BMC Pregnancy and Childbirth最新文献

Background: The magnitude of maternal and neonatal death and morbidity during the postnatal period remains too high. In contrast to the decline in global mortality rates, maternal and neonatal deaths continue to occur at the highest rate in sub-Saharan Africa. Appropriate care during the postpartum period is vital to prevent neonatal and maternal deaths. This study is aimed at delivering evidence on the pooled prevalence and associated factors of adequate postnatal care using the recent demographic and health surveys from 20 sub-Saharan African countries.

Methods: Data from the recent demographic and health surveys of 20 countries in sub-Saharan Africa conducted between 2015 and 2022 were used. A total weighted sample of 90,251 women aged 15-49 years with live births in the 2 years preceding the survey was included in the study. Multilevel logistic regression was used to determine the factors associated with the outcome variable. Intra-class correlation coefficient, likelihood ratio test, median odds ratio, and deviance (-2LLR) values were used for model comparison and fitness. Finally, variables with a p-value < 0.05 were declared statistically significant.

Results: The overall pooled prevalence of adequate postnatal care among women aged 15-49 years in SSA countries was 27.42% (95% CI: 27.13%, 27.71%). Factors like age [AOR = 1.10; 95% CI (1.05, 1.16)], educational status [AOR = 1.52; 95% CI (1.39, 1.67)], marital status [AOR = 0.83; 95% CI (0.79, 0.88)], working status [AOR = 0.81; 95% CI (0.78, 0.84)], media exposure [AOR = 1.05; 95% CI (1.01, 1.09)], sex of the household head [AOR = 1.13; 95% CI (1.08, 1.18)], household size [AOR = 1.07; 95% CI (1.03, 1.12)], number of ANC visits [AOR = 3.38; 95% CI (3.04, 3.75)], place of delivery [AOR = 3.77; 95% CI (3.57, 3.99)], prenatal community health workers visit [AOR = 1.45; 95% CI (1.39, 1.51)], and residence [AOR = 1.26; 95% CI (1.21, 1.32)] were significantly associated with adequate postnatal care.

Conclusion: Only nearly three out of ten women received adequate postnatal care in sub-Saharan African countries. Adequacy of postnatal care was determined by the age of respondents, educational status, current marital status, working status, media exposure, sex of the household head, household size, number of ANC visits, place of delivery, prenatal community health workers visit, and residence. Therefore, women's empowerment through education, employment, and decision-making involvement; strengthening ANC service utilization and health facility delivery; information dissemination through media; promoting prenatal care through community health workers home-to-home visits; and giving special attention to unmarried, young, and non-working women are strongly recommended.

Background: Preeclampsia (PE) is a multisystem progressive disease that occurs during pregnancy. Previous studies have shown that the immune system is involved in the placental trophoblast function and the pathological process of uterine vascular remodeling in PE. However, its molecular mechanism is still unclear. This study aimed to identify critical genes and immune cells involved in the pathological process of PE.

Methods: The PE-related GSE74341 and GSE160888 datasets were downloaded from the Gene Expression Omnibus (GEO) database, and differential expression analysis, weighted gene co-expression network analysis (WGCNA), and least absolute shrinkage and selection operator (LASSO) analysis were combined to screen the PE-related DEGs. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic specificity of obtained DEGs, and the GSE35574 dataset was used for preliminary validation. Furthermore, the single-sample Gene Set Enrichment Analysis (ssGSEA) was used to elucidate the correlation between the DEGs and the 28 types of infiltrating immune cells in PE. Real-time reverse transcription polymerase chain reaction (RT-PCR) was used to verify the differential expression of DEGs in the PE placental tissues.

Results: A total of 143 DEGs (DE-mRNAs) were screened using the PE datasets. The analysis of DEG modules and LASSO logistic regression were used to identify high-temperature requirement factor A4 (HtrA4), tumour suppressor candidate 3 (TUSC3), endothelial protein C receptor gene (PROCR), claudin 3 (CLDN3), and thioredoxin binding protein (TXNIP) as the hub DEGs in PE. Furthermore, validation with the GSE35574 dataset and ROC analysis was used to clarify that the HTRA4, PROCR, and TXNIP genes are potential markers of PE and are closely related to the infiltrating immune cells in PE, such as gamma delta T cells, mast cells, natural killer cells, and T follicular helper cells. Finally, differential HTRA4, PROCR, and TXNIP expression were confirmed in PE placental tissues (p < 0.001).

Conclusion: HTRA4, PROCR, and TXNIP can be used as potential PE biomarkers to provide a new strategy for early diagnosing and treating PE.

Background: Macrosomia presents significant risks to both maternal and neonatal health, however, accurate antenatal prediction remains a major challenge. This study aimed to develop machine learning approaches to enhance the prediction of fetal macrosomia at different stages of pregnancy.

Methods: This retrospective study involved 500 pregnant women who delivered singleton infants at Beijing Tsinghua Changgung Hospital between December 2019 and July 2024. The training set comprised 208 cases of macrosomia and 208 non-macrosomia cases, with 84 additional cases used for external validation. A total of 23 candidate variables, including maternal characteristics, physical measurements, and laboratory tests were collected for feature selection. Seven algorithms were applied in combination with three sets of selected features, resulting in 21 fitted models. Model performance was evaluated via the area under the receiver operating characteristic curve (AUC), accuracy, precision, sensitivity, specificity, and F1-score.

Results: Maternal height, pre-pregnancy weight, first-trimester weight, pre-labor weight, gestational age at birth, gestational weight gain, and the proportion of male neonates were significantly greater in the macrosomia group compared to non-macrosomia group in the training set (p < 0.05). The top five predictors for macrosomia were pre-labor weight, gestational weight gain, the Pre-labor Hb/First-trimester Hb ratio, first-trimester Hb, and maternal height. Logistic regression yielded the highest AUC values in the pre-pregnancy (0.790) and first-trimester (0.815) periods in the validation set, whereas the ensemble model achieved the highest AUC value of 0.930 before labor. SHapley Additive exPlanations (SHAP) analysis highlighted pre-labor weight, gestational age, gestational weight gain, first-trimester Hb, and neonatal sex as important factors for the prediction of macrosomia.

Conclusion: This is the first study to utilize machine learning with data from the pre-pregnancy, first-trimester, and pre-labor periods to predict macrosomia. The logistic regression model and the final ensemble model demonstrated strong predictive performance, offering valuable insights to improve pre-pregnancy counseling, antenatal assessment, and intrapartum decision-making.

Background: This study investigates the association of blood cell components and blood cell-derived inflammatory indices in early pregnancy with the development of gestational diabetes mellitus (GDM).

Methods: This research is part of the Qazvin maternal and neonatal metabolic study (QMNMS) conducted in Iran from 2018 to 2021. Pregnant women with gestational age ≤ 14 weeks were enrolled in the study. The association of blood cells and inflammatory indices, including neutrophil-to-lymphocyte ratio (NLR), systemic inflammatory response index (SIRI), systemic immune inflammation index (SII), and aggregate systemic inflammatory response index (AISI), in early pregnancy with subsequent GDM development was examined using multivariate logistic regression. This analysis was adjusted for age, pre-pregnancy body mass index (BMI), weight gain, and GDM history in previous pregnancies (Model 1), as well as for these factors in addition to the homeostatic model assessment for insulin resistance (HOMA-IR) (Model 2). The correlation of blood cells and inflammatory indices with insulin resistance was assessed through Spearman partial correlation, adjusted for the same risk factors.

Results: The final analysis included 612 participants, among whom GDM developed in 96 participants (15.7%). Neutrophil, platelet, and lymphocyte counts showed significant correlations with HOMA-IR (r = 0.14, r = 0.22, and r = 0.17, respectively; P < 0.01 for all). In univariate analysis, the highest quartile of neutrophil count was associated with a 5.9 times higher risk of GDM development (95% CI 2.6-13.2, P < 0.001). In multivariate logistic regression, neutrophil count quartiles remained significant predictors of GDM development, with relative risks of 3.7, 4.4, and 8.2 for the 2nd, 3rd, and 4th neutrophil quartiles compared to the 1st quartile (P < 0.001). While platelet count was initially associated with GDM development (RR = 2.6, 95% CI 1.3-5.1, P = 0.028), this association was no longer significant after adjusting for HOMA-IR. Neither lymphocyte nor monocyte counts were linked to GDM development. Additionally, inflammatory indices, such as NLR, SIRI, SII, and AISI, did not provide additional predictive value for GDM development.

Conclusion: Neutrophil count is an independent predictor of GDM development, and its role in GDM development is not influenced by early pregnancy insulin resistance. Moreover, novel inflammatory indices offer no additional predictive benefit for GDM.

Background: Sleep disorders are highly prevalent in pregnant people and have been associated with significant complications and morbidities for both pregnant people and their offspring. Despite this, the management of sleep issues during pregnancy remains suboptimal. There is an urgent need to explore novel treatment approaches that are safe, feasible, and widely implementable in daily routines. Given the demonstrated positive effects of physical activity (PA) on sleep in non-pregnant populations, PA interventions are a promising option. This meta-analysis was performed to evaluate the association between PA interventions and sleep disorders in pregnant people.

Methods: A systematic search of seven databases was conducted for English and Chinese articles published from inception to January 2024 using MeSH headings and keywords for 'sleep disorder', 'pregnancy', 'physical activity', and 'randomized controlled trial'. Two independent researchers selected studies assessing the effects of PA interventions on sleep disorders in pregnant people compared with a control condition. Data extraction was performed independently by two reviewers, and quality was assessed using the Cochrane Risk of Bias V1.0 tool. A random-effects model was applied for the meta-analysis, with results reported as standardised mean difference (SMD) and 95% confidence interval (CI).

Results: Eighteen studies involving 1,541 pregnant people were included, with 14 studies included in the meta-analysis. The results suggested that PA interventions were associated with a reduction in sleep disorders compared with the control condition (SMD = - 1.48, 95% CI = - 2.06 to - 0.90, P < 0.00001; night sleep MD = 0.52, 95% CI = 0.42 to 0.62, P < 0.00001; proportion of night sleep time MD = 5.65, 95% CI = 4.78 to 6.52, P < 0.00001). Subgroup analyses indicated that intervention characteristics (e.g., less than 8 weeks and more than 60 min at a session, individual or group settings, location, and activity type such as water-based exercise, progressive muscle relaxation, and aerobics) and participant characteristics (with or without complications) influenced the overall treatment effect.

Conclusion: This meta-analysis demonstrates that PA interventions positively impact sleep disorders in pregnant people, with effects influenced by participant type, duration, delivery method, and activity form. These findings provide valuable insights for healthcare professionals and hold significant implications for developing comprehensive, evidence-based guidance.

Background: The objective of this study was to investigate whether an isolated abnormal 1-hour glucose challenge test (GCT) among patients without gestational diabetes (GDM) is associated with adverse outcomes.

Methods: This is a retrospective cohort study of patients who underwent GDM screening at ≥ 24 weeks' gestation with a 1-hour GCT and delivered a singleton fetus at > 35 weeks' gestation at an urban tertiary hospital from 1/2013 to 10/2021. Data were extracted from an electronic medical record data warehouse using standardized billing/diagnosis codes. Individuals were categorized into 3 groups: normal screening (1-hour GCT value < 140 mg/dL), intermediate screening (1-hour GCT value ≥ 140 and < 200 but normal 3-hour glucose tolerance test (GTT)), and GDM (1-hour GCT ≥ 200 mg/dL or abnormal 3-hour GTT). The primary composite perinatal morbidity outcome included any of the following: large for gestational age (LGA) birthweight, birth injury, hypoglycemia with neonatal intensive care unit (NICU) admission, respiratory distress syndrome, transient tachypnea of the newborn, apnea, NICU admission, or perinatal death. Multiple secondary outcomes were also evaluated. Bivariable analyses and multivariable logistic regression modeling were performed.

Results: Of 37,277 eligible patients, 29,698 (79.7%) had normal screening results, 5092 (13.7%) had intermediate screening results, and 2487 (6.6%) were diagnosed with GDM. There were significant differences in baseline characteristics between the three groups, including age, parity, race and ethnicity, payer-type, obesity, and pre-pregnancy metformin use. Compared to normal screening, intermediate screening was associated with an increased risk for the composite perinatal morbidity outcome (OR 1.23, 95% CI 1.15-1.32), cesarean (OR 1.37, 95% CI 1.28-1.46), and hypertensive disorders of pregnancy (OR 1.30, 95% CI 1.20-1.40). Associations for these outcomes were further pronounced in those with GDM compared to normal screening (OR 1.86, 95% CI 1.70-2.03; OR 1.69, 95% CI 1.56-1.84; and OR 1.57, 95% CI 1.42-1.74, respectively). After adjusting for potential confounders, increased risks for the composite perinatal morbidity outcome persisted for those with intermediate screening (aOR 1.18, 95% CI 1.10-1.26).

Conclusions: In addition to patients with GDM, individuals an isolated abnormal 1-hour GCT without GDM were also at increased risks for adverse pregnancy outcomes. Further investigation is needed to understand if patients with mild dysregulation may still benefit from other interventions.

Objective: To investigate the relationship between first trimester subchorionic hematoma (SCH) and fetal growth restriction (FGR), and to develop a nomogram for predicting the risk of FGR in patients with SCH.

Methods: We conducted a retrospective cohort study from January 2021 to December 2021, involving singleton pregnancies that received routine prenatal care since the first trimester at our hospital. We excluded pregnancies loss before 20 weeks and ultimately included 1,055 individuals in the study, dividing them into two groups based on whether they were diagnosed with SCH. We compared the pregnancy outcomes in women with and without a SCH. After confirming an independent association between FGR and SCH detected in the first trimester, we conducted a subgroup analysis to identify high-risk factors for FGR among patients with SCH. Logistic regression was employed to identify risk factors for FGR in patients with SCH, and the nomogram was constructed based on the regression coefficients of relevant variables. The calibration of the prediction model was confirmed through the Hosmer-Lemeshow goodness-of-fit test (P > 0.05), and discrimination was assessed using the area under the receiver operating characteristic curve (ROC).

Results: A total of 1,055 individuals were enrolled in the study, with 504 identified with SCH during the first trimester ultrasound examination. Women with SCH had a higher likelihood of experiencing vaginal bleeding (36.5% vs. 21.8%, P < 0.001). The presence of a first trimester SCH was independently associated with FGR [adjusted odds ratio (OR) 4.30, 95% confidence interval (CI) 1.58-11.66]. Subgroup analysis of women with SCH showed that gestational age at diagnosis of SCH ≥ 7 weeks (OR 3.04, 95% CI 1.03-9.00), SCH persisting in the second trimester (OR 4.93, 95% CI 1.71-14.25), leiomyoma ≥ 4 cm (OR 17.23, 95% CI 3.78-78.56), and GDM (OR 3.42, 95% CI 1.18-9.87) were risk factors for FGR. The prediction model was developed based on these factors and presented as a nomogram. The AUC of the nomogram was 0.769 (95% CI: 0.655-0.883, P < 0.001), suggesting a good prediction capability.

Conclusions: First trimester SCH is independently associated with an increased risk of FGR, and the nomogram developed effectively predicts FGR in pregnancies with SCH.

Background: Fetal growth restriction (FGR) is a major determinant of perinatal morbidity and mortality. Our study aimed to develop a prediction model for the risk of FGR developing adverse perinatal outcome (APO) and evaluate its performance.

Methods: This was a prospective observational cohort study of consecutive singleton gestations meeting the ACOG-endorsed criteria for FGR from January 2022 to June 2023 at Obstetrics and Gynecology Hospital of Fudan University. Clinical information, ultrasound indicators and serum biomarkers were collected. The primary composite APO comprised one or more of: perinatal death, intrauterine demise, intraventricular hemorrhage, periventricular leukomalacia, seizures, necrotizing enterocolitis, neonatal respiratory distress syndrome, sepsis and the length of stay in the neonatal intensive care unit > 7 days. Least absolute shrinkage and selection operator regression was used to screen variables for nomogram model construction. The discrimination, calibration and clinical effectiveness of the nomogram were evaluated using receiver operating characteristic curve, calibration plots and decision curve analysis in training and validation cohorts.

Results: A total of 122 pregnancies were enrolled in the final statistical analysis. Five variables were identified to establish a nomogram, including gestational weeks at diagnosis, abnormal umbilical artery Doppler, abnormal uterine artery Doppler, and multiples of the median values of placental growth factor and soluble fms-like tyrosine kinase-1. The area under the receiver-operating-characteristics curve of 0.87 (95% CI, 0.75-0.99) and 0.86 (95% CI, 0.74-0.98) in the training and validation cohort respectively, indicated satisfactory discriminative ability of the nomogram. The calibration plots showed favorable consistency between the nomogram's predictions and actual observations. Decision curve analysis supported its practical value in a clinical setting.

Conclusions: A nomogram was developed and validated to possess the promising capacity of predicting APO in FGR-afflicted neonates, and may prove useful in counseling and management of pregnancies complicated by FGR.