Development and validation of a tool incorporating cervical length and quantitative fetal fibronectin to predict spontaneous preterm birth in asymptomatic high-risk women.

OBJECTIVE: To develop a predictive tool for spontaneous preterm birth (sPTB) in asymptomatic high-risk women that includes quantification of fetal fibronectin (fFN) along with cervical length (CL) measurement and other clinical factors.
METHODS: Data were analyzed that had been collected prospectively from 1249 women at high risk for sPTB attending preterm surveillance clinics. Clinicians were blinded to quantitative measurements of fFN (qfFN), although they were aware of qualitative fFN results. Parametric survival models for sPTB, with time-updated covariates, were developed and the best was selected using the Akaike and Bayesian information criteria. The model was developed on the first 624 consecutive women and validated on the subsequent 625. Fractional polynomials were used to accommodate possible non-linear effects of qfFN and CL. 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 analyzed as a predictive test for the actual occurrence of each event. Predictive statistics were calculated to compare training and validation sets.
RESULTS: The final model that was selected used a log-normal survival curve with CL, √qfFN and previous sPTB/preterm prelabor rupture of membranes as predictors. Predictive statistics were similar for training and validation sets. Areas under the receiver-operating characteristics curves ranged from 0.77 to 0.99, indicating accurate prediction across all five delivery outcomes.
CONCLUSIONS: sPTB in high-risk asymptomatic women can be predicted accurately using a model combining qfFN and CL, which supersedes the single-threshold fFN test, demographic information and obstetric history. This algorithm has been incorporated into an App (QUiPP) for widespread use.