title: Predicting stillbirth in a low resource setting.
abstract: Stillbirth is a major contributor to perinatal mortality and it is particularly common in low- and middle-income countries, where annually about three million stillbirths occur in the third trimester. This study aims to develop a prediction model for early detection of pregnancies at high risk of stillbirth.
  
  
  
  This retrospective cohort study examined 6,573 pregnant women who delivered at Federal Medical Centre Bida, a tertiary level of healthcare in Nigeria from January 2010 to December 2013. Descriptive statistics were performed and missing data imputed. Multivariable logistic regression was applied to examine the associations between selected candidate predictors and stillbirth. Discrimination and calibration were used to assess the model's performance. The prediction model was validated internally and over-optimism was corrected.
  
  
  
  We developed a prediction model for stillbirth that comprised maternal comorbidity, place of residence, maternal occupation, parity, bleeding in pregnancy, and fetal presentation. As a secondary analysis, we extended the model by including fetal growth rate as a predictor, to examine how beneficial ultrasound parameters would be for the predictive performance of the model. After internal validation, both calibration and discriminative performance of both the basic and extended model were excellent (i.e. C-statistic basic model = 0.80 (95 % CI 0.78-0.83) and extended model = 0.82 (95 % CI 0.80-0.83)).
  
  
  
  We developed a simple but informative prediction model for early detection of pregnancies with a high risk of stillbirth for early intervention in a low resource setting. Future research should focus on external validation of the performance of this promising model.
authors.affiliation: Julius Global Health, Julius Center for Health Sciences and Primary Care|University Medical Centre Utrecht, P.O. Box 85500, 3508, GA, Utrecht, The Netherlands. g.a.kayode@umcutrecht.nl.
authors.forename: Gbenga A
authors.initials: GA
authors.lastname: Kayode
authors.affiliation: Julius Global Health, Julius Center for Health Sciences and Primary Care|University Medical Centre Utrecht, P.O. Box 85500, 3508, GA, Utrecht, The Netherlands.|Global Geo and Health Data Center, Utrecht University, Utrecht, Netherlands.
authors.forename: Diederick E
authors.initials: DE
authors.lastname: Grobbee
authors.affiliation: Julius Global Health, Julius Center for Health Sciences and Primary Care|University Medical Centre Utrecht, P.O. Box 85500, 3508, GA, Utrecht, The Netherlands.
authors.forename: Mary
authors.initials: M
authors.lastname: Amoakoh-Coleman
authors.affiliation: Department of Health Information, Federal Medical Centre Bida, Bida, Nigeria.
authors.forename: Ibrahim Taiwo
authors.initials: IT
authors.lastname: Adeleke
authors.affiliation: Ghana Health Service, Greater Accra Region, Accra, Ghana.
authors.forename: Evelyn
authors.initials: E
authors.lastname: Ansah
authors.affiliation: Julius Global Health, Julius Center for Health Sciences and Primary Care|University Medical Centre Utrecht, P.O. Box 85500, 3508, GA, Utrecht, The Netherlands.
authors.forename: Joris A H
authors.initials: JA
authors.lastname: de Groot
authors.affiliation: Julius Global Health, Julius Center for Health Sciences and Primary Care|University Medical Centre Utrecht, P.O. Box 85500, 3508, GA, Utrecht, The Netherlands.|Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Science, University of Witwatersrand, Johannesburg, South Africa.|Global Geo and Health Data Center, Utrecht University, Utrecht, Netherlands.
authors.forename: Kerstin
authors.initials: K
authors.lastname: Klipstein-Grobusch
mesh_terms: D000328:Adult; D005260:Female; D047109:Fetal Development; D006295:Health Resources / Q000600:supply & distribution*; D006801:Humans; D016015:Logistic Models; D015999:Multivariate Analysis; D009549:Nigeria; D011237:Predictive Value of Tests; D011247:Pregnancy; D018566:Pregnancy, High-Risk; D011296:Prenatal Diagnosis / Q000379:methods / Q000706:statistics & numerical data*; D012189:Retrospective Studies; D050497:Stillbirth
keywords: Low-resource setting; Predicting; Stillbirth