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BMC Pregnancy and Childbirth
Published in: BMC Pregnancy and Childbirth 1/2016

Open Access 01-12-2016 | Research article

Predicting stillbirth in a low resource setting

Authors: Gbenga A. Kayode, Diederick E. Grobbee, Mary Amoakoh-Coleman, Ibrahim Taiwo Adeleke, Evelyn Ansah, Joris A. H. de Groot, Kerstin Klipstein-Grobusch

Published in: BMC Pregnancy and Childbirth | Issue 1/2016

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Abstract

Background

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.

Methods

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.

Results

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)).

Conclusion

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.
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Metadata
Title
Predicting stillbirth in a low resource setting
Authors
Gbenga A. Kayode
Diederick E. Grobbee
Mary Amoakoh-Coleman
Ibrahim Taiwo Adeleke
Evelyn Ansah
Joris A. H. de Groot
Kerstin Klipstein-Grobusch
Publication date
01-12-2016
Publisher
BioMed Central
Published in
BMC Pregnancy and Childbirth / Issue 1/2016
Electronic ISSN: 1471-2393
DOI
https://doi.org/10.1186/s12884-016-1061-2

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