2025年 · 第07卷第01期

Objective: To validate the hematocrit percentage drop cutoff points for blood loss in patients with postpartum hemorrhage (PPH) using the automated gravimetric method. Methods: A prospective cohort study was conducted from January 2023 to July 2023, in which 107 patients 18 years of age and above were scheduled for elective cesarean with obstetrical indications. We excluded cases with difficulty quantifying blood loss, those with incomplete data, and those of patients who did not consent to participate. Blood loss was measured by an automated gravimetric system integrated into a suction blood collector and surgical gauze weighing systems to automatically sum up blood loss immediately after hysterectomy and fetal delivery. The percentage drop in hematocrit was determined by subtracting the 8-hour postsurgical from presurgical hematocrit, divided by presurgical hematocrit. We performed the Pearson correlation test, and the receiver operating characteristic curve was used to determine cutoff points, their sensitivity, and their specificity. The κ index was used to determine the diagnostic agreement between the two methods. Results: A positive correlation was observed between the volume of blood loss and the percentage drop in hematocrit, with a Pearson correlation index of 0.70 and P < 0.0001. A 14% decrease in hematocrit had an 81.7% agreement rate, with a good κ index of 0.602, a sensitivity of 82.5%, and a specificity of 80.0%. A 10% drop in hemoglobin was sensitive (93.0%) but not very specific (56.0%) for blood loss greater than 1000 mL. Conclusion: The automated gravimetric method strongly correlates with hematocrit changes, providing an accurate real-time diagnosis of PPH. Additionally, a hematocrit percentage drop can retrospectively indicate significant blood loss, aiding in managing patients at risk for long-term PPH complications.

Objective: To report a fetus with ARCN1-related syndrome caused by a novel de novo heterozygous variant, highlighting the importance of early genetic diagnosis in prenatal care. Methods: The clinical and genetic data of a fetus with a complex combination of clinical signs and a novel de novo heterozygous variant were collected and have been summarized in this study. The potential pathogenic variant was identified throughout the whole exome sequencing and the effects of candidate variants were further validated by a minigene splicing assay. Results: Prenatal systematic ultrasound detected fetal growth restriction. Genetic analysis identified a novel de novo heterozygous variant within the ARCN1 gene—c.1241 +5G>A-located in intron 8. In vitro minigene splicing assays demonstrated that the variant led to two abnormal transcripts. The longer transcript retained 189 base pairs of intron 8, resulting in a truncated protein of 414 amino acids (p.Ser415*). The shorter transcript involved exon 8 skippings, producing a truncated protein of 407 amino acids (p.Ile378Serfs*31). Conclusion: A novel de novo heterozygous variant of the ARCN1 gene, namely NM_001655.5: c.1241 +5G>A, was discovered and identified in a fetus with rhizomelic short stature, microretrognathia, and developmental delays.

Objective: To evaluate the diagnostic accuracy of magnetic resonance imaging (MRI) in diagnosing placenta accreta spectrum (PAS). Methods: We conducted a comprehensive literature search from database inception to November 2023 using terms such as placenta creta, increta, percreta, PAS, MRI, and their respective Medical Subject Headings terms. All prospective and retrospective cohort, case-control, and cross-sectional studies involving prenatal magnetic resonance imaging diagnosis of PAS with subsequent pathological confirmation were included. Results: A total of 40 studies encompassing 3664 women met the inclusion criteria, with 1894 cases confirmed pathologically as PAS. The overall sensitivity of MRI was 0.867 (95% confidence interval (CI): 0.807-0.910), and the specificity was 0.860 (95% CI: 0.799-0.905), with a correlation of 0.693 between sensitivity and specificity. The estimated odds ratio was 28.693 (95% CI: 14.463-56.924), the negative likelihood ratio was 0.178 (95% CI: 0.122-0.258), and the positive likelihood ratio was 4.316 (95% CI: 3.186-5.846). Analysis of individual MRI criteria revealed estimates of sensitivity, specificity, odds ratio, negative likelihood ratio, and positive likelihood ratio for abnormal placental bed vascularization as 0.500, 0.740, 2.788, 0.571, and 1.645 respectively; 0.384, 0.985, 6.270, 0.471, and 2.720 for bladder wall interruption; 0.766, 0.818, 13.638, 0.262, and 3.375 for the presence of dark intraplacental bands; 0.691, 0.913, 10.828, 0.352, and 3.361 for heterogeneous placenta; 0.688, 0.984, 34.886, 0.254, and 7.164 for indistinctive myometrium; 0.757, 0.864, 8.496, 0.362, and 2.778 for loss of retroplacental dark zone; 0.828, 0.593, 5.829, 0.329, and 1.766 for myometrial thinning; and 0.518, 0.916, 9.473, 0.411, and 3.526 for placental bulge, respectively. Conclusion: MRI demonstrates significant utility in diagnosing PAS and its severity. It is recommended for use in all cases with inconclusive ultrasonographic findings. Registration: Registration number CRD42021267501.

Postpartum hemorrhage (PPH) remains a significant contributor to maternal mortality and morbidity worldwide, with approximately 14 million women affected annually and 70,000 resulting deaths. Despite advances in health care, PPH continues to pose challenges even in developed settings. Apart from mortality, PPH leads to various adverse outcomes and morbidity. Recently, there has been a surge in interest in using artificial intelligence (AI), including machine learning and deep learning, across many areas of health care. This article explores the application of AI in tackling PPH, including predictive modeling and risk stratification. Some studies have shown promising results in predicting PPH. However, external validation of these models is crucial and frequently lacking, with barriers including differences in cohort characteristics and variations in outcome measurement methods. Most of the existing research has taken place in well-resourced health care settings, and there is a lack of models applicable to resource-limited settings where the need is arguably greatest. Incorporating uterine contractility metrics and radiomics into predictive models offers new avenues for enhancing prediction accuracy. Beyond risk prediction, AI has also been explored in other aspects of PPH management, including blood product management and early detection using wearable devices. In conclusion, while AI presents exciting opportunities for PPH prediction and management, challenges such as model validation, clinical translation, and applicability in diverse health care settings remain. Further research, particularly in low-and middle-income countries, is necessary to realize the full potential of AI for addressing the global burden of PPH.

Postpartum hemorrhage (PPH) is an obstetric emergency and refers to excessive blood loss after birth. Loss of blood volume and oxygen-carrying capacity may lead to maternal hypovolemia and hypotension resulting in tissue hypoxia, the onset of anaerobic metabolism, and multiorgan failure. If timely and appropriate action is not taken, cardiac arrest and maternal death may occur. If the amount of blood loss exceeds 500 mL following a vaginal birth or 1000 mL during or following a cesarean section, it is termed PPH. Similar to any other surgical hemorrhage, PPH is classified into primary PPH (occurs within 24 hours of birth) or secondary PPH (between 24 hours and 12 weeks postpartum). PPH is a major contributor to maternal deaths worldwide, and it is estimated that a person dies because of PPH approximately every 5 minutes. Therefore, measures should be directed at prevention and early detection of PPH with prompt management. The prevalence of PPH varies globally and is influenced by location, socioeconomic factors, and the availability and quality of health care. The World Health Organization reported that PPH accounts for a quarter of global maternal deaths. The Mothers and Babies Reducing Risks through Audits and Confidential Enquiries report from the United Kingdom (2023) highlighted that despite rare mortality due to hemorrhage, the number of people dying of obstetric hemorrhage is not decreasing, particularly among people with abnormally invasive placentation. Additionally, substandard care was found to be responsible for more than 50% of deaths due to PPH in the United Kingdom. Therefore, it is vital that adequate healthcare infrastructure, trained and competent healthcare professionals, and immediate access to resources, interventions, and multidisciplinary teams are essential both in well-resourced and resource-restrained healthcare systems. Healthcare professionals must identify the potential risk factors for PPH and initiate preventive measures whenever possible. Additionally, they must respond swiftly if PPH occurs and ensure a multidisciplinary, multilayered approach for a synchronized response to optimize outcomes. This review article emphasizes the etiopathogenesis, diagnosis, and management of PPH based on current scientific evidence as well as international best practice recommendations.

Obstetric hemorrhage is the leading cause of maternal death in childbirth; it is estimated that one woman dies every four minutes from postpartum hemorrhage (PPH). PPH is the cause of approximately one-quarter of maternal deaths worldwide and is thus a major public health issue of great importance. Despite modern advances in medicine, hemorrhage continues to lead the causes of pregnancy-related death in most countries, with increasing disparity between countries with highly developed and underdeveloped national healthcare systems. Most deaths caused by PPH are preventable. All involved in the care of pregnant women must be aware of the gravity of this problem, ways of identifying women at risk for severe hemorrhage at childbirth, strategies for preventing and ameliorating blood loss at delivery, and finally ways to deal with obstetric hemorrhage when it does occur. This article reviews the impact of obstetric hemorrhage, the controversy regarding definitions, diagnosis, epidemiology, pathophysiology, and management of obstetric hemorrhage.