The QUiPP App: a safe alternative to a treat-all strategy for threatened preterm labor

Perinatal and neonatal outcomes of high-risk asymptomatic women from a specialist preterm birth surveillance clinic

OBJECTIVES

To report the perinatal outcomes of high-risk asymptomatic women who attended a specialist preterm surveillance clinic (PSC) to undergo screening for spontaneous preterm birth (PTB) in Ireland.

METHODS

Single center, retrospective cohort study of asymptomatic high risk women who attended the PSC between January 2019 and December 2022. A comprehensive database of all patients who attended the clinic during the study period was constructed and analyzed. Overall outcomes were reported, and stratified per the occurrence of preterm or term birth. Iatrogenic PTBs were included in the outcome data.

RESULTS

Following exclusions for loss-to-follow-up, 762 cases were analyzed, constituting 2262 PSC visits. Of those, 183 women were prescribed progesterone (24.0 %), and 100 women underwent cervical cerclage (13.1 %) to prevent spontaneous PTB. Overall, 2.4 %, 6.2 % and 18.6 % of participants gave birth prior to 30 weeks, 34 weeks, and 37 weeks, respectively. The median gestational age at birth for the entire cohort was 38.6 weeks (inter-quartile range (IQR) 37.2-39.6 weeks). Women who delivered < 37 weeks were significantly more likely to be smokers (p = 0.030), have a previous spontaneous PTB (p = 0.016), have multiple pregnancies (p < 0.001), type 1 or 2 diabetes (p = 0.044), or have a previous full dilatation caesarean section birth (p = 0.024). Infants born prior to 37 weeks were more likely to have a lower median birthweight (2270 vs 3300 g, p < 0.001), be admitted to a neonatal intensive care unit (53.8 % vs 2.3 %, p < 0.001) or experience short-term morbidity, including respiratory support (38.0 % vs 1.6 %, p < 0.001).

CONCLUSIONS

Over 80% of women deemed to be at high risk of PTB gave birth at term gestations following attendance at a PSC during pregnancy. Most women can be successfully managed without interventions, instead employing a policy of serial cervical surveillance, to identify those at greatest risk of PTB.

Performance of QUiPP App v.2 tool for prediction of preterm birth in asymptomatic high-risk women attending preterm specialist clinic: external validation study

OBJECTIVE

To validate externally the QUiPP App v.2 algorithms in an independent cohort of high-risk asymptomatic women attending a preterm birth (PTB) surveillance clinic in Ireland.

METHODS

This was a retrospective, single-center, observational study assessing discrimination and calibration of the QUiPP App v.2 at six predetermined clinical timepoints (PTB at < 30, < 34 and < 37 weeks of pregnancy and PTB within 1, 2 and 4 weeks of testing). Discrimination was assessed by estimating the area under the receiver-operating-characteristics curve (AUC) and sensitivity at fixed false-positive rates of 5%, 10% and 20%. Model calibration was assessed to evaluate the concordance between expected and observed outcomes. P-values < 0.05 were considered statistically significant. No adjustments for treatment effects were made.

RESULTS

Overall, 762 women with 1660 PTB surveillance clinic visits using the QUiPP App v.2 between 2019 and 2022 were analyzed. The study population included 142 (18.6%) patients who later experienced a PTB. The QuiPP App's performance in the prediction of short-term outcomes, such as birth within 1 week (AUC, 0.866 (95% CI, 0.755-0.955)), 2 weeks (AUC, 0.721 (95% CI, 0.569-0.854)) and 4 weeks (AUC, 0.775 (95% CI, 0.699-0.842)), and delivery at < 30 weeks (AUC, 0.747 (95% CI, 0.613-0.865)), was superior to its ability to predict longer-term outcomes (PTB at < 37 weeks: AUC, 0.631 (95% CI, 0.596-0.668)). Calibration was generally good for low-risk results, as the predicted risk in these patients tended to match the observed incidence. However, in women deemed to be at greater risk of PTB, the predicted probability superseded the observed incidence of PTB.

CONCLUSIONS

The QUiPP App v.2 accurately discriminates women who are at short-term risk of PTB. A treatment paradox may influence calibration in high-risk women. Further research is needed to ascertain if QuiPP treatment thresholds can be safely adjusted in women receiving prophylactic treatment to prevent PTB, and whether this improves the outcome. © 2024 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Preterm Birth: Screening and Prediction

Preterm birth (PTB) affects approximately 10% of births globally each year and is the most significant direct cause of neonatal death and of long-term disability worldwide. Early identification of women at high risk of PTB is important, given the availability of evidence-based, effective screening modalities, which facilitate decision-making on preventative strategies, particularly transvaginal sonographic cervical length (CL) measurement. There is growing evidence that combining CL with quantitative fetal fibronectin (qfFN) and maternal risk factors in the extensively peer-reviewed and validated QUanititative Innovation in Predicting Preterm birth (QUiPP) application can aid both the triage of patients who present as emergencies with symptoms of preterm labor and high-risk asymptomatic women attending PTB surveillance clinics. The QUiPP app risk of delivery thus supports shared decision-making with patients on the need for increased outpatient surveillance, in-patient treatment for preterm labor or simply reassurance for those unlikely to deliver preterm. Effective triage of patients at preterm gestations is an obstetric clinical priority as correctly timed administration of antenatal corticosteroids will maximise their neonatal benefits. This review explores the predictive capacity of existing predictive tests for PTB in both singleton and multiple pregnancies, including the QUiPP app v.2. and discusses promising new research areas, which aim to predict PTB through cervical stiffness and elastography measurements, metabolomics, extracellular vesicles and artificial intelligence.

Timing of antenatal corticosteroids and survival without neurologic disabilities at 5½ years in children born before 35 weeks of gestation

BACKGROUND

The efficacy of antenatal corticosteroids for neonatal preterm complications wanes beyond 7 days after treatment. The neurodevelopmental effects of longer treatment-to-birth intervals have not been adequately evaluated.

OBJECTIVE

This study aimed to assess the impact of antenatal corticosteroid timing on survival without moderate or severe neurologic disabilities at 5½ years.

STUDY DESIGN

This was a secondary analysis of the EPIPAGE-2 study, a national population-based cohort (France) that recruited neonates in 2011 and followed them up at 5½ years (results first reported in 2021). Participants were children born alive between 24+0 and 34+6 weeks, with a complete corticosteroid course, delivery >48 hours after the first injection, and neither limitation of care decided before birth nor severe congenital malformation. The study included 2613 children, 2427 of whom were alive at 5½ years; 71.9% (1739/2427) had a neurologic assessment at this age; 1537 had a clinical examination (complete for 1532), and 202 were assessed with a postal questionnaire. Exposure was defined as the interval between the first injection of the last antenatal corticosteroid course and delivery in days, studied in 2 categories (days 3-7 and after day 7), in 4 categories (days 3-7, 8-14, 15-21, and after day 21), and continuously in days. The main outcome was survival at 5½ years without moderate/severe neurologic disabilities, defined as moderate/severe cerebral palsy, or unilateral or bilateral blindness or deafness, or Full-Scale Intelligence Quotient 2 standard deviations below the mean. A multivariate analysis with a generalized estimated equation logistic regression model assessed the statistical association between the main outcomes and the interval from the first corticosteroid injection of the last course to birth. Multivariate analyses were adjusted for potential confounders, defined with a directed acyclic graph: gestational age in days, number of corticosteroid courses, multiple pregnancy, and cause of prematurity in 5 categories. Because neurologic follow-up was complete in only 63.2% of cases (1532/2427), the analyses used imputed data.

RESULTS

Among 2613 children, 186 died between birth and 5½ years. Overall survival was 96.6% (95% confidence interval, 95.9-97.0), and survival without moderate or severe neurologic disabilities was 86.0% (95% confidence interval, 84.7-87.0). Survival without moderate or severe neurologic disabilities was lower after day 7 (85.0%) than during the interval from day 3 to day 7 (87.0%) (adjusted odds ratio, 0.70; 95% confidence interval, 0.54-0.89).

CONCLUSION

The association of a >7-day interval between antenatal corticosteroid administration and birth with a lower rate of survival without moderate or severe neurologic disabilities among children aged 5½ years emphasizes the importance of better targeting women at risk of preterm delivery to optimize the timing and thus benefits of treatment.

Assessment of cervical softening and the prediction of preterm birth (STIPP): protocol for a prospective cohort study

INTRODUCTION

Preterm birth (PTB) is among the leading causes of perinatal and childhood morbidity and mortality. Therefore, accurate identification of pregnant women at high risk of PTB is key to enable obstetric healthcare professionals to apply interventions that improve perinatal and childhood outcomes. Serial transvaginal cervical length measurement is used to screen asymptomatic pregnant women with a history of PTB and identify those at high risk for a recurrent PTB. Cervical length measurement, fetal fibronectin test or a combination of both can be used to identify women at high risk of PTB presenting with symptoms of threatened PTB. The predictive capacity of these methods can be improved. Cervical softening is a precursor of cervical shortening, effacement and dilatation and could be a new marker to identify women a high risk of PTB. However, the predictive value of cervical softening to predict spontaneous PTB still needs to be determined.

METHODS AND ANALYSIS

This is a single-centre, prospective cohort study, conducted at the Amsterdam University Medical Centers in the Netherlands. Cervical softening will be investigated with a non-invasive CE-marked device called the Pregnolia System. This device has been developed to evaluate consistency of the cervix based on tissue elasticity. Two different cohorts will be investigated. The first cohort includes women with a history of spontaneous PTB <34 weeks. These women undergo biweekly measurements between 14 and 24 weeks of gestation. The second cohort includes women with symptoms of threatened PTB. These women will receive the measurement once at presentation between 24 and 34 weeks of gestation. The primary outcome is spontaneous PTB before 34 weeks for women with a history of PTB and delivery within 7 days for women with threatened PTB. The minimum sample size required to analyse the primary outcome is 227 women in the cohort of women with a history of PTB and 163 women in the cohort of women with symptoms of threatened PTB. Once this number is achieved, the study will be continued to investigate secondary objectives.

ETHICS AND DISSEMINATION

The study is approved by the Medical Ethics Committee of Amsterdam UMC (METC2022.0226). All patients will give oral and written informed consent prior to study entry. Results will be disseminated via a peer-reviewed journal.

TRIAL REGISTRATION NUMBER

NCT05477381.

Universal cervical length screening for preterm birth is not useful after 24 weeks of gestation

INTRODUCTION

Cervical length measurement using transvaginal sonography at 18+0 -24+0  weeks of gestation is used to identify women at risk of preterm delivery, who may benefit from treatment with progesterone to prevent premature birth. Few and conflicting data exist regarding the predictive value of cervical length measurement performed at later gestational ages. The primary objective of this study was to evaluate the predictive accuracy for spontaneous preterm birth of a single cervical length measurement performed between 24 and 32 weeks of gestation in asymptomatic singleton pregnancies at low risk for spontaneous preterm birth. The secondary objective was to test the predictive accuracy of different cervical length thresholds in the same population.

MATERIAL AND METHODS

This was a historical cohort study conducted in a tertiary referral hospital. A total of 2728 asymptomatic women with singleton pregnancy at low risk for spontaneous preterm birth were recruited. Of these women, 1548 had cervical length measured at 24+0 -27+6  weeks of gestation and 2191 women at 28+0 -32+0  weeks. In all, 1010 women were present in both gestational age windows. Maternal demographics, medical and obstetrical history, and pregnancy outcome were reviewed. The predictive value of cervical length for spontaneous preterm birth was evaluated through logistic regression analysis. Results were adjusted for confounding factors.

RESULTS

Overall, spontaneous preterm birth occurred in 53/2728 women (1.9%). In both the 24+0 -27+6 and 28+0 -32+0  weeks groups, a shorter cervical length was significantly associated with spontaneous preterm birth (p < 0.01), but it had a low predictive value, as shown by the receiver operating characteristics curve analysis (areas under the curve 0.62, 95% CI 0.50-0.74 for the 24+0 -27+6  weeks group, and 0.61, 95% CI 0.52-0.70 in the 28+0 -32+0  weeks group). When the predictive accuracy for preterm delivery of different cervical length cut-offs was evaluated, the sensitivity and positive predictive value were low in both gestational age windows, irrespective of the threshold used.

CONCLUSIONS

In asymptomatic women with singleton pregnancy at low risk for spontaneous preterm birth, the predictive value of cervical length after 24+0  weeks of gestation is low. Therefore, cervical length screening in these women should be discouraged.

Prediction of successful labor induction in persons with a low Bishop score using machine learning: Secondary analysis of two randomized controlled trials

BACKGROUND

The objective of this paper was to identify predictors of a vaginal birth in individuals with singleton pregnancies and a Bishop Score <4, following Induction of Labor (IoL) using dinoprostone vaginal insert (DVI). Secondarily, we sought to understand the association between oxytocin use for labor augmentation and IoL outcomes.

METHODS

We developed and internally validated a multivariate prediction model using machine learning (ML) applied to data from two Phase-III randomized controlled double-blind trials (NCT01127581, NCT00308711). The model was internally validated using 10-fold cross-validation.

RESULTS

This study included 1107 participants. Despite unfavorable cervical status and inclusion of high-risk pregnancies, 72% of participants had vaginal births. The model's area under receiver operating characteristic curve was 0.73. The following factors increased the chance of vaginal birth: being parous; being between 37 and 41 weeks of gestation; having a lower Body Mass Index; having a lower maternal age; having fewer maternal comorbidities; and having a higher Bishop score. Parity alone correctly predicted the outcome in ~50% of cases, at a ~10% false-negative rate. Participants whose labors progressed without requiring oxytocin had a higher probability of vaginal birth than those requiring oxytocin for either induction or augmentation (81% vs 70% vs 77%, respectively).

DISCUSSION

Even in high-risk pregnancies and with low Bishop scores, the use of DVI results in a high chance of vaginal birth. Parity is a critical predictor of success. The judicious use of oxytocin for labor induction or augmentation can increase the chance of vaginal birth. Our study validates the use of ML and predictive modeling for treatment response prediction when considering IoL.

The Tommy's Clinical Decision Tool, a device for reducing the clinical impact of placental dysfunction and preterm birth: protocol for a mixed-methods early implementation evaluation study

Background

Disparities in stillbirth and preterm birth persist even after correction for ethnicity and social deprivation, demonstrating that there is wide geographical variation in the quality of care. To address this inequity, Tommy’s National Centre for Maternity Improvement developed the Tommy’s Clinical Decision Tool, which aims to support the provision of “the right care at the right time”, personalising risk assessment and care according to best evidence. This web-based clinical decision tool assesses the risk of preterm birth and placental dysfunction more accurately than current methods, and recommends best evidenced-based care pathways in a format accessible to both women and healthcare professionals. It also provides links to reliable sources of pregnancy information for women. The aim of this study is to evaluate implementation of Tommy’s Clinical Decision Tool in four early-adopter UK maternity services, to inform wider scale-up.

Methods

The Tommy’s Clinical Decision Tool has been developed involving maternity service users and healthcare professionals in partnership. This mixed-methods study will evaluate: maternity service user and provider acceptability and experience; barriers and facilitators to implementation; reach (whether particular groups are excluded and why), fidelity (degree to which the intervention is delivered as intended), and unintended consequences. Data will be gathered over 25 months through interviews, focus groups, questionnaires and through the Tommy’s Clinical Decision Tool itself. The NASSS framework (Non-adoption or Abandonment of technology by individuals and difficulties achieving Scale-up, Spread and Sustainability) will inform data analysis.

Discussion

This paper describes the intervention, Tommy’s Clinical Decision Tool, according to TiDIER guidelines, and the protocol for the early adopter implementation evaluation study. Findings will inform future scale up.

Trial registration

This study was prospectively registered on the ISRCTN registry no. 13498237, on 31^st January 2022.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12884-022-04867-w.

Causes of perinatal deaths in Australia: Slow progress in the preterm period

AIM

The majority of perinatal deaths occur in the preterm period; however, current approaches predominantly focus on prevention in the term period. Reducing perinatal deaths in the preterm period is, therefore, key to reducing the rates of perinatal death overall in Australia. The aim was to understand the classifications of causes of preterm stillbirth and neonatal death in Victoria over time and by gestation.

MATERIALS AND METHODS

Retrospective study using state-wide, publicly available data. All births in Victoria between 2010 and 2018 included in the Victorian Perinatal Data Collection, excluding terminations of pregnancy for maternal psychosocial indications, were studied. Differences in causes of preterm perinatal mortality gestation group and over time were determined.

RESULTS

Out of 7977 perinatal deaths reported, 85.9% (n = 6849) were in the preterm period. The most common cause of preterm stillbirths was congenital anomalies (n = 1574, 29.8%), followed by unexplained antepartum deaths (n = 557, 14.2%). The most common cause of preterm neonatal death was spontaneous preterm birth (sPTB; n = 599, 38.2%), followed by congenital anomalies (n = 493, 31.4%). The rate of preterm stillbirths due to hypertension (-14.9% (95% CI -27.1% to -2.7%; P = 0.02)), maternal conditions (-24.1% (95% CI -44.2% to -4.0%; P = 0.03)) and those that were unexplained (-5.4% (95% CI -9.8% to -1.2%; P = 0.02)) decreased per annum between 2010 and 2018. All other classifications did not change significantly over time.

CONCLUSION

Prevention of congenital anomalies and sPTB is critical to reducing preterm perinatal mortality. Greater emphasis on understanding causes of preterm deaths through mortality investigations may reduce the proportion of those considered 'unexplained'.

Antenatal corticosteroids-to-birth interval in preterm birth

Objectives: The purpose of this study was to compare short-term outcomes in children born between 24 and 34 weeks' gestation, according to observed antenatal corticosteroids (ACS)-to-birth intervals. Research question: 'Is there a difference in short-term outcomes between observed ACS-to-birth intervals across a range of gestational ages at birth?'Methods: Cohort study assessing differences in incidence of short-term neonatal outcomes according to the observed interval between the last administration of ACS and birth. Linear, non-weighted GEE models with an independence working correlation structure were fitted to infant level data providing valid point estimates for either incidence or rate differences (binary outcomes) or average differences (continuous outcomes).Results: Of 886 children, 35.9% were born within 2 days after the last administration of ACS, 32.2% within 2 to 7 days, 14.1% within 8 to 14 days, and 17.8% more than 14 days after. Across gestational ages at birth, there were no differences in birth weight between children born at an ACS-to-birth interval of 7 days or less compared to more than 7 days, nor were there differences in respiratory outcomes, cerebral outcomes, or composite outcome.Conclusion: Drawing conclusions on the importance of the ACS-to-birth interval is difficult due to the post-hoc nature of the variable. In the absence of tools to better estimate if and when PTB will occur, it might not have any value in daily practice, regardless of whether there is an optimal ACS-to-birth interval or not.

How can obstetricians improve outcomes for infants born extremely preterm?

Prematurity remains a leading cause of perinatal morbidity and mortality, and also has significant implications for long-term health. Obstetricians have a key role to play in improving outcomes for infants born at extremely preterm gestations. This review explores the evidence for interventions available to obstetricians caring for women at risk of birthing at extremely preterm gestations, including antenatal corticosteroids, magnesium sulfate, tocolysis and antibiotics. It also addresses the importance of strategies to facilitate safe in-utero transfer, to maximise the chance of extremely preterm births occurring in tertiary centers, and the clinical value of strategies by which preterm birth can be predicted. The paper concludes with an appraisal of evidence for different modes of birth at extremely preterm gestations, and for delayed cord clamping.

Clinicians' experiences of using and implementing a medical mobile phone app (QUiPP V2) designed to predict the risk of preterm birth and aid clinical decision making

Background

As the vast majority of women who present in threatened preterm labour (TPTL) will not deliver early, clinicians need to balance the risks of over-medicalising the majority of women, against the potential risk of preterm delivery for those discharged home. The QUiPP app is a free, validated app which can support clinical decision-making as it produces individualised risks of delivery within relevant timeframes. Recent evidence has highlighted that clinicians would welcome a decision-support tool that accurately predicts preterm birth.

Methods

Qualitative interviews were undertaken as part of the EQUIPTT study (The Evaluation of the QUiPP app for Triage and Transfer) (REC: 17/LO/1802) which aimed to evaluate the impact of the QUiPP app on management of TPTL. Individual semi-structured telephone interviews were used to explore clinicians’ (obstetricians’ and midwives’) experiences of using the QUiPP app and how it was implemented at their hospital sites. Thematic analysis was chosen to explore the meaning of the data, through a framework approach.

Results

Nineteen participants from 10 hospital sites in England took part. Data analysis revealed three overarching themes which were: ‘experience of using the app’, ‘how QUiPP risk changes practice’ and ‘successfully adopting QUiPP: context is everything’. With these final themes we appeared to have achieved our aim of exploring the clinicians’ experiences of using and implementing the QUiPP app.

Conclusion

This study explored different clinician’s experiences of implementing the app. The organizational and cultural context at different sites appeared to have a large impact on how well the QUiPP app was implemented. Future work needs to be undertaken to understand how best to embed the intervention within different settings. This will inform scale up of QUiPP app use across the UK and ensure that clinicians have access to this free, easy-to-use tool which can positively aid clinical decision making when caring for women in TPTL.

Clinical trial registry and registration number

ISRCTN 17846337, registered 08th January 2018, https://doi.org/10.1186/ISRCTN17846337.

A prognostic model, including quantitative fetal fibronectin, to predict preterm labour: the QUIDS meta-analysis and prospective cohort study

BACKGROUND

The diagnosis of preterm labour is challenging. False-positive diagnoses are common and result in unnecessary, potentially harmful treatments (e.g. tocolytics, antenatal corticosteroids and magnesium sulphate) and costly hospital admissions. Measurement of fetal fibronectin in vaginal fluid is a biochemical test that can indicate impending preterm birth.

OBJECTIVES

To develop an externally validated prognostic model using quantitative fetal fibronectin concentration, in combination with clinical risk factors, for the prediction of spontaneous preterm birth and to assess its cost-effectiveness.

DESIGN

The study comprised (1) a qualitative study to establish the decisional needs of pregnant women and their caregivers, (2) an individual participant data meta-analysis of existing studies to develop a prognostic model for spontaneous preterm birth within 7 days in women with symptoms of preterm labour based on quantitative fetal fibronectin and clinical risk factors, (3) external validation of the prognostic model in a prospective cohort study across 26 UK centres, (4) a model-based economic evaluation comparing the prognostic model with qualitative fetal fibronectin, and quantitative fetal fibronectin with cervical length measurement, in terms of cost per QALY gained and (5) a qualitative assessment of the acceptability of quantitative fetal fibronectin.

DATA SOURCES/SETTING

The model was developed using data from five European prospective cohort studies of quantitative fetal fibronectin. The UK prospective cohort study was carried out across 26 UK centres.

PARTICIPANTS

Pregnant women at 22⁺⁰-34⁺⁶ weeks' gestation with signs and symptoms of preterm labour.

HEALTH TECHNOLOGY BEING ASSESSED

Quantitative fetal fibronectin.

MAIN OUTCOME MEASURES

Spontaneous preterm birth within 7 days.

RESULTS

The individual participant data meta-analysis included 1783 women and 139 events of spontaneous preterm birth within 7 days (event rate 7.8%). The prognostic model that was developed included quantitative fetal fibronectin, smoking, ethnicity, nulliparity and multiple pregnancy. The model was externally validated in a cohort of 2837 women, with 83 events of spontaneous preterm birth within 7 days (event rate 2.93%), an area under the curve of 0.89 (95% confidence interval 0.84 to 0.93), a calibration slope of 1.22 and a Nagelkerke R ² of 0.34. The economic analysis found that the prognostic model was cost-effective compared with using qualitative fetal fibronectin at a threshold for hospital admission and treatment of ≥ 2% risk of preterm birth within 7 days.

LIMITATIONS

The outcome proportion (spontaneous preterm birth within 7 days of test) was 2.9% in the validation study. This is in line with other studies, but having slightly fewer than 100 events is a limitation in model validation.

CONCLUSIONS

A prognostic model that included quantitative fetal fibronectin and clinical risk factors showed excellent performance in the prediction of spontaneous preterm birth within 7 days of test, was cost-effective and can be used to inform a decision support tool to help guide management decisions for women with threatened preterm labour.

FUTURE WORK

The prognostic model will be embedded in electronic maternity records and a mobile telephone application, enabling ongoing data collection for further refinement and validation of the model.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42015027590 and Current Controlled Trials ISRCTN41598423.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 52. See the NIHR Journals Library website for further project information.

Development and rapid rollout of The QUiPP App Toolkit for women who arrive in threatened preterm labour

BACKGROUND

Often the first opportunity for clinicians to assess risk of preterm birth is when women present with threatened preterm labour symptoms (such as period-like pain, tightening's or back ache). However, threatened preterm labour symptoms are not a strong predictor of imminent birth. Clinicians are then faced with a complex clinical dilemma, the need to ameliorate the consequences of preterm birth requires consideration with the side-effects and costs. The QUiPP app is a validated app which can aid clinicians when they triage a women who is in threatened preterm labour.

AIM

Our aim was to produce a toolkit to promote a best practice pathway for women who arrive in threatened preterm labour.

METHODS

We worked with two hospitals in South London. This included the aid of a toolkit midwife at each hospital. We also undertook stakeholder focus groups and worked with two Maternity Voice Partnership groups to ensure a diverse range of voices was heard in the toolkit development. While we aimed to produce the toolkit in September 2020, we rapidly rolled out and produced the first version of the toolkit in April 2020 due to COVID-19. As the QUiPP app can reduce admissions and hospital transfers, there was a need to enable all hospitals in England to have access to the toolkit as soon as possible.

RESULTS

While the rapid rollout of The QUiPP App Toolkit due to COVID-19 was not planned, it has demonstrated that toolkits to improve clinical practice can be produced promptly. Through actively welcoming continued feedback meant the initial version of the toolkit could be continually and iteratively refined. The toolkit has been recommended nationally, with National Health Service England recommending the app and toolkit in their COVID-19 update to the Saving Babies Lives Care Bundle and in the British Association of Perinatal Medicine Antenatal Optimisation Toolkit.

Leveraging Technology to Improve Diabetes Care in Pregnancy

Pregnant women with diabetes are at higher risk of adverse outcomes. Prevention of such outcomes depends on strict glycemic control, which is difficult to achieve and maintain. A variety of technologies exist to aid in diabetes management for nonpregnant patients. However, adapting such tools to meet the demands of pregnancy presents multiple challenges. This article reviews the key attributes digital technologies must offer to best support diabetes management during pregnancy, as well as some digital tools developed specifically to meet this need. Despite the opportunities digital health tools present to improve the care of people with diabetes, in the absence of robust data and large research studies, the ability to apply such technologies to diabetes in pregnancy will remain imperfect.

Role of cervical length and placental alpha microglobulin-1 to predict preterm birth

INTRODUCTION

Preterm labor is the leading cause of premature mortality and morbidity. Therefore, to rule-in and rule-out preterm delivery is a very important issue in our clinical practice.

OBJECTIVE

The aim of this study was to investigate the value of placental alpha microglobulin-1 (PAMG-1) molecule positivity in cervicovaginal secretions of women who have a CL <25 mm and presenting with preterm labor symptoms to predict spontaneous preterm birth within seven days.

MATERIALS AND METHODS

This was a prospective cohort study conducted in Ankara University Department of Obstetrics and Gynecology between August 2017 and February 2019 on the patients who had Preterm labor symptoms, <25 mm transvaginal cervical length (CL), clinically intact membranes. The primary outcome of the study was the power of CL and PAMG-1 positivity on the prediction of preterm birth in seven days.

RESULTS

Sensitivity and specificity values of PAMG-1 in our study population to predict spontaneous preterm birth in seven days were calculated 52.94% and 98.84%, respectively, negative predictive value (NPV) and positive predictive value (PPV) were calculated 91.4% and 90%, respectively. When we investigated our data according to different CL cutoffs, sensitivity and NPV for 20 mm cutoff were 88.24% and 96.3% that was better than PAMG-1, but specificity and PPV were 60.47% and 30.61%, respectively, that was more ineffective than PAMG-1. If we calculate the values according to 15 mm and 10 mm CL cutoffs sensitivity values were 58.8% and 23.53%, specificity values were 81.4% and 91.86%, NPV were 90.9% and 85.87%, PPV were 38.46% and 36.36%, respectively. Finally, accuracy value of PAMG-1 to predict spontaneous preterm birth in seven days was 91.26% that was better than other CL cutoffs (20 mm, 15 mm, and 10 mm).

CONCLUSION

PAMG-1 molecule with high NPV and PPV (91.4% and 90%) combination will contribute our clinical decision on the population who had preterm labor symptoms and a CL shorter than 25 mm.

Overly optimistic prediction results on imbalanced data: a case study of flaws and benefits when applying over-sampling

Information extracted from electrohysterography recordings could potentially prove to be an interesting additional source of information to estimate the risk on preterm birth. Recently, a large number of studies have reported near-perfect results to distinguish between recordings of patients that will deliver term or preterm using a public resource, called the Term/Preterm Electrohysterogram database. However, we argue that these results are overly optimistic due to a methodological flaw being made. In this work, we focus on one specific type of methodological flaw: applying over-sampling before partitioning the data into mutually exclusive training and testing sets. We show how this causes the results to be biased using two artificial datasets and reproduce results of studies in which this flaw was identified. Moreover, we evaluate the actual impact of over-sampling on predictive performance, when applied prior to data partitioning, using the same methodologies of related studies, to provide a realistic view of these methodologies' generalization capabilities. We make our research reproducible by providing all the code under an open license.

Clinical information extraction for preterm birth risk prediction

This paper contributes to the pursuit of leveraging unstructured medical notes to structured clinical decision making. In particular, we present a pipeline for clinical information extraction from medical notes related to preterm birth, and discuss the main challenges as well as its potential for clinical practice. A large collection of medical notes, created by staff during hospitalizations of patients who were at risk of delivering preterm, was gathered and analyzed. Based on an annotated collection of notes, we trained and evaluated information extraction components to discover clinical entities such as symptoms, events, anatomical sites and procedures, as well as attributes linked to these clinical entities. In a retrospective study, we show that these are highly informative for clinical decision support models that are trained to predict whether delivery is likely to occur within specific time windows, in combination with structured information from electronic health records.

All the right moves: why in utero transfer is both important for the baby and difficult to achieve and new strategies for change

The best way to ensure that preterm infants benefit from relevant neonatal expertise as soon as they are born is to transfer the mother and baby to an appropriately specialised neonatal facility before birth (“ in utero”). This review explores the evidence surrounding the importance of being born in the right unit, the advantages of in utero transfers compared to ex utero transfers, and how to accurately assess which women are at most risk of delivering early and the challenges of in utero transfers.

Accurate identification of the women most at risk of preterm birth is key to prioritising who to transfer antenatally, but the administrative burden and pathway variation of in utero transfer in the UK are likely to compromise optimal clinical care. Women reported the impact that in utero transfers have on them, including the emotional and financial burdens of being transferred and the anxiety surrounding domestic and logistical concerns related to being away from home. The final section of the review explores new approaches to reforming the in utero transfer process, including learning from outside the UK and changing policy and guidelines. Examples of collaborative regional guidance include the recent Pan-London guidance on in utero transfers. Reforming the transfer process can also be aided through technology, such as utilising the CotFinder app.

In utero transfer is an unavoidable aspect of maternity and neonatal care, and the burden will increase if preterm birth rates continue to rise in association with increased rates of multiple pregnancy, advancing maternal age, assisted reproductive technologies, and obstetric interventions. As funding and capacity pressures on health services increase because of the COVID-19 pandemic, better prioritisation and sustained multi-disciplinary commitment are essential to maximise better outcomes for babies born too soon.

Logistic regression has similar performance to optimised machine learning algorithms in a clinical setting: application to the discrimination between type 1 and type 2 diabetes in young adults

BACKGROUND

There is much interest in the use of prognostic and diagnostic prediction models in all areas of clinical medicine. The use of machine learning to improve prognostic and diagnostic accuracy in this area has been increasing at the expense of classic statistical models. Previous studies have compared performance between these two approaches but their findings are inconsistent and many have limitations. We aimed to compare the discrimination and calibration of seven models built using logistic regression and optimised machine learning algorithms in a clinical setting, where the number of potential predictors is often limited, and externally validate the models.

METHODS

We trained models using logistic regression and six commonly used machine learning algorithms to predict if a patient diagnosed with diabetes has type 1 diabetes (versus type 2 diabetes). We used seven predictor variables (age, BMI, GADA islet-autoantibodies, sex, total cholesterol, HDL cholesterol and triglyceride) using a UK cohort of adult participants (aged 18-50 years) with clinically diagnosed diabetes recruited from primary and secondary care (n = 960, 14% with type 1 diabetes). Discrimination performance (ROC AUC), calibration and decision curve analysis of each approach was compared in a separate external validation dataset (n = 504, 21% with type 1 diabetes).

RESULTS

Average performance obtained in internal validation was similar in all models (ROC AUC ≥ 0.94). In external validation, there were very modest reductions in discrimination with AUC ROC remaining ≥ 0.93 for all methods. Logistic regression had the numerically highest value in external validation (ROC AUC 0.95). Logistic regression had good performance in terms of calibration and decision curve analysis. Neural network and gradient boosting machine had the best calibration performance. Both logistic regression and support vector machine had good decision curve analysis for clinical useful threshold probabilities.

CONCLUSION

Logistic regression performed as well as optimised machine algorithms to classify patients with type 1 and type 2 diabetes. This study highlights the utility of comparing traditional regression modelling to machine learning, particularly when using a small number of well understood, strong predictor variables.

Commentary on a combined approach to the problem of developing biomarkers for the prediction of spontaneous preterm labor that leads to preterm birth

INTRODUCTION

Globally, preterm birth has replaced congenital malformation as the major cause of perinatal mortality and morbidity. The reduced rate of congenital malformation was not achieved through a single biophysical or biochemical marker at a specific gestational age, but rather through a combination of clinical, biophysical and biochemical markers at different gestational ages. Since the aetiology of spontaneous preterm birth is also multifactorial, it is unlikely that a single biomarker test, at a specific gestational age will emerge as the definitive predictive test.

METHODS

The Biomarkers Group of PREBIC, comprising clinicians, basic scientists and other experts in the field, with a particular interest in preterm birth have produced this commentary with short, medium and long-term aims: i) to alert clinicians to the advances that are being made in the prediction of spontaneous preterm birth; ii) to encourage clinicians and scientists to continue their efforts in this field, and not to be disheartened or nihilistic because of a perceived lack of progress and iii) to enable development of novel interventions that can reduce the mortality and morbidity associated with preterm birth.

RESULTS

Using language that we hope is clear to practising clinicians, we have identified 11 Sections in which there exists the potential, feasibility and capability of technologies for candidate biomarkers in the prediction of spontaneous preterm birth and how current limitations to this research might be circumvented.

DISCUSSION

The combination of biophysical, biochemical, immunological, microbiological, fetal cell, exosomal, or cell free RNA at different gestational ages, integrated as part of a multivariable predictor model may be necessary to advance our attempts to predict sPTL and PTB. This will require systems biological data using "omics" data and artificial intelligence/machine learning to manage the data appropriately. The ultimate goal is to reduce the mortality and morbidity associated with preterm birth.

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

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

Predictive statistics were similar between the training and the validation sets at most outcome time points and for each combination of predictors. Areas under the ROC curves (AUC) demonstrated that all three algorithms had good accuracy for the prediction of sPTB at < 30, < 34 and < 37 weeks' gestation and within 1 and 2 weeks' post-testing in the validation set, particularly the model combining risk factors plus qfFN alone (AUC: 0.96 at < 30 weeks; 0.85 at < 34 weeks; 0.77 at < 37 weeks; 0.91 at < 1 week from testing; and 0.92 at < 2 weeks from testing).

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.

The use of specialised preterm birth clinics for women at high risk of spontaneous preterm birth: a systematic review

Background

Specialised preterm birth clinics care for women at high risk of spontaneous preterm birth. This systematic review assesses current practice within preterm birth clinics globally.

Methods

A comprehensive search strategy was used to identify all studies on preterm birth clinics on the MEDLINE, Embase, PsycINFO, CENTRAL and CINAHL databases. There were no restrictions to study design. Studies were limited to the English language and publications from 1998 onwards. Two reviewers assessed studies for inclusion, performed data extraction and reviewed methodological quality. Primary outcomes were referral criteria, investigations and interventions offered in preterm birth clinics. Secondary outcomes were the timing of planned first and last appointments and frequency of review.

Results

Thirty-two records fulfilled eligibility criteria and 20 studies were included in the main analysis following grouping of records describing the same study or clinic. Studies were of mixed study design and methodological quality. A total of 39 clinics were described; outcome data was not available for all clinics. Referral criteria included previous spontaneous preterm birth (38/38, 100%), previous mid-trimester loss (34/38, 89%) and previous cervical surgery (33/38, 87%). All clinics offered transvaginal cervical length scans. Additional investigations varied, including urogenital swabs (16/28, 57%) and fetal fibronectin (8/28, 29%). The primary treatment of choice for a sonographic short cervix was cervical cerclage in 10/33 (30%) clinics and vaginal progesterone in 6/33 (18%), with 10/33 (30%) using multiple first-line options and 6/33 (18%) using a combination of treatments. The majority of clinics planned timing of first review for 12–16 weeks (30/35, 86%) and the frequency of review was usually determined by clinical findings (18/24, 75%). There was a wide variation in gestational age at clinic discharge between 24 and 37 weeks.

Conclusions

There is variation in the referral criteria, investigations and interventions offered in preterm birth clinics and in the timing and frequency of review. Consistency in practice may improve with the introduction of consensus guidelines and national preterm birth prevention programmes.

Trial registration

Systematic review registration number: CRD42019131470.

Mobile phone apps for clinical decision support in pregnancy: a scoping review

Background

The use of digital technology in healthcare has been found to be useful for data collection, provision of health information and communications. Despite increasing use of medical mobile phone applications (apps), by both clinicians and patients, there appears to be a paucity of peer-reviewed publications evaluating their use, particularly in pregnancy. This scoping review explored the use of mobile phone apps for clinical decision support in pregnancy. Specific objectives were to: 1. determine the current landscape of mobile phone app use for clinical decision support in pregnancy; 2. identify perceived benefits and potential hazards of use and 3. identify facilitators and barriers to implementation of these apps into clinical practice.

Methods

Papers eligible for inclusion were primary research or reports on the development and evaluation of apps for use by clinicians for decision support in pregnancy, published in peer-reviewed journals. Research databases included Medline, Embase, PsychoInfo, the Cochrane Database of Systematic Reviews and the online digital health journals JMIR mHealth and uHealth. Charting and thematic analysis was undertaken using NVivo qualitative data management software and the Framework approach.

Results

After screening for eligibility, 13 papers were identified, mainly reporting early stage development of the mobile app, and feasibility or acceptability studies designed to inform further development. Thematic analysis revealed four main themes across the included papers: 1. acceptability and satisfaction; 2. ease of use and portability; 3. multi-functionality and 4. the importance of user involvement in development and evaluation.

Conclusions

This review highlights the benefits of mobile apps for clinical decision support in pregnancy and potential barriers to implementation, but reveals a lack of rigorous reporting of evaluation of their use and data security. This situation may change, however, following the issue of FDA and MHRA guidelines and implementation of UK government and other international strategies. Overall, the findings suggest that ease of use, portability and multi-functionality make mobile apps for clinical decision support in pregnancy useful and acceptable tools for clinicians.

The Biomarkers for Preterm Birth Study-A prospective observational study comparing the impact of vaginal biomarkers on clinical practice when used in women with symptoms of preterm labor

INTRODUCTION

This study aims to compare the use of qualitative fetal fibronectin, quantitative fetal fibronectin, and placental α-microglobulin-1 in women with symptoms of preterm labor, to evaluate which vaginal biomarker performs the best in clinical practice.

MATERIAL AND METHODS

This prospective observational study included women who presented with symptoms of preterm labor at 24⁺⁰ to 34⁺⁰  weeks of gestation at a large tertiary maternity hospital in Auckland, New Zealand. Women were managed according to hospital guidelines using qualitative fetal fibronectin. Quantitative fetal fibronectin and placental α-microglobulin-1 tests were also taken, with clinicians blinded to the results. Management and delivery outcomes were collected from clinical records. The primary outcome was the rate of antenatal hospital admission. Analysis was performed according to predefined management protocols for each of the tests.

RESULTS

A total of 128 women had all three biomarkers tests taken. Spontaneous preterm birth rates were 7/128 (5.5%) ≤34⁺⁰  weeks and 20/128 (15.6%) <37⁺⁰  weeks of gestation; 5/128 (3.9%) delivered within 7 days of testing. Positive results were recorded in 28 qualitative fetal fibronectin tests, 25 quantitative fetal fibronectin tests with 11 ≥200 ng/mL, and 16 placental α-microglobulin-1 tests. The use of quantitative fetal fibronectin or placental α-microglobulin-1 would have lowered antenatal admission rates: 27/128 (21.1%) for qualitative fetal fibronectin, 11/128 (8.6%) for quantitative fetal fibronectin (admission threshold ≥200 ng/mL), and 15/128 (11.7%) for placental α-microglobulin-1. No additional women with quantitative fetal fibronectin <200 ng/mL delivered within 7 days or missed corticosteroids compared with standard care (qualitative fetal fibronectin); however, an additional 3 cases had a false-negative placental α-microglobulin-1 and clinical care may have been compromised (no antenatal corticosteroids or admission).

CONCLUSIONS

The use of quantitative fetal fibronectin (admission threshold ≥200 ng/mL) has the potential to reduce the rate of antenatal admissions for women with symptoms of preterm labor without compromising use of antenatal interventions that improve outcomes for babies born preterm.

The role of medical smartphone apps in clinical decision-support: A literature review

INTRODUCTION

The now ubiquitous smartphone has huge potential to assist clinical decision-making across the globe. However, the rapid pace of digitalisation contrasts starkly with the slower rate of medical research and publication. This review explores the evidence base that exists to validate and evaluate the use of medical decision-support apps. The resultant findings will inform appropriate and pragmatic evaluation strategies for future clinical app developers and provide a scientific and cultural context for research priorities in this field.

METHOD

Medline, Embase and Cochrane databases were searched for clinical trials concerning decision support and smart phones from 2007 (introduction of first smartphone iPhone) until January 2019.

RESULTS

Following exclusions, 48 trials and one Cochrane review were included for final analysis. Whilst diagnostic accuracy studies are plentiful, clinical trials are scarce. App research methodology was further interrogated according to setting and decision-support modality: e.g. camera-based, guideline-based, predictive models. Description of app development pathways and regulation were highly varied. Global health emerged as an early adopter of decision-support apps and this field is leading implementation and evaluation.

CONCLUSION

Clinical decision-support apps have considerable potential to enhance access to care and quality of care, but the medical community must rise to the challenge of modernising its approach if it is truly committed to capitalising on the opportunities of digitalisation.

Prediction of recurrent preterm delivery in asymptomatic women- an anxiety reducing measure?

Objectives

The QUiPP application is used to predict the risk of recurrent preterm birth (PTB) in asymptomatic high risk women with a previous PTB. Our study aims to evaluate the impact of the use of the QUiPP app on maternal anxiety levels.

Study design

A retrospective cohort study on asymptomatic pregnant women attending the Prevention of Preterm Birth Clinic in a busy tertiary unit. Women included in the study had a history of previous PTB. The study assessment occurred at approximately 4 weeks prior to the gestation of the earliest previous PTB and included measurement of cervical length and vaginal fetal fibronectin. Data was inputted into the QUiPP application, which in turn estimated risk of preterm delivery at specific intervals. Measured outcomes were gestation at delivery, time from risk assessment to delivery, infant birth weight, NICU admission and length of stay. In addition, maternal anxiety levels were retrospectively assessed using a questionnaire with a Likert scale.

Results

Seventy six women were included in the study. All women were asymptomatic for preterm labour at assessment. The mean gestation at the time of risk assessment was 27 weeks, the mean time from risk assessment to delivery was 72 days. Average gestation at time of delivery was 37 weeks (range 22-42 weeks). The preterm birth rate was 29% (n = 22).Seventy seven percent of women who delivered <37 weeks, and 80% who delivered <34 weeks were given QUiPP scores predicting a ≥5% chance of PTB within four weeks of their actual delivery date. Sixteen percent of infants were admitted to NICU (n = 12) with a mean length of stay of 21 days. All infants went home well with their parents.Eighty four percent of respondents to our questionnaire reported feeling anxious about their pregnancy prior to attending the clinic. After receiving a QUIPP score 90% said they felt reassured and 79% reported that the felt less anxious.

Conclusion

In asymptomatic women, the use of the QUiPP app helps to predict, prevent, and optimise PTB. This surveillance has a beneficial role for maternal mental well-being in that it reduces anxiety at a key time during a pregnancy.

Spontaneous preterm labour that leads to preterm birth: An update and personal reflection

OBJECTIVE

The objective was to provide an update of progress made over time (including personal reflection) of our attempts to reduce the mortality and morbidity associated with spontaneous preterm labour that leads to preterm birth.

METHODS

An experienced and evidence based approach was taken to provide an overview of progress made over a generation (∼40 years) in our understanding of spontaneous preterm labour.

RESULTS

It is evident that we have made significant progress in our understanding of the aetiology, the measurement of the burden, the basic science, systems biology and mechanical pathways of the preterm parturition syndrome. We have better ways of predicting, preventing and managing spontaneous preterm labour than existed a generation ago.

CONCLUSIONS

The profile of spontaneous preterm labour that leads to preterm birth, thanks to organisations such as the March of Dimes, WHO and PREBIC is much more evident than before. However, while we have come a long way, we must not be complacent, and clinicians and basic scientists must continue to work in harmony, while recruiting and encouraging young investigators to join the effort to improve survival and handicap in what is one of the Great Obstetric Syndromes.

EQUIPTT: The Evaluation of the QUiPP app for Triage and Transfer protocol for a cluster randomised trial to evaluate the impact of the QUiPP app on inappropriate management for threatened preterm labour

Background

Accurate diagnosis of preterm labour is needed to ensure correct management of those most at risk of preterm birth and to prevent the maternal and fetal risks incurred by unnecessary interventions given to the large majority of women, who do not deliver within a week of presentation. Intervention “just-in-case” results in many avoidable admissions, women being transferred out of their local hospital unnecessarily and most women receiving unwarranted drugs, such as steroids and tocolytics. It also precludes appropriate transfers for others as neonatal cots are blocked pre-emptively, resulting in more dangerous ex-utero transfers. We have developed the QUiPP App which is a clinical decision-making aid based on previous outcomes of women, quantitative fetal fibronectin (qfFN) values and cervical length. It is hypothesised that using the QUiPP app will reduce inappropriate admissions and transfers.

Methods

A multi-site cluster randomised trial will evaluate whether the QUiPP app reduces inappropriate management for threatened preterm labour. The 13 participating centres will be randomly allocated to receive either intervention or control. If the QUiPP app calculates risk of delivery within 7 days to be is less than 5%, clinicians are advised that interventions may be withheld. Women’s experience of threatened preterm labour assessment will be explored using self-completed questionnaires, with a subset of participants being invited to semi-structured interview. A health economics analysis is also planned.

Discussion

We hypothesise that the QUiPP app will improve identification of the most appropriate women for admission and transfer and ensure that therapies known to reduce risk of preterm neonatal morbidities are offered to those who need them. We will determine which women do not require these therapies, thereby reducing over-medicalisation and the associated maternal and fetal risks for these women. The findings will inform future national guidelines on threatened preterm labour. Beyond obstetrics, evaluating the impact of an app in an emergency setting, and our emphasis on balancing harms of over-treatment as well as under-treatment, make EQUIPTT a valuable contribution to translational medicine.

Trial registration

The EQUIPTT trial was prospectively registered on 16th January 2018 with the ISRCTN registry (no. 17846337).

The prediction of preterm delivery: What is new?

Preterm birth, defined as birth occurring prior to 37 weeks gestation, is a serious obstetric problem accounting for 11% of pregnancies worldwide. It is associated with significant neonatal morbidity and mortality. Predictive tests for preterm birth are incredibly important, given the huge personal, economic, and health impacts of preterm birth. They can provide reassurance for women who are unlikely to deliver early, but they are also important for highlighting those women at higher risk of premature delivery so that we can offer prophylactic interventions and help guide antenatal management decisions. Unfortunately, there is unlikely to be a single test for predicting preterm birth, but a combination of tests is likely to improve clinical prediction. This review explores the clinical utility of the currently marketed predictive tests for preterm birth in both singleton and multiple pregnancies, as well as discussing novel predictive tests that may be useful in the future.

Rationale & design of the PROMISES study: a prospective assessment and validation study of salivary progesterone as a test for preterm birth in pregnant women from rural India

Background

In India, 3.6 million pregnancies are affected by preterm birth annually, with many infants dying or surviving with disability. Currently, there is no simple test available for screening all women at risk of spontaneous PTB in low income setting, although high resource settings routinely use cervical length measurement and cervico-vaginal fluid fetal fibronectin for identification and care of women at risk due to clinical history. In rural India, where the public health system has limited infrastructure, trained staff and equipment, there is a greater need to develop a low-cost screening approach for providing early referral, treatment and remedial support for pregnant women at risk of preterm birth. There is interest in the use of a salivary progesterone test as a screening tool preliminary evidence from India, Egypt and UK has shown promise for this type of test. The test requires further validation in a low resource community setting.

Methods

The Promises study aims to validate and test the feasibility of introducing a low-cost salivary progesterone preterm birth prediction test in two rural districts in India with high rates of prematurity. It is a prospective study of 2000 pregnant women recruited from Panna and Satna in Madhya Pradesh over approximately 24 months. Demographic and pregnancy outcome data will be collected, and pregnancies will be dated by ultrasound sonography. Saliva progesterone will be measured by ELISA in samples obtained between 24–28 weeks of gestation. The association between salivary progesterone and preterm birth will be determined and the utility of salivary progesterone to predict preterm birth < 34, as well as < 30 and < 37 weeks assessed. Additional qualitative data will be obtained in terms of acceptability and feasibility of saliva progesterone testing and knowledge of PTB.

Discussion

A validated cost-effective saliva test, which has potential for further adaptation to a ‘point of care’ setting will allow early identification of pregnant women at risk of preterm birth, who can be linked to an effective pathway of care and support to reduce preterm birth and associated adverse consequences. This will reduce both economic and emotional burden on the affected women and their families.

The fetal fibronectin test: 25 years after its development, what is the evidence regarding its clinical utility? A systematic review and meta-analysis

Background: The identification of women at risk for preterm birth should allow interventions which could improve neonatal outcome. Fetal fibronectin, a glycoprotein which acts normally as glue between decidua and amniotic membranes could be a good marker of impending labour when its concentration in cervicovaginal secretions between 22 and 36 weeks of gestation is ≥50 ng/mL. Many authors worldwide have tested this marker with many different methodologies and clinical settings, but conclusions about its clinical use are mixed. It is time for a comprehensive update through a systematic review and meta-analysis.Methods: We searched PubMed, Cochrane Library, and Embase, supplemented by manual search of bibliographies of known primary and review articles, international conference papers, and contact with experts from 1-1990 to 2-2018. We have selected all type of studies involving fetal fibronectin test accuracy for preterm delivery. Two authors independently extracted data about study characteristics and quality from identified publications. Contingency tables were constructed. Reference standards were preterm delivery before 37, 36, 35, 34, and 32 weeks, within 28, 21, 14, or 7 d and within 48 h. Data were pooled to produce summary likelihood ratios for positive and negative tests results.Results: One hundred and ninety-three primary studies were identified allowing analysis of 53 subgroups. In all settings, none of the summary likelihood ratios were >10 or <0.1, thus indicating moderate prediction, particularly in asymptomatic women and in multiple gestations.Conclusions: The fetal fibronectin test should not be used as a screening test for asymptomatic women. For high-risk asymptomatic women, and especially for women with multiple pregnancies, the performance of the fetal fibronectin test was also too low to be clinically relevant. Consensual use as a diagnostic tool for women with suspected preterm labor, the best use policy probably still depends on local contingencies, future cost-effectiveness analysis, and comparison with other more recent available biochemical markers.

Threatened preterm labour: Women's experiences of risk and care management: A qualitative study

BACKGROUND

Preterm birth is a major cause of neonatal death and severe morbidity, so pregnant women experiencing symptoms of threatened preterm labour may be very anxious. The risk assessment and management that follows recognition of threatened preterm labour has the potential to either increase or decrease this anxiety. The aim of this study was to explore women's experience of threatened preterm labour, risk assessment and management in order to identify potential improvements in practice.

DESIGN

One-to-one semi-structured interviews with 19 women who experienced assessment for threatened preterm labour took place between March 2015 and January 2017. A purposive sample approach was employed to ensure participants from different risk and demographic backgrounds were recruited at an inner city UK NHS hospital. Interviews were recorded and transcribed. Data was managed with NVivo software and analysed using the Framework Approach. A public and patient involvement panel contributed to the design, analysis and interpretation of the findings.

FINDINGS

Data saturation was achieved after 19 interviews. 11 women were low risk and 8 were high risk for preterm birth. All high risk women had experience of being supported by a specialist preterm team. Four main themes emerged: (i) coping with uncertainty; (ii) dealing with conflicts; (iii) aspects of care and (iv) interactions with professionals. Both low and high risk women experiencing TPTL struggle to cope with the uncertainty of this unpredictable state. The healthcare management they receive can both help and hinder their ability to cope with this extremely stressful experience. High risk women were less likely to receive conflicting advice.

KEY CONCLUSIONS AND IMPLICATIONS FOR PRACTICE

Clinicians should acknowledge uncertainty, minimize conflicting information and advice, and promote continuity of care models for all women, including those attending high risk clinics and in the ward environment.