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Keywords:

  • Biomarkers;
  • cervicovaginal fluid;
  • pregnancy;
  • preterm labour;
  • proteomics

Objective

To identify cervicovaginal fluid (CVF) biomarkers predictive of spontaneous preterm birth in women with symptoms of preterm labour.

Design

Retrospective cohort study.

Setting

Melbourne, Australia.

Population

Women with a singleton pregnancy admitted to the Emergency Department between 22 and 36 weeks of gestation presenting with symptoms of preterm labour.

Methods

Two-dimensional electrophoresis was used to analyse the CVF proteome. Validation of putative biomarkers was performed using enzyme-linked immunosorbent assay (ELISA) in an independent cohort. Optimal concentration thresholds of putative biomarkers were determined and the predictive efficacy for preterm birth was compared with that of fetal fibronectin.

Main outcome measures

Prediction of spontaneous preterm labour within 7 days.

Results

Differentially expressed proteins were identified by proteomic analysis in women presenting with ‘threatened’ preterm labour without cervical change who subsequently delivered preterm (n = 12 women). ELISA validation using an independent cohort (n = 129 women) found albumin and vitamin D-binding protein (VDBP) to be significantly altered between women who subsequently experienced preterm birth and those who delivered at term. Prediction of preterm delivery within 7 days using a dual biomarker model (albumin/VDBP) provided 66.7% sensitivity, 100% specificity, 100% positive predictive value (PPV) and 96.7% negative predictive value (NPV), compared with fetal fibronectin yielding 66.7, 87.9, 36.4 and 96.2%, respectively (n = 64). Using the maximum number of screened samples, the predictive utility of albumin/VDBP yielded a sensitivity of 77.8%, specificity and PPV of 100% and NPV of 98.0% (n = 109).

Conclusions

The dual biomarker model of albumin/VDBP is more efficacious than fetal fibronectin in predicting spontaneous preterm delivery in symptomatic women within 7 days. A clinical diagnostic trial is required to test this model on a larger population to confirm these findings and to further refine the predictive values.