Prediction of neonatal metabolic acidosis in women with a singleton term pregnancy in cephalic presentation: An external validation study

Ewoud Schuit, Isis Amer-Wahlin, Rolf H H Groenwold, Ben W J Mol, Karel G M Moons, Anneke Kwee

Research output: Contribution to journalArticleResearchpeer-review

3 Citations (Scopus)

Abstract

Objective To externally validate two previously developed prognostic models that predict the risk for developing metabolic acidosis in newborns using both antepartum (model 1) and intrapartum (combined with antepartum, model 2) risk factors: parity, previous cesarean section, maternal diabetes mellitus, gestational age, induced onset of labor, meconium-stained amniotic fluid, and use of ST analysis. Study DesignThe two prediction models were applied in women in active labor at more than 36 gestational weeks with singleton fetuses in cephalic presentation and with high-risk pregnancies (n=5049) who were included in a Swedish randomized trial between December 1, 1998, and June 4, 2000. The prognostic ability of the models was determined using calibration and discrimination measures. ResultsOf 5049 infants in the validation population, 54 (1.1%) suffered from metabolic acidosis. After adjustment for incidence differences between the Dutch and Swedish cohorts, the prognostic models showed good calibration and moderate overall discrimination (C statistic 0.63, 95% confidence interval [CI] 0.55 to 0.71; and 0.64, 95% CI 0.55 to 0.72), for models 1 and 2, respectively). ConclusionExternal validation of the clinical prediction models for metabolic acidosis in Swedish infants showed good calibration and moderate discriminative ability. Updating of the models to enhance their predictive abilities seems indicated.

Original languageEnglish
Pages (from-to)681-686
Number of pages6
JournalAmerican Journal of Perinatology
Volume29
Issue number9
DOIs
Publication statusPublished - 28 May 2012
Externally publishedYes

Keywords

  • birth asphyxia
  • external validation
  • metabolic acidosis
  • neonate
  • prognostic model

Cite this