Development and validation of a tool incorporating quantitative fetal fibronectin to predict spontaneous preterm birth in symptomatic women

    loading  Checking for direct PDF access through Ovid



To develop a reliable and validated tool for prediction of spontaneous preterm birth (sPTB) in symptomatic women that incorporates quantitative measurements of fetal fibronectin (qfFN) and other relevant risk factors.


Data were analyzed that had been collected prospectively from 382 women who presented at an emergency assessment unit between 22 + 0 and 35 + 6 weeks' gestation with symptoms of preterm labor. Clinicians were blinded to qfFN although they were aware of qualitative fFN results. Parametric survival models for sPTB, with time-updated covariates, were developed for combinations of predictors and the best was selected using the Akaike and Bayesian information criteria. The model was developed on the first 190 consecutive women and validated on the subsequent 192. The estimated probability of delivery before 30, 34 or 37 weeks' gestation and within 2 or 4 weeks of testing was calculated for each patient and was compared to actual event rates. Predictive statistics were calculated to compare training and validation sets.


The final model that was selected used qfFN and previous sPTB/preterm prelabor rupture of membranes (PPROM) as predictors. Predictive statistics were similar for training and validation sets and there was good agreement between expected and observed sPTB for all outcomes. Areas under the receiver–operating characteristics curves ranged from 0.77 to 0.88, indicating accurate prediction across all five delivery outcomes.


sPTB in symptomatic women can be predicted accurately using a model combining qfFN and previous sPTB/PPROM. Clinicians can use this model, which has been incorporated into an App (QUiPP), to determine accurately a woman's risk of sPTB and potentially tailor management decisions appropriately. Copyright © 2015 ISUOG. Published by John Wiley & Sons Ltd.

Related Topics

    loading  Loading Related Articles