Catalan
Albanian
Arabic
Armenian
Azerbaijani
Belarusian
Bengali
Bosnian
Catalan
Czech
Danish
Deutsch
Dutch
English
Estonian
Finnish
Français
Greek
Haitian Creole
Hebrew
Hindi
Hungarian
Icelandic
Indonesian
Irish
Italian
Japanese
Korean
Latvian
Lithuanian
Macedonian
Mongolian
Norwegian
Persian
Polish
Portuguese
Romanian
Russian
Serbian
Slovak
Slovenian
Spanish
Swahili
Swedish
Turkish
Ukrainian
Vietnamese
Български
中文(简体)
中文(繁體)
American Journal of Obstetrics and Gynecology 2020-Aug

Pregnancy outcomes in nulliparous women with positive first-trimester preterm preeclampsia screening test: The Great Obstetrical Syndromes (GOS) cohort study

Només els usuaris registrats poden traduir articles
Inicieu sessió / registreu-vos
L'enllaç es desa al porta-retalls
Amélie Boutin
Paul Guerby
Cedric Gasse
Sylvie Tapp
Emmanuel Bujold

Paraules clau

Resum

Background: The Fetal Medicine Foundation (FMF) proposed a competing risks model for early identification of women at high risk of preterm preeclampsia, typically associated with deep placentation disorders. The Great Obstetrical Syndromes include a spectrum of pregnancy complications (preeclampsia, intrauterine growth restriction, preterm birth, late spontaneous abortion, and abruptio placentae) that are also associated with deep placentation disorders.

Objective: To estimate the rate of placenta-mediated pregnancy complications in nulliparous women with a positive first-trimester FMF preterm preeclampsia screening test.

Study design: We conducted a prospective cohort study of nulliparous women recruited at 11-14 weeks of gestation. Maternal characteristics, mean arterial blood pressure, levels of maternal serum biomarkers [pregnancy-associated plasma protein-A (PAPP-A), placental growth factor (PlGF) and soluble fms-like tyrosine kinase-1 (sFlt-1)] and mean uterine artery pulsatility index were obtained to calculate the risk of preterm preeclampsia according to the FMF algorithm. The predicted risks were dichotomised as a positive or negative test according to two risk cut-offs (1 in 70 and 1 in 100). The detection rate, false-positive rate, and positive and negative predictive values were calculated for placenta-mediated complications, including preeclampsia, small for gestational age (birthweight <10th centile), fetal death, preterm birth and a composite outcome including any of the foregoing. The same analyses were computed for a composite of severe outcomes including preterm preeclampsia, severe small for gestational age (<3rd centile), and fetal death.

Results: We included 4,575 participants with complete observations, of which 494 (10.8%) had an estimated risk of preterm preeclampsia ≥1 in 70 and 728 (15.9%) had a risk ≥1 in 100. The test based on a risk cut-off of 1 in 70 could have correctly predicted up to 27% of preeclampsia, 55% of preterm preeclampsia, 18% of small for gestational age, 24% of severe small for gestational age, and 37% of fetal deaths at a 10% false-positive rate. The test based on a cut-off of 1 in 100 could have predicted correctly up to 35% of preeclampsia, 69% of preterm preeclampsia, 25% of small for gestational age, 30% of severe small for gestational age, and 53% of fetal deaths at a 15% false-positive rate. The positive predictive value of a screening test for preterm preeclampsia ≥1 in 70, was 3% for preterm preeclampsia, 32% for the composite outcome and 9% for the severe composite outcome.

Conclusions: Nulliparous women with a first-trimester positive preterm preeclampsia FMF screening tests are at higher risk of both preterm preeclampsia, and other severe placenta-mediated pregnancy complications. Approximately 1 women out of 10 identified as high-risk by the FMF algorithm developed at least one severe placenta-mediated pregnancy complication.

Keywords: fetal death; fetal growth restriction; placenta-mediated complications; preeclampsia; preterm birth; risk assessment; screening; small for gestational age; validation.

Uneix-te a la nostra
pàgina de Facebook

La base de dades d’herbes medicinals més completa avalada per la ciència

  • Funciona en 55 idiomes
  • Cures a base d'herbes recolzades per la ciència
  • Reconeixement d’herbes per imatge
  • Mapa GPS interactiu: etiqueta les herbes a la ubicació (properament)
  • Llegiu publicacions científiques relacionades amb la vostra cerca
  • Cerqueu herbes medicinals pels seus efectes
  • Organitzeu els vostres interessos i estigueu al dia de les novetats, els assajos clínics i les patents

Escriviu un símptoma o una malaltia i llegiu sobre herbes que us poden ajudar, escriviu una herba i vegeu malalties i símptomes contra els quals s’utilitza.
* Tota la informació es basa en investigacions científiques publicades

Google Play badgeApp Store badge