BMC Pregnancy and Childbirth最新文献

Background: Excessive infant crying poses significant challenges to maternal well-being, often leading to parenting stress and postpartum depression. Midwifery assessment and support for mothers experiencing this distress remain inconsistent, emphasizing the need for evidence-based interventions in postpartum care.

Methods: This qualitative descriptive study recruited five midwives in Japan using a combination of purposive and snowball sampling. Semi-structured interviews were conducted between August and November 2023 to explore their perspectives on assessing and managing maternal distress related to excessive infant crying. Interview transcripts were analyzed using inductive content analysis to identify key assessment categories and nursing care approaches.

Results: Seven assessment categories emerged: (1) Maternal psychological burden, (2) Maternal physical burden, (3) Maternal engagement with excessive crying, (4) Infant physical factors related to excessive crying, (5) Infant sleep disturbances related to irregular routines, (6) Characteristics of crying, and (7) Breastfeeding-related issues. The findings illustrate that midwives provided holistic support by addressing both maternal and infant needs. Midwives offered psychological support to alleviate mothers' self-blame, promoted family involvement, and provided practical guidance on infant care, such as touch-based interventions and regulating sleep-wake cycles. While all participants acknowledged the necessity of a comprehensive approach, the primary focus of their interventions varied.

Conclusions: This study highlights the role of midwives in identifying and addressing maternal distress during the postpartum period. The findings suggest that excessive crying often stems from an intricate interplay between maternal psychological burdens and infant-specific factors. However, the varied emphasis of interventions underscores the need for structured, evidence-informed midwifery practices to ensure consistent care. Furthermore, this study suggests that many mothers in Japan tend to blame themselves when their infant cries, a reaction that may arise in other cultural contexts. Greater insight into how maternal self-perceptions shape coping strategies across cultures could help develop more effective postpartum care worldwide.

Aim: Obstetric anal sphincter injury (OASI) is a major complication of vaginal delivery. Tear severity can be categorized according to the structures involved. To date, little is known regarding risk factors for severe OASI tears. We aimed to evaluate the association between known risk factors for OASI and tear severity.

Methods: This was a retrospective cohort study, performed at a tertiary university hospital. The study population included women aged 18-50 years who underwent a vaginal delivery and were diagnosed with OASI. The study cohort was divided into two groups based on OASI severity according to OASI grade: low-grade (3A and 3B) and high-grade (3C and 4). A comparison was performed between the two groups regarding demographic, obstetric and labor related parameters. Quantitative independent variables were compared using Student's t or Mann-Whitney tests and presented as either mean ± standard deviation (SD) or median (interquartile range). The association between categorical variables was tested using either the Chi-squared test or Fisher's exact test, as appropriate. A multivariate regression model was applied to obtain adjusted odds ratios (aOR) for parameters found to be associated with high-grade OASI tear in the univariate model.

Results: Two hundred and sixteen patients within the study group and 405 within the control group were included in the final analysis. Patients with high-grade OASI had increased mean parity (0.56 ± 1.0 vs. 0.39 ± 0.7, p = 0.04) and were less likely to be nulliparous (66.2% vs. 74%, p = 0.03). Operative vaginal delivery (37.0% vs. 28.8%, p = 0.03) was associated with an increased risk of high-grade OASI while artificial rupture of membranes (AROM) was more common among the low-grade group (50.3% vs. 41.2%, P = 0.03). Multivariate logistic regression analysis was conducted for the dependent parameters of high grade OASI. Operative vaginal delivery (OR 1.72, CI 1.16-2.55, p < 0.01) and higher parity (OR 1.23, CI 1.00-1.51, p = 0.04) were associated with high-grade OASI. AROM (OR 1.49, CI 1.04-2.14, p = 0.02) was more common among the low grade OASI tears.

Conclusion: Operative vaginal delivery and higher parity are factors associated with more severe OASI, while artificial rupture of membranes was related to lower tear severity.

Background: The perinatal period is critical for maternal and child health, yet many women experience perinatal mental illnesses, particularly perinatal depression and anxiety. In Ghana, the burden of perinatal depression and anxiety is exacerbated by socioeconomic challenges and limited access to mental health services. This study explores the perceptions and experiences of pregnant women and new mothers regarding the role of artificial intelligence in addressing perinatal depression and anxiety.

Methods: A qualitative approach utilizing focus group discussions was employed to gather insights from 15 participants, including 8 pregnant women and 7 new mothers, in Accra, Ghana. Thematic analysis was used to identify key themes related to experiences with perinatal depression and anxiety and attitudes toward artificial intelligence technologies.

Results: Four main themes emerged: awareness of perinatal depression and anxiety and its emotional impact, scepticism and fear regarding artificial intelligence's ability to provide emotional support, perceived benefits and significant barriers associated with artificial intelligence tools. The participants acknowledged the potential for artificial intelligence to aid in self-monitoring and education but expressed concerns about privacy, trust, and the fear of losing human interaction in care.

Conclusion: This study highlights the complex interplay of awareness, emotional experiences, and attitudes toward artificial intelligence among perinatal women in Ghana. This highlights the need for culturally sensitive educational initiatives and ethical guidelines for artificial intelligence integration in maternal health, aiming to enhance mental health outcomes for women and their families in low- and middle-income contexts.

Background: The effect of maternal blood lipids and glucose on the relationship between pre-pregnancy body mass index (BMI) and newborn birthweight (NBW) in pregnant women with gestational diabetes mellitus (GDM) remains unclear. This study aimed to examine whether maternal blood lipids and glucose mediate the relationship between pre-pregnancy BMI and NBW in pregnant women with GDM.

Methods: A retrospective study was conducted on 1257 women with GDM who gave birth at the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China, between November 2017 and July 2021. Maternal blood lipids were measured at the first prenatal care (6-13 weeks of gestation). Glucoses, including mid-pregnancy fasting plasma glucose (FPG) and post-load glucose, were measured at 24-28 gestational weeks using an oral glucose tolerance test (OGTT). Multivariable linear regression was used to evaluate the significance of assumed paths. According to the results of multivariable linear regression, significant lipids and glucose were included in a multiple mediation analysis as mediators. Sensitivity analysis was performed to verify the robustness of the results.

Results: Early-pregnancy high-density lipoprotein cholesterol (HDL-C) and mid-pregnancy FPG were associated with pre-pregnancy BMI and NBW. Early-pregnancy HDL-C was associated with mid-pregnancy FPG. The multiple mediation analysis revealed that pre-pregnancy BMI not only had a direct effect on NBW (effect = 23.592, 95% CI [14.366, 32.306]) but also had an indirect effect (effect = 5.602, 95%CI [2.812, 8.648]) on NBW through three paths: the independent mediating role of early-pregnancy HDL-C (effect = 1.649, 95% CI [0.103, 3.472]); the independent mediating role of mid-pregnancy FPG (effect = 3.739, 95% CI [1.653, 6.164]); and the multiple chain-mediating role of early-pregnancy HDL-C and mid-pregnancy FPG (effect = 0.214, 95% CI [0.054, 0.508]). The mediated proportions of the three paths were 5.65%, 12.81%, and 0.73%.

Conclusions: Maternal early- pregnancy HDL-C and mid-pregnancy FPG partially mediate the association between pre-pregnancy BMI and NBW in pregnant women with GDM.

Background: To investigate fertility challenges, factors influencing spontaneous pregnancy, and pregnancy outcomes in females with partial X-chromosome deletions, and to provide evidence for genetic counseling.

Methods: A retrospective analysis of 101,018 individuals who underwent peripheral blood karyotype analysis was performed; females with partial X-deletions or 45,X karyotype were identified. Follow-up was conducted for females aged ≥ 15 years as of 2025. Clinical phenotypes were compared between females with partial X-deletions and those with homozygous 45,X karyotype. Females with partial X-deletions were stratified into spontaneous pregnancy and non-spontaneous pregnancy groups to identify predictive factors. Pregnancy outcomes were compared with 130 pregnant women with normal karyotype, with postnatal infant follow-up conducted.

Results: 68 partial X-deletion and 55 45,X cases were followed up. It revealed significantly higher rates of spontaneous menarche, normal uterine/ovarian ultrasound findings, and conception probability in partial X-deletion group (p < 0.05/p < 0.01). Among 68 females with partial X-deletions, 23 successfully delivered. Spontaneous pregnancy was significantly associated with earlier menarche and normal ultrasound findings (p < 0.05), while mosaicism without normal karyotypes and increased infertility-related gene deletions were adverse factors (p < 0.001). Compared with pregnant women with normal karyotypes, elevated rates of assisted reproductive technology utilization and cesarean section were observed (p < 0.05), with significantly increased abnormal fetal screening results (p < 0.001). Postnatally, 15 of 27 fetuses (55.6%) inherited maternal chromosomal abnormalities; 10 exhibited clinical phenotypes including one severe case.

Conclusions: This study is a preliminary exploration focusing on reproductive issues in females with partial X-chromosome deletions. These females face significant reproductive challenges, with infertility being the primary one. However, they have a significantly higher probability of spontaneous conception compared with females with a homozygous 45,X karyotype. Their offspring have a high probability of inheriting the maternal abnormal X-chromosome and often present with clinical phenotypes of varying severity. Larger cohort validation and long-term follow-up are necessary to further validate these findings.

Background: Prenatal diagnosis of fetal central nervous system (CNS) anomalies remains challenging and is influenced by various factors, including imaging equipment quality, and access to advanced modalities such as fetal magnetic resonance imaging (MRI). In resource-limited settings, these constraints highlight the need for practical and accessible screening methods. This study aims to evaluate the predictive value of the fetal corpus callosum length-to-head circumference (CCL/HC) and corpus callosum length-to-occipitofrontal (CCL/OFD) diameter ratios in the detection of fetal central CNS abnormalities.

Materials and methods: This prospective cohort study included 243 singleton pregnancies undergoing routine mid-trimester ultrasound between November 2024 and May 2025. 15 fetuses with CNS anomalies confirmed by fetal MRI constituted the patient group. Diagnosed anomalies included schizencephaly, lissencephaly, vermian agenesis, cerebellar hypoplasia, hydrocephalus, pachygyria, septo-optic dysplasia, and corpus callosum (CC) malformations, particularly hypoplasia. After excluding 22 cases with non-CNS anomalies, maternal diseases, or intrauterine growth restriction, 206 fetuses comprised the control group. The CCL/HC and CCL/OFD ratios were then calculated for analysis and compared between groups.

Results: A total of 221 fetuses were analyzed; CNS anomalies were confirmed in 15 of them, while 206 served as the control group. In the anomaly group, the mean corpus callosum length (CCL) was significantly shorter compared to healthy fetuses (19.27 ± 3.03 mm vs. 23.33 ± 3.50 mm; p < 0.001). Both the CCL/HC and CCL/OFD ratios were significantly lower in fetuses with CNS anomalies (p < 0.001 for both). ROC analysis showed AUC values of 0.833 for CCL/HC and 0.935 for CCL/OFD; the cutoff values were 0.0953 and 0.2658, respectively, with both ratios demonstrating high specificity (99.5%) for anomaly detection. Additionally, both ratios exhibited a moderate positive correlation with gestational age in the control group and remained within the ± 2 standard deviation limits in 97% (CCL/HC) and 95.7% (CCL/OFD) of healthy fetuses.

Conclusion: Fetal CCL/OFD and CCL/HC ratios may offer a practical method for both assessing normal CC development and identifying potential CNS anomalies.

Background: Globally, about 1.9 million stillbirths occurred at the end of 2021. This scenario has been worst in Sub-Saharan African countries, which contribute about 47% of the total stillbirths. In Ethiopia, though there was a previous study on the rate of stillbirths and its associated factors, it only explored the effect of antenatal care visits and previous stillbirth history. Yet, the meta-effects of other contributing factors such as antepartum hemorrhage, meconium-staining, labor duration, body weight, hypertension, prematurity, and premature rupture of membranes have not been investigated. Therefore, this systematic review and meta-analysis aimed to estimate the rate of stillbirths and identify its associated factors in Ethiopia.

Methods: PubMed, HINARI, Science Direct, and other sources including Google Scholar and African Journals Online were searched using different search engines. Critical appraisal was done using the Joanna Briggs Institute (JBI 2020) checklist. The heterogeneity among studies was tested using I-squared statistics. Publication bias was tested using a funnel plot visual inspection and Begg's test. The results were presented using forest plots and tables. The random effect model was fitted to estimate the pooled rate of stillbirths and odds ratio of factors with a 95% confidence interval. Finally, statistically significant association was declared at a P-value of < 0.05.

Results: A total of 28 studies with 18,454 study participants were included. The pooled rate of stillbirths in Ethiopia was found to be 71.2 (95% CI: 58.3, 96.7) per 1000 births. In subgroup analysis, the rate of stillbirth ranged from 53.4 per 1000 births in the Amhara region to 105.5 per 1000 births in the Oromia region. Antepartum hemorrhage (OR: 4.59, 95% CI: 3.58, 5.89), congenital abnormality (OR: 6.06, 95% CI: 4.21, 8.71), previous history of stillbirth (OR: 4.91, 95%CI: 2.42, 9.97), prolonged labor (OR: 3.22, 95% CI: 2.61, 3.98), low body weight (OR: 4.84, 95% CI: 3.92, 5.98), meconium-stained amniotic fluid (OR: 5.18, 95% CI: 3.72, 7.20), obstructed labor (OR: 2.40, 95% CI: 1.78, 3.23), not using partograph (OR: 4.03, 95% CI: 2.01, 8.08), preeclampsia (OR: 2.28, 95% CI: 1.71, 3.04), preterm delivery (OR: 5.11, 95% CI: 3.48, 7.50) and premature rupture of membranes(OR: 2.64, 95% CI: 1.47, 4.77) were factors associated with a higher odds of stillbirths. Whereas, having four or more antenatal care visits (OR: 0.37, 95% CI: 0.29, 0.46) was associated with a lower risk of stillbirths.

Conclusions: This systematic review and meta-analysis concludes that the rate of stillbirth remains high in Ethiopia. Therefore, pregnant mothers should be encouraged to utilize maternal and child healthcare services. Furthermore, all concerned stakeholders should consider the aforementioned factors in healthcare provision to reduce stillbirths in Ethiopia.

Objective: Current predictive models for gestational diabetes mellitus (GDM) largely overlook the role of social network factors. This study aimed to develop and validate an early GDM prediction model by integrating social network characteristics with traditional non-invasive predictors using machine learning (ML).

Methods: This prospective cohort study enrolled 2,433 pregnant individuals from four branches of Qingdao University Affiliated Hospital as the model development cohort and external validation cohort. After screening variables via univariate analysis, significant predictors were used to train seven ML algorithms: Logistic Regression (LR), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), Adaptive Boosting (AdaBoost) and Multilayer Perceptron (MLP). A 30-times repeated stratified 10-fold cross-validation procedure was employed, ensuring the preservation of the class distribution in every fold. Performance was assessed using the area under the receiver operating characteristic curve (AUC-ROC), accuracy, recall, specificity and F1 score. External validation evaluated the model's predictive performance and clinical effectiveness via ROC curves, Calibration curves, and decision curve analysis (DCA).

Results: One thousand seven hundred fifty-two cases were included in the model development cohort and 681 cases were included in the geographically independent external validation cohort. Twenty-two risk factors for GDM were screened out through univariate logistic regression, covering sociodemographic characteristics, social network characteristics (such as the scale of the structural network and the semi-annual total contact frequency), and personal behavioral characteristics. The XGBoost model demonstrated the optimal comprehensive performance (AUC = 0.980), significantly outperforming other algorithms. External validation further confirmed that the model has excellent generalization ability (AUC = 0.901), though with room to improve upon its sensitivity. The Calibration curve showed that the predicted results were in good agreement with actual observations, and DCA exhibited a superior net benefit across a wide range of threshold probabilities.

Conclusions: This study developed a high-performance GDM prediction model by integrating social network variables with conventional predictors. The XGBoost-based algorithm showed robust performance in external validation, demonstrating that social network metrics significantly enhance risk stratification beyond traditional clinical factors. It reveals the potential value of social network factors in predicting GDM, providing new ideas and methods for constructing GDM prediction models with higher predictive ability and more stable performance.