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Investigating maternal risk factors for stillbirth in a population-based cohort in the South of England - 05/07/18

Doi : 10.1016/j.respe.2018.05.097 
G. Gardner , N. Ziauddeen, N. Alwan
 Academic Unit of Primary Care and Population Sciences, Faculty of Medicine, University of Southampton, Southampton, United Kingdom 

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Résumé

Introduction

In the UK there has been little improvement on the rates of stillbirth; the stillbirth rate was 2.9 per 1000 in 2015 (≥28 weeks gestation) and the annual rate reduction (ARR) of stillbirths was 1.4% from 2000–2015, which is roughly in the bottom one-third of high-income countries. Until now, most studies have only identified individual maternal risk factors associated with stillbirth and, to our knowledge, only one study has produced a stillbirth prediction model in the UK. Our study aimed to investigate risk factors for stillbirth (defined as a baby delivered with no signs of life after 24 weeks of pregnancy) using routinely-collected healthcare data at a regional level in the South of England.

Methods

Analysis of a population-based cohort using routine antenatal care and delivery data collected between 2003–2017 in Southampton, UK. Stillbirth was defined as born with no signs of life after 24 weeks gestation. Univariable comparisons between livebirth and stillbirth pregnancies were made using chi-squared test for categorical variables and independent t-test for continuous variables. Logistic regression modelling produced unadjusted and adjusted odd ratios (aOR) for stillbirth risk, adjusting for within-participant clustering of multiple pregnancies. A stillbirth prediction model was developed using backward stepwise regression. The model's derivation area under receiver operator curve (AUROC) was calculated. For the purpose of generating and testing a stillbirth risk score, the cohort was divided chronologically into derivation (n=44,108) and internal validation cohorts (n=24,684). An unweighted risk score variable consisting of the number of risk factors was generated for each pregnancy. A weighted risk score variable was generated taking into account the magnitude of the association of each risk factor with stillbirth (OR).

Results

The total sample size was 81,798 (81,366 livebirth pregnancies and 432 stillbirths), giving a stillbirth incidence rate (≥24 weeks gestation) of 5.28/1000 (95% CI 4.80–5.80). A total of15,813 births were excluded in the multivariable model due to missing data, hence giving a total number of 65,985 births (65,663 livebirths and 322 stillbirths). Significant risk factors for stillbirth in the multivariable model of the whole cohort (n=65,985) included: maternal age35 years (aOR 1.39, 95% CI 1.03–1.87), Asian ethnicity (aOR 1.80, 95% CI 1.22–2.66), mothers with unemployed partners (aOR 1.53 95% CI 1.07–2.18]), no previous livebirths (aOR 1.49, 95% CI 1.13–1.97), maternal educational qualification of secondary school or below (aOR 1.46, 95% CI 1.07–2.00) and college (aOR 1.49, 95% CI 1.05–2.12) (compared to university qualification or above), maternal obesity (aOR 1.58, 95% CI 1.18, 2.12), diastolic hypertension (aOR 4.3, 95% CI 2.04–9.06) and pre-existing diabetes (aOR 2.35, 95% CI 1.35, 4.11). Upon investigating the proportion of women in the study population (n=65 985) with significant risk factors identified in the multivariable analysis; 5126 (7.42%) had none, 25,430 (36.80%) had one, 27,442 (39.71%) had two, 9518 (13.77%) had three and 1586 (2.30%) had4 significant risk factors. When conducting the multivariable model in the derivation cohort (n=44 108) and testing the risk score in the validation cohort (n=24 684), for each additional significant risk factor, there was a 16% increased risk of stillbirth (95% CI 6–27%, P=0.001). The stillbirth prediction model's AUROC was 0.64 (95% CI 0.61–0.67).

Conclusion

This study identifies the characteristics associated with stillbirth in this English cohort and that the risk of stillbirth is amplified in women with co-existing risk factors. Derived using routinely-collected healthcare data, this prediction model, if externally validated, can help early identification of pregnant mothers at risk of stillbirth from their first antenatal appointment. Interventions attempting to prevent stillbirth could then be targeted at the high-risk mothers.

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© 2018  Publié par Elsevier Masson SAS.
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Vol 66 - N° S5

P. S273 - juillet 2018 Retour au numéro
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