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

Stock, S. J. et al. (2021) A prognostic model, including quantitative fetal fibronectin, to predict preterm labour: the QUIDS meta-analysis and prospective cohort study. Health Technology Assessment, 25(52), (doi: 10.3310/hta25520) (PMID:34498576)

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. Study registration: This study is registered as PROSPERO CRD42015027590 and Current Controlled Trials ISRCTN41598423. Funding: This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 52. See the NIHR Journals Library website for further project information.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Heggie, Mr Robert and Jackson, Dr Lesley and Boyd, Professor Kathleen
• Authors: Stock, S. J., Horne, M., Bruijn, M., White, H., Heggie, R., Wotherspoon, L., Boyd, K., Aucott, L., Morris, R. K., Dorling, J., Jackson, L., Chandiramani, M., David, A., Khalil, A., Shennan, A., Baaren, G.-J. v., Hodgetts-Morton, V., Lavender, T., Schuit, E., Harper-Clarke, S., Mol, B., Riley, R. D., Norman, J., and Norrie, J.
• College/School: College of Medical Veterinary and Life Sciences > School of Health & Wellbeing > Health Economics and Health Technology Assessment; College of Medical Veterinary and Life Sciences > School of Medicine, Dentistry & Nursing
• Journal Name: Health Technology Assessment
• Publisher: NIHR Journals Library
• ISSN: 1366-5278
• ISSN (Online): 2046-4924
• Copyright Holders: Copyright © 2021 Queen’s Printer and Controller of HMSO
• First Published: First published in Health Technology Assessment 25(52)
• Publisher Policy: Reproduced in accordance with the publisher copyright policy