Background: With the advancement of prenatal diagnosis technology, the detection rate of fetal abnormalities continues to increase, imposing a significant burden on both society and families. A retrospective analysis of essential information about pregnant women, such as their pregnancy history and delivery details, is crucial for understanding the primary factors that influence pregnancy outcomes in women with fetal abnormalities. This analysis is of great significance for improving the level of pregnancy management and outcomes in pregnant women with fetal abnormalities.
Objective: To retrospectively analyze the pregnancy outcomes of women with fetal abnormalities and explore the factors that influence these outcomes.
Methods: Pregnant women's pregnancy outcomes were collected from the medical information system and through telephone follow-ups. The chi-square test and logistic regression were used to analyze the factors influencing pregnancy outcomes.
Results: Among 265 pregnant women diagnosed with fetal abnormalities, 190 chose to continue the pregnancy, while 75 chose to terminate it. Pregnant women with multiple fetal abnormalities (OR = 3.774, 95% CI [1.640-8.683]) were more likely to choose termination of pregnancy (TOP), and pregnant women who were advised to terminate their pregnancy or make a careful choice were more likely to terminate the pregnancy (OR = 41.113, 95% CI [11.028-153.267]).
Conclusion: The number of organs involved in fetal abnormalities and treatment recommendations were identified as the primary factors influencing pregnancy outcomes. Improving awareness of maternal health care during pregnancy, early pregnancy screening technology, and a multidisciplinary diagnosis and treatment approach are of great significance in assisting pregnant women in making informed decisions and improving fetal prognosis.
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http://dx.doi.org/10.7717/peerj.17284 | DOI Listing |
Int J Womens Health
January 2025
Department of Obstetrics and Gynecology, Hangzhou Women's Hospital, Hangzhou, Zhejiang, People's Republic of China.
Purpose: This study aimed to examine the effects of gestational diabetes mellitus (GDM) on the risk of pregnancy complications in twin pregnancies and to investigate the relationship between glycemic levels and the risk of preeclampsia (PE) and abnormal fetal growth.
Patients And Methods: A retrospective cohort study of 736 twin pregnancies was conducted at a tertiary hospital. Propensity score matching and multivariable logistic models were utilized to compare maternal and neonatal outcomes between twin pregnancies with GDM and those without GDM.
Ultrasound Obstet Gynecol
January 2025
Fetal Diagnostic Center, Suite 330, 50 Alessandro Place, Pasadena, CA, 91105, USA.
J Matern Fetal Neonatal Med
December 2025
Fetal Medicine Unit, St George's Hospital, London, UK.
Objective: To evaluate whether, in late pregnancy, the cerebral Doppler can identify very small fetuses that are less likely to experience intrapartum compromise (IC).
Material And Methods: This was a retrospective study of 282 singleton pregnancies that underwent an ultrasound scan at 32 + 0- 40 + 6 weeks and were delivered after induction, or spontaneous onset of labor. Very small fetuses were defined as fetuses with estimated weight less than the 3rd centile.
J Matern Fetal Neonatal Med
December 2025
Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
Objective: Fetal cerebellar abnormalities are associated with neurodevelopmental disorders and structural brain malformations. Accurate and early diagnosis is crucial for prenatal counseling and planning postnatal interventions. While prenatal ultrasound is a key tool for detecting fetal brain abnormalities, variations in diagnostic accuracy across studies necessitate a systematic evaluation of its effectiveness in diagnosing cerebellar abnormalities.
View Article and Find Full Text PDFSci Data
January 2025
School of Medicine, Anhui University of Science and Technology, Huainan, 232001, China.
Ultrasound is a primary diagnostic tool commonly used to evaluate internal body structures, including organs, blood vessels, the musculoskeletal system, and fetal development. Due to challenges such as operator dependence, noise, limited field of view, difficulty in imaging through bone and air, and variability across different systems, diagnosing abnormalities in ultrasound images is particularly challenging for less experienced clinicians. The development of artificial intelligence (AI) technology could assist in the diagnosis of ultrasound images.
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