Predicting stillbirth in a low resource setting

BMC Pregnancy and Childbirth

Research Article

Ibrahim Taiwo Adeleke¹  Evelyn Ansah²  Joris A. H. de Groot³  Mary Amoakoh-Coleman³  Gbenga A. Kayode³  Kerstin Klipstein-Grobusch⁴  Diederick E. Grobbee⁵ 

[1] Department of Health Information, Federal Medical Centre Bida, Bida, Nigeria;Ghana Health Service, Accra, Greater Accra Region, Ghana;Julius Global Health, Julius Center for Health Sciences and Primary Care

[2] University Medical Centre Utrecht, P.O. Box 85500, 3508, Utrecht, GA, The Netherlands;Julius Global Health, Julius Center for Health Sciences and Primary Care

[3] University Medical Centre Utrecht, P.O. Box 85500, 3508, Utrecht, GA, 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;Julius Global Health, Julius Center for Health Sciences and Primary Care

[4] University Medical Centre Utrecht, P.O. Box 85500, 3508, Utrecht, GA, The Netherlands;Global Geo and Health Data Center, Utrecht University, Utrecht, Netherlands;

关键词: Predicting;    Stillbirth;    Low-resource setting;   

DOI  :  10.1186/s12884-016-1061-2

 received in 2015-12-19, accepted in 2016-09-06,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundStillbirth 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.MethodsThis 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.ResultsWe 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)).ConclusionWe 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.

【 授权许可】

CC BY   
© The Author(s). 2016

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