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

Screening for women at risk of spontaneous preterm birth, including cervical incompetence

Preterm births remain one of the biggest challenges in obstetrics worldwide. With the advancement of neonatal care, more premature neonates survive with long term consequences. Therefore, preventing or delaying preterm births starting from the preconceptional or antenatal periods are important. Among the numerous screening strategies described, not one can fit into all. Nonetheless, approaches including identifying women with modifiable risk factors for preterm births, genitourinary infections and short cervical length are the most useful. In this article, the current evidence is summarized and the best strategies for common clinical scenerios including cervical incompetence, history of second trimester loss or early preterm births, incidental short cervix and multiple pregnancy are discussed.

Predicting Spontaneous Preterm Birth Using the Immunome

Throughout pregnancy, the maternal peripheral circulation contains valuable information reflecting pregnancy progression, detectable as tightly regulated immune dynamics. Local immune processes at the maternal-fetal interface and other reproductive and non-reproductive tissues are likely to be the pacemakers for this peripheral immune "clock." This cellular immune status of pregnancy can be leveraged for the early risk assessment and prediction of spontaneous preterm birth (sPTB). Systems immunology approaches to sPTB subtypes and cross-tissue (local and peripheral) interactions, as well as integration of multiple biological data modalities promise to improve our understanding of preterm birth pathobiology and identify potential clinically actionable biomarkers.

Prediction and prevention of preterm birth: Quality assessment and systematic review of clinical practice guidelines using the AGREE II framework

BACKGROUND

Prediction of pregnancies at risk of preterm birth (PTB) may allow targeted prevention strategies.

OBJECTIVES

To assess quality of clinical practice guidelines (CPGs) and identify areas of agreement and contention in prediction and prevention of spontaneous PTB.

SEARCH STRATEGY

We searched for CPGs regarding PTB prediction and prevention in asymptomatic singleton pregnancies without language restriction in January 2024.

SELECTION CRITERIA

CPGs included were published between July 2017 and December 2023 and contained statements intended to direct clinical practice.

DATA COLLECTION AND ANALYSIS

CPG quality was assessed using the AGREE-II tool. Recommendations were extracted and grouped under domains of prediction and prevention, in general populations and high-risk groups.

MAIN RESULTS

We included 37 CPGs from 20 organizations; all were of moderate or high quality overall. There was consensus in prediction of PTB by identification of risk factors and cervical length screening in high-risk pregnancies and prevention of PTB by universal screening and treatment for asymptomatic bacteriuria, screening and treatment for BV in high-risk pregnancies, and use of preventative progesterone and cerclage. Areas of contention or limited consensus were the role of PTB clinics, universal cervical length measurement, biomarkers and cervical pessaries.

CONCLUSIONS

This review identified strengths and limitations of current PTB CPGs, and areas for future research.

Development and validation of a risk prediction model for preterm birth in women with gestational diabetes mellitus

OBJECTIVES

This study aims to develop and validate a prediction model for preterm birth in women with gestational diabetes mellitus (GDM).

DESIGN

We conducted a retrospective study on women with GDM who gave birth at the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China, between November 2017 and July 2021. We divided 1879 patients into a development set (n = 1346) and a validation set (n = 533). The development set was used to construct the prediction model for preterm birth using the stepwise logistic regression model. A nomogram and a web calculator were established based on the model. Discrimination and calibration were assessed in both sets.

PATIENTS AND MEASUREMENTS

Patients were women with GDM. Data were collected from medical records. GDM was diagnosed with 75-g oral glucose tolerance test during 24-28 gestational weeks. Preterm birth was definied as gestational age at birth <37 weeks.

RESULTS

The incidence of preterm birth was 9.4%. The predictive model included age, assisted reproductive technology, hypertensive disorders of pregnancy, reproductive system inflammation, intrahepatic cholestasis of pregnancy, high-density lipoprotein, homocysteine, and fasting blood glucose of 75-g oral glucose tolerance test. The area under the receiver operating characteristic curve for the development and validation sets was 0.722 and 0.632, respectively. The model has been adequately calibrated using a calibration curve and the Hosmer-Lemeshow test, demonstrating a correlation between the predicted and observed risk.

CONCLUSION

This study presents a novel, validated risk model for preterm birth in pregnant women with GDM, providing an individualized risk estimation using clinical risk factors in the third trimester of pregnancy.

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.

Management of preterm labor: Clinical practice guideline and recommendation by the WAPM-World Association of Perinatal Medicine and the PMF-Perinatal Medicine Foundation

This practice guideline follows the mission of the World Association of Perinatal Medicine in collaboration with the Perinatal Medicine Foundation, bringing together groups and individuals throughout the world, with the goal of improving the management of preterm labor. In fact, this document provides further guidance for healthcare practitioners on the appropriate use of examinations with the aim to improve the accuracy in diagnosing preterm labor and allow timely and appropriate administration of tocolytics, antenatal corticosteroids and magnesium sulphate and avoid unnecessary or excessive interventions. Therefore, it is not intended to establish a legal standard of care. This document is based on consensus among perinatal experts throughout the world in the light of scientific literature and serves as a guideline for use in clinical practice.

Prediction of Preterm Delivery Using Serum Ischemia Modified Albumin, Biglycan, and Decorin Levels in Women with Threatened Preterm Labor

OBJECTIVE

 The serum ischemia modified albumin (IMA), biglycan, and decorin levels of pregnant women who were hospitalized for threatened preterm labor were measured.

METHODS

 Fifty-one consecutive pregnant women with a single pregnancy between the 24th and 36th weeks with a diagnosis of threatened preterm labor were included in the present prospective cohort study.

RESULTS

 As a result of multivariate logistic regression analysis for predicting preterm delivery within 24 hours, 48 hours, 7 days, 14 days, ≤ 35 gestational weeks, and ≤ 37 gestational weeks after admission, area under the curve (AUC) (95% confidence interval [CI[) values were 0.95 (0.89-1.00), 0.93 (0.86-0.99), 0.91 (0.83-0.98), 0.92 (0.85-0.99), 0.82 (0.69-0.96), and 0.89 (0.80-0.98), respectively. In the present study, IMA and biglycan levels were found to be higher and decorin levels lower in women admitted to the hospital with threatened preterm labor and who gave preterm birth within 48 hours compared with those who gave birth after 48 hours.

CONCLUSION

 In pregnant women admitted to the hospital with threatened preterm labor, the prediction preterm delivery of the combined model created by adding IMA, decorin, and biglycan in addition to the TVS CL measurement was higher than the TVS CL measurement alone.

CLINICAL TRIAL REGISTRATION

 The present trial was registered at ClinicalTrials.gov, number NCT04451928.

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.

On optimal timing of antenatal corticosteroids: time to reformulate the question

Administration of antenatal corticosteroids (ACS) for accelerating foetal lung maturation in threatened preterm birth is one of the cornerstones of prevention of neonatal mortality and morbidity. To identify the optimal timing of ACS administration, most studies have compared subgroups based on treatment-to-delivery intervals. Such subgroup analysis of the first placebo-controlled randomised controlled trial indicated that a one to seven day interval between ACS administration and birth resulted in the lowest rates of neonatal respiratory distress syndrome. This efficacy window was largely confirmed by a series of subgroup analyses of subsequent trials and observational studies and strongly influenced obstetric management. However, these subgroup analyses suffer from a methodological flaw that often seems to be overlooked and potentially has important consequences for drawing valid conclusions. In this commentary, we point out that studies comparing treatment outcomes between subgroups that are retrospectively identified at birth (i.e. after randomisation) may not only be plagued by post-randomisation confounding bias but, more importantly, may not adequately inform decision making before birth, when the projected duration of the interval is still unknown. We suggest two more formal interpretations of these subgroup analyses, using a counterfactual framework for causal inference, and demonstrate that each of these interpretations can be linked to a different hypothetical trial. However, given the infeasibility of these trials, we argue that none of these rescue interpretations are helpful for clinical decision making. As a result, guidelines based on these subgroup analyses may have led to suboptimal clinical practice. As an alternative to these flawed subgroup analyses, we suggest a more principled approach that clearly formulates the question about optimal timing of ACS treatment in terms of the protocol of a future randomised study. Even if this 'target trial' would never be conducted, its protocol may still provide important guidance to avoid repeating common design flaws when conducting observational 'real world' studies using statistical methods for causal inference.

Noninvasive prediction models of intra-amniotic infection in women with preterm labor

BACKGROUND

Among women with preterm labor, those with intra-amniotic infection present the highest risk of early delivery and the most adverse outcomes. The identification of intra-amniotic infection requires amniocentesis, perceived as too invasive by women and physicians. Noninvasive methods for identifying intra-amniotic infection and/or early delivery are crucial to focus early efforts on high-risk preterm labor women while avoiding unnecessary interventions in low-risk preterm labor women.

OBJECTIVE

This study modeled the best performing models, integrating biochemical data with clinical and ultrasound information to predict a composite outcome of intra-amniotic infection and/or spontaneous delivery within 7 days.

STUDY DESIGN

From 2015 to 2020, data from a cohort of women, who underwent amniocentesis to rule in or rule out intra-amniotic infection or inflammation, admitted with a diagnosis of preterm labor at <34 weeks of gestation at the Hospital Clinic and Hospital Sant Joan de Déu, Barcelona, Spain, were used. At admission, transvaginal ultrasound was performed, and maternal blood and vaginal samples were collected. Using high-dimensional biology, vaginal proteins (using multiplex immunoassay), amino acids (using high-performance liquid chromatography), and bacteria (using 16S ribosomal RNA gene amplicon sequencing) were explored to predict the composite outcome. We selected ultrasound, maternal blood, and vaginal predictors that could be tested with rapid diagnostic techniques and developed prediction models employing machine learning that was applied in a validation cohort.

RESULTS

A cohort of 288 women with preterm labor at <34 weeks of gestation, of which 103 (35%) had a composite outcome of intra-amniotic infection and/or spontaneous delivery within 7 days, were included in this study. The sample was divided into derivation (n=116) and validation (n=172) cohorts. Of note, 4 prediction models were proposed, including ultrasound transvaginal cervical length, maternal C-reactive protein, vaginal interleukin 6 (using an automated immunoanalyzer), vaginal pH (using a pH meter), vaginal lactic acid (using a reflectometer), and vaginal Lactobacillus genus (using quantitative polymerase chain reaction), with areas under the receiving operating characteristic curve ranging from 82.2% (95% confidence interval, ±3.1%) to 85.2% (95% confidence interval, ±3.1%), sensitivities ranging from 76.1% to 85.9%, and specificities ranging from 75.2% to 85.1%.

CONCLUSION

The study results have provided proof of principle of how noninvasive methods suitable for point-of-care systems can select high-risk cases among women with preterm labor and might substantially aid in clinical management and outcomes while improving the use of resources and patient experience.

Preterm birth and maternal heart disease: A machine learning analysis using the Korean national health insurance database

BACKGROUND

Maternal heart disease is suspected to affect preterm birth (PTB); however, validated studies on the association between maternal heart disease and PTB are still limited. This study aimed to build a prediction model for PTB using machine learning analysis and nationwide population data, and to investigate the association between various maternal heart diseases and PTB.

METHODS

A population-based, retrospective cohort study was conducted using data obtained from the Korea National Health Insurance claims database, that included 174,926 primiparous women aged 25-40 years who delivered in 2017. The random forest variable importance was used to identify the major determinants of PTB and test its associations with maternal heart diseases, i.e., arrhythmia, ischemic heart disease (IHD), cardiomyopathy, congestive heart failure, and congenital heart disease first diagnosed before or during pregnancy.

RESULTS

Among the study population, 12,701 women had PTB, and 12,234 women had at least one heart disease. The areas under the receiver-operating-characteristic curves of the random forest with oversampling data were within 88.53 to 95.31. The accuracy range was 89.59 to 95.22. The most critical variables for PTB were socioeconomic status and age. The random forest variable importance indicated the strong associations of PTB with arrhythmia and IHD among the maternal heart diseases. Within the arrhythmia group, atrial fibrillation/flutter was the most significant risk factor for PTB based on the Shapley additive explanation value.

CONCLUSIONS

Careful evaluation and management of maternal heart disease during pregnancy would help reduce PTB. Machine learning is an effective prediction model for PTB and the major predictors of PTB included maternal heart disease such as arrhythmia and IHD.

Development of prognostic model for preterm birth using machine learning in a population-based cohort of Western Australia births between 1980 and 2015

Preterm birth is a global public health problem with a significant burden on the individuals affected. The study aimed to extend current research on preterm birth prognostic model development by developing and internally validating models using machine learning classification algorithms and population-based routinely collected data in Western Australia. The longitudinal retrospective cohort study involved all births in Western Australia between 1980 and 2015, and the analytic sample contains 81,974 (8.6%) preterm births (< 37 weeks of gestation). Prediction models for preterm birth were developed using regularised logistic regression, decision trees, Random Forests, extreme gradient boosting, and multi-layer perceptron (MLP). Predictors included maternal socio-demographics and medical conditions, current and past pregnancy complications, and family history. Class weight was applied to handle imbalanced outcomes and stratified tenfold cross-validation was used to reduce overfitting. Close to half of the preterm births (49.1% at 5% FPR, 95% CI 48.9%,49.5%) were correctly classified by the best performing classifier (MLP) for all women when current pregnancy information was available. The sensitivity was boosted to 52.7% (95% CI 52.1%,53.3%) after including past obstetric history in a sub-population of births from multiparous women. Around half of the preterm birth can be identified antenatally at high specificity using population-based routinely collected maternal and pregnancy data. The performance of the prediction models depends on the available predictor pool that is individual and time specific.

Dense phenotyping from electronic health records enables machine learning-based prediction of preterm birth

BACKGROUND

Identifying pregnancies at risk for preterm birth, one of the leading causes of worldwide infant mortality, has the potential to improve prenatal care. However, we lack broadly applicable methods to accurately predict preterm birth risk. The dense longitudinal information present in electronic health records (EHRs) is enabling scalable and cost-efficient risk modeling of many diseases, but EHR resources have been largely untapped in the study of pregnancy.

METHODS

Here, we apply machine learning to diverse data from EHRs with 35,282 deliveries to predict singleton preterm birth.

RESULTS

We find that machine learning models based on billing codes alone can predict preterm birth risk at various gestational ages (e.g., ROC-AUC = 0.75, PR-AUC = 0.40 at 28 weeks of gestation) and outperform comparable models trained using known risk factors (e.g., ROC-AUC = 0.65, PR-AUC = 0.25 at 28 weeks). Examining the patterns learned by the model reveals it stratifies deliveries into interpretable groups, including high-risk preterm birth subtypes enriched for distinct comorbidities. Our machine learning approach also predicts preterm birth subtypes (spontaneous vs. indicated), mode of delivery, and recurrent preterm birth. Finally, we demonstrate the portability of our approach by showing that the prediction models maintain their accuracy on a large, independent cohort (5978 deliveries) from a different healthcare system.

CONCLUSIONS

By leveraging rich phenotypic and genetic features derived from EHRs, we suggest that machine learning algorithms have great potential to improve medical care during pregnancy. However, further work is needed before these models can be applied in clinical settings.

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.

Predictive RNA profiles for early and very early spontaneous preterm birth

BACKGROUND

Spontaneous preterm birth remains the main driver of childhood morbidity and mortality. Because of an incomplete understanding of the molecular pathways that result in spontaneous preterm birth, accurate predictive markers and target therapeutics remain elusive.

OBJECTIVE

This study sought to determine if a cell-free RNA profile could reveal a molecular signature in maternal blood months before the onset of spontaneous preterm birth.

STUDY DESIGN

Maternal samples (n=242) were obtained from a prospective cohort of individuals with a singleton pregnancy across 4 clinical sites at 12-24 weeks (nested case-control; n=46 spontaneous preterm birth <35 weeks and n=194 term controls). Plasma was processed via a next-generation sequencing pipeline for cell-free RNA using the Mirvie RNA platform. Transcripts that were differentially expressed in next-generation sequencing cases and controls were identified. Enriched pathways were identified in the Reactome database using overrepresentation analysis.

RESULTS

Twenty five transcripts associated with an increased risk of spontaneous preterm birth were identified. A logistic regression model was developed using these transcripts to predict spontaneous preterm birth with an area under the curve =0.80 (95% confidence interval, 0.72-0.87) (sensitivity=0.76, specificity=0.72). The gene discovery and model were validated through leave-one-out cross-validation. A unique set of 39 genes was identified from cases of very early spontaneous preterm birth (<25 weeks, n=14 cases with time to delivery of 2.5±1.8 weeks); a logistic regression classifier on the basis of these genes yielded an area under the curve=0.76 (95% confidence interval, 0.63-0.87) in leave-one-out cross validation. Pathway analysis for the transcripts associated with spontaneous preterm birth revealed enrichment of genes related to collagen or the extracellular matrix in those who ultimately had a spontaneous preterm birth at <35 weeks. Enrichment for genes in insulin-like growth factor transport and amino acid metabolism pathways were associated with spontaneous preterm birth at <25 weeks.

CONCLUSION

Second trimester cell-free RNA profiles in maternal blood provide a noninvasive window to future occurrence of spontaneous preterm birth. The systemic finding of changes in collagen and extracellular matrix pathways may serve to identify individuals at risk for premature cervical remodeling, with growth factor and metabolic pathways implicated more often in very early spontaneous preterm birth. The use of cell-free RNA profiles has the potential to accurately identify those at risk for spontaneous preterm birth by revealing the underlying pathophysiology, creating an opportunity for more targeted therapeutics and effective interventions.

Development and validation of a transcriptomic signature-based model as the predictive, preventive, and personalized medical strategy for preterm birth within 7 days in threatened preterm labor women

Preterm birth (PTB) is the leading cause of neonatal death. The essential strategy to prevent PTB is the accurate identification of threatened preterm labor (TPTL) women who will have PTB in a short time (< 7 days). Here, we aim to propose a clinical model to contribute to the effective prediction, precise prevention, and personalized medical treatment for PTB < 7 days in TPTL women through bioinformatics analysis and prospective cohort studies. In this study, the 1090 key genes involved in PTB < 7 days in the peripheral blood of TPTL women were ascertained using WGCNA. Based on this, the biological basis of immune-inflammatory activation (e.g., IFNγ and TNFα signaling) as well as immune cell disorders (e.g., monocytes and Th17 cells) in PTB < 7 days were revealed. Then, four core genes (JOSD1, IDNK, ZMYM3, and IL1B) that best represent their transcriptomic characteristics were screened by SVM and LASSO algorithm. Therefore, a prediction model with an AUC of 0.907 was constructed, which was validated in a larger population (AUC = 0.783). Moreover, the predictive value (AUC = 0.957) and clinical feasibility of this model were verified through the clinical prospective cohort we established. In conclusion, in the context of Predictive, Preventive, and Personalized Medicine (3PM), we have developed and validated a model to predict PTB < 7 days in TPTL women. This is promising to greatly improve the accuracy of clinical prediction, which would facilitate the personalized management of TPTL women to precisely prevent PTB < 7 days and improve maternal-fetal outcomes.

Timing of antenatal corticosteroids in relation to clinical indication

PURPOSE

This study aimed at determining the proportion of women who receive antenatal corticosteroids (ACS) within the optimal time window before birth based on the indication for ACS, and to explore in more detail indications that are associated with suboptimal timing.

METHODS

A retrospective cohort study of all women who received ACS in a single tertiary center between 2014 and 2017. The primary outcome was an ACS-to-birth interval ≤ 7 days. Secondary outcomes were ACS-to-birth interval of ≤ 14 days, and the proportion women who received ACS but ultimately gave birth at term (≥ 37^0/7 weeks). The study outcomes were stratified by the clinical indication for ACS.

RESULTS

A total of 1261 women met the study criteria, of whom 401 (31.8%) and 569 (45.1%) received ACS within ≤ 7 days and ≤ 14 days before birth, respectively, and 203 (16.1%) ultimately gave birth at term. The proportion of women who received ACS within 7 days before birth was highest for women with preeclampsia (50.4%), and was lowest for women with an incidental finding of a short cervix (8.4%). In the subgroup of women with an incidental finding of a short cervix, the likelihood of optimal timing was not related to the magnitude of cervical shortening, history of preterm birth, multifetal gestation, presence of cervical funneling, or the presence of cervical cerclage.

CONCLUSION

Over two-thirds of infants who are exposed to ACS do not get the maximal benefit from this intervention. The current study identified clinical indications for ACS that are associated with suboptimal timing of ACS where more research is needed to develop quantitative, indication-specific prediction models to guide the timing of ACS.

The web-based application "QUiPP v.2" for the prediction of preterm birth in symptomatic women is not yet ready for worldwide clinical use: ten reflections on development, validation and use

PURPOSE

In this correspondence, we highlight general and domain-specific caveats in the development and validation of prediction models.

METHODS

Development and use of the "QUiPP" application, a tool for preterm birth prediction which is supported by the United Kingdom National Health Service, is scrutinised and commented on.

RESULTS

We highlight and elaborate ten points which may be perceived to be unclear or potentially misleading.

CONCLUSION

While the QUiPP application has high potential, it lacks transparency (on certain aspects related to model development) and proper validation. This precludes transportability to settings with other treatment policies and to other countries where the app has been made publicly available.

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.

Evaluating the use of the QUiPP app and its impact on the management of threatened preterm labour: A cluster randomised trial

BACKGROUND

Preterm delivery (before 37 weeks of gestation) is the single most important contributor to neonatal death and morbidity, with lifelong repercussions. However, the majority of women who present with preterm labour (PTL) symptoms do not deliver imminently. Accurate prediction of PTL is needed in order ensure correct management of those most at risk of preterm birth (PTB) and to prevent the maternal and fetal risks incurred by unnecessary interventions given to the majority. The QUantitative Innovation in Predicting Preterm birth (QUIPP) app aims to support clinical decision-making about women in threatened preterm labour (TPTL) by combining quantitative fetal fibronectin (qfFN) values, cervical length (CL), and significant PTB risk factors to create an individualised percentage risk of delivery.

METHODS AND FINDINGS

EQUIPTT was a multi-centre cluster randomised controlled trial (RCT) involving 13 maternity units in South and Eastern England (United Kingdom) between March 2018 and February 2019. Pregnant women (n = 1,872) between 23+0 and 34+6 weeks' gestation with symptoms of PTL in the analysis period were assigned to either the intervention (762) or control (1,111). The mean age of the study population was 30.2 (+/- SD 5.93). A total of 56.0% were white, 19.6% were black, 14.2% were Asian, and 10.2% were of other ethnicities. The intervention was the use of the QUiPP app with admission, antenatal corticosteroids (ACSs), and transfer advised for women with a QUiPP risk of delivery >5% within 7 days. Control sites continued with their conventional management of TPTL. Unnecessary management for TPTL was a composite primary outcome defined by the sum of unnecessary admission decisions (admitted and delivery interval >7 days or not admitted and delivery interval ≤7 days) and the number of unnecessary in utero transfer (IUT) decisions/actions (IUT that occurred or were attempted >7 days prior to delivery) and ex utero transfers (EUTs) that should have been in utero (attempted and not attempted). Unnecessary management of TPTL was 11.3% (84/741) at the intervention sites versus 11.5% (126/1094) at control sites (odds ratio [OR] 0.97, 95% confidence interval [CI] 0.66-1.42, p = 0.883). Control sites frequently used qfFN and did not follow UK national guidance, which recommends routine treatment below 30 weeks without testing. Unnecessary management largely consisted of unnecessary admissions which were similar at intervention and control sites (10.7% versus 10.8% of all visits). In terms of adverse outcomes for women in TPTL <36 weeks, 4 women from the intervention sites and 12 from the control sites did not receive recommended management. If the QUiPP percentage risk was used as per protocol, unnecessary management would have been 7.4% (43/578) versus 9.9% (134/1,351) (OR 0.72, 95% CI 0.45-1.16). Our external validation of the QUiPP app confirmed that it was highly predictive of delivery in 7 days; receiver operating curve area was 0.90 (95% CI 0.85-0.95) for symptomatic women. Study limitations included a lack of compliance with national guidance at the control sites and difficulties in implementation of the QUiPP app.

CONCLUSIONS

This cluster randomised trial did not demonstrate that the use of the QUiPP app reduced unnecessary management of TPTL compared to current management but would safely improve the management recommended by the National Institute for Health and Care Excellence (NICE). Interpretation of qfFN, with or without the QUiPP app, is a safe and accurate method for identifying women most likely to benefit from PTL interventions.

TRIAL REGISTRATION

ISRCTN Registry ISRCTN17846337.

Next generation strategies for preventing preterm birth

Preterm birth (PTB) is defined as delivery before 37 weeks of gestation. Globally, 15 million infants are born prematurely, putting these children at an increased risk of mortality and lifelong health challenges. Currently in the U.S., there is only one FDA approved therapy for the prevention of preterm birth. Makena is an intramuscular progestin injection given to women who have experienced a premature delivery in the past. Recently, however, Makena failed a confirmatory trial, resulting the Center for Drug Evaluation and Research's (CDER) recommendation for the FDA to withdrawal Makena's approval. This recommendation would leave clinicians with no therapeutic options for preventing PTB. Here, we outline recent interdisciplinary efforts involving physicians, pharmacologists, biologists, chemists, and engineers to understand risk factors associated with PTB, to define mechanisms that contribute to PTB, and to develop next generation therapies for preventing PTB. These advances have the potential to better identify women at risk for PTB, prevent the onset of premature labor, and, ultimately, save infant lives.

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.

Ethical and Regulatory Considerations of Placental Therapeutics

PURPOSE

Placental therapeutics aim to treat placental disease; however, ethical and regulatory issues should be considered if the drug also potentially affects the fetus. Drugs that might transfer or edit genes carry a specific challenge because currently fetal gene editing and fetal gene therapy are considered unethical.

METHODS

This article reviews the literature on ethical and regulatory considerations for placental therapeutics.

FINDINGS

Proposals for maternal gene therapy, directed to the maternal side of the placenta, have been discussed with patients and stakeholders. No absolute ethical, legal, or regulatory barriers to this potential treatment were identified. Patients who have experienced placental disease, such as fetal growth restriction, are interested in these therapies; some would participate in first-in-human trials. Such trials need careful regulatory considerations, such as the steps required to indicate tolerability and efficacy in preclinical models and the optimal animals for reproductive toxicology studies. Ex vivo dual human placenta perfusion experiments and villous explant in vitro studies allow drugs to be tested in normal and diseased human placenta, providing short-term tolerability and toxicologic assessment. Testing drugs in nonhuman primates is an option but carries ethical and feasibility considerations. Selection of inclusion and exclusion criteria for clinical trial participants is important to ensure that the most suitable patients are exposed to a first-in-human drug. These patients will almost certainly be pregnant women with a high risk of perinatal loss and/or perinatal and maternal morbidity. Criteria should identify sufficient numbers of patients to make a trial feasible as well as a phenotype that will respond to the mechanism of action. How to dose escalate and to capture information on adverse events are also key to optimal clinical trial design.

IMPLICATIONS

Developing placental therapeutics requires input from scientists, practitioners, and regulators and close liaison with patients to ensure that new drugs are tested as carefully as possible.

Cervical Assessment for Predicting Preterm Birth-Cervical Length and Beyond

Preterm birth is considered one of the main etiologies of neonatal death, as well as short- and long-term disability worldwide. A number of pathophysiological processes take place in the final unifying factor of cervical modifications that leads to preterm birth. In women at high risk for preterm birth, cervical assessment is commonly used for prediction and further risk stratification. This review outlines the rationale for cervical length screening for preterm birth prediction in different clinical settings within existing and evolving new technologies to assess cervical remodeling.

Stress and Metabolomics for Prediction of Spontaneous Preterm Birth: A Prospective Nested Case-Control Study in a Tertiary Hospital

Spontaneous preterm birth (sPTB) is the leading cause of infant morbidity and mortality worldwide. Deficiency of effective predict methods is an urgent problem that needs to be solved. Numbers of researchers spare no efforts to investigate differential indicators. To evaluate the value of the differential indicators, a prospective nested case-control study was carried out. Among an overall cohort of 1,050 pregnancies, 20 sPTB pregnancies, and 20 full-term pregnancies were enrolled in this study. Participants were followed-up until labor. The psychological profile was evaluated utilizing the Zung Self-Rating Depression Scale at 11-14 weeks. Stress-related biomarker-cortisol and metabolites were detected by Electrochemiluminescence Immunoassay (ECLIA) and Gas Chromatography-Mass Spectrometry (GC-MS) in serum samples during pregnancy, respectively. The expression level of cortisol was up-regulated in serum and the score of the Zung Self-Rating Depression Scale was significantly higher in the sPTB group when compared to the control group. Note that, 29 metabolomics were differentially expressed between the sPTB group and the control group. The scores of the Zung Self-Rating Depression Scale, the level of cortisol, Eicosane, methyltetradecanoate, and stearic acid in serum were selected to establish the model with lasso logistic regression. Validation of the model yielded an optimum corrected AUC value of 89.5%, 95% CI: 0.8006-0.9889 with a sensitivity of 100.0%, and specificity of 78.9%. In conclusion, this study establishes a prediction model of sPTB with five variables, which may predict sPTB more accurately and sensitively in the second trimester.

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.

CRAFT (Cerclage after full dilatation caesarean section): protocol of a mixed methods study investigating the role of previous in-labour caesarean section in preterm birth risk

BACKGROUND

Full dilatation caesarean sections are associated with recurrent early spontaneous preterm birth and late miscarriage. The risk following first stage caesarean sections, are less well defined, but appears to be increased in late-first stage of labour. The mechanism for this increased risk of late miscarriage and early spontaneous preterm birth in these women is unknown and there are uncertainties with regards to clinical management. Current predictive models of preterm birth (based on transvaginal ultrasound and quantitative fetal fibronectin) have not been validated in these women and it is unknown whether the threshold to define a short cervix (≤25 mm) is reliable in predicting the risk of preterm birth. In addition the efficacy of standard treatments or whether benefit may be derived from prophylactic interventions such as a cervical cerclage is unknown.

METHODS

There are three distinct components to the CRAFT project (CRAFT-OBS, CRAFT-RCT and CRAFT-IMG).

CRAFT-OBS

Observational Study; To evaluate subsequent pregnancy risk of preterm birth in women with a prior caesarean section in established labour. This prospective study of cervical length and quantitative fetal fibronectin data will establish a predictive model of preterm birth.

CRAFT-RCT

Randomised controlled trial arm; To assess treatment for short cervix in women at high risk of preterm birth following a fully dilated caesarean section.

CRAFT-IMG

Imaging sub-study; To evaluate the use of MRI and transvaginal ultrasound imaging of micro and macrostructural cervical features which may predispose to preterm birth in women with a previous fully dilated caesarean section, such as scar position and niche.

DISCUSSION

The CRAFT project will quantify the risk of preterm birth or late miscarriage in women with previous in-labour caesarean section, define the best management and shed light on pathological mechanisms so as to improve the care we offer to women and their babies.

TRIAL REGISTRATION

CRAFT was prospectively registered on 25th November 2019 with the ISRCTN registry ( https://doi.org/10.1186/ISRCTN15068651 ).

Development and validation of a multivariable prediction model of spontaneous preterm delivery and microbial invasion of the amniotic cavity in women with preterm labor

BACKGROUND

Early spontaneous preterm delivery is often associated with microbial invasion of the amniotic cavity and/or intraamniotic inflammation.

OBJECTIVE

The objective of the study was to develop and validate clinically feasible multivariable prediction models of spontaneous delivery within 7 days and microbial invasion of the amniotic cavity in women admitted with diagnose of preterm labor and intact membranes below 34 weeks.

STUDY DESIGN

We used data from a cohort of women admitted from 2012 to 2018 with diagnosis of preterm labor below 34 weeks who had undergone amniocentesis to rule out microbial invasion of the amniotic cavity. The main outcome was spontaneous delivery within 7 days from admission. The secondary outcome was microbial invasion of the amniotic cavity, defined by a positive culture and/or 16S ribosomal RNA gene in the amniotic fluid. The sample (n = 358) was divided into derivation (2012-2016) and validation cohorts (2017-2018). Logistic regression models using a stepwise selection of variables were developed for the outcomes evaluated. We explored as predictive variables ultrasound cervical length measurement at admission, maternal C-reactive protein, gestational age, amniotic fluid glucose, and interleukin-6 (expressed as log units). Models were developed in the derivation cohort and applied to the validation cohort and diagnostic performance was calculated.

RESULTS

The derivation cohort included 263 women and the validation cohort 95 women. One hundred five of the women (39%, 105 of 268) spontaneously delivered in the following 7 days and 68 (19%, 68 of 358) had microbial invasion of the amniotic cavity. For spontaneous delivery within 7 days after admission, 4 predictors were identified: cervical length at admission, gestational age, amniotic fluid glucose, and interleukin-6. The diagnostic performance of the model was assessed in the validation cohort using the receiver operating characteristic curve and showed an area under curve of 0.86 (95% confidence interval, 0.77-0.95) with a detection rate of spontaneous delivery within 7 days of 87%, a false-positive rate of 33%, a negative predictive value of 80%, and a negative likelihood ratio of 0.1908. For microbial invasion of the amniotic cavity, 2 independent predictors of the amniotic cavity were identified: amniotic fluid glucose and maternal C-reactive protein. The receiver operating characteristic curve and an area under curve in the validation cohort was 0.83 (95% confidence interval, 0.70-0.96) with a detection rate of 76%, a false-positive rate of 8%, a negative predictive value of 93%, and a negative likelihood ratio of 0.2591.

CONCLUSION

In women with preterm labor, we propose 2 clinically feasible prediction models to classify as low vs high risk of spontaneous delivery within 7 days and of microbial invasion of the amniotic cavity. The models showed a high diagnostic performance and could be of value to optimize clinical management.

Prevention of preterm birth: Proactive and reactive clinical practice-are we on the right track?

Preterm birth remains the major cause of death and disability among children under the age of five. In developing countries antenatal preterm birth prevention clinics are set up to provide cervical length surveillance and/or treatment modalities such as cerclage or progesterone for those women with identified risk factors such as previous cervical treatment or preterm birth. However, 85% of women have no risk factors for PTB and currently there is no biomarker to screen women early in pregnancy. Women will present unexpectedly in threatened preterm labour and we have no choice but to adopt a re-active approach to their care by using predication and preparation strategies such as fetal fibronectin, tocolytic therapy and steroids. Despite these strategies approximately 15-20% of these women will give birth preterm before 34 weeks. There is a urgent need to re-design primary, secondary and tertiary prevention strategies for spontaneous preterm labour (sPTL) in singleton pregnancies aimed at identifying and addressing key gaps in clinical practice and research.

Development and validation of predictive models for QUiPP App v.2: tool for predicting preterm birth in asymptomatic high-risk women

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.