The preterm prediction study: a clinical risk assessment system

Exploring the potential of machine learning in gynecological care: a review

Gynecological health remains a critical aspect of women's overall well-being, with profound implications for maternal and reproductive outcomes. This comprehensive review synthesizes the current state of knowledge on four pivotal aspects of gynecological health: preterm birth, breast cancer and cervical cancer and infertility treatment. Machine learning (ML) has emerged as a transformative technology with the potential to revolutionize gynecology and women's healthcare. The subsets of AI, namely, machine learning (ML) and deep learning (DL) methods, have aided in detecting complex patterns from huge datasets and using such patterns in making predictions. This paper investigates how machine learning (ML) algorithms are employed in the field of gynecology to tackle crucial issues pertaining to women's health. This paper also investigates the integration of ultrasound technology with artificial intelligence (AI) during the initial, intermediate, and final stages of pregnancy. Additionally, it delves into the diverse applications of AI throughout each trimester.This review paper provides an overview of machine learning (ML) models, introduces natural language processing (NLP) concepts, including ChatGPT, and discusses the clinical applications of artificial intelligence (AI) in gynecology. Additionally, the paper outlines the challenges in utilizing machine learning within the field of gynecology.

Recent Advances and Challenges in the Early Diagnosis and Treatment of Preterm Labor

Preterm birth (PTB) is the primary cause of neonatal mortality and long-term disabilities. The unknown mechanism behind PTB makes diagnosis difficult, yet early detection is necessary for controlling and averting related consequences. The primary focus of this work is to provide an overview of the known risk factors associated with preterm labor and the conventional and advanced procedures for early detection of PTB, including multi-omics and artificial intelligence/machine learning (AI/ML)- based approaches. It also discusses the principles of detecting various proteomic biomarkers based on lateral flow immunoassay and microfluidic chips, along with the commercially available point-of-care testing (POCT) devices and associated challenges. After briefing the therapeutic and preventive measures of PTB, this review summarizes with an outlook.

Predicting preterm birth using explainable machine learning in a prospective cohort of nulliparous and multiparous pregnant women

Preterm birth (PTB) presents a complex challenge in pregnancy, often leading to significant perinatal and long-term morbidities. "While machine learning (ML) algorithms have shown promise in PTB prediction, the lack of interpretability in existing models hinders their clinical utility. This study aimed to predict PTB in a pregnant population using ML models, identify the key risk factors associated with PTB through the SHapley Additive exPlanations (SHAP) algorithm, and provide comprehensive explanations for these predictions to assist clinicians in providing appropriate care. This study analyzed a dataset of 3509 pregnant women in the United Arab Emirates and selected 35 risk factors associated with PTB based on the existing medical and artificial intelligence literature. Six ML algorithms were tested, wherein the XGBoost model exhibited the best performance, with an area under the operator receiving curves of 0.735 and 0.723 for parous and nulliparous women, respectively. The SHAP feature attribution framework was employed to identify the most significant risk factors linked to PTB. Additionally, individual patient analysis was performed using the SHAP and the local interpretable model-agnostic explanation algorithms (LIME). The overall incidence of PTB was 11.23% (11 and 12.1% in parous and nulliparous women, respectively). The main risk factors associated with PTB in parous women are previous PTB, previous cesarean section, preeclampsia during pregnancy, and maternal age. In nulliparous women, body mass index at delivery, maternal age, and the presence of amniotic infection were the most relevant risk factors. The trained ML prediction model developed in this study holds promise as a valuable screening tool for predicting PTB within this specific population. Furthermore, SHAP and LIME analyses can assist clinicians in understanding the individualized impact of each risk factor on their patients and provide appropriate care to reduce morbidity and mortality related to PTB.

A new model based on artificial intelligence to screening preterm birth

OBJECTIVE

The objective of this study is to create a new screening for spontaneous preterm birth (sPTB) based on artificial intelligence (AI).

METHODS

This study included 524 singleton pregnancies from 18th to 24th-week gestation after transvaginal ultrasound cervical length (CL) analyzes for screening sPTB < 35 weeks. AI model was created based on the stacking-based ensemble learning method (SBELM) by the neural network, gathering CL < 25 mm, multivariate unadjusted logistic regression (LR), and the best AI algorithm. Receiver Operating Characteristics (ROC) curve to predict sPTB < 35 weeks and area under the curve (AUC), sensitivity, specificity, accuracy, predictive positive and negative values were performed to evaluate CL < 25 mm, LR, the best algorithms of AI and SBELM.

RESULTS

The most relevant variables presented by LR were cervical funneling, index straight CL/internal angle inside the cervix (≤ 0.200), previous PTB < 37 weeks, previous curettage, no antibiotic treatment during pregnancy, and weight (≤ 58 kg), no smoking, and CL < 30.9 mm. Fixing 10% of false positive rate, CL < 25 mm and SBELM present, respectively: AUC of 0.318 and 0.808; sensitivity of 33.3% and 47,3%; specificity of 91.8 and 92.8%; positive predictive value of 23.1 and 32.7%; negative predictive value of 94.9 and 96.0%. This machine learning presented high statistical significance when compared to CL < 25 mm after T-test (p < .00001).

CONCLUSION

AI applied to clinical and ultrasonographic variables could be a viable option for screening of sPTB < 35 weeks, improving the performance of short cervix, with a low false-positive rate.

Node embedding-based graph autoencoder outlier detection for adverse pregnancy outcomes

Adverse pregnancy outcomes, such as low birth weight (LBW) and preterm birth (PTB), can have serious consequences for both the mother and infant. Early prediction of such outcomes is important for their prevention. Previous studies using traditional machine learning (ML) models for predicting PTB and LBW have encountered two important limitations: extreme class imbalance in medical datasets and the inability to account for complex relational structures between entities. To address these limitations, we propose a node embedding-based graph outlier detection algorithm to predict adverse pregnancy outcomes. We developed a knowledge graph using a well-curated representative dataset of the Emirati population and two node embedding algorithms. The graph autoencoder (GAE) was trained by applying a combination of original risk factors and node embedding features. Samples that were difficult to reconstruct at the output of GAE were identified as outliers considered representing PTB and LBW samples. Our experiments using LBW, PTB, and very PTB datasets demonstrated that incorporating node embedding considerably improved performance, achieving a 12% higher AUC-ROC compared to traditional GAE. Our study demonstrates the effectiveness of node embedding and graph outlier detection in improving the prediction performance of adverse pregnancy outcomes in well-curated population datasets.

Pregnancy and birth complications associations with long-term adverse maternal mental health outcomes: a systematic review and meta-analysis protocol

Background

Existing studies have established an association between pregnancy, birth complications, and mental health in the first few weeks postpartum. However, there is no clear understanding of whether pregnancy and birth complications increase the risk of adverse maternal mental outcomes in the longer term. Research on maternal adverse mental health outcomes following pregnancy and birth complications beyond 12 months postpartum is scarce, and findings are inconsistent.

Objective

This systematic review and meta-analysis will examine the available evidence on the association between pregnancy and birth complications and long-term adverse maternal mental health outcomes.

Methods and analysis

We will include cohort, cross-sectional, and case-control studies in which a diagnosis of pregnancy and/or birth complication (preeclampsia, pregnancy loss, caesarean section, preterm birth, perineal laceration, neonatal intensive care unit admission, major obstetric haemorrhage, and birth injury/trauma) was reported and maternal mental disorders (depression, anxiety disorders, bipolar disorders, psychosis, and schizophrenia) after 12 months postpartum were the outcomes. A systematic search of PubMed, Embase, CINAHL, PsycINFO, and Web of Science will be conducted following a detailed search strategy until August 2022. Three authors will independently review titles and abstracts of all eligible studies, extract data using pre-defined standardised data extraction and assess the quality of each study using the Newcastle-Ottawa Scale. We will use random-effects meta-analysis for each exposure and outcome variable to calculate overall pooled estimates using the generic inverse variance method. This systematic review will follow the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines.

Ethical consideration

The proposed systematic review and meta-analysis is based on published data; ethics approval is not required. The results will be presented at scientific meetings and publish in a peer-reviewed journal.

PROSPERO registration

CRD42022359017.

Before Is Better: Innovative Multidisciplinary Preconception Care in Different Clinical Contexts

CONTEXT

Implementation of pre-conception care units is still very limited in Italy. Nowadays, the population's awareness of the reproductive risks that can be reduced or prevented is very low. Purpose and main findings: We presented a new personalized multidisciplinary model of preconception care aimed at identifying and possibly reducing adverse reproductive events. We analyzed three cohorts of population: couples from the general population, infertile or subfertile couples, and couples with a previous history of adverse reproductive events. The proposal involves a deep investigation regarding family history, the personal histories of both partners, and reproductive history.

PRINCIPAL CONCLUSIONS

Preconception care is still neglected in Italy and under-evaluated by clinicians involved in natural or in vitro reproduction. Adequate preconception counseling will improve maternal and fetal obstetrical outcomes.

Effectiveness and Costs of Molecular Screening and Treatment for Bacterial Vaginosis to Prevent Preterm Birth: The AuTop Randomized Clinical Trial

Importance

Bacterial vaginosis (BV) is a well-known risk factor for preterm birth. Molecular diagnosis of BV is now available. Its impact in the screening and treatment of BV during pregnancy on preterm births has not been evaluated to date.

Objective

To evaluate the clinical and economic effects of point-of-care quantitative real-time polymerase chain reaction screen and treat for BV in low-risk pregnant women on preterm birth.

Design, Setting, and Participants

The AuTop trial was a prospective, multicenter, parallel, individually randomized, open-label, superiority trial conducted in 19 French perinatal centers between March 9, 2015, and December 18, 2017. Low-risk pregnant women before 20 weeks' gestation without previous preterm births or late miscarriages were enrolled. Data were analyzed from October 2021 to November 2022.

Interventions

Participants were randomized 1:1 to BV screen and treat using self-collected vaginal swabs (n = 3333) or usual care (n = 3338). BV was defined as Atopobium vaginae (Fannyhessea vaginae) load of 108 copies/mL or greater and/or Gardnerella vaginalis load of 109 copies/mL or greater, using point-of-care quantitative real-time polymerase chain reaction assays. The control group received usual care with no screening of BV.

Main Outcomes and Measures

Overall rate of preterm birth before 37 weeks' gestation and total costs were calculated in both groups. Secondary outcomes were related to treatment success as well as maternal and neonate health. Post hoc subgroup analyses were conducted.

Results

Among 6671 randomized women (mean [SD] age, 30.6 [5.0] years; mean [SD] gestational age, 15.5 [2.8] weeks), the intention-to-treat analysis of the primary clinical and economic outcomes showed no evidence of a reduction in the rate of preterm birth and total costs with the screen and treat strategy compared with usual care. The rate of preterm birth was 3.8% (127 of 3333) in the screen and treat group and 4.6% (153 of 3338) in the control group (risk ratio [RR], 0.83; 95% CI, 0.66-1.05; P = .12). On average, the cost of the intervention was €203.6 (US $218.0) per participant, and the total average cost was €3344.3 (US $3580.5) in the screen and treat group vs €3272.9 (US $3504.1) in the control group, with no significant differences being observed. In the subgroup of nulliparous women (n = 3438), screen and treat was significantly more effective than usual care (RR, 0.62; 95% CI, 0.45-0.84; P for interaction = .003), whereas no statistical difference was found in multiparous (RR, 1.30; 95% CI, 0.90-1.87).

Conclusion and Relevance

In this clinical trial of pregnant women at low risk of preterm birth, molecular screening and treatment for BV based on A vaginae (F vaginae) and/or G vaginalis quantification did not significantly reduce preterm birth rates. Post hoc analysis suggests a benefit of screen and treat in low-risk nulliparous women, warranting further evaluation in this group.

Trial Registration

ClinicalTrials.gov Identifier: NCT02288832.

Risk Scoring Systems for Preterm Birth and Their Performance: A Systematic Review

Introduction: Nowadays, the risk stratification of preterm birth (PTB) and its prediction remain a challenge. Many risk factors associated with PTB have been identified, and risk scoring systems (RSSs) have been developed to face this challenge. The objectives of this systematic review were to identify RSSs for PTB, the variables they consist of, and their performance. Materials and methods: Two databases were searched, and two authors independently performed the screening and eligibility phases. Records studying an RSS, based on specified variables, with an evaluation of the predictive value for PTB, were considered eligible. Reference lists of eligible studies and review articles were also searched. Data from the included studies were extracted. Results: A total of 56 studies were included in this review. The most frequently incorporated variables in the RSS included in this review were maternal age, weight, history of smoking, history of previous PTB, and cervical length. The performance measures varied widely among the studies, with sensitivity ranging between 4.2% and 92.0% and area under the curve (AUC) between 0.59 and 0.95. Conclusions: Despite the recent technological and scientifical evolution with a better understanding of variables related to PTB and the definition of new ultrasonographic parameters and biomarkers associated with PTB, the RSS's ability to predict PTB remains poor in most situations, thus compromising the integration of a single RSS in clinical practice. The development of new RSSs, the identification of new variables associated with PTB, and the elaboration of a large reference dataset might be a step forward to tackle the problem of PTB.

Pregnancy and birth complications associations with long-term adverse maternal mental health outcomes: a systematic review and meta-analysis protocol

Background: Existing studies have established an association between pregnancy, birth complications, and mental health in the first few weeks postpartum. However, there is no clear understanding of whether pregnancy and birth complications increase the risk of adverse maternal mental outcomes in the longer term. Research on maternal adverse mental health outcomes following pregnancy and birth complications beyond 12 months postpartum is scarce, and findings are inconsistent. Objective: This systematic review and meta-analysis will examine the available evidence on the association between pregnancy and birth complications and long-term adverse maternal mental health outcomes. Methods and analysis: We will include cohort, cross-sectional, and case-control studies in which a diagnosis of pregnancy and/or birth complication (preeclampsia, pregnancy loss, caesarean section, preterm birth, perineal laceration, neonatal intensive care unit admission, major obstetric haemorrhage, and birth injury/trauma) was reported and maternal mental disorders (depression, anxiety disorders, bipolar disorders, psychosis, and schizophrenia) after 12 months postpartum were the outcomes. A systematic search of PubMed, Embase, CINAHL, PsycINFO, and Web of Science will be conducted following a detailed search strategy until August 2022. Three authors will independently review titles and abstracts of all eligible studies, extract data using pre-defined standardised data extraction and assess the quality of each study using the Newcastle-Ottawa Scale. We will use random-effects meta-analysis for each exposure and outcome variable to calculate overall pooled estimates using the generic inverse variance method. This systematic review will follow the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines. Ethical consideration: The proposed systematic review and meta-analysis is based on published data; ethics approval is not required. The results will be presented at scientific meetings and publish in a peer-reviewed journal. PROSPERO registration: CRD42022359017

Does vaginal progesterone prevent recurrent preterm birth in women with a singleton gestation and a history of spontaneous preterm birth? Evidence from a systematic review and meta-analysis

OBJECTIVE

To assess the efficacy and safety of vaginal progesterone to prevent recurrent preterm birth and adverse perinatal outcomes in singleton gestations with a history of spontaneous preterm birth.

DATA SOURCES

MEDLINE, Embase, LILACS, and CINAHL (from their inception to February 28, 2022), Cochrane databases, Google Scholar, bibliographies, and conference proceedings.

STUDY ELIGIBILITY CRITERIA

Randomized controlled trials that compared vaginal progesterone to placebo or no treatment in asymptomatic women with a singleton gestation and a history of spontaneous preterm birth.

METHODS

The primary outcomes were preterm birth <37 and <34 weeks of gestation. The secondary outcomes included adverse maternal and perinatal outcomes. Pooled relative risks with 95% confidence intervals were calculated. We assessed the risk of bias in the included studies, heterogeneity (I2 test), small-study effects, publication bias, and quality of evidence; performed subgroup and sensitivity analyses; and calculated 95% prediction intervals and adjusted relative risks.

RESULTS

Ten studies (2958 women) met the inclusion criteria: 7 with a sample size <150 (small studies) and 3 with a sample size >600 (large studies). Among the 7 small studies, 4 were at high risk of bias, 2 were at some concerns of bias, and only 1 was at low risk of bias. All the large studies were at low risk of bias. Vaginal progesterone significantly decreased the risk of preterm birth <37 weeks (relative risk, 0.64; 95% confidence interval, 0.50-0.81; I2=75%; 95% prediction interval, 0.31-1.32; very low-quality evidence) and <34 weeks (relative risk, 0.62; 95% confidence interval, 0.42-0.92; I2=66%; 95% prediction interval, 0.23-1.68; very low-quality evidence), and the risk of admission to the neonatal intensive care unit (relative risk, 0.53; 95% confidence interval, 0.33-0.85; I2=67%; 95% prediction interval, 0.16-1.79; low-quality evidence). There were no significant differences between the vaginal progesterone and the placebo or no treatment groups in other adverse perinatal and maternal outcomes. Subgroup analyses revealed that vaginal progesterone decreased the risk of preterm birth <37 weeks (relative risk, 0.43; 95% confidence interval, 0.33-0.55; I2=0%) and <34 weeks (relative risk, 0.27; 95% confidence interval, 0.15-0.49; I2=0%) in the small but not in the large studies (relative risk, 0.98; 95% confidence interval, 0.88-1.09; I2=0% for preterm birth <37 weeks; and relative risk, 0.94; 95% confidence interval, 0.78-1.13; I2=0% for preterm birth <34 weeks). Sensitivity analyses restricted to studies at low risk of bias indicated that vaginal progesterone did not reduce the risk of preterm birth <37 weeks (relative risk, 0.96; 95% confidence interval, 0.84-1.09) and <34 weeks (relative risk, 0.90; 95% confidence interval, 0.71-1.15). There was clear evidence of substantial small-study effects in the meta-analyses of preterm birth <37 and <34 weeks of gestation because of funnel plot asymmetry and the marked differences in the pooled relative risks obtained from fixed-effect and random-effects models. The adjustment for small-study effects resulted in a markedly reduced and nonsignificant effect of vaginal progesterone on preterm birth <37 weeks (relative risk, 0.86; 95% confidence interval, 0.68-1.10) and <34 weeks (relative risk, 0.92; 95% confidence interval, 0.60-1.42).

CONCLUSION

There is no convincing evidence supporting the use of vaginal progesterone to prevent recurrent preterm birth or to improve perinatal outcomes in singleton gestations with a history of spontaneous preterm birth.

The amniotic fluid proteome predicts imminent preterm delivery in asymptomatic women with a short cervix

Preterm birth, the leading cause of perinatal morbidity and mortality, is associated with increased risk of short- and long-term adverse outcomes. For women identified as at risk for preterm birth attributable to a sonographic short cervix, the determination of imminent delivery is crucial for patient management. The current study aimed to identify amniotic fluid (AF) proteins that could predict imminent delivery in asymptomatic patients with a short cervix. This retrospective cohort study included women enrolled between May 2002 and September 2015 who were diagnosed with a sonographic short cervix (< 25 mm) at 16–32 weeks of gestation. Amniocenteses were performed to exclude intra-amniotic infection; none of the women included had clinical signs of infection or labor at the time of amniocentesis. An aptamer-based multiplex platform was used to profile 1310 AF proteins, and the differential protein abundance between women who delivered within two weeks from amniocentesis, and those who did not, was determined. The analysis included adjustment for quantitative cervical length and control of the false-positive rate at 10%. The area under the receiver operating characteristic curve was calculated to determine whether protein abundance in combination with cervical length improved the prediction of imminent preterm delivery as compared to cervical length alone. Of the 1,310 proteins profiled in AF, 17 were differentially abundant in women destined to deliver within two weeks of amniocentesis independently of the cervical length (adjusted p-value < 0.10). The decreased abundance of SNAP25 and the increased abundance of GPI, PTPN11, OLR1, ENO1, GAPDH, CHI3L1, RETN, CSF3, LCN2, CXCL1, CXCL8, PGLYRP1, LDHB, IL6, MMP8, and PRTN3 were associated with an increased risk of imminent delivery (odds ratio > 1.5 for each). The sensitivity at a 10% false-positive rate for the prediction of imminent delivery by a quantitative cervical length alone was 38%, yet it increased to 79% when combined with the abundance of four AF proteins (CXCL8, SNAP25, PTPN11, and MMP8). Neutrophil-mediated immunity, neutrophil activation, granulocyte activation, myeloid leukocyte activation, and myeloid leukocyte-mediated immunity were biological processes impacted by protein dysregulation in women destined to deliver within two weeks of diagnosis. The combination of AF protein abundance and quantitative cervical length improves prediction of the timing of delivery compared to cervical length alone, among women with a sonographic short cervix.

Machine learning modeling for preterm birth prediction using health record: A systematic review (Preprint)

Background

Preterm birth (PTB), a common pregnancy complication, is responsible for 35% of the 3.1 million pregnancy-related deaths each year and significantly affects around 15 million children annually worldwide. Conventional approaches to predict PTB lack reliable predictive power, leaving >50% of cases undetected. Recently, machine learning (ML) models have shown potential as an appropriate complementary approach for PTB prediction using health records (HRs).

Objective

This study aimed to systematically review the literature concerned with PTB prediction using HR data and the ML approach.

Methods

This systematic review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. A comprehensive search was performed in 7 bibliographic databases until May 15, 2021. The quality of the studies was assessed, and descriptive information, including descriptive characteristics of the data, ML modeling processes, and model performance, was extracted and reported.

Results

A total of 732 papers were screened through title and abstract. Of these 732 studies, 23 (3.1%) were screened by full text, resulting in 13 (1.8%) papers that met the inclusion criteria. The sample size varied from a minimum value of 274 to a maximum of 1,400,000. The time length for which data were extracted varied from 1 to 11 years, and the oldest and newest data were related to 1988 and 2018, respectively. Population, data set, and ML models’ characteristics were assessed, and the performance of the model was often reported based on metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve.

Conclusions

Various ML models used for different HR data indicated potential for PTB prediction. However, evaluation metrics, software and package used, data size and type, selected features, and importantly data management method often remain unjustified, threatening the reliability, performance, and internal or external validity of the model. To understand the usefulness of ML in covering the existing gap, future studies are also suggested to compare it with a conventional method on the same data set.

Using principal component analysis to examine associations of early pregnancy inflammatory biomarker profiles and adverse birth outcomes

OBJECTIVE

Inflammation as a risk factor for preterm birth is well-established. The primary objective of this analysis was to examine whether individual cytokines versus a composite indicator of mid-pregnancy inflammation are significantly associated with risk for adverse birth outcomes.

STUDY DESIGN

A multi-site prospective study was conducted in a socio-demographically diverse cohort of 610 pregnant participants. At a study visit between 12 and 20 6/7 weeks' gestation, low-grade inflammation was measured via log-transformed serum concentrations of the biomarkers IFN-γ, IL-10, IL-13, IL-6, IL-8, TNF-α, and CRP. Principal component analysis (PCA) was used to identify underlying dimensions of inflammatory activity from the seven biomarkers measured. Gestational age and birth weight at delivery were obtained from medical chart review. The associations between inflammatory profiles and birth outcomes were assessed via linear and logistic regression models. Results were compared with those from individual inflammatory biomarkers, and model fit was assessed using Akaike's Information Criterion (AIC).

RESULTS

Principal component analysis analysis yielded a two-factor solution, with the first factor (IF1) composed of IL-8, IL-10, IL-13, IFN-ɣ, and TNF-α, and the second factor (IF2) containing IL-6 and CRP. When adjusted for race, education, BMI, smoking status, gestational age at time of blood draw, and study site, a one standard deviation (SD) increase in IF1 remained significantly associated with a decrease in standardized gestational age (β = -.13, 95% CI: -.21, -.05) and an increase in odds of preterm delivery (OR = 1.46, 95% CI: 1.13, 1.88) (Table 3). A one SD increase in IF2 was similarly associated with a decrease in standardized gestational age at delivery (β = -.13, 95% CI: -.23, -.04) and an increase in odds of preterm delivery (OR: 1.46, 95% CI: 1.04, 2.05). Neither IF1 nor IF2 was associated with measures of fetal growth. AIC identified that IL-6 was a slightly better fit for length of gestation compared to either composite measure, though all performed similarly.

CONCLUSION

Independent of known sociodemographic risk factors, an elevated mid-pregnancy inflammatory profile was associated with a nearly 50% increase in odds of preterm delivery. The composite performed similarly to IL-6. These results suggest that maternal low-grade inflammation is a risk factor for preterm delivery, and that mid-pregnancy inflammatory biomarkers may be useful in predicting risk for preterm delivery.

Prediction and Prevention of Preterm Birth: A Prospective, Randomized Intervention Trial

OBJECTIVE

 The study aimed to determine if a program of mid-trimester serum proteomics screening of women at low risk for spontaneous preterm birth (sPTB) and the use of a PTB risk-reduction protocol in those whose results indicated an increased risk of sPTB would reduce the likelihood of sPTB and its sequelae.

STUDY DESIGN

 Prospective comparison of birth outcomes in singleton pregnancies with mid-trimester cervical length ≥2.5 cm and at otherwise low risk for sPTB randomized to undergo or not undergo mid-trimester serum proteomics screening for increased risk of sPTB (NCT03530332). Screen-positive women were offered a group of interventions aimed at reducing the risk of spontaneous PTB. The primary outcome was the rate of sPTB <37 weeks, and secondary outcomes were gestational age at delivery, total length of neonatal stay, and NICU length of stay (LOS). Unscreened and screen-negative women received standard care. The adaptive study design targeted a sample size of 3,000 to 10,000 women to detect a reduction in sPTB from 6.4 to 4.7%. Due to limited resources, the trial was stopped early prior to data unblinding.

RESULTS

 A total of 1,191 women were randomized. Screened and unscreened women were demographically similar. sPTB <37 weeks occurred in 2.7% of screened women and 3.5% of controls (p = 0.41). In the screened compared with the unscreened group, there were no between-group differences in the gestational age at delivery, total length of neonatal stay, and NICU LOS. However, the NICU LOS among infants admitted for sPTB was significantly shorter (median = 6.8 days, interquartile range [IQR]: 1.8-8.0 vs. 45.5 days, IQR: 34.6-79.0; p = 0.005).

CONCLUSION

 Mid-trimester serum proteomics screening of women at low risk for sPTB and the use of a sPTB risk-reduction protocol in screen-positive patients did not significantly reduce the rate of sPTB compared with women not screened, though the trial was underpowered thus limiting the interpretation of negative findings. Infants in the screened group had a significantly shorter NICU LOS, a difference likely due to a reduced number of infants in the screened group that delivered <35 weeks.

KEY POINTS

· Mid-trimester serum proteomics screening of women at low risk for sPTB and the use of a sPTB risk-reduction protocol in screen-positive patients did not significantly reduce the rate of sPTB, though the trial was underpowered.. · NICU LOS following sPTB was significantly shortened among women who underwent screening and risk-reduction management.. · The use of serum biomarkers may contribute to a practical strategy to reduce sPTB sequelae..

Prediction and Prevention of Spontaneous Preterm Birth: ACOG Practice Bulletin, Number 234

Preterm birth is among the most complex and important challenges in obstetrics. Despite decades of research and clinical advancement, approximately 1 in 10 newborns in the United States is born prematurely. These newborns account for approximately three-quarters of perinatal mortality and more than one half of long-term neonatal morbidity, at significant social and economic cost (1-3). Because preterm birth is the common endpoint for multiple pathophysiologic processes, detailed classification schemes for preterm birth phenotype and etiology have been proposed (4, 5). In general, approximately one half of preterm births follow spontaneous preterm labor, about a quarter follow preterm prelabor rupture of membranes (PPROM), and the remaining quarter of preterm births are intentional, medically indicated by maternal or fetal complications. There are pronounced racial disparities in the preterm birth rate in the United States. The purpose of this document is to describe the risk factors, screening methods, and treatments for preventing spontaneous preterm birth, and to review the evidence supporting their roles in clinical practice. This Practice Bulletin has been updated to include information on increasing rates of preterm birth in the United States, disparities in preterm birth rates, and approaches to screening and prevention strategies for patients at risk for spontaneous preterm birth.

A Machine Learning-Based Prediction Model for Preterm Birth in Rural India

Preterm birth (PTB) in a pregnant woman is the most serious issue in the field of Gynaecology and Obstetrics, especially in rural India. In recent years, various clinical prediction models for PTB have been developed to improve the accuracy of learning models. However, to the best of the authors' knowledge, most of them suffer from selecting the most accurate features from the medical dataset in linear time. The present paper attempts to design a machine learning model named as risk prediction conceptual model (RPCM) for the prediction of PTB. In this paper, a feature selection approach is proposed based on the notion of entropy. The novel approach is used to find the best maternal features (responsible for PTB) from the obstetrical dataset and aims to predict the classifier's accuracy at the highest level. The paper first deals with the review of PTB cases (which is neglected in many developing countries including India). Next, we collect obstetrical data from the Community Health Centre of rural areas (Kamdara, Jharkhand). The suggested approach is then applied on collected data to identify the excellent maternal features (text-based symptoms) present in pregnant women in order to classify all birth cases into term birth and PTB. The machine learning part of the model is implemented using three different classifiers, namely, decision tree (DT), logistic regression (LR), and support vector machine (SVM) for PTB prediction. The performance of the classifiers is measured in terms of accuracy, specificity, and sensitivity. Finally, the SVM classifier generates an accuracy of 90.9%, which is higher than other learning classifiers used in this study.

Mid-pregnancy maternal blood nitric oxide-related gene and miRNA expression are associated with preterm birth

Aim: The nitric oxide (NO) pathway modulates inflammation and may influence birth timing. Patients & methods: Case-control analysis of 136 pregnant women with RNA obtained <28 weeks; n = 212 mRNAs and n = 108 miRNAs in the NO pathway were evaluated. NO-pathway mRNA and miRNA transcript counts in women delivering preterm versus at term were compared, miRNA-mRNA expression levels correlated and prediction models generated. Results: Fourteen genes were differentially expressed in women delivering <37 weeks; 13/14 were also differentially expressed in those delivering <34 weeks (q <0.10) versus term births. Multiple miRNA-mRNA pairs were correlated. Models with gene expression better predicted prematurity than models with only clinical or nongenomic predictors. Conclusion: Maternal blood NO pathway-related mRNA and miRNA expression is associated with prematurity.

Machine Learning Methods for Preterm Birth Prediction: A Review

Preterm births affect around 15 million children a year worldwide. Current medical efforts focus on mitigating the effects of prematurity, not on preventing it. Diagnostic methods are based on parent traits and transvaginal ultrasound, during which the length of the cervix is examined. Approximately 30% of preterm births are not correctly predicted due to the complexity of this process and its subjective assessment. Based on recent research, there is hope that machine learning can be a helpful tool to support the diagnosis of preterm births. The objective of this study is to present various machine learning algorithms applied to preterm birth prediction. The wide spectrum of analysed data sets is the advantage of this survey. They range from electrohysterogram signals through electronic health records to transvaginal ultrasounds. Reviews of works on preterm birth already exist; however, this is the first review that includes works that are based on a transvaginal ultrasound examination. In this work, we present a critical appraisal of popular methods that have employed machine learning methods for preterm birth prediction. Moreover, we summarise the most common challenges incurred and discuss their possible application in the future.

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 ).

Deep learning predicts extreme preterm birth from electronic health records

OBJECTIVE

Models for predicting preterm birth generally have focused on very preterm (28-32 weeks) and moderate to late preterm (32-37 weeks) settings. However, extreme preterm birth (EPB), before the 28th week of gestational age, accounts for the majority of newborn deaths. We investigated the extent to which deep learning models that consider temporal relations documented in electronic health records (EHRs) can predict EPB.

STUDY DESIGN

EHR data were subject to word embedding and a temporal deep learning model, in the form of recurrent neural networks (RNNs) to predict EPB. Due to the low prevalence of EPB, the models were trained on datasets where controls were undersampled to balance the case-control ratio. We then applied an ensemble approach to group the trained models to predict EPB in an evaluation setting with a nature EPB ratio. We evaluated the RNN ensemble models with 10 years of EHR data from 25,689 deliveries at Vanderbilt University Medical Center. We compared their performance with traditional machine learning models (logistical regression, support vector machine, gradient boosting) trained on the datasets with balanced and natural EPB ratio. Risk factors associated with EPB were identified using an adjusted odds ratio.

RESULTS

The RNN ensemble models trained on artificially balanced data achieved a higher AUC (0.827 vs. 0.744) and sensitivity (0.965 vs. 0.682) than those RNN models trained on the datasets with naturally imbalanced EPB ratio. In addition, the AUC (0.827) and sensitivity (0.965) of the RNN ensemble models were better than the AUC (0.777) and sensitivity (0.819) of the best baseline models trained on balanced data. Also, risk factors, including twin pregnancy, short cervical length, hypertensive disorder, systemic lupus erythematosus, and hydroxychloroquine sulfate, were found to be associated with EPB at a significant level.

CONCLUSION

Temporal deep learning can predict EPB up to 8 weeks earlier than its occurrence. Accurate prediction of EPB may allow healthcare organizations to allocate resources effectively and ensure patients receive appropriate care.

Mouse models of preterm birth: suggested assessment and reporting guidelines

Preterm birth affects approximately 1 out of every 10 births in the United States, leading to high rates of mortality and long-term negative health consequences. To investigate the mechanisms leading to preterm birth so as to develop prevention strategies, researchers have developed numerous mouse models of preterm birth. However, the lack of standard definitions for preterm birth in mice limits our field's ability to compare models and make inferences about preterm birth in humans. In this review, we discuss numerous mouse preterm birth models, propose guidelines for experiments and reporting, and suggest markers that can be used to assess whether pups are premature or mature. We argue that adoption of these recommendations will enhance the utility of mice as models for preterm birth.

Preterm birth: seven-year retrospective study in a single centre population

Background

Preterm birth is a health and social problem, considered the leading cause of neonatal mortality worldwide. It is associated with higher rates of neurodevelopmental morbidity, sensorineural impairments and other complications. The aim of the study was to describe the incidence and the major risk factors associated with preterm birth.

Methods

We performed a single center, observational and retrospective Cohort study in the Division of Obstetrics and Gynaecology, University Hospital “G. Martino”, Messina. Clinical records of all pregnant women who delivered from 1st January 2010 to 31 of December 2016 were collected.

Results

In the 7 years considered, a total of 7954 pregnant women were included in our study. The majority of all preterm births were due to infants born late preterm (71.83%), 26.45% were due to preterm and 1.72% to extremely preterm. The preterm cohort had a higher proportion of history of preterm delivery (p < 0.0001), and unmarried (p = 0.003) and underweight or obese patients (p < 0.0001). In addition, prematurity was associated with presence of uterine anomalies (p < 0.0001), vaginal/urinary infections (p = 0.02), poli/oligohydramnios (p < 0.0001), maternal diabetes (p = 0.004), hypertension (p < 0.0001), short cervical length (p < 0.0001).

Conclusions

We suggest prompt identification of all risk factors associated with preterm birth to apply immediate and appropriate specific interventions.

Early clindamycin for bacterial vaginosis in pregnancy (PREMEVA): a multicentre, double-blind, randomised controlled trial

BACKGROUND

Preterm delivery during pregnancy (<37 weeks' gestation) is a leading cause of perinatal mortality and morbidity. Treating bacterial vaginosis during pregnancy can reduce poor outcomes, such as preterm birth. We aimed to investigate whether treatment of bacterial vaginosis decreases late miscarriages or spontaneous very preterm birth.

METHODS

PREMEVA was a double-blind randomised controlled trial done in 40 French centres. Women aged 18 years or older with bacterial vaginosis and low-risk pregnancy were eligible for inclusion and were randomly assigned (2:1) to three parallel groups: single-course or triple-course 300 mg clindamycin twice-daily for 4 days, or placebo. Women with high-risk pregnancy outcomes were eligible for inclusion in a high-risk subtrial and were randomly assigned (1:1) to either single-course or triple-course clindamycin. The primary outcome was a composite of late miscarriage (16-21 weeks) or spontaneous very preterm birth (22-32 weeks), which we assessed in all patients with delivery data (modified intention to treat). Adverse events were systematically reported. This study is registered with ClinicalTrials.gov, number NCT00642980.

FINDINGS

Between April 1, 2006, and June 30, 2011, we screened 84 530 pregnant women before 14 weeks' gestation. 5630 had bacterial vaginosis, of whom 3105 were randomly assigned to groups in the low-risk trial (n=943 to receive single-course clindamycin, n=968 to receive triple-course clindamycin, and n=958 to receive placebo) or high-risk subtrial (n=122 to receive single-course clindamycin and n=114 to receive triple-course clindamycin). In 2869 low-risk pregnancies, the primary outcome occurred in 22 (1·2%) of 1904 participants receiving clindamycin and 10 (1·0%) of 956 participants receiving placebo (relative risk [RR] 1·10, 95% CI 0·53-2·32; p=0·82). In 236 high-risk pregnancies, the primary outcome occurred in 5 (4·4%) participants in the triple-course clindamycin group and 8 (6·0%) participants in the single-course clindamycin group (RR 0·67, 95% CI 0·23-2·00; p=0·47). In the low-risk trial, adverse events were more common in the clindamycin groups than in the placebo group (58 [3·0%] of 1904 vs 12 [1·3%] of 956; p=0·0035). The most commonly reported adverse event was diarrhoea (30 [1·6%] in the clindamycin groups vs 4 [0·4%] in the placebo group; p=0·0071); abdominal pain was also observed in the clindamycin groups (9 [0·6%] participants) versus none in the placebo group (p=0·034). No severe adverse event was reported in any group. Adverse fetal and neonatal outcomes did not differ significantly between groups in the high-risk subtrial.

INTERPRETATION

Systematic screening and subsequent treatment for bacterial vaginosis in women with low-risk pregnancies shows no evidence of risk reduction of late miscarriage or spontaneous very preterm birth. Use of antibiotics to prevent preterm delivery in this patient population should be reconsidered.

FUNDING

French Ministry of Health.

Pre-pregnancy or first-trimester risk scoring to identify women at high risk of preterm birth

Objective To develop a pre-pregnancy or first-trimester risk score to identify women at high risk of preterm birth. Study design In this retrospective cohort analysis, the sample was drawn from California singleton livebirths from 2007 to 2012 with linked birth certificate and hospital discharge records. The dataset was divided into a training (2/3 of sample) and a testing (1/3 of sample) set for discovery and validation. Predictive models for preterm birth using pre-pregnancy or first-trimester maternal factors were developed using backward stepwise logistic regression on a training dataset. A risk score for preterm birth was created for each pregnancy using beta-coefficients for each maternal factor remaining in the final multivariable model. Risk score utility was replicated in a testing dataset and by race/ethnicity and payer for prenatal care. Results The sample included 2,339,696 pregnancies divided into training and testing datasets. Twenty-three maternal risk factors were identified including several that were associated with a two or more increased odds of preterm birth (preexisting diabetes, preexisting hypertension, sickle cell anemia, and previous preterm birth). Approximately 40% of women with a risk score ≥ 3.0 in the training and testing samples delivered preterm (40.6% and 40.8%, respectively) compared to 3.1-3.3% of women with a risk score of 0.0 [odds ratio (OR) 13.0, 95% confidence interval (CI) 10.7-15.8, training; OR 12.2, 95% CI 9.4-15.9, testing). Additionally, over 18% of women with a risk score ≥ 3.0 had an adverse outcome other than preterm birth. Conclusion Maternal factors that are identifiable prior to pregnancy or during the first-trimester can be used create a cumulative risk score to identify women at the lowest and highest risk for preterm birth regardless of race/ethnicity or socioeconomic status. Further, we found that this cumulative risk score could also identify women at risk for other adverse outcomes who did not have a preterm birth. The risk score is not an effective screening test, but does identify women at very high risk of a preterm birth.

The association of different progesterone preparations with preterm birth prevention

Objective: We aimed to compare the efficacy of commonly available progesterone preparations for preterm birth prevention. Methods: A retrospective cohort study of all women treated with progesterone to prevent preterm birth and delivered in a single university-affiliated tertiary medical-center. Four progesterone preparations were compared: vaginal Endometrin 100 mg twice daily, vaginal Crinone 8% gel 90 mg daily, vaginal Utrogestan 200 mg daily, and intramuscular 17α-hydroxyprogesterone caproate (17-OHPC) 250 mg weekly. All women were considered at risk for preterm birth according to: prior preterm birth or cervical length below 25 mm measured during the second trimester. Significant maternal morbidity, pregnancy achieved by artificial reproductive technique and cerclage placement were excluded. Primary outcome was the rate of preterm birth prior to 37 weeks of gestation. Results: Overall, 422 women were allocated to four study groups according to progesterone preparation: Endometrin 175 (41.5%), Crinone 73 (17.3%), Utrogestan 154 (36.5%), and 17-OHPC 20 (4.7%). Rates of preterm birth prior to 37 gestational weeks were lowest on the Endometrin treatment group (12.6 versus 20.5, 17.5, and 35% in the rest, p = .05). Multivariate analysis revealed that the progesterone preparation was associated with preterm birth prior to 37 gestational weeks (LR = 8.3, p = .004). The need for maternal red blood cells transfusion was significantly higher in the Endometrin subgroup (4% versus 0 in all others, p = .018). This finding remained significant after adjustment to potential confounders (LR 16.44, p < .001). Neonatal outcomes did not differ between groups. Conclusions: Different progesterone preparations prescribed to women at risk, may possess different efficacy in preventing preterm delivery prior to 37 weeks of gestation.

Screening for spontaneous preterm birth and resultant therapies to reduce neonatal morbidity and mortality: A review

Despite considerable effort aimed at decreasing the incidence of spontaneous preterm birth, it remains the leading cause of perinatal morbidity and mortality. Screening strategies are imperfect. Approaches used to identify women considered by historical factors to be low risk for preterm delivery (generally considered to be women with singleton pregnancies without a history of a previous preterm birth) as well as those at high risk for preterm birth (those with a previous preterm birth, short cervix, or multiple gestation) continue to evolve. Herein, we review the current evidence and approaches to screening women for preterm birth, and examine future directions for clinical practice. Further research is necessary to better identify at-risk women and provide evidence-based management.

Mechanical strain induced phospho-proteomic signaling in uterine smooth muscle cells

Mechanical strain associated with the expanding uterus correlates with increased preterm birth rates. Mechanical signals result in a cascading network of protein phosphorylation events. These signals direct cellular activities and may lead to changes in contractile phenotype and calcium signaling. In this study, the complete phospho-proteome of uterine smooth muscle cells subjected to mechanical strain for 5 min was compared to un-strained controls. Statistically significant, differential phosphorylation events were annotated by Ingenuity Pathway Analysis to elucidate mechanically induced phosphorylation networks. Mechanical strain leads to the direct activation of ERK1/2, HSPB1, and MYL9, in addition to phosphorylation of PAK2, vimentin, DOCK1, PPP1R12A, and PTPN11 at previously unannotated sites. These results suggest a novel network reaction to mechanical strain and reveal proteins that participate in the activation of contractile mechanisms leading to preterm labor.

Proteomic analysis of first trimester maternal serum to identify candidate biomarkers potentially predictive of spontaneous preterm birth

Spontaneous preterm birth (sPTB) remains a major clinical dilemma; current diagnostics and interventions have not reduced the rate of this serious healthcare burden. This study characterizes differential protein profiles and post-translational modifications (PTMs) in first trimester maternal serum using a refined top-down approach coupling two-dimensional gel electrophoresis (2DE) and mass spectrometry (MS) to directly compare subsequent term and preterm labour events and identify marked protein differences. 30 proteoforms were found to be significantly increased or decreased in the sPTB group including 9 phosphoproteins and 11 glycoproteins. Changes occurred in proteins associated with immune and defence responses. We identified protein species that are associated with several clinically relevant biological processes, including interrelated biological networks linked to regulation of the complement cascade and coagulation pathways, immune modulation, metabolic processes and cell signalling. The finding of altered proteoforms in maternal serum from pregnancies that delivered preterm suggests these as potential early biomarkers of sPTB and also possible mediators of the disorder.

BIOLOGICAL SIGNIFICANCE

Identifying changes in protein profiles is critical in the study of cell biology, and disease treatment and prevention. Identifying consistent changes in the maternal serum proteome during early pregnancy, including specific protein PTMs (e.g. phosphorylation, glycosylation), is likely to provide better opportunities for prediction, intervention and prevention of preterm birth. This is the first study to examine first trimester maternal serum using a highly refined top-down proteomic analytical approach based on high resolution 2DE coupled with mass spectrometry to directly compare preterm (<37 weeks) and preterm (≥37 weeks) events and identify select protein differences between these conditions. As such, the data present a promising avenue for translation of biomarker discovery to a clinical setting as well as for future investigation of underlying aetiological processes.

Is Midtrimester Cervical Length Associated with Preterm Birth in Women Evaluated for Preterm Labor?

OBJECTIVE

 This article aims to evaluate whether midtrimester cervical length (CL) is associated with improved prediction of preterm delivery in women presenting with preterm labor.

STUDY DESIGN

 This is a retrospective cohort study of women with a singleton gestation who underwent routine CL screening between 18 and 24 weeks of gestation between 2010 and 2014 who were later evaluated for preterm labor. Women were stratified by midtrimester CL quartile. Bivariable and multivariable analyses were performed to identify factors independently associated with preterm birth <37 weeks, <34 weeks, and delivery within 7 days of evaluation. Receiver operating characteristic (ROC) curves were created for multivariable equations with and without CL quartile to determine whether addition of CL improved the predictive capacity of the model for predicting preterm birth.

RESULTS

 A total of 460 women were evaluated for preterm labor and had midtrimester CL measurements available. When CL quartile was incorporated into a regression model including demographic and clinical characteristics associated with preterm birth, the area under the ROC curve was not improved (0.775 vs. 0.786, p = 0.20).

CONCLUSION

 While a shorter midtrimester CL quartile is associated with an increased incidence of preterm delivery in women evaluated for preterm labor, the addition of this variable to an existing model does not improve prediction of preterm birth.

Tensile Mechanical Properties and Dynamic Collagen Fiber Re-Alignment of the Murine Cervix are Dramatically Altered Throughout Pregnancy

The cervix is a unique organ able to dramatically change its shape and function by serving as a physical barrier for the growing fetus and then undergoing dramatic dilation allowing for delivery of a term infant. As a result, the cervix endures changing mechanical forces from the growing fetus. There is an emerging concept that the cervix may change or remodel "early" in many cases of spontaneous preterm birth (sPTB). However, the mechanical role of the cervix in both normal and preterm birth remains unclear. Therefore, the primary objective of this study was to determine the mechanical and structural responses of murine cervical tissue throughout a normal gestational time course. In this study, both tissue structural and material properties were determined via a quasi-static tensile load-to-failure test, while simultaneously obtaining dynamic collagen fiber re-alignment via cross-polarization imaging. This study demonstrated that the majority of the mechanical properties evaluated decreased at midgestation and not just at term, while collagen fiber re-alignment occurred earlier in the loading curve for cervices at term. This suggests that although structural changes in the cervix occur throughout gestation, the differences in material properties function in combination with collagen fiber re-alignment as mechanical precursors to regulate term gestation. This work lays a foundation for investigating cervical biomechanics and the role of the cervix in preterm birth.

Body Mass Index and the Spontaneous Onset of Parturition

OBJECTIVE

To investigate the relationship between body mass index (BMI) and the onset of parturition throughout gestation.

METHODS

This was a secondary analysis of the Maternal-Fetal Medicine Units Network Preterm Prediction Study. Time-to-spontaneous-birth-event (ie, "survival") methods were used to study the association of BMI with the timing of spontaneous onset of labor throughout gestation with indicated births censored at delivery. A Kaplan-Meier estimate of the probability of spontaneous labor was compared with a log rank test across five categories of BMI (kg/m): underweight (less than 18.5), normal weight (18.5-24.99), preobese (25-29.99), obese I (30-34.99), and obese II+ (35 or greater). A proportional hazards model was estimated to compare time to spontaneous onset of labor adjusted for multiple variables known to be associated with the onset of labor.

RESULTS

Normal-weight women (n=1,054) had a median delivery gestational age of 39 3/7 weeks. Obese II+ women (n=178) had a median delivery gestational age 5 days later than normal-weight women (P<.001). Delivery gestational age of preobese (n=866) and obese I (n=548) women was not significantly different from normal-weight women. Underweight women (n=41) had a median delivery gestational age 5 days earlier than normal-weight women (P<.001). Compared with women with normal BMIs, obese II+ women were significantly less likely and underweight women significantly more likely to enter spontaneous labor at all gestational ages. In the multivariable model, BMI was significantly associated with spontaneous onset of labor throughout pregnancy (BMI [five-unit] adjusted hazard ratio 0.874, 0.829-0.921).

CONCLUSION

Body mass index is significantly associated with the likelihood of the spontaneous onset of labor at all gestational ages with gestational age at the time of delivery and BMI being inversely related. This novel observation unifies previous reports focusing on the association of overweight and underweight BMIs and preterm and postterm birth and may inform discussions surrounding elective induction of labor at term.

Association among Maternal Obesity, Cervical Length, and Preterm Birth

Objective The objective of this study was to determine if mid-trimester cervical length is associated with the inverse relationship between maternal body mass index (BMI) at delivery and spontaneous preterm birth (SPTB). Materials and Methods This was a retrospective cohort of women with a singleton pregnancy without prior SPTB who underwent routine transvaginal cervical length assessment between 18 and 24 weeks. Women were categorized into four BMI groups: (1) 18.5 to 24.9, (2) 25 to 29.9, (3) 30 to 34.9, and (4) ≥ 35 kg/m2. Univariable and multivariable analyses were conducted to determine whether BMI group was associated with SPTB at < 37, 34, or 32 weeks independent of the cervical length. Results Of the 18,100 women in this analysis, 43.5% had a BMI ≥ 30. In univariable analysis, increasing BMI group was associated with longer cervical length but not with cervical length < 10th percentile. SPTB at < 37, 35, and 32 weeks was less common among women with higher BMI. In multivariable regression, a higher BMI group was associated with a lower frequency of SPTB at 37 weeks (adjusted odds ratios [aORs] of 0.64, 0.68, and 0.51), at 34 weeks (aORs of 0.53, 0.54, and 0.31) and at 32 weeks (aORs of 0.47, 0.60, and 0.27) for BMI groups 2 to 4, respectively. This association persisted even when cervical length was entered into the model as a covariate. Conclusion Women with a higher BMI group had longer mid-trimester cervical length, and correspondingly reduced SPTB. However, the decreased risk of SPTB was not associated with cervical length. The reason for the potential protective effect from prematurity is unknown and its mechanisms require further investigation.

Contributions to the dynamics of cervix remodeling prior to term and preterm birth

Major clinical challenges for obstetricians and neonatologists result from early cervix remodeling and preterm birth. Complications related to cervix remodeling or delivery account for significant morbidity in newborns and peripartum mothers. Understanding morphology and structure of the cervix in pregnant women is limited mostly to the period soon before and after birth. However, evidence in rodent models supports a working hypothesis that a convergence of factors promotes a physiological inflammatory process that degrades the extracellular collagen matrix and enhances biomechanical distensibility of the cervix well before the uterus develops the contractile capabilities for labor. Contributing factors to this remodeling process include innervation, mechanical stretch, hypoxia, and proinflammatory mediators. Importantly, the softening and shift to ripening occurs while progesterone is near peak concentrations in circulation across species. Since progesterone is required to maintain pregnancy, the premise of this review is that loss of responsiveness to progesterone constitutes a common final mechanism for remodeling the mammalian cervix in preparation for birth at term. Various inputs are suggested to promote signaling between stromal cells and resident macrophages to drive proinflammatory processes that advance the soft cervix into ripening. With infection, pathophysiological processes may prematurely drive components of this remodeling mechanism and lead to preterm birth. Identification of critical molecules and pathways from studies in various rodent models hold promise for novel endpoints to assess risk and provide innovative approaches to treat preterm birth or promote the progress of ripening at term.

A Review on Various Uses of N-Acetyl Cysteine

N-acetyl cysteine (NAC), as a nutritional supplement, is a greatly applied antioxidant in vivo and in vitro. NAC is a precursor of L-cysteine that results in glutathione elevation biosynthesis. It acts directly as a scavenger of free radicals, especially oxygen radicals. NAC is a powerful antioxidant. It is also recommended as a potential treatment option for different disorders resulted from generation of free oxygen radicals. Additionally, it is a protected and endured mucolytic drug that mellows tenacious mucous discharges. It has been used for treatment of various diseases in a direct action or in a combination with some other medications. This paper presents a review on various applications of NAC in treatment of several diseases.

Genetic variation associated with preterm birth in African-American women

BACKGROUND

Preterm birth is considered a multifactorial condition; however, emerging evidence suggests that genetic variation among individuals may have an important role. Prior studies have suggested that single-nucleotide polymorphisms associated with genes related to the immune system, and particularly the maternal inflammatory response, may be associated with an increased risk of preterm delivery.

OBJECTIVE

The objective of the study was to identify single-nucleotide polymorphisms associated with spontaneous preterm birth <37 weeks within a cohort of African-American women.

STUDY DESIGN

This is a secondary analysis of a randomized trial that evaluated periodontal disease and preterm birth. Women were enrolled between 6 and 20 weeks' gestation at 3 prenatal care clinics between 2004 and 2007. Maternal DNA samples were collected and analyzed using a custom 1536 single-nucleotide polymorphismgenotyping array designed to assess genes involved in inflammation. Women were included in this study if they self-identified as African American. We excluded women with a multiple gestation or an indicated preterm delivery. We performed allele- and genotype-based analyses to evaluate the association between spontaneous preterm birth and tag single-nucleotide polymorphisms. We used a logistic regression to adjust for prior preterm birth in our genotype-based analysis. In a subgroup analysis, we compared women who delivered at <34 weeks' gestation to women who delivered at term. Within the microarray, we identified ancestry informative markers and compared global ancestry estimates among women who delivered preterm with those who delivered at term.

RESULTS

Of the 833 African-American women in the study with genotype data, 77 women (9.2%) had a spontaneous preterm birth, whereas 756 women delivered at term. In an allele-based analysis, 4 single-nucleotide polymorphisms related to the genes for protein kinase C-α (PRKCA) were associated with increased risk of spontaneous preterm birth <37 weeks, whereas a single single-nucleotide polymorphism related to fms-related tyrosine kinase 1 (FLT1) was associated with spontaneous preterm birth <34 weeks. A genotype-based analysis revealed similar associations between single-nucleotide polymorphisms related to the PRKCA genes and spontaneous premature delivery. Additionally, single-nucleotide polymorphisms related to matrix metalloproteinase-2 (MMP2), tissue inhibitor of matrix metalloproteinase-2 (TIMP2), and interleukin 16 (IL16) genes were associated with spontaneous preterm birth <37 weeks in genotype-based analysis. Genetic variants related to MMP2, matrix metalloproteinase-1 (MMP1), and leukemia inhibitory factor receptor antisense RNA 1 (LIFR-AS1) genes were associated with higher rates of preterm birth <34 weeks. Ancestry estimates were similar between the women who had a spontaneous preterm birth and those who delivered at term.

CONCLUSION

We identified tag single-nucleotide polymorphisms related to 7 genes that are critical to inflammation, extracellular remodeling, and cell signaling that were associated with spontaneous preterm birth in African-American women. Specifically, we found a strong association with the PRKCA gene. Genetic variation in these regions of the genome may be important in the pathogenesis of preterm birth. Our results should be considered in the design of future genomic studies in prematurity.

Predicting Preterm Birth Among Women Screened by North Carolina's Pregnancy Medical Home Program

OBJECTIVE

To determine which combination of risk factors from Community Care of North Carolina's (CCNC) Pregnancy Medical Home (PMH) risk screening form was most predictive of preterm birth (PTB) by parity and race/ethnicity.

METHODS

This retrospective cohort included pregnant Medicaid patients screened by the PMH program before 24 weeks gestation who delivered a live birth in North Carolina between September 2011-September 2012 (N = 15,428). Data came from CCNC's Case Management Information System, Medicaid claims, and birth certificates. Logistic regression with backward stepwise elimination was used to arrive at the final models. To internally validate the predictive model, we used bootstrapping techniques.

RESULTS

The prevalence of PTB was 11 %. Multifetal gestation, a previous PTB, cervical insufficiency, diabetes, renal disease, and hypertension were the strongest risk factors with odds ratios ranging from 2.34 to 10.78. Non-Hispanic black race, underweight, smoking during pregnancy, asthma, other chronic conditions, nulliparity, and a history of a low birth weight infant or fetal death/second trimester loss were additional predictors in the final predictive model. About half of the risk factors prioritized by the PMH program remained in our final model (ROC = 0.66). The odds of PTB associated with food insecurity and obesity differed by parity. The influence of unsafe or unstable housing and short interpregnancy interval on PTB differed by race/ethnicity.

CONCLUSIONS

Evaluation of the PMH risk screen provides insight to ensure women at highest risk are prioritized for care management. Using multiple data sources, salient risk factors for PTB were identified, allowing for better-targeted approaches for PTB prevention.

Screening for post 32-week preterm birth risk: how helpful is routine perinatal data collection?

Background

Preterm birth is a clinical event significant but difficult to predict. Biomarkers such as fetal fibronectin and cervical length are effective, but the often are used only for women with clinically suspected preterm risk. It is unknown whether routinely collected data can be used in early pregnancy to stratify preterm birth risk by identifying asymptomatic women. This paper tries to determine the value of the Victorian Perinatal Data Collection (VPDC) dataset in predicting preterm birth and screening for invasive tests.

Methods

De-identified VPDC report data from 2009 to 2013 were extracted for patients from Barwon Health in Victoria. Logistic regression models with elastic-net regularization were fitted to predict 37-week preterm, with the VPDC antenatal variables as predictors. The models were also extended with two additional variables not routinely noted in the VPDC: previous preterm birth and partner smoking status, testing the hypothesis that these two factors add prediction accuracy. Prediction performance was evaluated using a number of metrics, including Brier scores, Nagelkerke’s R², c statistic.

Results

Although the predictive model utilising VPDC data had a low overall prediction performance, it had a reasonable discrimination (c statistic 0.646 [95% CI: 0.596–0.697] for 37-week preterm) and good calibration (goodness-of-fit p = 0.61). On a decision threshold of 0.2, a Positive Predictive Value (PPV) of 0.333 and a negative predictive value (NPV) of 0.941 were achieved. Data on previous preterm and partner smoking did not significantly improve prediction.

Conclusions

For multiparous women, the routine data contains information comparable to some purposely-collected data for predicting preterm risk. But for nulliparous women, the routine data contains insufficient data related to antenatal complications.

Sonographic Methods of Evaluating Cervical Length: A Literature Review

Labor that begins between 20 and 37 weeks gestation is appropriately termed preterm labor. Some of the precipitating factors of preterm labor are changes in cervical status including dilatation and effacement. Until recently, a digital pelvic examination was considered the gold standard for evaluating cervical changes. Current research promotes the use of sonography for the prediction of preterm labor. It is essential for sonographers to become familiar with the various methods of cervical imaging including transabdominal, translabial, and transvaginal approaches. Each technique has its costs and benefits; however, a review of the current literature will show that the transvaginal method of cervical measurement is the most reliable.

Targeted Sequencing and Meta-Analysis of Preterm Birth

Understanding the genetic contribution(s) to the risk of preterm birth may lead to the development of interventions for treatment, prediction and prevention. Twin studies suggest heritability of preterm birth is 36–40%. Large epidemiological analyses support a primary maternal origin for recurrence of preterm birth, with little effect of paternal or fetal genetic factors. We exploited an “extreme phenotype” of preterm birth to leverage the likelihood of genetic discovery. We compared variants identified by targeted sequencing of women with 2–3 generations of preterm birth with term controls without history of preterm birth. We used a meta-genomic, bi-clustering algorithm to identify gene sets coordinately associated with preterm birth. We identified 33 genes including 217 variants from 5 modules that were significantly different between cases and controls. The most frequently identified and connected genes in the exome library were IGF1, ATM and IQGAP2. Likewise, SOS1, RAF1 and AKT3 were most frequent in the haplotype library. Additionally, SERPINB8, AZU1 and WASF3 showed significant differences in abundance of variants in the univariate comparison of cases and controls. The biological processes impacted by these gene sets included: cell motility, migration and locomotion; response to glucocorticoid stimulus; signal transduction; metabolic regulation and control of apoptosis.

Development and validation of a spontaneous preterm delivery predictor in asymptomatic women

BACKGROUND

Preterm delivery remains the leading cause of perinatal mortality. Risk factors and biomarkers have traditionally failed to identify the majority of preterm deliveries.

OBJECTIVE

To develop and validate a mass spectrometry-based serum test to predict spontaneous preterm delivery in asymptomatic pregnant women.

STUDY DESIGN

A total of 5501 pregnant women were enrolled between 17(0/7) and 28(6/7) weeks gestational age in the prospective Proteomic Assessment of Preterm Risk study at 11 sites in the United States between 2011 and 2013. Maternal blood was collected at enrollment and outcomes collected following delivery. Maternal serum was processed by a proteomic workflow, and proteins were quantified by multiple reaction monitoring mass spectrometry. The discovery and verification process identified 2 serum proteins, insulin-like growth factor-binding protein 4 (IBP4) and sex hormone-binding globulin (SHBG), as predictors of spontaneous preterm delivery. We evaluated a predictor using the log ratio of the measures of IBP4 and SHBG (IBP4/SHBG) in a clinical validation study to classify spontaneous preterm delivery cases (<37(0/7) weeks gestational age) in a nested case-control cohort different from subjects used in discovery and verification. Strict blinding and independent statistical analyses were employed.

RESULTS

The predictor had an area under the receiver operating characteristic curve value of 0.75 and sensitivity and specificity of 0.75 and 0.74, respectively. The IBP4/SHBG predictor at this sensitivity and specificity had an odds ratio of 5.04 for spontaneous preterm delivery. Accuracy of the IBP4/SHBG predictor increased using earlier case-vs-control gestational age cutoffs (eg, <35(0/7) vs ≥35(0/7) weeks gestational age). Importantly, higher-risk subjects defined by the IBP4/SHBG predictor score generally gave birth earlier than lower-risk subjects.

CONCLUSION

A serum-based molecular predictor identifies asymptomatic pregnant women at risk of spontaneous preterm delivery, which may provide utility in identifying women at risk at an early stage of pregnancy to allow for clinical intervention. This early detection would guide enhanced levels of care and accelerate development of clinical strategies to prevent preterm delivery.

Prognostic models in obstetrics: available, but far from applicable

Health care provision is increasingly focused on the prediction of patients' individual risk for developing a particular health outcome in planning further tests and treatments. There has been a steady increase in the development and publication of prognostic models for various maternal and fetal outcomes in obstetrics. We undertook a systematic review to give an overview of the current status of available prognostic models in obstetrics in the context of their potential advantages and the process of developing and validating models. Important aspects to consider when assessing a prognostic model are discussed and recommendations on how to proceed on this within the obstetric domain are given. We searched MEDLINE (up to July 2012) for articles developing prognostic models in obstetrics. We identified 177 papers that reported the development of 263 prognostic models for 40 different outcomes. The most frequently predicted outcomes were preeclampsia (n = 69), preterm delivery (n = 63), mode of delivery (n = 22), gestational hypertension (n = 11), and small-for-gestational-age infants (n = 10). The performance of newer models was generally not better than that of older models predicting the same outcome. The most important measures of predictive accuracy (ie, a model's discrimination and calibration) were often (82.9%, 218/263) not both assessed. Very few developed models were validated in data other than the development data (8.7%, 23/263). Only two-thirds of the papers (62.4%, 164/263) presented the model such that validation in other populations was possible, and the clinical applicability was discussed in only 11.0% (29/263). The impact of developed models on clinical practice was unknown. We identified a large number of prognostic models in obstetrics, but there is relatively little evidence about their performance, impact, and usefulness in clinical practice so that at this point, clinical implementation cannot be recommended. New efforts should be directed toward evaluating the performance and impact of the existing models.

Risk Prediction for Adverse Pregnancy Outcomes in a Medicaid Population

BACKGROUND

Despite prior efforts to develop pregnancy risk prediction models, there remains a lack of evidence to guide implementation in clinical practice. The current aim was to develop and validate a risk tool grounded in social determinants theory for use among at-risk Medicaid patients.

METHODS

This was a retrospective cohort study of 409 women across 17 Cincinnati health centers between September 2013 and April 2014. The primary outcomes included preterm birth, low birth weight, intrauterine fetal demise, and neonatal death. After random allocation into derivation and validation samples, a multivariable model was developed, and a risk scoring system was assessed and validated using area under the receiver operating characteristic curve (AUROC) values.

RESULTS

The derived multivariable model (n=263) included: prior preterm birth, interpregnancy interval, late prenatal care, comorbid conditions, history of childhood abuse, substance use, tobacco use, body mass index, race, twin gestation, and short cervical length. Using a weighted risk score, each additional point was associated with an odds ratio of 1.57 for adverse outcomes, p<0.001, AUROC=0.79. In the validation sample (n=146), each additional point conferred an odds ratio of 1.20, p=0.03, AUROC=0.63. Using a cutoff of 20% probability for the outcome, sensitivity was 29%, with specificity 82%. Positive and negative predictive values were 22% and 85%, respectively.

CONCLUSIONS

Risk scoring based on social determinants can discriminate pregnancy risk within a Medicaid population; however, performance is modest and consistent with prior prediction models. Future research is needed to evaluate whether implementation of risk scoring in Medicaid prenatal care programs improves clinical outcomes.

An instrument for broadened risk assessment in antenatal health care including non-medical issues

Introduction

Growing evidence on the risk contributing role of non-medical factors on pregnancy outcomes urged for a new approach in early antenatal risk selection. The evidence invites to more integration, in particular between the clinical working area and the public health domain. We developed a non-invasive, standardized instrument for comprehensive antenatal risk assessment. The current study presents the application-oriented development of a risk screening instrument for early antenatal detection of risk factors and tailored prevention in an integrated care setting.

Methods

A review of published instruments complemented with evidence from cohort studies. Selection and standardization of risk factors associated with small for gestational age, preterm birth, congenital anomalies and perinatal mortality. Risk factors were weighted to obtain a cumulative risk score. Responses were then connected to corresponding care pathways. A cumulative risk threshold was defined, which can be adapted to the population and the availability of preventive facilities. A score above the threshold implies multidisciplinary consultation between caregivers.

Results

The resulting digital score card consisted of 70 items, subdivided into four non-medical and two medical domains. Weighing of risk factors was based on existing evidence. Pilot-evidence from a cohort of 218 pregnancies in a multi-practice urban setting showed a cut-off of 16 points would imply 20% of all pregnant women to be assessed in a multidisciplinary setting. A total of 28 care pathways were defined.

Conclusion

The resulting score card is a universal risk screening instrument which incorporates recent evidence on non-medical risk factors for adverse pregnancy outcomes and enables systematic risk management in an integrated antenatal health care setting.

Precocious cervical ripening as a screening target to predict spontaneous preterm delivery among asymptomatic singleton pregnancies: a systematic review

Routine second-trimester transvaginal ultrasonographic (TVU) screening for short cervical length (CL) predicts spontaneous preterm delivery (SPTD), albeit with limited sensitivity (35-40%) and a moderate positive likelihood ratio of 4-6. However, CL describes one of the multidimensional changes that are associated with precocious cervical ripening (PCCR) and that also include cervical softening, cervical funneling (CF), and dilation. PCCR, a precursor and a strong predictor for SPTD, was proposed as a potential screening target. We hypothesized that screening for composite measures of PCCR (eg, CL, CF, cervical consistency, and dilation) with the use of either digital examination or TVU would improve the prediction of SPTD compared with screening for short CL alone. We searched PubMed and EMBASE electronic databases for observational cohort studies to evaluate cervical screening in asymptomatic obstetric populations. Multidimensional composite cervical measures were assessed in 10 datasets (n = 22,050 pregnancies) and 12 publications. Appreciable heterogeneity in cervical measurements, data quality, and outcomes across studies prevented quantitative metaanalysis. Only one study reported intra- and interobserver reliability of cervical measurements. The prevalence of CF ranged from 0.7-9.1%. Five studies compared composite measures of PCCR (ie, CL and CF) with short CL alone and consistently reported improved screening performance. Among 3 TVU studies, gains in sensitivity ranged from 5-27%, and increases in positive likelihood ratio ranged from 3-16. Our findings suggest that composite measures of PCCR might serve as valuable screening targets. High-quality interdisciplinary studies that integrate epidemiologic approaches are needed to test this hypothesis and to accelerate the translation of advances in cervical pathophysiology into effective preventive interventions.

Interleukin 6 and fetal fibronectin as a predictors of preterm delivery in symptomatic patients

Preterm delivery is the leading cause of neonatal mortality and morbidity. The rate of preterm births has been estimated to be about 15 million, which accounts for 11.1% of all live births worldwide. The purpose of this study was to evaluate the cervico-vaginal (CVF) cytokine IL-6 and fetal fibronectin (fFN) status as predictors of preterm delivery in patients with symptoms of preterm labor. Patients with symptoms suggestive of preterm labor were recruited from September 2013 to March 2014. Vaginal swabs were taken for fetal fibronectin test (fFN) and CVF IL-6. Antibiotics, steroids and tocolytics were administered, where appropriate. The outcome was measured by the occurrence of preterm delivery within 14 days from the day of hospital admission. Cut-off value of 1305 pg/mL for the concentration of IL-6 in the CVF was the best predictor of preterm delivery, with the sensitivity of 69.4% and specificity of 68.2%. Patients with positive fFN test had the OR of 6.429 (95%CI 1.991-20.758) to deliver prematurely. The multivariate analysis of combined fFN and CVF IL-6 tests resulted in risk of 86.7% to deliver prematurely, if both tests were positive. The combination of both tests performed better than the individual tests and decreased the false positive rate, which in turn reduced the chances for inappropriate patient treatment, bringing down the costs.