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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


Background Preterm 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 findings EQUIPTT 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. Conclusions This 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.

Original languageEnglish
Article numbere1003689
JournalPLoS Medicine
Issue number7
Publication statusPublished - Jul 2021


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