News Release

New risk model may improve the prediction of preterm birth

Peer-Reviewed Publication


The health outcomes of preterm babies can be significantly improved by timely and appropriate interventions in women presenting with preterm labor. However, the non-specific nature of presenting signs and symptoms of preterm labor make it challenging to diagnose, and unnecessary overtreatment is both common and costly. A study published in PLOS Medicine by Sarah Stock at the University of Edinburgh, United Kingdom and colleagues suggests that a newly developed risk prediction model may improve the prediction of impending preterm births.

To improve the prediction of impending preterm birth, the researchers developed and validated a risk prediction model. The researchers first identified clinical risk factors for preterm labor by analyzing individual participant data from five European prospective cohort studies, including 1,783 pregnant European women, and used these to develop a model to predict risk of spontaneous preterm birth. This model was then externally validated in a prospective cohort study of 2,924 women with signs and symptoms of preterm labor from 26 consultant-led obstetric units in the United Kingdom, to demonstrate the difference between predicted and observed outcomes.

The authors found that using a risk prediction model that included vaginal fluid fetal fibronectin concentration analysis alongside clinical risk factors improved the prediction of impending spontaneous preterm birth and was cost-effective in comparison to fetal fibronectin alone. The study noted several limitations, including few non-White participants, as well as missing data in the risk predictor development cohort. Further studies are required to determine whether the risk prediction model improves clinical outcomes in practice.

According to the authors, "The risk prediction model showed promising performance in the prediction of spontaneous preterm birth within seven days of testing and can be used as part of a decision support tool to help guide management decisions for women at risk of preterm labor. It is readily implementable, with potential for immediate benefit to women and babies and health services, through avoidance of unnecessary admission and treatment".

Dr Stock notes, "The vast majority of women with signs and symptoms of preterm labour don't actually give birth early, but many receive unnecessary hospital admission just in case of preterm birth. The risk predictor developed by our research team will help women to understand their chance of giving birth early, so they can decide whether or not to have admission and treatment. We are now working towards linking the predictor to maternity records, so it can easily be used as part of women's care and be continually improved as more women use it."


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Funding: This publication presents independent research funded by the National Institute for Health Research (NIHR) HTA Programme (Project Number 14/32/01). All listed authors were funded via this programme. Funder website URL: The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. SJS is funded by the Wellcome Trust (209560/Z/17/Z). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We are grateful to HOLOGIC (Marlborough, MA, USA) for providing adapted analyzers at each site allowing quantitative fFN values to be masked from clinicians and offered training and support to each site for fFN testing. They also allowed QUIDS sites to purchase tests at the lowest list price (NHS treatment cost). HOLOGIC had no involvement in data collection, analysis or interpretation, and no role in the writing of this manuscript or the decision to submit for publication. We are also grateful to PARSAGEN DIAGNOSTICS who gifted Partosure test kits for a substudy comparing different biochemical test of preterm labour (to be reported separately) and offered training and support to sites and MEDIX BIOCHEMICA who provided test kits at a reduced cost and offered training and support to sites. These companies had no other involvement in study design, implementation, analysis or interpretation of results.

Competing Interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: SJS is a member of PLOS Medicine's Editorial Board. All authors had financial support from National Institute for Health Research (NIHR) National Institute for Healthcare Research; SJS reports financial support form the Wellcome Trust (209560/Z/17/Z), non-financial support from HOLOGIC, non-financial support from PARSAGEN, and non-financial support from MEDIX BIOCHEMICA to support the conduct of the study; JD reports grants from Nutrinia in 2017 and 2018 which were part of his salary to work as an expert advisor on a trial; MC reports that she has done advisory work for HOLOGIC unrelated to the submitted work, and has been supported by HOLOGIC to attend a conference in the last 12 months; ALD reports personal fees from HOLOGIC outside the submitted work; and salary support from the NIHR UCLH/UCL Biomedical Research Centre; AK reports grants and prediction tests from Parsagen Diagnostics, paid to the institution, outside the submitted work; AS has grants and prediction tests from HOLOGIC, paid to the institution outside the submitted work; BM reports other grants from NHMRC, personal fees from ObsEva, Merck, Guerbet, and grants from Guerbet and Merck outside the submitted work; RDR reports personal fees from Roche outside the submitted work; JEN reports personal fees from Dilafor outside the submitted work; no other relationships or activities that could appear to have influenced the submitted work. All authors have completed the ICMJE uniform disclosure form at

Citation: Stock SJ, Horne M, Bruijn M, White H, Boyd KA, Heggie R, et al. (2021) Development and validation of a risk prediction model of preterm birth for women with preterm labour symptoms (the QUIDS study): A prospective cohort study and individual participant data meta-analysis. PLoS Med 18(7): e1003686.

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