New labor curves of dilation and station to improve the accuracy of predicting labor progress.

BACKGROUND: The diagnosis of failure to progress, the most common indication for intrapartum cesarean delivery, is based on the assessment of cervical dilation and station over time. Labor curves serve as references for expected changes in dilation and fetal descent. The labor curves of Friedman, Zhang et al, and others are based on time alone and derived from mothers with spontaneous labor onset. However, labor induction is now common, and clinicians also consider other factors when assessing labor progress. Labor curves that consider the use of labor induction and other factors that influence labor progress have the potential to be more accurate and closer to clinical decision-making. OBJECTIVE: This study aimed to compare the prediction errors of labor curves based on a single factor (time) or multiple clinically relevant factors using two modeling methods: mixed-effects regression, a standard statistical method, and Gaussian processes, a machine learning method. STUDY DESIGN: This was a longitudinal cohort study of changes in dilation and station based on data from 8022 births in nulliparous women with a live, singleton, vertex-presenting fetus ≥35 weeks of gestation with a vaginal delivery. New labor curves of dilation and station were generated with 10-fold cross-validation. External validation was performed using a geographically independent group. Model variables included time from the first examination in the 20 hours before delivery; dilation, effacement, and station recorded at the previous examination; cumulative contraction counts; and use of epidural anesthesia and labor induction. To assess model accuracy, differences between each model's predicted value and its corresponding observed value were calculated. These prediction errors were summarized using mean absolute error and root mean squared error statistics. RESULTS: Dilation curves based on multiple parameters were more accurate than those derived from time alone. The mean absolute error of the multifactor methods was better (lower) than those of the single-factor methods (0.826 cm [95% confidence interval, 0.820-0.832] for the multifactor machine learning and 0.893 cm [95% confidence interval, 0.885-0.901] for the multifactor mixed-effects method and 2.122 cm [95% confidence interval, 2.108-2.136] for the single-factor methods; P<.0001 for both comparisons). The root mean squared errors of the multifactor methods were also better (lower) than those of the single-factor methods (1.126 cm [95% confidence interval, 1.118-1.133] for the machine learning [P<.0001] and 1.172 cm [95% confidence interval, 1.164-1.181] for the mixed-effects methods and 2.504 cm [95% confidence interval, 2.487-2.521] for the single-factor [P<.0001 for both comparisons]). The multifactor machine learning dilation models showed small but statistically significant improvements in accuracy compared to the mixed-effects regression models (P<.0001). The multifactor machine learning method produced a curve of descent with a mean absolute error of 0.512 cm (95% confidence interval, 0.509-0.515) and a root mean squared error of 0.660 cm (95% confidence interval, 0.655-0.666). External validation using independent data produced similar findings. CONCLUSION: Cervical dilation models based on multiple clinically relevant parameters showed improved (lower) prediction errors compared to models based on time alone. The mean prediction errors were reduced by more than 50%. A more accurate assessment of departure from expected dilation and station may help clinicians optimize intrapartum management.

Classification of preeclampsia according to molecular clusters with the goal of achieving personalized prevention.

The prevention of pre-eclampsia is difficult due to the syndromic nature and multiple underlying mechanisms of this severe complication of pregnancy. The current clinical distinction between early- and late-onset disease, although clinically useful, does not reflect the true nature and complexity of the pathologic processes leading to pre-eclampsia. The current gaps in knowledge on the heterogeneous molecular pathways of this syndrome and the lack of adequate, specific diagnostic methods are major obstacles to early screening and tailored preventive strategies. The development of novel diagnostic tools for detecting the activation of the identified disease pathways would enable early, accurate screening and personalized preventive therapies. We implemented a holistic approach that includes the utilization of different proteomic profiling methods of maternal plasma samples collected from various ethnic populations and the application of systems biology analysis to plasma proteomic, maternal demographic, clinical characteristic, and placental histopathologic data. This approach enabled the identification of four molecular subclasses of pre-eclampsia in which distinct and shared disease mechanisms are activated. The current review summarizes the results and conclusions from these studies and the research and clinical implications of our findings.

The vaginal immunoproteome for the prediction of spontaneous preterm birth: A retrospective longitudinal study.

Background: Preterm birth is the leading cause of neonatal morbidity and mortality worldwide. Most cases of preterm birth occur spontaneously and result from preterm labor with intact (spontaneous preterm labor [sPTL]) or ruptured (preterm prelabor rupture of membranes [PPROM]) membranes. The prediction of spontaneous preterm birth (sPTB) remains underpowered due to its syndromic nature and the dearth of independent analyses of the vaginal host immune response. Thus, we conducted the largest longitudinal investigation targeting vaginal immune mediators, referred to herein as the immunoproteome, in a population at high risk for sPTB. Methods: Vaginal swabs were collected across gestation from pregnant women who ultimately underwent term birth, sPTL, or PPROM. Cytokines, chemokines, growth factors, and antimicrobial peptides in the samples were quantified via specific and sensitive immunoassays. Predictive models were constructed from immune mediator concentrations. Results: Throughout uncomplicated gestation, the vaginal immunoproteome harbors a cytokine network with a homeostatic profile. Yet, the vaginal immunoproteome is skewed toward a pro-inflammatory state in pregnant women who ultimately experience sPTL and PPROM. Such an inflammatory profile includes increased monocyte chemoattractants, cytokines indicative of macrophage and T-cell activation, and reduced antimicrobial proteins/peptides. The vaginal immunoproteome has improved predictive value over maternal characteristics alone for identifying women at risk for early (<34 weeks) sPTB. Conclusions: The vaginal immunoproteome undergoes homeostatic changes throughout gestation and deviations from this shift are associated with sPTB. Furthermore, the vaginal immunoproteome can be leveraged as a potential biomarker for early sPTB, a subset of sPTB associated with extremely adverse neonatal outcomes. Funding: This research was conducted by the Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services (NICHD/NIH/DHHS) under contract HHSN275201300006C. ALT, KRT, and NGL were supported by the Wayne State University Perinatal Initiative in Maternal, Perinatal and Child Health.

Human pregnancies last 40 weeks on average. Preterm births, defined as live births before 37 weeks, occur in about one in ten pregnancies. Being born too early is the main cause of a number of diseases and death in newborn babies. Preterm births are further divided into those that happen early ­ before 34 weeks ­ and those that happen late ­ between 34 and 37 weeks. There are also differences between preterm births in which the amniotic sac ruptures before or after the start of labor. Although several factors can lead to spontaneous preterm birth, bacteria getting into the amniotic fluid around the fetus are a well-known trigger. These bacteria usually come from the vagina. In the past, researchers have studied the number and types of bacteria in the vagina of people who had a normal pregnancy and those that had a preterm birth to predict who is more at risk of preterm birth. However, predictions based only on data about bacteria have been less useful so far. Instead, it might be better to investigate a person's immune response during pregnancy. Shaffer et al. addressed this gap by asking whether measuring the levels of proteins involved in the immune response could help predict preterm births. Shaffer et al. collected vaginal fluids from 739 individuals of predominately African American ethnicity with an average BMI of 28.7 ­ representing a population at high risk for spontaneous preterm birth. The swabs were taken at multiple points during their pregnancy, and 31 different immune-related proteins in those fluids were measured. The researchers further noted whether these individuals had a normal or a preterm birth. The data showed that, compared to normal births, preterm births are associated with higher levels of proteins that attract white blood cells and promote inflammation, such as IL-6 and IL-1ß. Vaginal fluids from individuals who went on to have an early preterm birth where the amniotic sac ruptured before labor, contained lower levels of proteins known as defensins, which defend the body from bacteria. With these new data from vaginal swabs, Shaffer et al. could make better predictions about the likelihood of preterm birth in general and early preterm birth with the amniotic sac ruptured before labor. For the latter scenario, the predictions were not improved when combining immune protein data with other characteristics of the pregnant person, such as age. These findings suggest that clinicians may be able to use measurements of immune-related proteins to help predict preterm births, so that pregnant individuals at high risk can receive extra care. Further research will have to validate the data and determine whether the findings apply more widely.

Deciphering maternal-fetal cross-talk in the human placenta during parturition using single-cell RNA sequencing.

Labor is a complex physiological process requiring a well-orchestrated dialogue between the mother and fetus. However, the cellular contributions and communications that facilitate maternal-fetal cross-talk in labor have not been fully elucidated. Here, single-cell RNA sequencing (scRNA-seq) was applied to decipher maternal-fetal signaling in the human placenta during term labor. First, a single-cell atlas of the human placenta was established, demonstrating that maternal and fetal cell types underwent changes in transcriptomic activity during labor. Cell types most affected by labor were fetal stromal and maternal decidual cells in the chorioamniotic membranes (CAMs) and maternal and fetal myeloid cells in the placenta. Cell-cell interaction analyses showed that CAM and placental cell types participated in labor-driven maternal and fetal signaling, including the collagen, C-X-C motif ligand (CXCL), tumor necrosis factor (TNF), galectin, and interleukin-6 (IL-6) pathways. Integration of scRNA-seq data with publicly available bulk transcriptomic data showed that placenta-derived scRNA-seq signatures could be monitored in the maternal circulation throughout gestation and in labor. Moreover, comparative analysis revealed that placenta-derived signatures in term labor were mirrored by those in spontaneous preterm labor and birth. Furthermore, we demonstrated that early in gestation, labor-specific, placenta-derived signatures could be detected in the circulation of women destined to undergo spontaneous preterm birth, with either intact or prelabor ruptured membranes. Collectively, our findings provide insight into the maternal-fetal cross-talk of human parturition and suggest that placenta-derived single-cell signatures can aid in the development of noninvasive biomarkers for the prediction of preterm birth.

A mitochondrial regulator protein, MNRR1, is elevated in the maternal blood of women with preeclampsia.

OBJECTIVE: Preeclampsia, one of the most serious obstetric complications, is a heterogenous disorder resulting from different pathologic processes. However, placental oxidative stress and an anti-angiogenic state play a crucial role. Mitochondria are a major source of cellular reactive oxygen species. Abnormalities in mitochondrial structures, proteins, and functions have been observed in the placentae of patients with preeclampsia, thus mitochondrial dysfunction has been implicated in the mechanism of the disease. Mitochondrial nuclear retrograde regulator 1 (MNRR1) is a newly characterized bi-organellar protein with pleiotropic functions. In the mitochondria, this protein regulates cytochrome c oxidase activity and reactive oxygen species production, whereas in the nucleus, it regulates the transcription of a number of genes including response to tissue hypoxia and inflammatory signals. Since MNRR1 expression changes in response to hypoxia and to an inflammatory signal, MNRR1 could be a part of mitochondrial dysfunction and involved in the pathologic process of preeclampsia. This study aimed to determine whether the plasma MNRR1 concentration of women with preeclampsia differed from that of normal pregnant women. METHODS: This retrospective case-control study included 97 women with preeclampsia, stratified by gestational age at delivery into early (<34 weeks, n = 40) and late (≥34 weeks, n = 57) preeclampsia and by the presence or absence of placental lesions consistent with maternal vascular malperfusion (MVM), the histologic counterpart of an anti-angiogenic state. Women with an uncomplicated pregnancy at various gestational ages who delivered at term served as controls (n = 80) and were further stratified into early (n = 25) and late (n = 55) controls according to gestational age at venipuncture. Maternal plasma MNRR1 concentrations were determined by an enzyme-linked immunosorbent assay. RESULTS: 1) Women with preeclampsia at the time of diagnosis (either early or late disease) had a significantly higher median (interquartile range, IQR) plasma MNRR1 concentration than the controls [early preeclampsia: 1632 (924-2926) pg/mL vs. 630 (448-4002) pg/mL, p = .026, and late preeclampsia: 1833 (1441-5534) pg/mL vs. 910 (526-6178) pg/mL, p = .021]. Among women with early preeclampsia, those with MVM lesions in the placenta had the highest median (IQR) plasma MNRR1 concentration among the three groups [with MVM: 2066 (1070-3188) pg/mL vs. without MVM: 888 (812-1781) pg/mL, p = .03; and with MVM vs. control: 630 (448-4002) pg/mL, p = .04]. There was no significant difference in the median plasma MNRR1 concentration between women with early preeclampsia without MVM lesions and those with an uncomplicated pregnancy (p = .3). By contrast, women with late preeclampsia, regardless of MVM lesions, had a significantly higher median (IQR) plasma MNRR1 concentration than women in the control group [with MVM: 1609 (1392-3135) pg/mL vs. control: 910 (526-6178), p = .045; and without MVM: 2023 (1578-8936) pg/mL vs. control, p = .01]. CONCLUSIONS: MNRR1, a mitochondrial regulator protein, is elevated in the maternal plasma of women with preeclampsia (both early and late) at the time of diagnosis. These findings may reflect some degree of mitochondrial dysfunction, intravascular inflammation, or other unknown pathologic processes that characterize this obstetrical syndrome.

COVID-19 is associated with early emergence of preeclampsia: results from a large regional collaborative.

Objective: To investigate the relationship between preeclampsia and SARS-CoV-2 infection during pregnancy. Methods: This was a retrospective cohort study of pregnant women between March and October 2020. Pregnant patients admitted to 14 obstetrical centers in Michigan, USA formed the study population. Of the N = 1458 participants, 369 had SARS-CoV-2 infection (cases). Controls were uninfected pregnancies that were delivered in the same obstetric unit within 30 days of the index case. Robust Poisson regression was used to estimate relative risk (RR) of preterm and term preeclampsia and preeclampsia involving placental lesions. The analysis included adjustment for relevant clinical and demographic risk factors.Results: SARS-CoV-2 infection during pregnancy increased the risk of preeclampsia [adjusted aRR = 1.69 (1.26-2.26)], preeclampsia involving placental lesions [aRR = 1.97(1.14-3.4)] and preterm preeclampsia 2.48(1.48-4.17). Although the highest rate of preeclampsia was observed in patients infected with SARS-CoV-2 who were symptomatic (18.4%), there was increased risk even in asymptomatic SARS-CoV-2 infected patients (14.2%) relative to non-infected controls (8.7%) (p < 0.05). This association with symptomatology was also noted with preterm preeclampsia for which the rate doubled from 2.7% in controls to 5.2% in asymptomatic cases and reached 11.8% among symptomatic cases (p < 0.05). The rate of preterm preeclampsia among cases of pregnant people self-identified as Black reached 10.1% and was almost double the rate of the reminder of the group of infected pregnancies (5.3%), although the rate among uninfected was almost the same (2.7%) for both Black and non-Black groups (interaction p = 0.05).Conclusions: Infection with SARS-CoV-2 increases the risk of preeclampsia even in the absence of symptoms, although symptomatic persons are at even higher risk. Racial disparities in the development of preterm preeclampsia after SARS-CoV-2 infection may explain discrepancies in prematurity between different populations.

Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research.

Every year, 11% of infants are born preterm with significant health consequences, with the vaginal microbiome a risk factor for preterm birth. We crowdsource models to predict (1) preterm birth (PTB; <37 weeks) or (2) early preterm birth (ePTB; <32 weeks) from 9 vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from public raw data via phylogenetic harmonization. The predictive models are validated on two independent unpublished datasets representing 331 samples from 148 pregnant individuals. The top-performing models (among 148 and 121 submissions from 318 teams) achieve area under the receiver operator characteristic (AUROC) curve scores of 0.69 and 0.87 predicting PTB and ePTB, respectively. Alpha diversity, VALENCIA community state types, and composition are important features in the top-performing models, most of which are tree-based methods. This work is a model for translation of microbiome data into clinically relevant predictive models and to better understand preterm birth.