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Predicting Risk of Spontaneous Preterm Delivery in Women with a Singleton Pregnancy
Author(s) -
Morken NilsHalvdan,
Källen Karin,
Jacobsson Bo
Publication year - 2014
Publication title -
paediatric and perinatal epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.667
H-Index - 88
eISSN - 1365-3016
pISSN - 0269-5022
DOI - 10.1111/ppe.12087
Subject(s) - medicine , singleton , logistic regression , confidence interval , obstetrics , preterm delivery , pregnancy , population , gynecology , gestation , genetics , environmental health , biology
Background Prediction 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). Methods A population‐based register study of women born in S weden with spontaneous onset of delivery was designed using S wedish M edical B irth R egister 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. Results For 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 . Conclusions Spontaneous 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.

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