title: Prediction of neonatal outcome in women with gestational hypertension or mild preeclampsia after 36 weeks of gestation.
abstract: BACKGROUND
  There is little knowledge about neonatal complications in GH and PE and induction at term, we aim to assess whether they can be predicted from clinical data.
  
  
  METHODS
  We used data of the HYPITAT trial and evaluated whether adverse neonatal outcome (Apgar score < 7, pH < 7.05, NICU admission) could be predicted from clinical data. Logistic regression, ROC analysis and calibration were used to identify predictors and evaluate the predictive capacity in an antepartum and intrapartum model.
  
  
  RESULTS
  We included 1153 pregnancies, of whom 76 (6.6%) had adverse neonatal outcome. Parity (primipara OR 2.75), BMI (OR 1.06), proteinuria (dipstick +++ OR 2.5), uric acid (OR 1.4) and creatinine (OR 1.02) were independent antepartum predictors; In the intrapartum model, meconium stained amniotic fluid (OR 2.2), temperature (OR 1.8), duration of first stage of labour (OR 1.15), proteinuria (dipstick +++ OR 2.7), creatinine (OR 1.02) and uric acid (OR 1.5) were predictors of adverse neonatal outcome. Both models showed good discrimination (AUC 0.75 and 0.78), but calibration was limited (Hosmer-Lemeshow p = 0.41, and p = 0.20).
  
  
  CONCLUSIONS
  In women with GH or PE at term, it is difficult to predict neonatal complications, possibly since they are rare in the term pregnancy. However, the identified individual predictors may guide physicians to anticipate requirements for neonatal care.
authors.affiliation: Department of Obstetrics and Gynecology, University Medical Centre Groningen, University of Groningen , Groningen , the Netherlands .
authors.forename: K
authors.initials: K
authors.lastname: van der Tuuk
authors.forename: M A G
authors.initials: MA
authors.lastname: Holswilder-Olde Scholtenhuis
authors.forename: C M
authors.initials: CM
authors.lastname: Koopmans
authors.forename: E S A
authors.initials: ES
authors.lastname: van den Akker
authors.forename: P J M
authors.initials: PJ
authors.lastname: Pernet
authors.forename: L S M
authors.initials: LS
authors.lastname: Ribbert
authors.forename: C A
authors.initials: CA
authors.lastname: van Meir
authors.forename: K
authors.initials: K
authors.lastname: Boers
authors.forename: A P
authors.initials: AP
authors.lastname: Drogtrop
authors.forename: A J
authors.initials: AJ
authors.lastname: van Loon
authors.forename: M J C P
authors.initials: MJ
authors.lastname: Hanssen
authors.forename: J M J
authors.initials: JM
authors.lastname: Sporken
authors.forename: B W J
authors.initials: BW
authors.lastname: Mol
authors.forename: P P
authors.initials: PP
authors.lastname: van den Berg
authors.forename: H
authors.initials: H
authors.lastname: Groen
authors.forename: M G
authors.initials: MG
authors.lastname: van Pampus
mesh_terms: D000328:Adult; D001034:Apgar Score; D015331:Cohort Studies; D003661:Decision Support Techniques; D005260:Female; D005865:Gestational Age; D006801:Humans; D046110:Hypertension, Pregnancy-Induced; D007231:Infant, Newborn; D015931:Intensive Care, Neonatal / Q000706:statistics & numerical data*; D007751:Labor, Induced; D016015:Logistic Models; D011225:Pre-Eclampsia; D011247:Pregnancy; D011256:Pregnancy Outcome; D012372:ROC Curve; D012307:Risk Factors
keywords: Hypertension; neonatal outcome; prediction models; preeclampsia