Development and validation of predictive models for QUiPP App v.2: tool for predicting preterm birth in asymptomatic high‐risk women
Authors: Watson H. A. , Seed P. T. , Carter J. , Hezelgrave N. L. , Kuhrt K. , Tribe R. M. , Shennan A. H. 
Published: July 2019

Objectives

Accurate mid‐pregnancy prediction of spontaneous preterm birth (sPTB) is essential to ensure appropriate surveillance of high‐risk women. Advancing the QUiPP App prototype, QUiPP App v.2 aimed to provide individualized risk of delivery based on cervical length (CL), quantitative fetal fibronectin (qfFN) or both tests combined, taking into account further risk factors, such as multiple pregnancy. Here we report development of the QUiPP App v.2 predictive models for use in asymptomatic high‐risk women, and validation using a distinct dataset in order to confirm the accuracy and transportability of the QUiPP App, overall and within specific clinically relevant time frames.

Methods

This was a prospective secondary analysis of data of asymptomatic women at high risk of sPTB recruited in 13 UK preterm birth clinics. Women were offered longitudinal qfFN testing every 2–4 weeks and/or transvaginal ultrasound CL measurement between 18 + 0 and 36 + 6 weeks' gestation. A total of 1803 women (3878 visits) were included in the training set and 904 women (1400 visits) in the validation set. Prediction models were created based on the training set for use in three groups: patients with risk factors for sPTB and CL measurement alone, with risk factors for sPTB and qfFN measurement alone, and those with risk factors for sPTB and both CL and qfFN measurements. Survival analysis was used to identify the significant predictors of sPTB, and parametric structures for survival models were compared and the best selected. The estimated overall probability of delivery before six clinically important time points (< 30, < 34 and < 37 weeks' gestation and within 1, 2 and 4 weeks after testing) was calculated for each woman and analyzed as a predictive test for the actual occurrence of each event. This allowed receiver‐operating‐characteristics curves to be plotted, and areas under the curve (AUC) to be calculated. Calibration was performed to measure the agreement between expected and observed outcomes.

Results

All three algorithms demonstrated high accuracy for the prediction of sPTB at < 30, < 34 and < 37 weeks' gestation and within 1, 2 and 4 weeks of testing, with AUCs between 0.75 and 0.90 for the use of qfFN and CL combined, between 0.68 and 0.90 for qfFN alone, and between 0.71 and 0.87 for CL alone. The differences between the three algorithms were not statistically significant. Calibration confirmed no significant differences between expected and observed rates of sPTB within 4 weeks and a slight overestimation of risk with the use of CL measurement between 22 + 0 and 25 + 6 weeks' gestation.

Conclusions

The QUiPP App v.2 is a highly accurate prediction tool for sPTB that is based on a unique combination of biomarkers, symptoms and statistical algorithms. It can be used reliably in the context of communicating to patients the risk of sPTB. Whilst further work is required to determine its role in identifying women requiring prophylactic interventions, it is a reliable and convenient screening tool for planning follow‐up or hospitalization for high‐risk women. Copyright © 2019 ISUOG. Published by John Wiley & Sons Ltd.