First-Trimester Prediction of Preterm Prelabour Rupture of Membranes

Premature rupture of membranes and changes in the vaginal microbiome - Probiotics

Preterm birth affects approximately 15 million women worldwide, of which 30 % is due to preterm premature rupture of membranes (PPROM). The reasons for shortening the duration of pregnancy are seen in genetic, hormonal, immunological and socio-economic conditions. Recent years have provided a lot of evidence on the impact of the microbiota and whole microbiome on pregnant women, suggesting that the microorganisms inhabiting the vagina significantly affect the risk of preterm delivery. The aim of the study was to review studies evaluating the composition of the vaginal microflora and its role in the occurrence of preterm labor caused by PPROM, and to evaluate the potential beneficial effect of probiotics on preventing the development of preterm labor. Vaginal microbial dysbiosis is observed in PPROM, which, due to its association with a high risk of prematurity and infection, increases neonatal morbidity and mortality. Further research on biomarkers for screening, early prognosis and diagnosis of PPROM seems advisable. Probiotics as a potential intervention can prevent the development of pathological vaginal flora, reducing the risk of infection in women planning pregnancy and pregnant women.

Non-invasive prediction model of histologic chorioamnionitis with preterm prelabour rupture of membranes

BACKGROUND

The aim of this study is to identify risk factors associated with histological chorioamnionitis (HCA) and develop a predictive model for antepartum assessment of the risk of PPROM with HCA.

METHODS

This study retrospectively analyzed pregnant women who experienced PPROM between 25 + 0 and 35 + 0 weeks of gestational age. The women were divided into two groups based on the presence or absence of HCA. Univariate and multivariate logistic regression analyses were conducted to identify maternal risk factors and develop a clinical prediction model for HCA. The model's discrimination and consistency were evaluated using receiver operating characteristic (ROC) and calibration curves.

RESULTS

Seventeen thousand one hundred forty-six (17,146) pregnant women were screened, and 726 (4.23 %) had PPROM. Out of the 286 subjects with PPROM, 160 developed HCA. The maternal age of these subjects ranged from 18 to 43 years (30.0 ± 5.4), while their gestational age (GA) ranged from 25 + 0 to 35 + 0 weeks (31.6 ± 2.0). The average GA at delivery was 32.2 ± 2.0 (weeks).Compared with the non-HCA group, the expectant time > 48 h, GA at delivery > 32 weeks, twin pregnancy, HGB (<110 g/Lg/L), degree of LGB (IIb-III), and WBC (>9.5 × 109 /L) were significantly more than in the PPROM with HCA group. The results show that the best model was obtained by leave-one-out logistic regression (AUC = 0.785, CA = 0.741, F1 = 0.739, Precision = 0.740, Recall = 0.741). In the validation set, logistic regression also achieved good results (AUC = 0.710, CA = 0.671, F1 = 0.654, Precision = 0.683, Recall = 0.671). Combining the previous analysis, we found that the prognostic model constructed using the core six features had the best predictive effect.

CONCLUSIONS

Six features were associated with the occurrence of chorioamnionitis. These features were used to construct a diagnostic model that can accurately predict the probability of chorioamnionitis occurrence and provide a beneficial tool for the prevention and management of PPROM with HCA.

Determinants of prelabor rupture of membrane among pregnant women attending governmental hospitals in Jimma zone, Oromia region, Ethiopia: A multi-center case-control study

BACKGROUND

Prelabor rupture of membrane defined as the rupture of fetal membranes before the beginning of uterine contractions, is a common complication of pregnancy and the leading cause of preterm birth. In Ethiopia, the prevalence of prelabor rupture of membrane varied significantly between settings due to variations in risk factors. Besides, there was no study conducted using primary data, particularly in the Jimma zone, Ethiopia. Therefore, this study aimed to identify determinants of prelabor rupture of membrane among pregnant women attending governmental hospitals in the Jimma zone, Oromia region, Ethiopia.

METHODS

An institutional-based unmatched case-control study design was conducted from October 15 to December 15, 2021, at four governmental hospitals. A consecutive sampling technique was used to select 316 participants (79 cases and 237 controls). Women with prelabor rupture of the membrane were confirmed by history, sterile vaginal examination, and ultrasound as cases, and their counterparts as controls. An interviewer-administered questionnaire was used to collect data on maternal (obstetric, medical, behavioral) and fetal-related characteristics. The data were entered into Epi Data version 4.6 and analyzed using SPSS version 25. Descriptive statistics, bi-variable, and multivariable logistic regression were computed. The odds ratio with a 95% confidence level was used, and the significance level was declared at a p-value < 0.05.

RESULTS

A total of 316 participants (79 cases and 237 controls) were included in this study. Pregnancy-induced hypertension (AOR = 3.06, 95% CI: 1.41-6.64), history of abortion (AOR = 3.67, 95% CI: 1.56-8.65), urinary tract infections (AOR = 2.61, 95% CI: 1.13-6.06), abnormal vaginal discharge (AOR = 2.65, 95% CI: 1.21-5.79), maternal khat chewing (AOR = 3.40, 95% CI: 1.70-6.80), mid-upper arm circumference less than 23 cm (AOR = 2.80, 95% CI: 1.51-5.19), and fetal presentation (breech) (AOR = 2.63, 95% CI: 1.10-6.28) were determinants of prelabor rupture of membrane among pregnant women.

CONCLUSION

This study revealed that the aforementioned factors were found to be determinants of prelabor rupture of membrane among pregnant women. Therefore, hospitals should give focus to the early screening, diagnosis, and treatment of pregnancy-induced hypertension, urinary tract infection, and abnormal vaginal discharge to reduce the burden of prelabor rupture of membranes.

Novel genetic variants linked to prelabor rupture of membranes among Chinese pregnant women

INTRODUCTION

The etiology of prelabor rupture of membranes (PROM), either preterm or term PROM (PPROM or TPROM), remains largely unknown. This study aimed to investigate the association between maternal genetic variants (GVs) and PROM and further establish a GV-based prediction model for PROM.

METHODS

In this case-cohort study (n = 1166), Chinese pregnant women with PPROM (n = 51), TPROM (n = 283) and controls (n = 832) were enrolled. A weighted Cox model was applied to identify the GVs (single nucleotide polymorphisms [SNPs], insertions/deletions, and copy number variants) associated with either PPROM or TPROM. Gene set enrichment analysis (GSEA) was to explore the mechanisms. The suggestively significant GVs were applied to establish a random forest (RF) model.

RESULTS

PTPRT variants (rs117950601, P = 4.37 × 10^-9; rs147178603, P = 8.98 × 10^-9) and SNRNP40 variant (rs117573344, P = 2.13 × 10^-8) were associated with PPROM. STXBP5L variant (rs10511405, P = 4.66 × 10^-8) was associated with TPROM. GSEA results showed that genes associated with PPROM were enriched in cell adhesion, and TPROM in ascorbate and glucuronidation metabolism. The area under the receiver operating characteristic curve of SNP-based RF model for PPROM was 0.961, with a sensitivity of 100.0% and specificity of 83.3%.

DISCUSSION

Maternal GVs in PTPRT and SNRNP40 were associated with PPROM, and GV in STXBP5L was associated with TPROM. Cell adhesion participated in PPROM, while ascorbate and glucuronidation metabolism contributed in TPROM. The PPROM might be well predicted using the SNP-based RF model.

First-trimester prediction of preterm prelabour rupture of membranes incorporating cervical length measurement

OBJECTIVES

To examine early pregnancy risk factors for preterm prelabour rupture of membranes (PPROM) and develop a predictive model.

STUDY DESIGN

Retrospective analysis of a cohort of mixed-risk singleton pregnancies screened in the first and second trimesters in three Danish tertiary fetal medicine centres, including a cervical length measurement at 11-14 weeks, at 19-21 weeks and at 23-24 weeks of gestation. Univariable and multivariable logistic regression analyses were employed to identify predictive maternal characteristics, biochemical and sonographic factors. Receiver operating characteristic (ROC) curve analysis was used to determine predictors for the most accurate model.

RESULTS

Of 3477 screened women, 77 (2.2%) had PPROM. Maternal factors predictive of PPROM in univariable analysis were nulliparity (OR 2.0 (95% CI 1.2-3.3)), PAPP-A < 0.5 MoM (OR 2.6 (1.1-6.2)), previous preterm birth (OR 4.2 (1.9-8.9)), previous cervical conization (OR 3.6 (2.0-6.4)) and cervical length ≤ 25 mm on transvaginal imaging (first-trimester OR 15.9 (4.3-59.3)). These factors all remained statistically significant in a multivariable adjusted model with an AUC of 0.72 in the most discriminatory first-trimester model. The detection rate using this model would be approximately 30% at a false-positive rate of 10%. Potential predictors such as bleeding in early pregnancy and pre-existing diabetes mellitus affected very few cases and could not be formally assessed.

CONCLUSIONS

Several maternal characteristics, placental biochemical and sonographic features are predictive of PPROM with moderate discrimination. Larger numbers are required to validate this algorithm and additional biomarkers, not currently used for first-trimester screening, may improve model performance.

Human-guided deep learning with ante-hoc explainability by convolutional network from non-image data for pregnancy prognostication

BACKGROUND AND OBJECTIVE

Deep learning is applied in medicine mostly due to its state-of-the-art performance for diagnostic imaging. Supervisory authorities also require the model to be explainable, but most explain the model after development (post hoc) instead of incorporating explanation into the design (ante hoc). This study aimed to demonstrate a human-guided deep learning with ante-hoc explainability by convolutional network from non-image data to develop, validate, and deploy a prognostic prediction model for PROM and an estimator of time of delivery using a nationwide health insurance database.

METHODS

To guide modeling, we constructed and verified association diagrams respectively from literatures and electronic health records. Non-image data were transformed into meaningful images utilizing predictor-to-predictor similarities, harnessing the power of convolutional neural network mostly used for diagnostic imaging. The network architecture was also inferred from the similarities.

RESULTS

This resulted the best model for prelabor rupture of membranes (n=883, 376) with the area under curves 0.73 (95% CI 0.72 to 0.75) and 0.70 (95% CI 0.69 to 0.71) respectively by internal and external validations, and outperformed previous models found by systematic review. It was explainable by knowledge-based diagrams and model representation.

CONCLUSIONS

This allows prognostication with actionable insights for preventive medicine.

Prediction of spontaneous preterm birth and preterm prelabor rupture of membranes using maternal factors, obstetric history and biomarkers of placental function at 11-13 weeks

OBJECTIVES

To determine whether first-trimester biomarkers of placental function can be used to screen for spontaneous preterm birth (sPTB), and to develop prediction models using maternal factors, obstetric history and biomarkers of placental function at 11-13 weeks for the calculation of patient-specific risk for sPTB.

METHODS

This was a retrospective secondary analysis of data derived from a prospective cohort study on first-trimester screening for pre-eclampsia in singleton pregnancies attending for routine Down syndrome screening at 11 + 0 to 13 + 6 weeks' gestation at a tertiary obstetric unit between December 2016 and September 2019. A split-sample internal validation method was used to explore and develop prediction models for all sPTB at < 37 weeks and for PTB at < 37 weeks after preterm prelabor rupture of membranes (PPROM) using maternal risk factors, uterine artery Doppler indices, serum placental growth factor (PlGF), pregnancy-associated plasma protein-A (PAPP-A) and β-human chorionic gonadotropin (β-hCG). Screening performance was assessed using receiver-operating-characteristics (ROC)-curve analysis, with calculation of the areas under the ROC curves (AUCs).

RESULTS

A total of 9298 singleton pregnancies were included in this study. sPTB at < 37 weeks occurred in 362 (3.89%) cases, including 231 (2.48%) cases of PPROM. sPTB at < 34 weeks occurred in 87 (0.94%) cases, including 39 (0.42%) cases of PPROM. Identified maternal risk factors for sPTB at < 37 weeks included chronic hypertension, conception using in-vitro fertilization and history of PTB. Maternal risk factors for PPROM at < 37 weeks included conception using in-vitro fertilization and history of PTB. Median PlGF multiples of the median (MoM) and PAPP-A MoM were significantly reduced in women with sPTB at < 37 weeks, as well as in those who had PPROM, compared to those who delivered at term. Screening by a combination of maternal risk factors, PAPP-A and PlGF achieved better performance in predicting sPTB at < 37 weeks (AUC, 0.630 vs 0.555; detection rate (DR), 24.8% vs 16.6% at a false-positive rate (FPR) of 10%; P ≤ 0.0001) and PPROM at < 37 weeks (AUC, 0.643 vs 0.558; DR, 28.1% vs 17.0% at a FPR of 10%; P ≤ 0.0001) than using maternal risk factors alone. Both models were successfully applied to the internal validation dataset, with AUCs of 0.628 and 0.650, respectively.

CONCLUSIONS

We demonstrated that low levels of maternal serum PAPP-A and PlGF in the first trimester are associated with increased risks of sPTB and PPROM at < 37 weeks. However, further research is needed to identify additional biomarkers to improve the screening performance of the combined model that includes maternal risk factors, PAPP-A and PlGF before clinical application. © 2022 International Society of Ultrasound in Obstetrics and Gynecology.

Relationship between extreme values of first trimester maternal pregnancy associated plasma Protein-A, free-β-human chorionic gonadotropin, nuchal translucency and adverse pregnancy outcomes

OBJECTIVE

The aim of our study was to investigate the relationship between extreme values of first trimester screening markers and adverse obstetric outcomes.

MATERIALS AND METHODS

Our study was conducted by examining the prenatal and postnatal perinatal records of 786 singleton gestations between the ages of 18-40, who applied to Prof. Dr. Cemil Taşçıoğlu City Hospital outpatient clinics for first-trimester screening for aneuploidy, between January 1, 2017 and December 31, 2019.

RESULTS

The presence of small for gestational age (SGA) was found to be statistically significant for the <5 percentile (<0.37) pregnancy-associated plasma protein A (PAPP-A) group (p = 0.016). For <5 percentile β-hCG group, the presence of gestational diabetes mellitus (GDM), premature rupture of membrane (PROM) and preterm premature rupture of membrane (PPROM) was determined as a statistically significant risk (p = 0.015, p = 0.005, p = 0.02 respectively) In the univariate test, fetal death rate was found to be high for ≥90 percentile at nuchal translucency (NT), but the presence of fetal death was found to be statistically insignificant in logistic regression analysis. (p: 0.057).

CONCLUSION

First trimester screening test can be used in predicting pregnancy complications. In this study we found that serum levels of PAPP-A are associated with developing SGA, while GDM, PROM and PPROM are more common in low serum free β-hCG.

Is there a maternal blood biomarker that can predict spontaneous preterm birth prior to labour onset? A systematic review

INTRODUCTION

The ability to predict spontaneous preterm birth (sPTB) prior to labour onset is a challenge, and it is currently unclear which biomarker(s), may be potentially predictive of sPTB, and whether their predictive power has any utility. A systematic review was conducted to identify maternal blood biomarkers of sPTB.

METHODS

This study was conducted according to PRISMA protocol for systematic reviews. Four databases (MEDLINE, EMBASE, CINAHL, Scopus) were searched up to September 2021 using search terms: "preterm labor", "biomarker" and "blood OR serum OR plasma". Studies assessing blood biomarkers prior to labour onset against the outcome sPTB were eligible for inclusion. Risk of bias was assessed based on the Newcastle Ottawa scale. Increased odds of sPTB associated with maternal blood biomarkers, as reported by odds ratios (OR), or predictive scores were synthesized. This review was not prospectively registered.

RESULTS

Seventy-seven primary research articles met the inclusion criteria, reporting 278 unique markers significantly associated with and/or predictive of sPTB in at least one study. The most frequently investigated biomarkers were those measured during maternal serum screen tests for aneuploidy, or inflammatory cytokines, though no single biomarker was clearly predictive of sPTB based on the synthesized evidence. Immune and signaling pathways were enriched within the set of biomarkers and both at the level of protein and gene expression.

CONCLUSION

There is currently no known predictive biomarker for sPTB. Inflammatory and immune biomarkers show promise, but positive reporting bias limits the utility of results. The biomarkers identified may be more predictive in multi-marker models instead of as single predictors. Omics-style studies provide promising avenues for the identification of novel (and multiple) biomarkers. This will require larger studies with adequate power, with consideration of gestational age and the heterogeneity of sPTB to identify a set of biomarkers predictive of sPTB.

Single Nucleotide Polymorphisms from CSF2, FLT1, TFPI and TLR9 Genes Are Associated with Prelabor Rupture of Membranes

A prelabor rupture of membranes (PROM) and its subtypes, preterm PROM (pPROM) and term PROM (tPROM), are associated with disturbances in the hemostatic system and angiogenesis. This study was designed to demonstrate the role of single nucleotide polymorphisms (SNPs), localized in CSF2 (rs25881), FLT1 (rs722503), TFPI (C-399T) and TLR9 (rs352140) genes, in PROM. A population of 360 women with singleton pregnancy consisted of 180 PROM cases and 180 healthy controls. A single-SNP analysis showed a similar distribution of genotypes in the studied polymorphisms between the PROM or the pPROM women and the healthy controls. Double-SNP TT variants for CSF2 and FLT1 polymorphisms, CC variants for TLR9 and TFPI SNPs, TTC for CSF2, FLT1 and TLR9 polymorphisms, TTT for FLT1, TLR9 and TFPI SNPs and CCCC and TTTC complex variants for all tested SNPs correlated with an increased risk of PROM after adjusting for APTT, PLT parameters and/or pregnancy disorders. The TCT variants for the CSF2, FLT1 and TLR9 SNPs and the CCTC for the CSF2, FLT1, TLR9 and TFPI polymorphisms correlated with a reduced risk of PROM when corrected by PLT and APTT, respectively. We concluded that the polymorphisms of genes, involved in hemostasis and angiogenesis, contributed to PROM.