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Development and validation of a predictive tool for spontaneous preterm birth, incorporating quantitative fetal fibronectin, in symptomatic women

Research output: Contribution to journalArticlepeer-review

Original languageEnglish
Pages (from-to)210-216
JournalUltrasound in Obstetrics and Gynecology
Issue number2
Early online date11 May 2015
Accepted/In press5 May 2015
E-pub ahead of print11 May 2015
PublishedFeb 2016

King's Authors


OBJECTIVE: Every year 15 million babies are born preterm but prediction of spontaneous preterm birth (sPTB) is poor which makes it difficult to target interventions appropriately. The aim of this study is to develop a reliable and validated tool, incorporating quantitative fetal fibronectin (qfFN) and other relevant risk factors, to predict sPTB in symptomatic women.

METHODS: The model was created by analyzing a prospective, observational, masked secondary dataset of 382 women (22(+0) -35(+6)  week's gestation) symptomatic of preterm labour. Parametric survival models, with time updated covariates for sPTB, were compared for combinations of predictors and the best selected using the Akaike and Bayesian Information Criteria. The model was developed on the first 190 women and validated on the remaining 192. Probabilities of delivery before 5 gestations (30, 34, 37 weeks' and within 2 or 4 weeks of test) were compared to actual event rates. Predictive statistics were calculated to compare training and validation sets.

RESULTS: The final model included qfFN and previous sPTB/ preterm pre-labour rupture of membranes (PPROM). Predictive statistics were similar for training and validation sets and there was good agreement between expected and observed sPTB across all outcomes. ROC areas, ranging from 0.77-0.88, indicated good prediction across a range of fFN thresholds.

CONCLUSIONS: sPTB can be more accurately predicted using a model combining qfFN and previous sPTB/ PPROM. Clinicians can use this model, which will be made available as an App, to accurately determine a woman's risk and to potentially tailor management decisions appropriately.

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