Preterm birth prediction in asymptomatic women at mid-gestation using a panel of novel protein biomarkers: the Prediction of PreTerm Labor (PPeTaL) study

San Min Leow; Megan K W Di Quinzio; Zhen Long Ng; Claire Grant; Tal Amitay; Ying Wei; Moshe Hod; Penelope M Sheehan; Shaun P Brennecke; Nir Arbel; Harry M Georgiou

doi:10.1016/j.ajogmf.2019.100084

Preterm birth prediction in asymptomatic women at mid-gestation using a panel of novel protein biomarkers: the Prediction of PreTerm Labor (PPeTaL) study

Am J Obstet Gynecol MFM. 2020 May;2(2):100084. doi: 10.1016/j.ajogmf.2019.100084. Epub 2020 Jan 9.

Authors

Affiliations

¹ Carmentix Pte Ltd, Singapore.
² Department of Obstetrics and Gynecology University of Melbourne, Australia; Department of Obstetrics and Gynecology, Mercy Hospital for Women, Heidelberg VIC, Australia.
³ Department of Maternal-Fetal Medicine, Pregnancy Research Centre, Royal Women's Hospital, Parkville VIC, Australia.
⁴ Carmentix Australia Pty Ltd, Collingwood VIC, Australia.
⁵ Department of Obstetrics and Gynecology University of Melbourne, Australia; Department of Maternal-Fetal Medicine, Pregnancy Research Centre, Royal Women's Hospital, Parkville VIC, Australia.
⁶ Carmentix Pte Ltd, Singapore; Carmentix Australia Pty Ltd, Collingwood VIC, Australia.
⁷ Department of Obstetrics and Gynecology University of Melbourne, Australia; Department of Obstetrics and Gynecology, Mercy Hospital for Women, Heidelberg VIC, Australia; Department of Maternal-Fetal Medicine, Pregnancy Research Centre, Royal Women's Hospital, Parkville VIC, Australia. Electronic address: harrymg@unimelb.edu.au.

PMID: 33345955
DOI: 10.1016/j.ajogmf.2019.100084

Abstract

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.

Keywords: biomarker; cervical remodeling; cervicovaginal fluid; predictive test; pregnancy; prognostic test; protein biomarker; spontaneous preterm birth.

Publication types

Observational Study
Research Support, Non-U.S. Gov't

MeSH terms

Australia
Biomarkers
Cohort Studies
Female
Humans
Infant, Newborn
Obstetric Labor, Premature* / diagnosis
Pregnancy
Premature Birth* / diagnosis

Substances

Biomarkers