A prognostic model, including quantitative fetal fibronectin, to predict preterm labour: the QUIDS meta-analysis and prospective cohort study

Sarah J. Stock* (Corresponding Author), Margaret A Horne, Merel M.C. Bruijn, Helen White, Robert Heggie, Lisa M. Wotherspoon, Kathleen A. Boyd, Lorna Aucott, Rachel K. Morris, Jon Dorling, Lesley Jackson, Manju Chandiramani, Anna L David, Asma Khalil, Andrew Shennan, Gert-Janvan Baaren, Hodgetts Morton Victoria, Tina Lavender, Ewoud Schuit, Susan Harper-ClarkeBen Willem J. Mol, Richard D. Riley, Jane Elizabeth Norman, John Norrie

*Corresponding author for this work

Research output: Book/ReportOther Report

Abstract

Background: The diagnosis of preterm labour is challenging. False-positive diagnoses are common and result in unnecessary, potentially harmful treatments (e.g. tocolytics, antenatal corticosteroids and magnesium sulphate) and costly hospital admissions. Measurement of fetal fibronectin in vaginal fluid is a biochemical test that can indicate impending preterm birth.
Objectives: To develop an externally validated prognostic model using quantitative fetal fibronectin concentration, in combination with clinical risk factors, for the prediction of spontaneous preterm birth and to assess its cost-effectiveness.
Design: The study comprised (1) a qualitative study to establish the decisional needs of pregnant women and their caregivers, (2) an individual participant data meta-analysis of existing studies to develop a prognostic model for spontaneous preterm birth within 7 days in women with symptoms of preterm labour based on quantitative fetal fibronectin and clinical risk factors, (3) external validation of the prognostic model in a prospective cohort study across 26 UK centres, (4) a model-based economic evaluation comparing the prognostic model with qualitative fetal fibronectin, and quantitative fetal fibronectin with cervical length measurement, in terms of cost per QALY gained and (5) a qualitative assessment of the acceptability of quantitative fetal fibronectin.
Data sources/setting: The model was developed using data from five European prospective cohort studies of quantitative fetal fibronectin. The UK prospective cohort study was carried out across 26 UK centres.
Participants: Pregnant women at 22+0–34+6 weeks’ gestation with signs and symptoms of preterm labour. Health technology being assessed: Quantitative fetal fibronectin.
Main outcome measures: Spontaneous preterm birth within 7 days.
Results: The individual participant data meta-analysis included 1783 women and 139 events of spontaneous preterm birth within 7 days (event rate 7.8%). The prognostic model that was developed included quantitative fetal fibronectin, smoking, ethnicity, nulliparity and multiple pregnancy. The model was externally validated in a cohort of 2837 women, with 83 events of spontaneous preterm birth within 7 days (event rate 2.93%), an area under the curve of 0.89 (95% confidence interval 0.84 to 0.93), a calibration slope of 1.22 and a Nagelkerke R2 of 0.34. The economic analysis found that the prognostic model was cost-effective compared with using qualitative fetal fibronectin at a threshold
for hospital admission and treatment of ≥ 2% risk of preterm birth within 7 days.
Limitations: The outcome proportion (spontaneous preterm birth within 7 days of test) was 2.9% in the validation study. This is in line with other studies, but having slightly fewer than 100 events is a limitation in model validation.
Conclusions: A prognostic model that included quantitative fetal fibronectin and clinical risk factors showed excellent performance in the prediction of spontaneous preterm birth within 7 days of test, was cost-effective and can be used to inform a decision support tool to help guide management decisions for women with threatened preterm labour.
Future work: The prognostic model will be embedded in electronic maternity records and a mobile telephone application, enabling ongoing data collection for further refinement and validation of
the model.
Original languageEnglish
PublisherNIHR Health Technology Assessment programme
Number of pages198
Volume25 (52)
DOIs
Publication statusPublished - 1 Sep 2021

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