To compare the effect of small for gestational age (SGA) on mortality, major morbidity and resource utilization among singleton very preterm infants (<33 weeks gestation) admitted to neonatal intensive care units (NICUs) across Canada. Infants admitted to participating NICUs from 2003 to 2008 were divided into SGA (defined as birth weight <10th percentile for gestational age and sex) and non-small gestational age (non-SGA) groups. The risk-adjusted effects of SGA on neonatal outcomes and resource utilization were examined using multivariable analyses. SGA infants (n = 1249 from a cohort of 11,909) had a higher odds of mortality (adjusted odds ratio [AOR] 2.46; 95% confidence interval [CI], 1.93-3.14), necrotizing enterocolitis (AOR 1.57; 95% CI, 1.22-2.03), bronchopulmonary dysplasia (AOR 1.78; 95% CI, 1.48-2.13), and severe retinopathy of prematurity (AOR 2.34; 95% CI, 1.71-3.19). These infants also had lower odds of survival free of major morbidity (AOR 0.50; 95% CI, 0.43-0.58) and respiratory distress syndrome (AOR 0.79; 95% CI, 0.68-0.93). In addition, SGA infants had a more prolonged stay in the NICU, and longer use of ventilation continuous positive airway pressure, and supplemental oxygen (p < 0.01 for all). SGA infants had a higher risk of mortality, major morbidities, and higher resource utilization compared with non-SGA infants.
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http://dx.doi.org/10.1055/s-0031-1295647 | DOI Listing |
Prenat Diagn
January 2025
Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia.
Objective: The first objective is to develop a nuchal thickness reference chart. The second objective is to compare rule-based algorithms and machine learning models in predicting small-for-gestational-age infants.
Method: This retrospective study involved singleton pregnancies at University Malaya Medical Centre, Malaysia, developed a nuchal thickness chart and evaluated its predictive value for small-for-gestational-age using Malaysian and Singapore cohorts.
Case Rep Neurol Med
January 2025
Department of Obstetrics and Gynecology, The Jikei University School of Medicine, Tokyo, Japan.
Determining the differential diagnosis of small scalp cysts identified on a fetus is difficult. In particular, many physicians have difficulty differentiating small meningoceles from small scalp cysts during the prenatal period. Volume contrast imaging increases contrast between tissues, thereby allowing an enhanced view of target structures.
View Article and Find Full Text PDFHeliyon
January 2025
BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
Deformable image registration is a cornerstone of many medical image analysis applications, particularly in the context of fetal brain magnetic resonance imaging (MRI), where precise registration is essential for studying the rapidly evolving fetal brain during pregnancy and potentially identifying neurodevelopmental abnormalities. While deep learning has become the leading approach for medical image registration, traditional convolutional neural networks (CNNs) often fall short in capturing fine image details due to their bias toward low spatial frequencies. To address this challenge, we introduce a deep learning registration framework comprising multiple cascaded convolutional networks.
View Article and Find Full Text PDFSisli Etfal Hastan Tip Bul
December 2024
Division of Pediatric Endocrinology, Department of Pediatrics, University of Health Sciences Türkiye, Sisli Hamidiye Etfal Training and Research Hospital, Istanbul, Türkiye.
Chromosome 15q26 deletion is a rare condition that causes short stature and is associated with intrauterine growth restriction (IUGR), failure to thrive, congenital heart disease and many congenital malformations. The insulin growth factor receptor (IGF-1R) on chromosome 15 has many important roles, especially in growth regulation. Our case is an 18-month-old small for gestational age girl who presented with severe short stature, microcephaly and minor dysmorphic features.
View Article and Find Full Text PDFUltrasound Obstet Gynecol
January 2025
Harris Birthright Research Centre for Fetal Medicine, King's College Hospital, London, UK.
Objective: Globally, one in four pregnant women is classified as overweight or obese, based on their prepregnancy body mass index (BMI). Obese pregnant women are at increased risk of adverse pregnancy outcomes and long-term cardiovascular disease that occurs earlier in life. This study aimed to assess maternal hemodynamic and vascular parameters at 35-37 weeks' gestation, to understand the alterations that may occur in association with increased maternal BMI and gestational weight gain, and to evaluate obesity-related pregnancy outcomes.
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