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Pregnancy is hazardous with pulmonary arterial hypertension, but maternal mortality may have fallen in recent years. We sought to systematically evaluate pulmonary arterial hypertension and pregnancy-related outcomes in the last decade. We searched for articles describing outcomes in pregnancy cohorts published between 2008 and 2018. A total of 3658 titles were screened and 13 studies included for analysis. Pooled incidences and percentages of maternal and perinatal outcomes were calculated. Results showed that out of 272 pregnancies, 214 pregnancies advanced beyond 20 gestational weeks. The mean maternal age was 28 ± 2 years, mean pulmonary artery systolic pressure on echocardiogram was 76 ± 19 mmHg. Etiologies include idiopathic pulmonary arterial hypertension (22%), congenital heart disease (64%), and others (15%). Majority (74%) had good functional class I/II. Only 48% of women received pulmonary arterial hypertension-specific therapy. Premature deliveries occur in 58% of pregnancies at mean of 34 ± 1 weeks, most (76%) had Cesarean section. Maternal mortality rate was 12% overall (n = 26); even higher for idiopathic pulmonary arterial hypertension etiology alone (20%). Reported causes of death included right heart failure, cardiac arrest, pulmonary arterial hypertension crises, pre-eclampsia, and sepsis; 61% of maternal deaths occur at 0–4 days postpartum. Stillbirth rate was 3% and neonatal mortality rate was 1%. In conclusion, pulmonary arterial hypertension in pregnancy continues to be perilous with high maternal mortality rate. Continued prospective studies are needed.

Background Low birth weight (LBW) is closely associated with foetal and neonatal mortality and morbidity, inhibite growth and cognitive development and resulted chronic diseases later in life. Many factors affect foetal growth and thus, the birth weight. These factors operate to various extents in different environments and cultures. The prevalence of low birth weight in the study area is the highest in the country. To the investigator’s knowledge in Bale Zone, no study has yet been done to elucidate the risk factors for low birth weight using case control study design. This study was aimed to identify the risk factors of low birth weight in Bale zone hospitals. Methods A case–control study design was applied from April 1st to August 30th, 2013. A total of 387 mothers (136 cases and 272 controls) were interviewed using structured and pretested questionnaire by trained data collectors working in delivery ward. For each case, two consecutive controls were included in the study. All cases and controls were mothers with singleton birth, full term babies, no diabetes mellitus and no hypertensive. The data were entered and analyzed using SPSS version 16.0 statistical package. The association between the independent variables and dependent variable (birth weight) was evaluated through bivariate and multiple logistic regression analyses. Result Maternal age at delivery <20 years (adjusted odds ratio (AOR) = 3; 95 % confidence interval (CI) = 1.65–5.73), monthly income <26 United States Dollarr (USD) (AOR = 3.8; 95 % CI = 1.54–9.41), lack of formal education (AOR = 6; 95 % CI = 1.34–26.90), being merchant (AOR = 0.1; 95 %CI = 0.02–0.52) and residing in rural area (AOR = 2.1; 95 % CI = 1.04–4.33) were socio-economic variables associated with low birth weight. Maternal risk factors like occurrence of health problems during pregnancy (AOR = 6.3; 95 % CI = 2.75–14.48), maternal body mass index <18 kg/m2 (AOR = 6.7; 95 % CI = 1.21–37.14), maternal height <1.5m (AOR = 3.7; 95 % CI = 1.22–11.28), inter-pregnancy interval <2 years (AOR = 3; 95 % CI = 1.58–6.31], absence of antenatal care (OR = 2.9; 95 % CI = 1.23–6.94) and history of khat chewing (AOR = 6.4; 95 % CI = 2.42–17.10) and environmental factors such as using firewood for cooking (AOR = 2.7; 95 % CI = 1.01–7.17), using kerosene for cooking (AOR = 8.9; 95 % CI = 2.54–31.11), wash hands with water only (AOR = 2.2; 95 % CI = 1.30–3.90) and not having separate kitchen room (AOR = 2.6; 95 % CI = 1.36–4.85) were associated with low birth weight. Conclusion Women who residing in rural area, faced health problems during current pregnancy, had no antenatal care follow-up and use firewood as energy source were found to be more likely to give low birth weight babies. Improving a mother’s awareness and practice for a healthy pregnancy needs to be emphasized to reverse LBW related problems.

Background Stillbirth is a profound and devastating outcome of pregnancy that has a long-lasting emotional and physiological impact on parents and families. Current risk assessment approaches largely rely on maternal characteristics and clinical history, yet their predictive accuracy remains poor, particularly among nulliparous women (women with no previous birth beyond 24 weeks of gestation). We evaluated the extent to which routinely collected pregnancy risk factors can predict stillbirth and assessed their contribution among singleton births in nulliparous women. Methods We conducted a population-based retrospective cohort study of 876,279 nulliparous women receiving maternity care across 130 National Health Service (NHS) Trusts in England between 2015 and 2019. Thirty-one maternal and pregnancy factors routinely collected during antenatal care were analysed. We used modified Poisson regressions with generalised estimating equations to account for clustering of women within Trusts to compute risk ratios (RR) and 95% confidence intervals (CI). We calculated adjusted population attributable risks (PARs) for significant factors. Results Among 876,279 nulliparous women receiving maternity care, 2568 stillbirths occurred. Modifiable maternal characteristics associated with increased risk included elevated body mass index (BMI) (RR 1.22, 95% CI 1.03–1.45 for BMI 35– < 40 kg/m 2 ; RR 1.70, 95% CI 1.39–2.07 for BMI ≥ 40 kg/m 2 , both compared to BMI 18.5– < 25 kg/m 2 ), smoking at booking (RR 1.34, 95% CI 1.19–1.51), current substance misuse (RR 1.52, 95% CI 1.16–1.98), lack of folic acid consumption before conception (RR 1.28, 95% CI 1.16–1.40) or during pregnancy (RR 1.38, 95% CI 1.18–1.61), and late antenatal booking after 12 weeks of gestation (RR 1.18, 95% CI 1.07–1.30). Fetal growth restriction accounted for the largest population attributable risk for stillbirth (RR 2.96, 95% CI 2.73–3.21). Conclusions Maternal and clinical risk factors explain only a fraction of stillbirths in nulliparous women and cannot underpin a clinically useful prediction model. These findings demonstrate the limitations of risk-based screening strategies and highlight the need for integrated approaches that combine maternal characteristics with biochemical, biophysical, and system-level factors to achieve meaningful advances in stillbirth prevention.

Background Preterm birth (PTB), which is defined as delivery before 37 weeks of gestation, is the leading cause of neonatal morbidity, long-term developmental impairment, and infant mortality. In South Korea, PTB has become a critical concern amid declining fertility, delayed childbearing, and an increased reliance on assisted reproductive technology (ART). Comprehensive population-based evidence of contemporary maternal and healthcare-related risk factors is limited. Methods We conducted a retrospective cohort study using Health Insurance Review & Assessment Service claims data for all singleton live births between 2018 and 2022 ( N  = 795,715). Cox proportional hazards model with gestational age as the time axis were used to estimate the adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs). Preterm birth is inherently a time-to-event process, where risk evolves dynamically over gestational age, and key exposures (e.g., pregnancy complications) arise during follow-up. Cox proportional hazards modeling with gestational age as the time axis is therefore epidemiologically justified and widely used in obstetric research. Pregnancy complications were modelled as time-varying covariates, and baseline hazards were stratified according to the delivery facility level. Results Significant risk factors of PTB included a history of PTB (aHR 4.05, 95% CI 2.57–6.36), adolescent pregnancy (< 20 years; aHR 2.35, 95% CI 1.26–3.44), severe pregnancy complications (aHR 2.21, 95% CI 2.04–2.38), ART conception (aHR 1.32, 95% CI 1.18–1.47), and a history of miscarriage (aHR 1.34, 95% CI 1.21–1.48). Women covered by Medical Aid, reflecting a lower socioeconomic status, were also at an increased risk (aHR 1.65, 95% CI 1.06–2.57). Although the established model demonstrated excellent discrimination, False labor, while strongly associated with preterm birth, represents a clinically proximate predictive marker rather than an etiological cause, reflecting imminent risk as pregnancy progresses. Conclusions This nationwide analysis identified significant associations between recurrent obstetric history, adolescent pregnancy, ART, socioeconomic disadvantage, and the risk of PTB. These findings underscore the importance of early antenatal risk stratification, targeted support for vulnerable populations, and implementation of policies to address the structural determinants of maternal health. Insights from the demographic and healthcare context of Korea may inform global strategies to reduce PTBs. Graphical Abstract

Background The Obstetric Comorbidity Index (OBCMI) is an internationally validated scoring system for maternal risk factors intended to reliably predict the occurrence of severe maternal morbidity (SMM). This retrospective cohort study applied the OBCMI to pregnant women in Qatar to validate its performance in predicting SMM and cumulative fetal morbidity. Methods Data from 1000 women who delivered in July 2021 in a large tertiary centre was extracted from medical records. The OBCMI index included maternal demographics, pre-existing comorbidities, and various current pregnancy risk factors such as hypertension, including preeclampsia, intrauterine fetal death, prolonged rupture of membranes and unbooked pregnancies. SMM was based on the ACOG consensus definition, and the cumulative fetal morbidity (CFM) included fetal distress in labour, low APGAR and umbilical artery (UA) pH, admission to neonatal intensive care (NICU), and hypoxic-ischemic encephalopathy (HIE). A c-statistic or area under curve (AUC) was calculated to determine the ability of OBCMI to predict SMM and CFM. Results The median OBCMI score for the cohort was 1 (interquartile range- 0 to 2); 50% of women scored 0, while 85% ( n  = 842) had a score ranging from 0 to 2. Ten women (1%) scored ≥ 7; the highest score was 10. The incidence of SMM was 13%. According to the modified scoring system, the mean OBCMI score in those who developed SMM was 2.18 (± 2.20) compared to a mean of 1.04 (± 1.40) in those who did not (median 1, IQR:1–3 versus median 0, IQR: 0–2; p  < 0.001). The incidence of CFM was 11.3%. The incidence of low APGAR score, HIE and NICU admission was nearly 1 in 1000. Around 5% of the babies had fetal distress in labour and low UA pH. For every 1 unit increase in OBCMI score, the odds of SMM increased by 44% (OR 1.44 95% CI 1.30–1.59; p  < 0.001; AUC 0.66), and CFM increased by 28% (OR 1.28 95% CI 1.15–1.42; p  < 0.001; AUC 0.61). A cut-off score of 4 had a high specificity (> 90%); 1 in 4 and 1 in 6 women with OBCMI score ≥ 4 developed SMM and CFM, respectively. Conclusion The OBCMI performed moderately well in predicting SMM in pregnant women of Qatar and can be effectively used as a risk assessment tool to red-flag high-risk cases so that appropriate and timely multidisciplinary care can be initiated to reduce SMM and maternal mortality. The index is also helpful in predicting fetal morbidity; however, further prospective studies are required to validate OBCMI for CFM.

IntroductionInterconception care (ICC) is recommended to reduce maternal risk factors for poor birth outcomes between pregnancies. The IMPLICIT ICC model includes screening and brief intervention for mothers at well child visits (WCVs) for smoking, depression, multivitamin use, and family planning. Prior studies demonstrate feasibility and acceptability among providers and mothers, but not whether mothers recall receipt of targeted messages.MethodsMothers accompanying their child at 12- and 24-month WCVs at four sites of a family medicine academic practice were surveyed pre (2012) and post (2018) ICC model implementation. Survey items assessed health history, behaviors, and report of whether their child’s physician addressed maternal depression, tobacco use, family planning, and folic acid supplementation during WCVs. Pre and post results are compared using logistic regression adjusting for demographics and insurance.ResultsOur sample included 307 distinct mothers with 108 and 199 respondents in the pre and post periods, respectively. Mothers were more likely to report discussions with their child’s doctor post-intervention for family planning (31% pre to 86% post; aOR 18.65), depression screening (63–85%; aOR 5.22), and taking a folic acid supplement (53–68%; aOR 2.54). Among mothers who smoked, the percentage that reported their child’s doctor recommended cessation increased from 56 to 75% (aOR = 3.66).DiscussionThe IMPLICIT ICC model resulted in increased reported health care provider discussions of four key areas of interconception health by mothers attending WCVs. This model holds promise as a primary care strategy to systematically address maternal risks associated with poor pregnancy outcomes.

Introduction Research has documented modest positive impacts of early childhood home visiting programs. However, understanding more about what home visitors do during visits and how much time they spend on specific topics may provide insight into the variability in effectiveness of services. Methods Outcome data were collected via parent survey at program enrollment and 12 months from 123 women in three MIECHV-funded home visiting models. Home visitors completed weekly home visit content and activity logs. Results Families received an average of 28 visits during the study (3.1 visits per month). Of ten content areas, the three most often discussed were early childhood development, physical care of children, and the parent–child-relationship. Multivariate regression models were used to explore the association of home visit dosage, home visit content and cumulative risk factors on parenting outcomes. Women whose visits were focused more on parenting topics reported lower parenting-related stress at follow-up compared to those whose visits had less parenting content. Additionally, higher-risk women who received greater numbers of home visits showed larger reductions in their attitudes about harsh punishment over time, compared to high-risk women with fewer home visits. Discussion Receiving home visits that emphasize parenting content may contribute to reduced parenting-related stress. For high-risk women in particular, receiving more visits overall may be important to achieving positive outcomes. Implications for practice include working to engage and retain high-risk families. Future home visiting research calls for improved methods for collecting data on content/activity during visits, the necessity for long-term follow-up, and testing for the effectiveness of varied and flexible visit schedules/content focus for women and families with trauma exposure.

Background: Diagnosis and timely treatment of neonatal jaundice and prevention of dangerous side effects of pathologic neonatal jaundice remain a serious debate. The first step in prevention of jaundice is the identification of predisposing factors. The present study aims to systematically review the maternal risk factors of neonatal hyperbilirubinemia.Methods: For this study, we searched databases including Science Direct, Cochrane Library, ISI, PubMed and Google Scholar from 1993 to 2017. The keywords searched based on MESH included hyperbilirubinemia, jaundice, infants, mothers and risk factors. The present systematic review was conducted on studies reporting maternal risk factors for neonatal jaundice. The inclusion criteria were: study on neonates; examination of maternal factors or both maternal and neonatal factors. Papers associated with the diagnosis and treatment of neonatal jaundice were excluded from the study, as well as those articles for which only abstracts were available. The limitations of this study include lack of access to all relevant articles, lack of qualified reports in some papers, and the limitation in number of articles related to maternal risk factors, and therefore inability to judge accurately about their effects on neonatal jaundice.Results: Of 500 searched articles, 17 articles (1 prospective article, 2 retrospective papers, 12 cross-sectional papers and 2 historical cohort articles) were finally investigated. Maternal risk factors included hypertension, diabetes, type of delivery, vaginal bleeding, premature rupture of membranes (PROM), maternal age, lack of initiation of feeding during the first hours of life, inappropriate breastfeeding techniques and presence of maternal breast problems.Conclusion: The most common maternal risk factors for neonatal jaundice were prematurity, blood type incompatibilities, preeclampsia, hypertension, diabetes mellitus, vaginal bleeding, delivery problems (type of delivery, labor injuries, delivery at home, skin ecchymosis, and cephalohematoma), mothers and community cultural beliefs (use of traditional supplements), breast problems, and decrease in breastfeeding.

Maternal health risks can cause a range of complications for women during pregnancy. High blood pressure, abnormal glucose levels, depression, anxiety, and other maternal health conditions can all lead to pregnancy complications. Proper identification and monitoring of risk factors can assist to reduce pregnancy complications. The primary goal of this research is to use real-world datasets to identify and predict Maternal Health Risk (MHR) factors. As a result, we developed and implemented the Quad-Ensemble Machine Learning framework to predict Maternal Health Risk Classification (QEML-MHRC). The methodology used a vacxsriety of Machine Learning (ML) models, which then integrated with four ensemble ML techniques to improve prediction. The dataset collected from various maternity hospitals and clinics subjected to nineteen training and testing tests. According to the exploratory data analysis, the most significant risk factors for pregnant women include high blood pressure, low blood pressure, and high blood sugar levels. The study proposed a novel approach to dealing with high-risk factors linked to maternal health. Dealing with class-specific performance elaborated further to properly understand the distinction between high, low, and medium risks. All tests yielded outstanding results when predicting the amount of risk during pregnancy. In terms of class performance, the dataset associated with the “HR” class outperformed the others, predicting 90% correctly. GBT with ensemble stacking outperformed and demonstrated remarkable performance for all evaluation measure (0.86) across all classes in the dataset. The key success of the models used in this work is the ability to measure model performance using a class-wise distribution. The proposed approach can help medical experts assess maternal health risks, saving lives and preventing complications throughout pregnancy. The prediction approach presented in this study can detect high-risk pregnancies early on, allowing for timely intervention and treatment. This study’s development and findings have the potential to raise public awareness of maternal health issues.

According to the World Health Organization, maternal mortality rates remain a critical public health issue, with 94% of maternal deaths occurring in low- and middle-income countries (LMICs), where the rates reached 430 per 100,000 live births in 2020 compared to 13 in high-income countries. Despite this difference, only a few studies have investigated whether sparse data and features such as vital signs can effectively predict maternal health risks. This study addresses this gap by evaluating the predictive capability of vital sign data using machine learning models trained on a dataset of 1014 pregnant women from rural Bangladesh. This study developed multiple machine learning models using a dataset containing age, blood pressure, temperature, heart rate, and blood glucose of 1014 pregnant women from rural Bangladesh. The models’ performance were evaluated using regular, random and stratified sampling techniques. Additionally, we developed a stacking ensemble machine learning model combining multiple methods to evaluate predictive accuracy. A key contribution of this study is developing a stacking ensemble model combined with stratified sampling, an approach not previously considered in maternal health risk prediction. The ensemble model using stratified sampling achieved the highest accuracy (87.2%), outperforming CatBoost (84.7%), XGBoost (84.2%), random forest (81.3%) and decision trees (80.3%) without stratified sampling. Observations from our study demonstrate the feasibility of using sparse data and features for maternal health risk prediction using algorithms. By focusing on data from resource-constrained settings, we show that machine learning offers a convenient and accessible solution to improve prenatal care and reduce maternal deaths in LMICs.