• Paediatr Perinat Epidemiol · Jan 2014

    Predicting risk of spontaneous preterm delivery in women with a singleton pregnancy.

    • Nils-Halvdan Morken, Karin Källen, and Bo Jacobsson.
    • Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway; National Institute of Environmental Health Sciences, Epidemiology Branch, Durham, NC; Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway.
    • Paediatr Perinat Epidemiol. 2014 Jan 1; 28 (1): 11-22.

    BackgroundPrediction of a woman's risk of a spontaneous preterm delivery (PTD) is a core challenge and an unresolved problem in today's obstetric practice. The objective of this study was to develop prediction models for spontaneous PTD (<37 weeks).MethodsA population-based register study of women born in Sweden with spontaneous onset of delivery was designed using Swedish Medical Birth Register data for 1992-2008. Predictive variables were identified by multiple logistic regression analysis, and outputs were used to calculate adjusted likelihood ratios in primiparous (n = 199 272) and multiparous (n = 249 580) singleton pregnant women. The predictive ability of each model was validated in a separate test sample for primiparous (n = 190 936) and multiparous (n = 239 203) women, respectively.ResultsFor multiparous women, the area under the ROC curve (AUC) of 0.74 [95% confidence interval (CI) 0.73, 0.74] indicated a satisfying performance of the model, while for primiparous women, it was rather poor {AUC: 0.58 [95% CI 0.57, 0.58]}. For both primiparous and multiparous women, the prediction models were quite good for pregnancies with comparatively low risk for spontaneous PTD, whereas more limited to predict pregnancies with ≥30% risk of spontaneous PTD.ConclusionsSpontaneous PTD is difficult to predict in multiparous women and nearly impossible in primiparous, by using this statistical method in a large and unselected sample. However, adding clinical data (like cervical length) may in the future further improve its predictive performance.© 2013 John Wiley & Sons Ltd.

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