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Development and validation of prediction models for the QUiPP App v.2: a tool for predicting preterm birth in women with symptoms of threatened preterm labor

Carter, J; Seed, PT; Watson, HA; David, AL; Sandall, J; Shennan, AH; Tribe, RM; (2020) Development and validation of prediction models for the QUiPP App v.2: a tool for predicting preterm birth in women with symptoms of threatened preterm labor. Ultrasound in Obstetrics & Gynecology , 55 (3) pp. 357-367. 10.1002/uog.20422. Green open access

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Abstract

OBJECTIVE: To develop enhanced prediction models to update the QUiPP app, a tool for predicting spontaneous preterm birth in women with symptoms of threatened preterm labour (TPTL), incorporating risk factors, transvaginal ultrasound assessment of cervical length (CL, mm) and cervicovaginal fluid quantitative fetal fibronectin test results (qfFN). METHODS: Participants were pregnant women between 23+0 and 34+6 weeks' gestation with symptoms of TPTL, recruited as part of four prospective cohort studies carried out at 16 UK hospitals between October 2010 and October 2017. The training set comprised all women where outcomes were known at May 2017 (n=1032). The validation set comprised women where outcomes were gathered between June 2017 and March 2018 (n=506). Parametric survival models were developed for three combinations of predictors: risk factors plus qfFN test, risk factors plus CL only, and risk factors plus both tests. The best models were selected using the Akaike and Bayesian information criteria. The estimated probability of delivery before 30, 34 or 37 weeks' gestation and within 1 or 2 weeks of testing was calculated and Receiver Operating Characteristic (ROC) curves were created to demonstrate the diagnostic ability of the prediction models. RESULTS: Predictive statistics were similar in training and validation sets. Areas under the ROC curves (validation set) demonstrated good prediction at all time points, particularly in the combination of risk factors plus qfFN model: 0.96 (<30 weeks); 0.85 (<34 weeks); 0.77 (<37 weeks); 0.91 (<1 week) and 0.92 (<2 weeks). CONCLUSIONS: Validation of these prediction models suggests the QUiPP v.2 app can reliably calculate risk of preterm delivery in women with TPTL. Use of the QUiPP app in practice could lead to better targeting of intervention, while providing reassurance and avoiding unnecessary intervention in women at low risk. This article is protected by copyright. All rights reserved.

Type: Article
Title: Development and validation of prediction models for the QUiPP App v.2: a tool for predicting preterm birth in women with symptoms of threatened preterm labor
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1002/uog.20422
Publisher version: https://doi.org/10.1002/uog.20422
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Preterm, eHealth, mHealth, mobile apps, prediction, risk assessment
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health > Maternal and Fetal Medicine
URI: https://discovery.ucl.ac.uk/id/eprint/10079989
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