Evidence map›Paper›PMID 34228735›Full record

Trial reportPLoS medicine2021

Evaluating the use of the QUiPP app and its impact on the management of threatened preterm labour: A cluster randomised trial.

Helena A Watson, Naomi Carlisle, Paul T Seed, Jenny Carter, Katy Kuhrt, Rachel M Tribe, Andrew H Shennan

Open access · goldAbstract readRandomized Controlled Trial
In one paragraph

Trial report in PLoS medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
2.6field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
  6. External validation of QUiPP App in three independent European cohorts of symptomatic women.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 2025
    Article
  7. Article
  8. Article
  9. Article
  10. Preterm Birth: Screening and Prediction.International journal of women's health · 2023
    Review
  11. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Helena A WatsonDepartment of Women and Children's Health, School of Life Course Sciences, King's College London, St Thomas' Hospital, London.ORCID 0000-0002-6332-1907
Naomi CarlisleDepartment of Women and Children's Health, School of Life Course Sciences, King's College London, St Thomas' Hospital, London.ORCID 0000-0001-8943-8700
Paul T SeedDepartment of Women and Children's Health, School of Life Course Sciences, King's College London, St Thomas' Hospital, London.ORCID 0000-0001-7904-7933
Jenny CarterDepartment of Women and Children's Health, School of Life Course Sciences, King's College London, St Thomas' Hospital, London.ORCID 0000-0003-2550-6465
Katy KuhrtDepartment of Women and Children's Health, School of Life Course Sciences, King's College London, St Thomas' Hospital, London.ORCID 0000-0001-5016-0430
Rachel M TribeDepartment of Women and Children's Health, School of Life Course Sciences, King's College London, St Thomas' Hospital, London.ORCID 0000-0003-3675-9978
Andrew H ShennanDepartment of Women and Children's Health, School of Life Course Sciences, King's College London, St Thomas' Hospital, London.ORCID 0000-0001-5273-3132
St Thomas' Hospital · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPreterm delivery (before 37 weeks of gestation) is the single most important contributor to neonatal death and morbidity, with lifelong repercussions. However, the majority of women who present with preterm labour (PTL) symptoms do not deliver imminently. Accurate prediction of PTL is needed in order ensure correct management of those most at risk of preterm birth (PTB) and to prevent the maternal and fetal risks incurred by unnecessary interventions given to the majority. The QUantitative Innovation in Predicting Preterm birth (QUIPP) app aims to support clinical decision-making about women in threatened preterm labour (TPTL) by combining quantitative fetal fibronectin (qfFN) values, cervical length (CL), and significant PTB risk factors to create an individualised percentage risk of delivery. METHODS AND

findingsEQUIPTT was a multi-centre cluster randomised controlled trial (RCT) involving 13 maternity units in South and Eastern England (United Kingdom) between March 2018 and February 2019. Pregnant women (n = 1,872) between 23+0 and 34+6 weeks' gestation with symptoms of PTL in the analysis period were assigned to either the intervention (762) or control (1,111). The mean age of the study population was 30.2 (+/- SD 5.93). A total of 56.0% were white, 19.6% were black, 14.2% were Asian, and 10.2% were of other ethnicities. The intervention was the use of the QUiPP app with admission, antenatal corticosteroids (ACSs), and transfer advised for women with a QUiPP risk of delivery >5% within 7 days. Control sites continued with their conventional management of TPTL. Unnecessary management for TPTL was a composite primary outcome defined by the sum of unnecessary admission decisions (admitted and delivery interval >7 days or not admitted and delivery interval ≤7 days) and the number of unnecessary in utero transfer (IUT) decisions/actions (IUT that occurred or were attempted >7 days prior to delivery) and ex utero transfers (EUTs) that should have been in utero (attempted and not attempted). Unnecessary management of TPTL was 11.3% (84/741) at the intervention sites versus 11.5% (126/1094) at control sites (odds ratio [OR] 0.97, 95% confidence interval [CI] 0.66-1.42, p = 0.883). Control sites frequently used qfFN and did not follow UK national guidance, which recommends routine treatment below 30 weeks without testing. Unnecessary management largely consisted of unnecessary admissions which were similar at intervention and control sites (10.7% versus 10.8% of all visits). In terms of adverse outcomes for women in TPTL <36 weeks, 4 women from the intervention sites and 12 from the control sites did not receive recommended management. If the QUiPP percentage risk was used as per protocol, unnecessary management would have been 7.4% (43/578) versus 9.9% (134/1,351) (OR 0.72, 95% CI 0.45-1.16). Our external validation of the QUiPP app confirmed that it was highly predictive of delivery in 7 days; receiver operating curve area was 0.90 (95% CI 0.85-0.95) for symptomatic women. Study limitations included a lack of compliance with national guidance at the control sites and difficulties in implementation of the QUiPP app.

conclusionsThis cluster randomised trial did not demonstrate that the use of the QUiPP app reduced unnecessary management of TPTL compared to current management but would safely improve the management recommended by the National Institute for Health and Care Excellence (NICE). Interpretation of qfFN, with or without the QUiPP app, is a safe and accurate method for identifying women most likely to benefit from PTL interventions.

trial registrationISRCTN Registry ISRCTN17846337.

Indexed as

Mobile ApplicationsAdultFemaleHumansObstetric Labor, PrematurePregnancy

Identifiers

PMID34228735
PMCPMC8291648
OpenAlexW3178976736

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.