Preterm birth prediction in asymptomatic women at mid-gestation using a panel of novel protein biomarkers: the Prediction of PreTerm Labor (PPeTaL) study.
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Date
2020-05Source Title
American Journal of Obstetrics and Gynecology MFMPublisher
ElsevierAffiliation
Obstetrics and GynaecologyMetadata
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Journal ArticleCitations
Leow, S. M., Di Quinzio, M. K. W., Ng, Z. L., Grant, C., Amitay, T., Wei, Y., Hod, M., Sheehan, P. M., Brennecke, S. P., Arbel, N. & Georgiou, H. M. (2020). Preterm birth prediction in asymptomatic women at mid-gestation using a panel of novel protein biomarkers: the Prediction of PreTerm Labor (PPeTaL) study.. American Journal of Obstetrics and Gynecology MFM, 2 (2), pp.1-16. https://doi.org/10.1016/j.ajogmf.2019.100084.Access Status
Open AccessAbstract
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.
OBJECTIVE: The aim of this study was to develop and validate an immunoassay-based, multi-biomarker test to predict spontaneous preterm birth.
MATERIALS AND 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.
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%).
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.
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