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

摘要

ABSTRACT Objective To develop enhanced prediction models to update the QUiPP App prototype, a tool providing individualized risk of spontaneous preterm birth (sPTB), for use in women with symptoms of threatened preterm labor (TPTL), incorporating risk factors, transvaginal ultrasound assessment of cervical length (CL) and cervicovaginal fluid quantitative fetal fibronectin (qfFN) test results. 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 whose outcomes were known in May 2017 ( n ?=?1032). The validation set comprised women whose 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 results alone, risk factors plus CL alone, and risk factors plus both qfFN and CL. The best models were selected using the Akaike and Bayesian information criteria. The estimated probability of sPTB ?30, ?34 or ?37?weeks' gestation and within 1 or 2?weeks of testing was calculated and receiver‐operating‐characteristics (ROC) curves were created to demonstrate the diagnostic ability of the prediction models. Results Conclusions Validation of the new prediction models suggests that the QUiPP App v.2 can reliably calculate risk of sPTB 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. Copyright ? 2019 ISUOG. Published by John Wiley & Sons Ltd.