Prediction Of Preterm Birth Research Articles

Use of uterine electromyography in the prediction of preterm birth after transvaginal cervical cerclage

BackgroundPreterm birth (PTB), complications of which account for approximately 35% of deaths among neonates, remains a crucial issue. Cervical insufficiency (CI) is defined as the inability of the utrine cervix to retain a pregnancy, leading to PTB. Cervical cerclage is an efficient surgery for CI patients by preventing the cervix from being further mechanically shortened. Unfortunately, a certain number of patients who had cerclage still delivered prematurely, raising the urgent need to accurately assess the risk of PTB in patients with cerclage. Uterine electromyography (uEMG) is an emerging technology that characterizes uterine contractions by describing the actual evolution process of uterine activity and has been used to predict PTB in recent years.MethodIn this single-center retrospective case-control study, singleton pregnancy women who received cervical cerclage and uEMG assessment between January 2018 and January 2022 at the Sun Yat-sen Memorial Hospital of Sun Yat-sen University were enrolled.Results32 PTBs were observed of the 69 women who underwent assessment. Based on multivariate logistic regression analysis, PTB after cerclage was significantly associated with previous PTB history or mid-trimester pregnancy loss (OR: 2.87, 95%CI: 1.49–5.54) and contraction frequency detected by uEMG (OR: 2.24, 95%CI: 1.44–3.49). The AUC of contraction frequency (0.766, P<0.001) was observed, and the optimal cut-off value suggested by Youden Index was 1.75 times per hour. Combined with previous preterm history and cervical length, the AUC of contraction frequency reached 0.858. After stratification by contraction frequency, the median duration was 11 weeks in the high frequency group (> 1.75 times per hour) and 15 weeks in the low frequency group (≤ 1.75 times per hour) (P<0.001).ConclusionsThe uEMG effectively predicts PTB after transvaginal cervical cerclage and provides a new method for clinicians to evaluate the pregnancy outcome of CI patients.

Clinical and dental predictors of preterm birth using machine learning methods: the MOHEPI study.

Preterm birth (PTB) is one of the most common and serious complications of pregnancy, leading to mortality and severe morbidities that can impact lifelong health. PTB could be associated with various maternal medical condition and dental status including periodontitis. The purpose of this study was to identify major predictors of PTB among clinical and dental variables using machine learning methods. Prospective cohort data were obtained from 60 women who delivered singleton births via cesarean section (30 PTB, 30 full-term birth [FTB]). Dependent variables were PTB and spontaneous PTB (SPTB). 15 independent variables (10 clinical and 5 dental factors) were selected for inclusion in the machine learning analysis. Random forest (RF) variable importance was used to identify the major predictors of PTB and SPTB. Shapley additive explanation (SHAP) values were calculated to analyze the directions of the associations between the predictors and PTB/SPTB. Major predictors of PTB identified by RF variable importance included pre-pregnancy body mass index (BMI), modified gingival index (MGI), preeclampsia, decayed missing filled teeth (DMFT) index, and maternal age as in top five rankings. SHAP values revealed positive correlations between PTB/SPTB and its major predictors such as premature rupture of the membranes, pre-pregnancy BMI, maternal age, and MGI. The positive correlations between these predictors and PTB emphasize the need for integrated medical and dental care during pregnancy. Future research should focus on validating these predictors in larger populations and exploring interventions to mitigate these risk factors.

7828 First Trimester Serum 11-Deoxycorticosterone Predicts Extreme Premature Childbirth

Abstract Disclosure: E.K. Ronde Salminen: None. S. Schoenmakers: None. B.D. van Zelst: None. S.A. van den Berg: None. First Trimester Serum 11-Deoxycorticosterone Predicts Extreme Premature Childbirth Background: Preterm birth is the leading cause of perinatal mortality and morbidity worldwide. Unfortunately, the prediction of preterm birth remains challenging, causing unnecessary hospital admittance and overexposure to medication. Studies have shown that metabolites of progesterone can influence uterine contractility[1] and serum levels of 11-deoxycorticosterone have been shown to correlate with an increased risk of preterm birth before 32 weeks of gestation2. The aim of this prospective study was to determine whether first trimester measurement of the progesterone metabolites 11-deoxycorticosterone (11-DOC) and 16-alpha hydroxyprogesterone (16-OHP) could be used as a predictive marker for preterm birth. Methods and results: A cohort of 386 pregnant women were included at 12 weeks of gestation between 2014-2016. Their obstetric histories and comorbidities were noted and their plasma was frozen. Of these women, n=26 delivered prior to 32 weeks of gestation and n=286 delivered at term. 11-DOC and 16-OHP were determined using LC-MS/MS. Mann-Whitney U test was used to statistically analyze differences between the groups. There was a highly significant difference in the level of 11-DOC between women delivering prior to 32 weeks of gestation in comparison to women delivering after 37 weeks of gestation (p=0.007). 16-OHP levels tended to be different between the two groups, but this did not reach statistical significance (p=0.062). No difference was found in the ratio 16-alpha hydroxyprogesterone/11-deoxycorticosterone between the two groups (p=0.916). Conclusion: Plasma levels of progesterone metabolites collected late in the first trimester of pregnancy predicts preterm birth prior to 32 weeks of pregnancy.

7828 First Trimester Serum 11-Deoxycorticosterone Predicts Extreme Premature Childbirth

Abstract Disclosure: E.K. Ronde Salminen: None. S. Schoenmakers: None. B.D. van Zelst: None. S.A. van den Berg: None. Background: Preterm birth is the leading cause of perinatal mortality and morbidity worldwide. Unfortunately, the prediction of preterm birth remains challenging, causing unnecessary hospital admittance and overexposure to medication. Studies have shown that metabolites of progesterone can influence uterine contractility[1] and serum levels of 11-deoxycorticosterone have been shown to correlate with an increased risk of preterm birth before 32 weeks of gestation2. The aim of this prospective study was to determine whether first trimester measurement of the progesterone metabolites 11-deoxycorticosterone (11-DOC) and 16-alpha hydroxyprogesterone (16-OHP) could be used as a predictive marker for preterm birth. Methods and results: A cohort of 386 pregnant women were included at 12 weeks of gestation between 2014-2016. Their obstetric histories and comorbidities were noted and their plasma was frozen. Of these women, n=26 delivered prior to 32 weeks of gestation and n=286 delivered at term. 11-DOC and 16-OHP were determined using LC-MS/MS. Mann-Whitney U test was used to statistically analyze differences between the groups. There was a highly significant difference in the level of 11-DOC between women delivering prior to 32 weeks of gestation in comparison to women delivering after 37 weeks of gestation (p=0.007). 16-OHP levels tended to be different between the two groups, but this did not reach statistical significance (p=0.062). No difference was found in the ratio 16-alpha hydroxyprogesterone/11-deoxycorticosterone between the two groups (p=0.916). Conclusion: Plasma levels of progesterone metabolites collected late in the first trimester of pregnancy predicts preterm birth prior to 32 weeks of pregnancy.

Changing Indications for Cervical Cerclage Following the Introduction of Routine Ultrasound Surveillance of Cervical Length for Prediction and Prevention of Preterm Birth.

Preterm birth (PTB) is associated with significant neonatal mortality and morbidity. Universal measurement of cervical length has been proposed as a screening tool to direct intervention to prevent PTB. To assess the impact of the introduction of sonographic mid-trimester cervical length screening on the use of cervical cerclage and PTB. A retrospective cohort study reviewed two groups of women who underwent cervical cerclage before and after the introduction of universal sonographic cervical length screening. Demographics and outcomes were compared using Student's t test, Fisher's Exact test and Kaplan-Meier analysis. Following introduction of universal cervical length screening, the overall rate of cerclage increased from 2.5/1000 births to 6.0/1000 births (p < 0.01). There was a reduction in the proportion of sutures placed purely based on maternal history (50.0% to 30.4%; p < 0.001), while the proportion of sutures placed following ultrasound assessment increased in both high- (21.7 to 36.6%) and low-risk (11.7% to 30.4%) women (p < 0.001). The overall rate of PTB <37weeks in women has a cerclage was 25.7% and was highest in women undergoing rescue cerclage (64.3%; p < 0.01). There was no difference in the rate of PTB between high- and low-risk women undergoing history- or ultrasound-indicated cerclage. Mean pregnancy length was most prolonged in low-risk women undergoing ultrasound-indicated cerclage, extending gestation from 33.9 to 38.3weeks (p < 0.01). Universal cervical length screening results in an increase in the use of cerclage, specifically on the basis of the ultrasound findings. Women who were at low risk but then underwent ultrasound-indicated cerclage experienced most prolongation of pregnancy. Women who were at high risk but had a suture on the basis of ultrasound findings-indicated cerclage represent an alternative method of management with no significant difference in the gestational age of delivery.

Evaluation of antenatal care quality for preterm birth prevention using an auditable scoring system: A retrospective, descriptive, longitudinal study in Sydney, Australia.

Preterm birth continues to be one of the most significant contributors to perinatal death. This study aims to evaluate the quality of antenatal care provided to women delivering preterm. This was a retrospective, descriptive, longitudinal review of all women who had antenatal care within a single Australian tertiary hospital and delivered spontaneously between 24 and 37 weeks of gestation, using an auditable scoring system assessing potential interventions for prevention of preterm birth. The review was limited to singleton pregnancies without fetal abnormalities delivering between January 2013 and April 2015. The audit tool was developed by reference to established 'best practice' guidance for prediction and prevention of preterm birth based on Royal Australian and New Zealand College of Obstetricians and Gynaecologists guidelines and published literature. Different pathways were assessed for women deemed either low- or high-risk at the outset of antenatal care. A series of 161 pregnancies that delivered preterm (between 24 and 37 weeks' gestation) were reviewed. The quality of antenatal care was scored 'good' in 42.9% and 50% of high-risk and low-risk women, respectively. Care was scored 'adequate', with room for improvement in 51.4% and 45.2% of the two corresponding groups. The main deficiencies in care were recorded evidence of assessment of cervical length (absent in 35% of cases) and failure to screen for bacterial vaginosis in high-risk women. Auditing antenatal care for prevention of preterm birth allows identification of suboptimal practice allowing service improvement and potential intervention for preterm birth prevention.

Prediction of preterm birth in multiparous women using logistic regression and machine learning approaches

To predict preterm birth (PTB) in multiparous women, comparing machine learning approaches with traditional logistic regression. A population-based cohort study was conducted using data from the Ontario Better Outcomes Registry and Network (BORN). The cohort included all multiparous women who delivered a singleton birth at 20–42 weeks’ gestation in an Ontario hospital between April 1, 2012 and March 31, 2014. The primary outcome was PTB < 37 weeks, with spontaneous PTB analyzed as a secondary outcome. Stepwise logistic regression and the Boruta machine learning were used to select the important variables during the first and second trimester. For building prediction models, the whole data set were divided for the two independent parts: two-third for training the classifiers (Logistic regression, random forests, decision trees, and artificial neural networks) and one-third for model validation. Then, the training data set were balanced by random over sampling technique. The best hyper parameters were obtained by the tenfold cross validation. The performance of all models was evaluated by sensitivity, specificity, positive predictive value, negative predictive value, and the area under the receiver operating characteristics (AUC). The cohort included 145,846 births, of which 8125 (5.57%) were preterm. In first-trimester models, the strongest predictors of PTB were previous PTB, preexisting diabetes, and abnormal pregnancy‐associated plasma protein-A. In the testing data set, the highest predictive ability was seen for artificial neural networks, with an area under the receiver operating characteristic curve (AUC) of 68.8% (95% CI 67.6–70.1%). In second-trimester models, addition of infant sex, attendance at first-trimester appointment, medication exposure, and abnormal alpha-fetoprotein concentrations increased the AUC to 72.1% (95% CI 71.1–73.1%) with logistic regression. With the inclusion of the variable complications during pregnancy, the AUC increased to 80.5% (95% CI 79.6–81.5%) using logistic regression. For both overall and spontaneous PTB, during both the first and second trimesters, models yielded negative predictive values of 97%. Overall, machine learning and logistic regression produced similar performance for prediction of PTB. For overall and spontaneous PTB, both first- and second-trimester models provided negative predictive values of ~ 97%, higher than that of fetal fibronectin.

Predictors of preterm birth following emergency and ultrasound-indicated cervical cerclage: A retrospective study on 136 singleton pregnancies

ObjectiveWe aimed to retrospectively evaluate obstetric and neonatal outcomes in patients who underwent ultrasound-indicated and rescue cervical cerclage and to identify predictors for cerclage failure and consequent preterm birth (PTB). Materials and methodsWe conducted a retrospective analysis on singleton pregnancies between 16 and 27 weeks of gestation who presented with a transvaginal sonographic cervical length (TVS-CL) <25 mm and a previous PTB/second-trimester miscarriage or prolapsed amniotic membranes and/or a TVS-CL <15 mm and underwent cervical cerclage at Mangiagalli Center, Milan, between September 2011 and December 2021. Univariate and multivariate logistic regression analyses were used to identify possible predictive factors of cerclage failure. ResultsDuring the study period, a total of 136 singletons met the inclusion criteria. Overall, 3 % of pregnancies did not reach fetal viability, mostly due to post-cerclage preterm premature rupture of membranes and/or chorioamnionitis. The mean gestational age at delivery was 35.9 ± 4.5 weeks. Neonates were delivered beyond 37 weeks in 63.2 % of cases, between 28 and 37 weeks in 26.5 %, and less than 28 gestational weeks in 10.3 %. At the multivariate analysis, independent risk factors for miscarriage or PTB were cervical length <10 mm (odds ratio, OR 3.44), advanced cervical dilatation (OR 4.76), and in vitro fertilization (OR 4.66). A history of previous miscarriage, premature delivery, and uterine malformations did not increase the risk of post-cerclage delivery before 37 weeks. In the preterm delivery group, 14 % of patients experienced preterm premature rupture of membranes (pPROM) and 10 % had chorioamnionitis, while no case was registered in the term delivery group. A positive vaginal swab at the time of cervical cerclage was not significantly associated with PTB at the multivariate analysis but it emerged as a significant risk factor for both chorioamnionitis (OR 11.03) and pPROM (OR 5.28). ConclusionsUltrasound-indicated and rescue cervical cerclage were effective in prolonging pregnancy, even when placed beyond 24 weeks of gestation. Preoperative cervical length of less than 10 mm, advanced dilatation, and in vitro fertilization are associated with an increased risk of cerclage failure. A positive vaginal swab before cerclage is associated with increased rates of intrauterine infectious-inflammatory processes.

The roles of maternal height and body mass index in preterm birth prediction

Objective: To investigate the role of maternal height and body mass index in the prediction of preterm birth Material and Method: Patients who had either cesarean section and/or normal delivery in Department of Obstetrics and Gynecology in the last 5 years were included in the study. Demographic data, weeks of gestation at the time of delivery, maternal height and body mass index of the patients with preterm delivery were recorded retrospectively for the study group. The control group consisted of retrospectively selected patients who had given birth after the expected date of delivery using the Naegele method. Results: In the study, 295 of 541 pregnant women had a preterm birth (study group), while 246 women had given birth after the expected date of delivery (control group). Maternal height and body mass index had an influence on preterm birth. The sensitivity and specificity values for a height of 152.5 cm and a body mass index of 21.3 kg/m2 were (1.0 and 0.13) and (1.0 and 0.06), respectively. In the receiver characteristics analysis of these variables affecting preterm birth, the area under the curve for body mass index and maternal height were 0.763 and 0.708, respectively. Conclusion: We conclude that height and body mass index were found to be good discriminators for the prediction of preterm birth.

Maternal Plasma miRNAs as Early Biomarkers of Moderate-to-Late-Preterm Birth.

Globally, preterm birth (PTB) is a primary cause of mortality and morbidity in infants, with PTB rates increasing worldwide over the last two decades. Biomarkers for accurate early prediction of PTB before the clinical event do not currently exist. Given their roles in the development and progression of many disease states, there has been increasing interest in the utility of microRNAs (miRNAs) as early biomarkers for pregnancy-related disorders, including PTB. The present study was designed to examine potential differences in miRNA abundances in maternal plasma from mothers with infants born following a moderate to late (28-36 weeks' gestation, n = 54) spontaneous PTB (SPTB) compared to mothers with matched term infants (n = 54). Maternal plasma collected at 15 weeks' gestation were utilised from the Auckland and Adelaide cohorts from the Screening for Pregnancy Endpoints (SCOPE) study. miRNAs in plasma were quantified using the NanoString nCounter expression panel (800 miRNAs). The top four most abundant miRNAs were significantly decreased in the plasma of mothers in the SPTB group with results consistent across both cohorts and pathway analysis was undertaken to examine the biological processes linked to the dysregulated miRNAs. The top candidate miRNAs (miRs-451a, -223-3p, let-7a-5p, and -126-3p) were linked to gene pathways associated with inflammation, apoptosis, and mitochondrial biogenesis. Moreover, miRNAs were consistently less abundant in the plasma of mothers of preterm infants across both sites, suggesting potential global dysregulation in miRNA biogenesis. This was supported by a significant downregulation in expression of key genes that are involved in miRNA biogenesis (DROSHA, DICER, and AGO2) across both sites in the SPTB group. In summary, the present study has identified miRNAs in maternal plasma that may provide predictive utility as early biomarkers for the risk of later SPTB. Importantly, these observations were conserved across two independent cohorts. Further, our data provide evidence for a persistent decrease in miRNA abundance in mothers who later experienced an SPTB, which is likely to have widespread consequences for gene regulation and epigenetic processes.

Classification of Term and Preterm Birth Data from Elektrohisterogram (EHG) Data by Empirical Wavelet Transform Based Machine Learning Methods

Accurate prediction of preterm birth can significantly reduce birth complications for both mother and baby. This situation increases the need for an effective technique in early diagnosis. Therefore, machine learning methods and techniques used on Electrohysterogram (EHG) data are increasing day by day. The aim of this study is to evaluate the effectiveness of the Empirical Wavelet Transform (EWT) approach on EHG data and to propose an algorithm for estimating preterm birth using single EHG signal. The data used in the study were taken from Physionet's Term-Preterm Electrohysterogram Database (TPEHGDB) and scored in one-minute windows. The feature matrix was obtained by calculating the sample entropy value from each of the discretized EHG modes obtained as a result of this method, which was used for the first time on EHG data, and the average energy value from the signal obtained by recombining the modes. The obtained features were applied to Random Forest (RF), Support Vector Machine (SVM), Long Short-Term Memory (LSTM) algorithms to predict preterm birth. Among the classifier algorithms, the RF algorithm achieved the best result with a success rate of 98,20%.

Predictive value of short cervix in early third trimester for preterm birth in women with normal mid-trimester cervical length

BackgroundShort cervical length in the mid-trimester is a powerful predictor of preterm birth (PTB). However, clinical significance of cervical length in the third trimester for predicting PTB has not been established yet. ObjectiveTo examine the predictive role of a shortened cervix in the third trimester for PTB in women who had a normal cervical length in the second trimester Study designThis retrospective cohort study included women who underwent cervical length measured at least once in both the second trimester (16+0 weeks to 27+6weeks) and the early third trimester (28+0 weeks to 33+6 weeks). Women with short cervical length in the second trimester, those with multiple pregnancies, those who underwent cerclage operation, and those who had iatrogenic PTB were excluded. The study population was divided into two groups based on cervical length in the third trimester: a short cervix (≤25 mm) group and a control group (>25 mm). Rates of PTB (<37 weeks) were compared between two groups. Predictive performances of cervical length in the third trimester for PTB were assessed. ResultsWomen with a short cervical length at 28+0 to 33+6 weeks accounted for 12.6% (n=717) of the total study population (n=5682). PTB rate was 9.5% in the short cervix group, which was significantly higher than that (3.2%) in the control group (P<.001). The adjusted odds ratio for short cervical length on PTB was 2.73 (95% CI: 1.96–3.79). A short cervix in the third trimester had a sensitivity of 30.1%, a specificity of 88.1%, a positive predictive value of 9.5%, and a negative predictive value (NPV) of 96.8% in predicting PTB. The addition of third-trimester cervical length to the predictive model significantly increased the area under the curve from 0.64 (95% CI: 0.60–0.68) to 0.67 (95% CI: 0.63–0.71) (P=.002), demonstrating improved predictive performance. ConclusionApproximately 13% of women with normal cervical length in the mid-trimester had a short cervical length after 28 weeks, which increased the risk of PTB. The high specificity and NPV of third-trimester cervical length measurements underscore their critical utility in identifying women at low risk for preterm delivery.

The value of real-time shear wave elastography in spontaneous preterm birth.

This study aimed to investigate the predictive value of real-time shear wave elastography (SWE) for spontaneous preterm birth (SPB). This study prospectively selected 175 women with singleton pregnancies at 16 to 36 weeks of gestation. Cervical length (CL) and uterocervical angle (UCA) were measured using transvaginal ultrasonography. Real-time shear wave elastography was used to measure Young's modulus values, including the average Young's modulus (Emean) and the maximum Young's modulus (Emax) at 4 points: point A on the inner lip of the cervical os, point B on the outer lip of the cervical os, point C on the inner lip of the external os, and point D on the outer lip of the external os. Receiver operating characteristic (ROC) curve analysis was performed to compare the accuracy of Young's modulus values at the 4 points, CL, and UCA in predicting SPB. Significant variables were used to construct a binary logistic regression model to predict the multifactorial predictive value of SPB, which was evaluated using an ROC curve. A total 176 valid cases, including 160 full-term pregnancies and 16 SPB, were included in this study. Receiver operating characteristic curve analysis revealed that Emean at point A, as well as Emean and Emax at point D, had a relatively high accuracy in diagnosing SPB, with area under the curve values of 0.704, 0.708, and 0.706, respectively followed by CL (0.670), SWE at point C (Emean 0.615, Emax 0.565), SWE at point B (Emean 0.577, Emax 0.584), and UCA (0.476). Binary logistic regression analysis showed that comorbidities during pregnancy (including diabetes mellitus, hypertension, cholestasis and thyroid dysfunction), CL, and Emean at point A were independent predictors of preterm birth. In addition, the AUC value of the logistic regression model's ROC curve was 0.892 (95% CI: 0.804-0.981), with a sensitivity of 0.867, specificity of 0.792, and Youden's index of 0.659, indicating that the regression model has good predictive ability for SPB. Real-time shear wave elastography showed a higher predictive value for SPB than CL and UCA. The SWE combined with CL and comorbidities during pregnancy model has a good predictive ability for SPB.

Maternal peripheral blood telomere length and preterm birth in African American women: a pilot study.

This study aimed to explore the association between preterm birth and telomere length of maternal peripheral blood in African American women. 78 African American women were recruited for this study between 2018 and 2023 from 2 prenatal clinics in central and east Texas. Participants provided blood samples and completed clinic questionnaires, with clinical data collected from their post-delivery medical records. Telomere length was measured using monochrome multiplex quantitative real-time polymerase chain reaction. Linear regression and multinomial logistic regression were used to analyze the association between telomere length and gestational length. Kruskal-Wallis's test and Fisher's exact test were used to compare preterm birth, early-term birth and full-term birth by telomere length, social-demographic characteristics, stress and discrimination. The rates of preterm birth was higher in pregnant women with shorter telomeres. After adjusting for confounders, for every 10-units increase in the relative telomere-to-single-copy gene (T/S) ratio, gestational days increased by 1.090 days (90% CI 0.182, 1.997), and for every 10-units decrease in the T/S ratio, the odds of preterm birth was 2.664 (90% CI 1.064, 6.673) times greater than the odds of full-term birth. No statistically significant associations were observed between stress, discrimination, and either preterm birth or telomere length. Maternal peripheral blood telomere shortening is associated with preterm birth, providing support to further explore the clinical utility of maternal telomere testing for prediction and early intervention of preterm birth and the study of biological mechanisms of spontaneous preterm birth.

Impact of Complete Blood Count (CBC) Parameters in Preterm Birth Prediction in Cases with Threatened Preterm Labour (TPL)

Impact of Complete Blood Count (CBC) Parameters in Preterm Birth Prediction in Cases with Threatened Preterm Labour (TPL)

Amniotic fluid proteomic analysis identifies IL1RL1, APOE, and NECTIN4 as new biomarkers for preterm birth

BackgroundDespite extensive research, the identification of effective biomarkers for early prediction of preterm birth (PTB) continues to be a challenging endeavor. This study aims to identify amniotic fluid (AF) protein biomarkers useful for the early diagnosis of PTB.MethodsWe initially identified the protein expression profiles in the AF of women with PTB (n = 22) and full-term birth (FTB, n = 22), from the First People’s Hospital of Yunnan Province who underwent amniocentesis from November 2019 to February 2020, using mass spectrometry employing the data-independent acquisition (DIA) technique, and then analyzed differentially expressed proteins (DEPs). Subsequently, the least absolute shrinkage and selection operator (LASSO) and random forest analysis were employed to further screen the key proteins for PTB biomarker identification. The receiver operating characteristic (ROC) analysis, calibration plots, and decision curve analyses (DCA) were utilized to assess the discrimination and calibration of the key biomarkers.ResultsA total of 25 DEPs were identified between the PTB and FTB groups, comprising 13 up-regulated and 12 down-regulated proteins. Three key protein biomarkers for early PTB diagnosis were identified: IL1RL1 (interleukin-1 receptor-like 1), APOE (apolipoprotein E), and NECTIN4 (nectin cell adhesion molecule 4). The results of the ROC analysis showed that the area under the curve (AUC) of the three proteins combined as a biomarker for early diagnosis of PTB was 0.913 (95% CI: 0.823-1.000), with a sensitivity of 0.864 and a specificity of 0.955, both superior to those of the individual biomarkers. Bootstrap internal validation revealed a concordance index (C-index) of 0.878, with a sensitivity of 0.812 and a specificity of 0.773, indicating the robust predictive performance of these biomarkers.ConclusionsWe identified three previously unexplored yet potentially useful protein biomarkers in AF for early PTB diagnosis: IL1RL1, APOE, and NECTIN4.

Predictors of Inflammation-Mediated Preterm Birth.

Preterm birth remains a worldwide health concern due to ongoing challenges in prediction and prevention. Current predictors are limited by poor performance, need for invasive sampling, and an inability to identify patients in a timely fashion to allow for effective intervention. The multiple etiologies of preterm birth often have an inflammatory component. Thus, a deeper understanding of the inflammatory mechanisms involved in preterm birth may provide opportunities to identify new predictors of preterm birth. This review will discuss the multiple etiologies of preterm birth, their links to inflammation, current predictors available, and new directions for the field.

Routine first trimester Preeclampsia screening and prediction of preterm birth

Routine first trimester Preeclampsia screening and prediction of preterm birth

Black-White Differences in Chronic Stress Exposures to Predict Preterm Birth: Interpretable, Race/Ethnicity-Specific Machine Learning Models.

We developed Multivariate Adaptive Regression Splines (MARS) machine learning models of chronic stressors using the Pregnancy Risk Assessment Monitoring System data (2012-2017) to predict preterm birth (PTB) more accurately and identify chronic stressors driving PTB among non-Hispanic (N-H) Black and N-H White pregnant women in the U.S. We trained the MARS models using 5-fold cross-validation, whose performance was evaluated with AUC. We computed variable importance for PTB prediction. Our models showed high accuracy (AUC: 0.754-0.765). The number of prenatal care visits, premature rupture of membrane, and medical conditions were the most important variables in predicting PTB across the populations. Chronic stressors (e.g., low maternal education and violence) and their correlates were pivotal for PTB prediction only for N-H Black women. Interpretable, race/ethnicity-specific MARS models can predict PTB accurately and explain the most impactful life stressors and their magnitude of effect on PTB risk among N-H Black and N-H White women.

Uterocervical angle and cervical length measurements for spontaneous preterm birth prediction in low-risk singleton pregnant women: a prospective cohort study.

Preterm birth is the leading cause of early neonatal morbidity and mortality. Strategies to predict preterm birth risk can help improve pregnancy outcomes. Even pregnant women without known risk factors for preterm birth can also experience it. This study aimed to evaluate the ability of the uterocervical angle and cervical length to predict spontaneous preterm birth in low-risk singleton pregnant women. A prospective study on 1107 singleton pregnant women between 16+0 and 23+6weeks gestation at low risk for spontaneous preterm birth who were treated at the Haiphong Hospital of Obstetrics and Gynecology, Vietnam, between September 2020 and September 2021 was conducted. A single sonographer assessed the cervical length and the uterocervical angle using transvaginal ultrasonography. The patients were followed up until delivery to determine the main pregnancy outcome (spontaneous preterm birth before 37weeks gestation). The cut-off points for the uterocervical angle and cervical length were established by analyzing the receiver operating characteristic curve. The sensitivity, specificity, likelihood ratio, positive and negative predictive values, and accuracy of the uterocervical angle and cervical length for predicting spontaneous preterm birth were determined. A uterocervical angle ≥ 99° predicted spontaneous preterm birth at < 37weeks, with a sensitivity and specificity of 91% and 76%, respectively. A cervical length ≤ 33.8mm predicted preterm birth at < 37weeks with a sensitivity and specificity of 25% and 66%, respectively. A uterocervical angle ≥ 99° combined with a cervical length ≤ 33.8mm yielded the sensitivity, specificity, positive predictive value, likelihood ratio, and accuracy of spontaneous preterm birth prediction of 66%, 93%, 36%, 9, and 91%, respectively; thus provided a significant increase of specificity with an acceptable reduction of sensitivity as compared to cervical length alone. Besides the cervical length, the uterocervical angle can be considered a valuable ultrasound parameter for predicting spontaneous preterm birth in low-risk singleton pregnant women. Combining the uterocervical angle and cervical length yielded stronger spontaneous preterm birth prediction values.