QUIDS Quantitative fibronectin to help decision-making in women with symptoms of preterm labour

Record ID 32015001237
Authors' objectives: The clinical diagnosis of preterm labour that leads to delivery is notoriously challenging. Up to 80% of women who have signs and symptoms of preterm labour remain pregnant after 7 days. This means that many women unnecessarily receive therapies aimed at preventing complications in preterm babies, to ensure benefit for the few babies that are actually born preterm. Possible treatments include steroids given to the mother to help mature preterm babies lungs; magnesium sulphate to help prevent brain damage in children born preterm; and transfer to a hospital so delivery will occur at a hospital with appropriate neonatal care facilities. In addition, treatments called tocolytics can be given to try to delay delivery until steroids are effective (48 hours) and to allow transfer to a different hospital, but there is little evidence that they improve outcomes for babies. If however, preterm delivery doesn t occur, these treatments are costly and potentially harmful to babies and women. Hospital admission and transfer can be particularly difficult for families, both financially and emotionally. A test called quantitative fetal Fibronectin (fFN) may help improve diagnosis of preterm labour. The test involves them measurement of fFN in a swab taken at speculum examination (like a smear test), which is part of the assessment of a woman presenting with signs and symptoms of preterm labour. The amount of fFN present in the sample can be measured in an analyzer that provides results in less than 10 minutes. The lower the concentration of fFN in the sample, the less likely preterm delivery is to occur. Although another type of fFN test, which provided a positive or negative result, has been available for some time, the ability to measure the absolute amount of fibronectin is new. This new test has the potential to more accurately rule out preterm labour. The main aim of this research is to see if qfFN can accurately rule out preterm delivery within 7 days of testing. We will analyse previous research data to see if qfFN is likely to be a useful test - either on its own, or in combination with clinical features that may increase the likelihood of preterm delivery (such as history of previous preterm labour or twin pregnancy). We will then determine which combination of features can help diagnose preterm labour most effectively, whilst still being good value to the NHS. In order to ensure that this 'model' works in UK populations, we will try using it to predict preterm delivery in women attending 8 UK maternity units with symptoms of preterm labour, and then adapt it as necessary. We use our findings to develop decision support tool, to help women and clinicians assess how likely preterm delivery is, and decide whether to start treatment or not. We will ask women, their partners and their caregivers which outcomes are most important when making decisions, and how best to present the decision support, to make sure it is relevant to them. We will make the decision support freely available, most likely as a web-based application. The work will be carried out over 30 months, by a team with the necessary expertise to complete the research. Public representatives will be involved in trial design, management and interpretation and dissemination of results. Patient advisory groups will also be regularly consulted, and women and their partners will be involved in the needs assessment to design the decision support.
Project Status: Ongoing
Anticipated Publish Date: 2021
English language abstract: An English language summary is available
Publication Type: Not Assigned
Country: England, United Kingdom
MeSH Terms
  • Decision Support Systems, Clinical
  • Decision Making
  • Female
  • Fibronectins
  • Obstetric Labor, Premature
  • Pregnancy
  • Infant, Newborn
  • Premature Birth
Organisation Name: NIHR Health Technology Assessment programme
Contact Address: NIHR Journals Library, National Institute for Health and Care 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
This is a bibliographic record of a published health technology assessment from a member of INAHTA or other HTA producer. No evaluation of the quality of this assessment has been made for the HTA database.