clinically useful screening tools, our results indicate that the biomarker profile in women with risk factors for PE is different according to the subtype of PE. The heterogeneous nature of PE results in difficulty to find new, clinically useful biomarkers for prediction of PE in early pregnancy in high-risk cohorts. Trial registration International Standard Randomised Controlled Trial number ISRCTN14030412, Date of registration 6/09/2007, retrospectively registered. Ausführliche Beschreibung

Autor*in:	Murtoniemi, Katja [verfasserIn] Villa, Pia M. Matomäki, Jaakko Keikkala, Elina Vuorela, Piia Hämäläinen, Esa Kajantie, Eero Pesonen, Anu-Katriina Räikkönen, Katri Taipale, Pekka Stenman, Ulf-Håkan Laivuori, Hannele

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Pre-eclampsia Screening Biomarkers Early-onset pre-eclampsia Late-onset pre-eclampsia Severe pre-eclampsia Placental growth factor hCGβ Hyperglycosylated human chorionic gonadotropin Pregnancy associated plasma protein a

Anmerkung:	© The Author(s). 2018

Übergeordnetes Werk:	Enthalten in: BMC pregnancy and childbirth - London : BioMed Central, 2001, 18(2018), 1 vom: 03. Juli
Übergeordnetes Werk:	volume:18 ; year:2018 ; number:1 ; day:03 ; month:07

Links:	Volltext

DOI / URN:	10.1186/s12884-018-1908-9

Katalog-ID:	SPR027592685