Background
Accurate prediction of spontaneous preterm labor/preterm birth in asymptomatic women remains an elusive clinical challenge because of the multi-etiological nature of preterm birth.
Conclusion
We have identified a panel of biomarkers that yield clinically useful diagnostic values when combined in a multiplex algorithm. The early identification of asymptomatic women at risk for preterm birth would allow women to be triaged to specialist clinics for further assessment and appropriate preventive treatment.
Methods
This was an observational cohort study of women delivering from December 2017 to February 2019 at 2 maternity hospitals in Melbourne, Australia. Cervicovaginal fluid samples were collected from asymptomatic women at gestational week 16+0-24+0, and biomarker concentrations were quantified by enzyme-linked immunosorbent assay. Women were assigned to a training cohort (n = 136) and a validation cohort (n = 150) based on chronological delivery dates.
Objective
The aim of this study was to develop and validate an immunoassay-based, multi-biomarker test to predict spontaneous preterm birth. Materials and
Results
Seven candidate biomarkers representing key pathways in utero-cervical remodeling were discovered by high-throughput bioinformatic search, and their significance in both in vivo and in vitro studies was assessed. Using a combination of the biomarkers for the first 136 women allocated to the training cohort, we developed an algorithm to stratify term birth (n = 124) and spontaneous preterm birth (n = 12) samples with a sensitivity of 100% (95% confidence interval, 76-100%) and a specificity of 74% (95% confidence interval, 66-81%). The algorithm was further validated in a subsequent cohort of 150 women (n = 139 term birth and n = 11 preterm birth), achieving a sensitivity of 91% (95% confidence interval, 62-100%) and a specificity of 78% (95% confidence interval, 70-84%).