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

Stock SJ, Horne M, Bruijn M, White H, Heggie R, Wotherspoon L, et al.
Record ID 32018001664
English
Authors' objectives: 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. 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.
Authors' results and conclusions: 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 ā‰„ā€‰2% risk of preterm birth within 7 days. 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.
Authors' methods: 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. 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. Pregnant women at 22+0ā€“34+6 weeksā€™ gestation with signs and symptoms of preterm labour. Quantitative fetal fibronectin. Spontaneous preterm birth within 7 days. 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.
Authors' identified further reserach: 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.
Details
Project Status: Completed
Year Published: 2021
URL for additional information: English
English language abstract: An English language summary is available
Publication Type: Full HTA
Country: England
DOI: 10.3310/hta25520
MeSH Terms
  • Premature Birth
  • Fibronectins
  • Biomarkers
  • Pregnancy
  • Pregnancy Complications
Keywords
  • PRETERM LABOUR
  • PREGNANCY
  • FETAL FIBRONECTIN
  • PROGNOSTIC MODEL
  • INDIVIDUAL PARTICIPANT DATA LEVEL META-ANALYSIS
  • NEONATAL
  • PARTURITION
Contact
Organisation Name: NIHR Health Technology Assessment programme
Contact Address: NIHR Journals Library, National Institute for Health Research, Evaluation, Trials and Studies Coordinating Centre, Alpha House, University of Southampton Science Park, Southampton SO16 7NS, UK
Contact Name: journals.library@nihr.ac.uk
Contact Email: journals.library@nihr.ac.uk
Copyright: Queen's Printer and Controller of HMSO
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