Changes in pelvic floor electrical activity and vulvar pain after botulinum toxin treatment of vestibulodynia: are clinical and electrophysiological outcomes related?

Vestibulodynia is a gynecological condition with different treatment options available, including botulinum neurotoxin type A (BoNT/A) injections into the vulvar vestibule. Unlike other treatments, no studies have assessed changes in the myoelectrical activity of the pelvic floor muscles (PFM) after BoNT/A treatment. The aim of this study was thus to evaluate these changes and to correlate them with changes in vulvar pain sensitivity. To do this, 35 patients with vestibulodynia were recruited, the myoelectrical activity of their left and right PFM was recorded with surface electromyography (sEMG), and their vulvar pain sensitivity was monitored according to Visual Analogue Scale (VAS) and an algometer, both before and after BoNT/A treatment. According to our results, patients' signals during PFM relaxation showed a significantly higher power than those of healthy women at baseline, as shown by their root mean square values (RMS), but became similar at follow-up. Patients' mean vulvar pain VAS scores significantly decreased after treatment. Furthermore, baseline-to-follow-up differences of RMS at PFM rest vs. mean VAS were significantly correlated (CC=0.48, p<0.01) so that higher reductions in the PFM activity power were associated with higher decreases in vulvar pain.Clinical Relevance- Altered PFM electrophysiological condition of patients with vestibulodynia becomes similar to healthy women's after BoNT/A treatment. This study also points to a relationship between the evolution of clinical and PFM electrophysiological conditions.

Outperformance of interleukin-6 over placental alpha microglobulin-1 in predicting preterm delivery in symptomatic women.

BACKGROUND: Threatened preterm labor is the major cause of hospital admission during the second half of pregnancy. An early diagnosis is crucial for adopting pharmacologic measures to reduce perinatal mortality and morbidity. Current diagnostic criteria are based on symptoms and short cervical length. However, there is a high false-positive rate when using these criteria, which implies overtreatment, causing unnecessary side effects and an avoidable economic burden. OBJECTIVE: This study aimed to compare the use of placental alpha microglobulin-1 and interleukin-6 as vaginal biomarkers combined with cervical length and other maternal characteristics to improve the prediction of preterm delivery in symptomatic women. STUDY DESIGN: A prospective observational study was conducted in women with singleton pregnancies complicated by threatened preterm labor with intact membranes at 24+0 to 34+6 weeks of gestation. A total of 136 women were included in this study. Vaginal fluid was collected with a swab for placental alpha microglobulin-1 determination using the PartoSure test, interleukin-6 was assessed by electrochemiluminescence immunoassay, cervical length was measured by transvaginal ultrasound, and obstetrical variables and newborn details were retrieved from clinical records. These characteristics were used to fit univariate binary logistic regression models to predict time to delivery <7 days, time to delivery <14 days, gestational age at delivery ≤34 weeks, and gestational age at delivery ≤37 weeks, and multivariate binary logistic regression models were fitted with imbalanced and balanced data. Performance of models was assessed by their F2-scores and other metrics, and the association of their variables with a risk or a protective factor was studied. RESULTS: A total of 136 women were recruited, of whom 8 were lost to follow-up and 7 were excluded. Of the remaining 121 patients, 22 had a time to delivery <7 days and 31 had a time to delivery <14 days, and 30 deliveries occurred with a gestational age at delivery ≤34 weeks and 55 with a gestational age at delivery ≤37 weeks. Univariate binary logistic regression models fitted with the log transformation of interleukin-6 showed the greatest F2-scores in most studies, which outperformed those of models fitted with placental alpha microglobulin-1 (log[interleukin-6] vs placental alpha microglobulin-1 in time to delivery <7 days: 0.38 vs 0.30; time to delivery <14 days: 0.58 vs 0.29; gestational age at delivery ≤34 weeks: 0.56 vs 0.29; gestational age at delivery ≤37 weeks: 0.61 vs 0.16). Multivariate logistic regression models fitted with imbalanced data sets outperformed most univariate models (F2-score in time to delivery <7 days: 0.63; time to delivery <14 days: 0.54; gestational age at delivery ≤34 weeks: 0.62; gestational age at delivery ≤37 weeks: 0.73). The performance of prediction of multivariate models was drastically improved when data sets were balanced, and was maximum for time to delivery <7 days (F2-score: 0.88±0.2; positive predictive value: 0.86±0.02; negative predictive value: 0.89±0.03). CONCLUSION: A multivariate assessment including interleukin-6 may lead to more targeted treatment, thus reducing unnecessary hospitalization and avoiding unnecessary maternal-fetal treatment.

GESTACOVID Project: Psychological and Perinatal Effects in Spanish Pregnant Women Subjected to Strict Confinement Due to the COVID-19 Pandemic and Their Evolution during De-Escalation.

The lockdown and de-escalation process following the COVID-19 pandemic led to a period of new normality. This study aimed to assess the confinement impact on the mental health of peripartum women, as their psychological well-being may be particularly vulnerable and thus affect their offspring's development. A cross-sectional epidemiological study was conducted among women who gave birth during strict confinement (G0) and the new normality period (G1), in which a self-administered paper-based questionnaire assessed 15 contextual factors and the General Health Questionnaire-12 (GHQ-12). For each item, it was verified whether the positive screening rate differed in each confinement phase, and a risk factor study was conducted. For G0, significantly higher positive screening and preterm birth rates were observed in the positive screening group. In the case of G1, maternal age (>35 years), decreased physical activity, and normal weight were found to be protective factors against distress. This study underscores the heightened mental health risk for postpartum women during major psychosocial upheavals (war, economic crisis, natural disasters, or pandemics), along with their resilience as the positive screening rate decreases with the new normality. Findings encourage adopting strategies to identify high-risk women and promote effective measures, such as promoting physical activity.

Social Support and Mental Health in the Postpartum Period in Times of SARS-CoV-2 Pandemic: Spanish Multicentre Cohort Study.

BACKGROUND: To explore the depression and anxiety symptoms in the postpartum period during the SARS-CoV-2 pandemic and to identify potential risk factors. METHODS: A multicentre observational cohort study including 536 women was performed at three hospitals in Spain. The Edinburgh Postnatal Depression Scale (EPDS), the State-Trait Anxiety Inventory (STAI) Scale, the Medical Outcomes Study Social Support Survey (MOS-SSS), and the Postpartum Bonding Questionnaire (PBQ) were assessed after birth. Depression (EPDS) and anxiety (STAI) symptoms were measured, and the cut-off scores were set at 10 and 13 for EPDS, and at 40 for STAI. RESULTS: Regarding EPDS, 32.3% (95% CI, 28% to 36.5%) of women had a score ≥ 10, and 17.3% (95% CI, 13.9% to 20.7%) had a score ≥ 13. Women with an STAI score ≥ 40 accounted for 46.8% (95% CI, 42.3% to 51.2%). A lower level of social support (MOS-SSS), a fetal malformation diagnosis and a history of depression (p = 0.000, p = 0.019 and p = 0.043) were independent risk factors for postpartum depression. A lower level of social support and a history of mental health disorders (p = 0.000, p = 0.003) were independent risk factors for postpartum anxiety. CONCLUSION: During the SARS-CoV-2 pandemic, an increase in symptoms of anxiety and depression were observed during the postpartum period.

Combination of Feature Selection and Resampling Methods to Predict Preterm Birth Based on Electrohysterographic Signals from Imbalance Data.

Due to its high sensitivity, electrohysterography (EHG) has emerged as an alternative technique for predicting preterm labor. The main obstacle in designing preterm labor prediction models is the inherent preterm/term imbalance ratio, which can give rise to relatively low performance. Numerous studies obtained promising preterm labor prediction results using the synthetic minority oversampling technique. However, these studies generally overestimate mathematical models' real generalization capacity by generating synthetic data before splitting the dataset, leaking information between the training and testing partitions and thus reducing the complexity of the classification task. In this work, we analyzed the effect of combining feature selection and resampling methods to overcome the class imbalance problem for predicting preterm labor by EHG. We assessed undersampling, oversampling, and hybrid methods applied to the training and validation dataset during feature selection by genetic algorithm, and analyzed the resampling effect on training data after obtaining the optimized feature subset. The best strategy consisted of undersampling the majority class of the validation dataset to 1:1 during feature selection, without subsequent resampling of the training data, achieving an AUC of 94.5 ± 4.6%, average precision of 84.5 ± 11.7%, maximum F1-score of 79.6 ± 13.8%, and recall of 89.8 ± 12.1%. Our results outperformed the techniques currently used in clinical practice, suggesting the EHG could be used to predict preterm labor in clinics.

GESTACOVID project: psychological and perinatal effects in Spanish pregnant women subjected to confinement due to the COVID-19 pandemic.

INTRODUCTION: COVID-19 was declared a pandemic and confinement with movement restriction measures were applied in Spain. Postnatal mental disorders are common but frequently undiagnosed, being a risk period to develop anxiety and depression symptoms. The aim of this study is to evaluate the impact of confinement as depressive and anxiety symptoms in pregnant women (PrW) and puerperal women (PuW) mental health, as well as obstetric and perinatal outcomes during this period. MATERIALS AND METHODS: The self-administered survey consists of a total of 28 questions, the first 16 providing contextual information and the following ones corresponding to the GHQ-12 that has been evaluated in a binomial form. A logistic regression model has been used to assess whether the contextual variables acted as a protective or risk factor and its fitting has been represented by a receiver operating curve. RESULTS: Of the 754 PrW interviewed, 58.22% were screened positive. Confinement time for these was 54.93 ± 9.75 days. The risk factors that were identified after the refinement have been to have a worse general state of health, to be sadder and to be more nervous. Among the protectors have been found to have a higher Apgar 10 score and induction of labor. The area under the adjusted regression adjustment curve was 0.8056. CONCLUSIONS: Our results show a high prevalence of depression and anxiety symptoms with strict confinement measures. PrW and PuW must be considered a risk group to develop mental health disorders during disruption circumstances. Using a mental health screening tool could help to identify a group of patients with more risk and to carry out a careful monitoring to allow adequate management.

Disrupted PGR-B and ESR1 signaling underlies defective decidualization linked to severe preeclampsia.

Background: Decidualization of the uterine mucosa drives the maternal adaptation to invasion by the placenta. Appropriate depth of placental invasion is needed to support a healthy pregnancy; shallow invasion is associated with the development of severe preeclampsia (sPE). Maternal contribution to sPE through failed decidualization is an important determinant of placental phenotype. However, the molecular mechanism underlying the in vivo defect linking decidualization to sPE is unknown. Methods: Global RNA sequencing was applied to obtain the transcriptomic profile of endometrial biopsies collected from nonpregnant women who suffer sPE in a previous pregnancy and women who did not develop this condition. Samples were randomized in two cohorts, the training and the test set, to identify the fingerprinting encoding defective decidualization in sPE and its subsequent validation. Gene Ontology enrichment and an interaction network were performed to deepen in pathways impaired by genetic dysregulation in sPE. Finally, the main modulators of decidualization, estrogen receptor 1 (ESR1) and progesterone receptor B (PGR-B), were assessed at the level of gene expression and protein abundance. Results: Here, we discover the footprint encoding this decidualization defect comprising 120 genes-using global gene expression profiling in decidua from women who developed sPE in a previous pregnancy. This signature allowed us to effectively segregate samples into sPE and control groups. ESR1 and PGR were highly interconnected with the dynamic network of the defective decidualization fingerprint. ESR1 and PGR-B gene expression and protein abundance were remarkably disrupted in sPE. Conclusions: Thus, the transcriptomic signature of impaired decidualization implicates dysregulated hormonal signaling in the decidual endometria in women who developed sPE. These findings reveal a potential footprint that could be leveraged for a preconception or early prenatal screening of sPE risk, thus improving prevention and early treatments. Funding: This work has been supported by the grant PI19/01659 (MCIU/AEI/FEDER, UE) from the Spanish Carlos III Institute awarded to TGG. NCM was supported by the PhD program FDGENT/2019/008 from the Spanish Generalitat Valenciana. IMB was supported by the PhD program PRE2019-090770 and funding was provided by the grant RTI2018-094946-B-100 (MCIU/AEI/FEDER, UE) from the Spanish Ministry of Science and Innovation with CS as principal investigator. This research was funded partially by Igenomix S.L.

Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals.

One of the remaining challenges for the scientific-technical community is predicting preterm births, for which electrohysterography (EHG) has emerged as a highly sensitive prediction technique. Sample and fuzzy entropy have been used to characterize EHG signals, although they require optimizing many internal parameters. Both bubble entropy, which only requires one internal parameter, and dispersion entropy, which can detect any changes in frequency and amplitude, have been proposed to characterize biomedical signals. In this work, we attempted to determine the clinical value of these entropy measures for predicting preterm birth by analyzing their discriminatory capacity as an individual feature and their complementarity to other EHG characteristics by developing six prediction models using obstetrical data, linear and non-linear EHG features, and linear discriminant analysis using a genetic algorithm to select the features. Both dispersion and bubble entropy better discriminated between the preterm and term groups than sample, spectral, and fuzzy entropy. Entropy metrics provided complementary information to linear features, and indeed, the improvement in model performance by including other non-linear features was negligible. The best model performance obtained an F1-score of 90.1 ± 2% for testing the dataset. This model can easily be adapted to real-time applications, thereby contributing to the transferability of the EHG technique to clinical practice.

Optimized Feature Subset Selection Using Genetic Algorithm for Preterm Labor Prediction Based on Electrohysterography.

Electrohysterography (EHG) has emerged as an alternative technique to predict preterm labor, which still remains a challenge for the scientific-technical community. Based on EHG parameters, complex classification algorithms involving non-linear transformation of the input features, which clinicians found difficult to interpret, were generally used to predict preterm labor. We proposed to use genetic algorithm to identify the optimum feature subset to predict preterm labor using simple classification algorithms. A total of 203 parameters from 326 multichannel EHG recordings and obstetric data were used as input features. We designed and validated 3 base classifiers based on k-nearest neighbors, linear discriminant analysis and logistic regression, achieving F1-score of 84.63 ± 2.76%, 89.34 ± 3.5% and 86.87 ± 4.53%, respectively, for incoming new data. The results reveal that temporal, spectral and non-linear EHG parameters computed in different bandwidths from multichannel recordings provide complementary information on preterm labor prediction. We also developed an ensemble classifier that not only outperformed base classifiers but also reduced their variability, achieving an F1-score of 92.04 ± 2.97%, which is comparable with those obtained using complex classifiers. Our results suggest the feasibility of developing a preterm labor prediction system with high generalization capacity using simple easy-to-interpret classification algorithms to assist in transferring the EHG technique to clinical practice.

Optimization of Imminent Labor Prediction Systems in Women with Threatened Preterm Labor Based on Electrohysterography.

Preterm birth is the leading cause of death in newborns and the survivors are prone to health complications. Threatened preterm labor (TPL) is the most common cause of hospitalization in the second half of pregnancy. The current methods used in clinical practice to diagnose preterm labor, the Bishop score or cervical length, have high negative predictive values but not positive ones. In this work we analyzed the performance of computationally efficient classification algorithms, based on electrohysterographic recordings (EHG), such as random forest (RF), extreme learning machine (ELM) and K-nearest neighbors (KNN) for imminent labor (<7 days) prediction in women with TPL, using the 50th or 10th-90th percentiles of temporal, spectral and nonlinear EHG parameters with and without obstetric data inputs. Two criteria were assessed for the classifier design: F1-score and sensitivity. RFF1_2 and ELMF1_2 provided the highest F1-score values in the validation dataset, (88.17 ± 8.34% and 90.2 ± 4.43%) with the 50th percentile of EHG and obstetric inputs. ELMF1_2 outperformed RFF1_2 in sensitivity, being similar to those of ELMSens (sensitivity optimization). The 10th-90th percentiles did not provide a significant improvement over the 50th percentile. KNN performance was highly sensitive to the input dataset, with a high generalization capability.

Robust Characterization of the Uterine Myoelectrical Activity in Different Obstetric Scenarios.

Electrohysterography (EHG) has been shown to provide relevant information on uterine activity and could be used for predicting preterm labor and identifying other maternal fetal risks. The extraction of high-quality robust features is a key factor in achieving satisfactory prediction systems from EHG. Temporal, spectral, and non-linear EHG parameters have been computed to characterize EHG signals, sometimes obtaining controversial results, especially for non-linear parameters. The goal of this work was to assess the performance of EHG parameters in identifying those robust enough for uterine electrophysiological characterization. EHG signals were picked up in different obstetric scenarios: antepartum, including women who delivered on term, labor, and post-partum. The results revealed that the 10th and 90th percentiles, for parameters with falling and rising trends as labor approaches, respectively, differentiate between these obstetric scenarios better than median analysis window values. Root-mean-square amplitude, spectral decile 3, and spectral moment ratio showed consistent tendencies for the different obstetric scenarios as well as non-linear parameters: Lempel-Ziv, sample entropy, spectral entropy, and SD1/SD2 when computed in the fast wave high bandwidth. These findings would make it possible to extract high quality and robust EHG features to improve computer-aided assessment tools for pregnancy, labor, and postpartum progress and identify maternal fetal risks.

A Comparative Study of Vaginal Labor and Caesarean Section Postpartum Uterine Myoelectrical Activity.

Postpartum hemorrhage (PPH) is one of the major causes of maternal mortality and morbidity worldwide, with uterine atony being the most common origin. Currently there are no obstetrical techniques available for monitoring postpartum uterine dynamics, as tocodynamometry is not able to detect weak uterine contractions. In this study, we explored the feasibility of monitoring postpartum uterine activity by non-invasive electrohysterography (EHG), which has been proven to outperform tocodynamometry in detecting uterine contractions during pregnancy. A comparison was made of the temporal, spectral, and non-linear parameters of postpartum EHG characteristics of vaginal deliveries and elective cesareans. In the vaginal delivery group, EHG obtained a significantly higher amplitude and lower kurtosis of the Hilbert envelope, and spectral content was shifted toward higher frequencies than in the cesarean group. In the non-linear parameters, higher values were found for the fractal dimension and lower values for Lempel-Ziv, sample entropy and spectral entropy in vaginal deliveries suggesting that the postpartum EHG signal is extremely non-linear but more regular and predictable than in a cesarean. The results obtained indicate that postpartum EHG recording could be a helpful tool for earlier detection of uterine atony and contribute to better management of prophylactic uterotonic treatment for PPH prevention.