Abstract
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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12884-016-1061-2) contains supplementary material, which is available to authorized users.
Highlights
Stillbirth is a major contributor to perinatal mortality and it is common in low- and middle-income countries, where annually about three million stillbirths occur in the third trimester
In this study we aimed to develop a prediction model to be applied in the second trimester of a pregnancy to identify pregnancies at high risk of stillbirth using routine clinical and non-clinical profiles of pregnant women who received care at a tertiary hospital in a low resource setting
Of previous pregnancies carried beyond viability i.e. up to 28 weeks gestational age), maternal education, maternal occupation, ethnicity, place of residence, previous fetal loss, bleeding in pregnancy, maternal height, number of previous caesarean sections, maternal weight, multiple gestation, sex, fetal presentation, fetal growth rate, and number of comorbid conditions
Summary
Stillbirth is a major contributor to perinatal mortality and it is common in low- and middle-income countries, where annually about three million stillbirths occur in the third trimester. Stillbirth is a major but silent contributor to perinatal mortality [1], and about 3 million third-trimester stillbirths [2, 3] occur annually, mainly (98 %) in lowand middle-income countries (LMICs) [4]. Only few attempts have been made to develop a decision making tool for early detection of pregnancies with a high risk of stillbirth but these models cannot be applied to low-resource settings. In this study we aimed to develop a prediction model to be applied in the second trimester of a pregnancy to identify pregnancies at high risk of stillbirth using routine clinical and non-clinical profiles of pregnant women who received care at a tertiary hospital in a low resource setting
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