Objective To develop a predictive tool for spontaneous preterm birth (sPTB)

Almost two thirds of women presenting with CPP have BPS. Large variations in prevalence may be due to variable study selection and quality. Clinicians need to actively investigate patients for BPS, a condition that appears to co-exist with endometriosis.

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ObjectiveTo develop a reliable and validated tool for prediction of spontaneous preterm birth (sPTB)

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 fol...

Key content The physiology of pregnancy and responses to exercise. The benefits of exercise in pregnancy. The potential harms related to exercise in pregnancy. Guidelines and recommendations for exercise in pregnancy. Learning objectives To understand the interaction between the physiology of exercise and pregnancy. To be able to counsel women regarding the benefits and harms of exercise in pregnancy. To recognise longer term benefits of exercise on maternal and fetal health. Ethical issues Consider the variable national recommendations and their impact on pregnant women. Acknowledge the lack of specific research on intensity and nature of exercise.

BackgroundIncreasingly, women of reproductive age participate in recreational running, but its impact on pregnancy outcome is unknown. We investigated whether running affects gestational age at delivery and birth weight as indicators of cervical integrity and placental function, respectively.Methods1293 female participants were recruited from parkrun, which organises weekly runs involving 1.25 million runners across 450 parks worldwide. Those under 16 or unable to provide outcome data were excluded. Women were categorised according to whether they continued to run during pregnancy or not. Those who continued were further stratified dependent on average weekly kilometres, and which trimester they ran until. Retrospectively collected primary outcomes were gestational age at delivery and birthweight centile. Other outcomes included assisted vaginal delivery rate and prematurity at clinically important gestations.ResultsThere was no significant difference in gestational age at delivery: 279.0 vs 279.6 days (mean difference 0.6 days, CI −1.3 to 2.4 days; P=0.55) or birthweight centile: 46.9%vs 44.9% (mean difference 2.0%, CI −1.3% to −5.3%; P=0.22) in women who stopped running and those who continued, respectively. Assisted vaginal delivery rate was increased in women who ran: 195/714 (27%) vs 128/579 (22%) (OR 1.32; CI 1.02 to 1.71; P=0.03).ConclusionContinuing to run during pregnancy does not appear to affect gestational age or birthweight centile, regardless of mean weekly distance or stage of pregnancy. Assisted vaginal delivery rates were higher in women who ran, possibly due to increased pelvic floor muscle tone. Randomised prospective analysis is necessary to further explore these findings.

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