International Journal of Gynecology & Obstetrics最新文献

Objective: To evaluate the perceived educational value of a multimodal surgical training model designed to improve obstetricians' skills in managing placenta accreta spectrum (PAS).

Methods: A prospective, descriptive observational study was conducted to evaluate a multimodal educational model during three standardized workshops held in Bolivia and Peru between January and August 2023. The educational strategy combined flipped-classroom preparation, simulation-based training, interactive theoretical discussions, live ultrasound and surgical sessions, and follow-up mentorship. A total of 290 participants were enrolled, and 175 practicing obstetricians who completed all three surveys (precourse, immediate postcourse, and 6-month follow-up) were included in the analysis. Surveys assessed participants' prior exposure to PAS, perceived usefulness of each educational component, and implementation of learned techniques.

Results: Before the workshops, most participants had prior exposure to PAS but limited experience with uterine-sparing procedures. Following the workshop, participants rated the overall clarity and usefulness of simulation and theoretical sessions around an 8 out of 10 and the value of integrating live surgery as a 9 out of 10. At 6-month follow-up, between 16% and 52% reported having performed at least one technique taught during the workshop. All participants would recommend the course to colleagues. Nearly 30% maintained contact with instructors for diagnostic or surgical support.

Conclusion: A multimodal educational model combining theory, simulation, and hands-on experience is well received by obstetricians and gynecologists and facilitates the integration of new surgical skills into clinical practice, supporting individualized management of PAS.

Objective: Postpartum hemorrhage (PPH) is the leading cause of maternal mortality globally. Most PPH deaths are preventable through evidence-based interventions. This study assessed the availability, direct costs, and economic implications of World Health Organization-recommended PPH interventions in public hospitals in Zambia.

Methods: A cross-sectional survey was conducted in 31 purposively selected public hospitals across seven provinces. Data (June 2019 to May 2020) on PPH prevalence, resource availability, and direct costs of PPH interventions were collected via a structured questionnaire administered to hospital administrators. An ingredients-based costing model compared an ideal pathway (severe PPH managed at a fully equipped primary-level health facility) to a referral pathway (patients unresponsive to early-stage interventions transferred to a tertiary hospital). Data were analyzed using Python.

Results: Among 74 238 deliveries, 1957 (2.6%) were PPH cases, resulting in 94 (4.8%) fatalities. Most cases (86.9%) received medical management, primarily by nurse-midwives. Tranexamic acid (TXA) was available in only 58.1% of the hospitals (subsidized cost: US$1.91/dose). Managing a severe PPH case in the ideal pathway cost US$133.46-US$276.22, compared to US$153.34-US$332.53 via the referral pathway , representing an 18.6% increase. Scaled nationally (approximately 672 000 births), this inefficiency translates to an avoidable annual burden of US$133121.

Conclusion: While oxytocin is universally available, access to critical treatments such as TXA and advanced interventions remains limited. The low cost of preventive interventions contrasts with the high cost of managing severe PPH, which is exacerbated by system fragmentation. Strengthening primary-level facilities with essential commodities, surgical capacity, and training is critical to containing costs and reducing maternal mortality due to PPH.

Objective: This study aims to enhance antenatal detection of placenta accreta spectrum (PAS) and predict severe hemorrhage at delivery using machine learning by evaluating the association between antenatal hematologic index trends across trimesters, imaging markers, and patient history.

Methods: We retrospectively analyzed 2017-2023 data from a PAS referral center, including demographics, laboratory results, ultrasounds, and outcomes. Patients with confirmed PAS (cases) were compared to those with antenatal risk but no histopathologic evidence of PAS (controls). Statistical analyses and machine learning models were developed to predict PAS. We also used machine learning to predict severe hemorrhage (>1500 mL) in the cases.

Results: A total of 186 PAS cases and 217 controls were identified, showing significant differences in body mass index, gravidity, parity, prior cesarean deliveries, gestational age at delivery, and PAS ultrasound findings. Logistic regression highlighted prior cesarean deliveries (odds ratio [OR] 1.8; 95% confidence interval [CI] 1.3-2.4) and second (OR 28.1; 95% CI 12.7-60.8) or third trimester ultrasound markers (OR 27.6; 95% CI 13.2-61.1) as strong predictors of PAS. Third trimester mean platelet volume was inversely associated with PAS (OR 0.55; 95% CI 0.39-0.78). Machine learning models achieved high accuracy. Model 1 predicted PAS with 90% accuracy. Model 2 predicted PAS with 88.8% accuracy using early gestational hematologic markers. Model 3 predicted severe hemorrhage (>1500 mL) with 74.3% accuracy.

Conclusion: Machine learning models combining patient history, imaging, and hematologic markers detect PAS and predict hemorrhage with up to 90% accuracy. These tools improve antenatal diagnosis of PAS, which enhances maternal outcomes by enabling early identification and better resource allocation.

Objective: To describe the types of cesarean delivery on maternal request (CDMR), and to analyze the distribution of delivering types according to the Robson Classification (RC) and the distribution of comorbidities among the different groups of delivering types.

Methods: Cross-sectional study using electronic records of deliveries that occurred in a public, academic high-risk maternity hospital in Brazil, from October 2017 to October 2021. CDMR included first or second cesarean delivery (CD), subdived into three groups: elective (CDMRele), induction withdrawal (CDMRiw), and labor withdrawal (CDMRlw).

Results: In 7779 deliveries, 53.1% (4132) were CD. The CDMR group corresponded to 10.2% (420/4132) of all CD and 5.4% (420/7779) of all deliveries. CDMRele were the main group (246 - 58.6%), followed by CDMRiw (118 - 28.1%), and CDMRlw (56 - 13.3%). The CDMR and its subdivisions were concentrated in RC group 5.1 (CDMR 240 - 57.1%, CDMRele 156 - 63.4%, CDMRiw 54 - 45.7% and CDMRlw 30 - 53.6%). Second CD corresponded to 3.1% (247/7779) of all deliveries and 58.8% (247/420) of CDRM. Among CDMR, 72.1% (303/420) of patients had comorbidities and the most frequent diseases were hypertension and diabetes.

Conclusion: The definition of CDRM in the international literature is not a consensus and Brazil's law supports CDMR in circumstances not previously described. The RC is limited for understanding the growing phenomenon of CDMR among a group of pregnant women with high prevalence of comorbidities.

Pre-eclampsia is a difficult pregnancy condition that causes high blood pressure and can lead to health complications in both mother and newborn, resulting in a higher fatality rate. It presents with a wide range of symptoms and lacks specific indicators, as the contemporary diagnostic techniques, including proteinuria testing and blood pressure measurements, are not reliable. The current evolution in artificial intelligence (AI) technology tends to show a promising transformation of pre-eclampsia management. AI algorithms are applied to process larger sets of clinical, biochemical, and image data that facilitate timely medical interventions by bringing up the early-onset and severity of pre-eclampsia. By analyzing the red cell distribution width (blood test indicators for pre-eclampsia), it is recognized as a cost-effective way of detecting inflammation. The application of AI technology on non-invasive diagnostic (wearable) devices enables continuous monitoring with imaging techniques for the placenta and retina via cloud-based systems. These developments are not only applied for early detection of pre-eclampsia, but also assist decision making capabilities in both high- and low-resource environments. This article explains how the growing use of AI is changing the way that pre-eclampsia is understood and managed, with the aim of improving accuracy and offering more personalized care for pregnant women.

Objective: Concerns have been raised about the nutritional adequacy of a gluten-free (GF) diet during pregnancy, specifically in the absence of celiac disease, and its impact on fetal development. The objective of this study was to investigate the association between a GF diet during pregnancy and obstetrical outcomes, with further sensitivity analyses exploring those without celiac disease.

Methods: Using data from the ongoing P3 Cohort study, self-reported data were obtained on participant demographics, GF diet, celiac disease, and obstetrical outcomes. Fisher exact, χ², Mann-Whitney U and two-sample t-tests, and regression models were used to examine differences in patient characteristics and outcomes between those who did and did not follow a GF diet in pregnancy. A sensitivity analysis was conducted to examine outcomes among those without celiac disease.

Results: Of the 2359 participants included, 108 (4.6%) reported following a GF diet in pregnancy. The majority of these individuals (n = 68, 63.0%) were non-celiac. Participants in the GF group were older, more likely to identify as white, and more likely to take prenatal vitamin B supplements. There were no significant differences in obstetrical outcomes between the GF and non-GF participants. Sensitivity analyses excluding patients with celiac disease demonstrated similar results, with no difference in obstetrical outcomes observed.

Conclusion: Following a GF diet in pregnancy, even in individuals without celiac disease, was not associated with adverse obstetrical outcomes. These results provide some reassurance on the safety of a GF diet in pregnancy but require validation in larger studies.

Objective: To investigate maternal serum and placental osteopontin levels in pregnancies complicated with fetal growth restriction (FGR) and to evaluate their association with composite adverse neonatal outcomes (CANO).

Methods: A prospective case-control study was conducted at Etlik City Hospital between March and September 2024, including 40 women with pregnancies affected by FGR (34-39 weeks) and 40 gestational age-matched healthy control women. FGR was diagnosed using Delphi criteria. Maternal venous blood and placental samples were collected at delivery. Serum and placental osteopontin levels were measured using enzyme-linked immunosorbent assay. Placental osteopontin concentrations were normalized to total protein by the Bradford method (ng/mg protein). Statistical analyses included Mann-Whitney U, χ², and Fisher exact tests, and receiver operating characteristic (ROC) analysis.

Results: A total of 80 participants were analyzed. Gravidity and parity were lower in the FGR group, whereas maternal age and body mass index were comparable. As expected, adverse perinatal outcomes were more frequent in the FGR group. Both maternal serum and placental osteopontin levels were significantly decreased in FGR cases compared with controls (P = 0.002 and P < 0.001, respectively). ROC analysis demonstrated the best predictive performance for placental osteopontin normalized to total protein, with a cut-off of 61.2 ng/mg (area under the curve [AUC] 0.791, sensitivity 77.5%, specificity 70.0%). Lower osteopontin levels were also predictive of CANO (AUC up to 0.760, P < 0.001).

Conclusions: In pregnancies complicated by FGR, osteopontin levels were significantly reduced in both maternal serum and placental tissue. This decrease may reflect impaired spiral artery remodeling and placental angiogenesis, contributing to the molecular mechanisms of placental insufficiency and adverse neonatal outcomes.

Objective: To assess the prevalence and associations between period poverty and depressive symptoms among college students with food and/or housing insecurity in the University of Hawai'i system. Period poverty is an important public health concern within the USA and has been linked with poor mental health among university students in prior research.

Methods: An anonymous, online survey was conducted in February-March, 2023, with college students in Hawai'i (N = 462) through an academic-community partnership. Unadjusted and adjusted logistic regressions assessed relationships between period poverty and depressive symptoms, stratified by food insecurity and housing.

Results: Nearly 43% (n = 199) of students (N =462) reported past-year period poverty and 28% reported moderate/severe depressive symptoms. Among women with food insecurity and/or housing insecurity, those experiencing period poverty were nearly two times more likely to report depressive symptoms (adjusted odds ratio [aOR] 1.86, 95% confidence interval [CI] 1.03-3.40; aOR 1.98, 95% CI 1.10-3.40, respectively) compared with those without period poverty.

Conclusion: Period poverty may be an added mental health burden for those experiencing other forms of basic needs insecurity. Understanding the impact of period poverty on students' well-being is critical to implementing effective policies and interventions to foster student success.