Background: Independent predictive factors of preterm delivery were evaluated using clinical data at hospitalisation by multivariate analysis.
Aim: The aim of this study was to clarify independent predictive factors related to preterm delivery by multivariate analysis of clinical data at hospitalisation of patients with threatened preterm delivery or premature rupture of membranes (PROM), and to realise the early and highly reliable prediction of preterm delivery in pregnant women at risk.
Methods: The subjects were 200 patients, which diagnosed with threatened preterm delivery or PROM and admitted at gestational ages of 22-35 weeks. Univariate and multivariate analyses were performed; 20 factors were evaluated concerning clinical data, and we extracted prognostic factors using logistic regression analysis.
Results: The mean age of the patients was 30.5 years, and the mean gestational age at admission was 30.0 weeks. Preterm delivery was observed in 55 (27.5%), and term delivery in 145 (72.5%). On multivariate analysis, haemorrhage, prepregnancy body mass index, fetal fibronectin and cervical length were extracted as independent predictive factors related to preterm delivery.
Conclusions: If the reliable and reproducible prediction of preterm delivery becomes possible using the four factors extracted in this study, further evaluation of these factors may lead to clarification of the mechanism of preterm delivery.
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http://dx.doi.org/10.1111/j.1479-828X.2008.00930.x | DOI Listing |
Curr Opin Gastroenterol
January 2025
Assistant Professor of Neonatal-Perinatal Medicine, Department of Pediatrics, School of Medicine, Ayatollah Mousavi Hospital, Zanjan University of Medical Sciences, Zanjan, Iran.
Purpose Of Review: Whether low birth weight (LBW) and preterm delivery (PD) are associated with the risk of developing celiac disease (CD) in children remains unclear. This systematic review and meta-analysis aimed to evaluate the association between LBW and PD with CD development in children.
Recent Findings: We searched PubMed, Embase, Scopus, Web of Science, and Google Scholar databases based on the Mesh terms to find observational studies that investigated the association of LBW and PD with CD development in children up to July 18, 2024.
Acta Derm Venereol
January 2025
Department of Preventive Medicine, Korea University College of Medicine, Seoul, Korea.
Data on pregnancy outcomes in patients with alopecia areata (AA) are limited. The aim of this study is to determine the association between maternal AA and risk of adverse birth outcomes in children. A retrospective cohort study was conducted on 45,328 children born to mothers with AA and 4,703,253 controls born to mothers without AA using the Korean National Health Insurance Claims database from 2002 to 2016.
View Article and Find Full Text PDFBMC Pregnancy Childbirth
January 2025
Department of Gynecology, Jiangnan University Medical Center, 68 Zhongshan Road, Liangxi Strict, Wuxi, Jiangsu, 214002, China.
Background: This study aimed to analyze the impact of preoperative cervical length before cervical cerclage on the extension of gestational days in patients with various diagnostic types of cervical insufficiency, including obstetric history-based diagnosis, ultrasound-based diagnosis, and physical examination-based diagnosis.
Methods: 168 patients were segregated into four categories based on cervical length: 0-0.4 cm, 0.
Nat Commun
January 2025
MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.
Structural brain organization in infancy is associated with later cognitive, behavioral, and educational outcomes. Due to practical limitations, such as technological advancements and data availability of fetal MRI, there is still much we do not know about the early emergence of topological organization. We combine the developing Human Connectome Project's large infant dataset with generative network modeling to simulate the emergence of network organization over early development.
View Article and Find Full Text PDFNat Commun
January 2025
Department of Metabolism, Digestion, and Reproduction, Imperial College London, London, UK.
Infertility affects one-in-six couples, often necessitating in vitro fertilization treatment (IVF). IVF generates complex data, which can challenge the utilization of the full richness of data during decision-making, leading to reliance on simple 'rules-of-thumb'. Machine learning techniques are well-suited to analyzing complex data to provide data-driven recommendations to improve decision-making.
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