Background: Prematurity is a major cause of neonatal and infantile morbidity and morbidity. Late preterm delivery increases the prevalence of respiratory illness, as respiratory distress syndrome (RDS), transient tachypnea of the newborn (TTN), and the need for ventilator support. Objectives: To assess the respiratory morbidities & outcome in late preterm infants in neonatal ICU of Assiut University Children Hospital (AUCH).Methods: A one-year hospital-based prospective cohort study of all newborns of 34+0 to 36+6 weeks of gestational age, born at AUCH, Neonates with congenital heart diseases and those with multiple congenital anomalies were excluded.Results: A total of 132 neonates were studied (34 neonates aged 34 weeks, another 34 neonates aged 35 weeks, and 64 neonates aged 36 weeks), 40.2% of them with RDS, pneumonia in 20.5%, LOS in 14.4%, EOS in 10.6%, TTN in 12.1%, diaphragmatic hernia in 1.5%, and IDM in 0.8%. Low birth weight, low APGAR score at 5 minutes, anemia need for blood transfusion, thrombocytopenia, abnormal liver function test, pneumonia on x-ray, and need to be connected to MV were significant predictors for poor outcome among the studied sample. However, on multivariate analysis; only low birth weight and need of MV were still significant predictors for poor outcome.Conclusion: Late preterm were found to be more prone to respiratory distress, and sepsis. The best treatment for late prematurity is to prevent it. Every effort should be made to delay the delivery of newborns until at least 38 weeks gestation .
Background: Hypoxic Ischemic Encephalopathy (HIE) is an injury to the brain, happening in the neonatal period (under 28 days old) in which there is deprivation of oxygen supply to the brain. It is a common cause of neonatal death and developmental psychomotor illnesses in the pediatric population worldwide and is one of the major causes of cerebral palsy."Aim: To compare between TCUS, MRI and CT brain in cases with HIE as regards diagnostic accuracy and which correlates more closely to the clinical picture.Methods: This cross-sectional observational study included 48 neonates with clinical signs of HIE admitted to the NICU at Assiut University Children’s Hospital.Results: Neuroimaging demonstrated high abnormality detection rates across all modalities. MRI detected abnormalities in (91.7%), CT in (85.4%), and TCUS in (64.6%) of neonates. MRI and CT were both significantly superior to TCUS (p = 0.004 and p = 0.013, correspondingly), with no significant variance between MRI and CT (p = 1.000). In Grade I HIE, MRI detected lesions in (87.0%), CT in (78.3%), and TCUS in (47.8%). In Grade II/III HIE, all modalities detected abnormalities, but MRI provided the clearest visualization of watershed and deep gray matter injury.Conclusion: MRI is the most accurate diagnostic tool for neonatal HIE, detecting early subtle lesions, while TCUS is useful for bedside screening and CT serves as a second-line option in emergencies; choice depends on timing, clinical context, and availability.
Climate change poses significant environmental challenges to neonatal health, particularly for preterm and critically ill newborns in neonatal intensive care units (NICUs). Rising temperatures, deteriorating air quality, and extreme weather events exacerbate risks, compromising physiological stability and long-term outcomes. This narrative review examines how environmental factors—air pollution, extreme temperatures, and humidity—impact neonatal health and NICU care practices. Air pollution, including particulate matter and ozone, increases respiratory complications, such as bronchopulmonary dysplasia, in vulnerable newborns. Extreme temperatures strain thermoregulatory systems, heightening risks of hypothermia or hyperthermia, especially in preterm infants with underdeveloped regulatory mechanisms. High humidity levels elevate infection risks by fostering microbial growth in NICU environments. The review also explores adaptive strategies to mitigate these challenges, including advanced air filtration systems to reduce pollutant exposure, optimized thermal regulation technologies to maintain stable infant temperatures, and eco-friendly NICU designs incorporating sustainable materials and energy-efficient systems.By synthesizing current evidence, this review underscores the urgent need for environmentally responsive strategies to safeguard neonatal health. Implementing these adaptations ensures NICUs remain safe havens for vulnerable newborns amid a warming planet, promoting resilience and improved health outcomes.
Background: Infants of diabetic mothers (IDMs) are at increased risk of developing pulmonary hypertension (PH), a condition associated with significant neonatal morbidity. Echocardiography is a key non-invasive tool for early PH detection. Recently, pulmonary artery acceleration time (PAAT) and the PAAT to right ventricular ejection time (PAAT/RVET) ratio have emerged as promising indicators of pulmonary vascular resistance. Aim: To evaluate the diagnostic accuracy of PAAT and PAAT/RVET ratio in detecting PH in IDMs and compare them with conventional echocardiographic parameters. Patients and Methods: A cross-sectional study was conducted on 50 IDMs admitted to the NICU at Minia University Hospital and 50 age- and sex-matched healthy neonates. All underwent transthoracic echocardiography to assess PAAT, PAAT/RVET ratio, tricuspid regurgitation peak gradient (TRPG), and systolic pulmonary artery pressure (SPAP). Results: IDMs with PH had significantly lower PAAT and PAAT/RVET ratios, and higher SPAP compared to controls (p < 0.001). PAAT showed excellent diagnostic performance (AUC 0.957) with a cut-off <74 ms (92% sensitivity, 96% specificity). The PAAT/RVET ratio had even higher accuracy (AUC 0.968) at a cut-off ≤0.36 (94% sensitivity, 96% specificity). SPAP showed lower diagnostic value (AUC 0.838). Conclusions: PAAT and PAAT/RVET ratio are effective, non-invasive echocardiographic markers for early PH detection in IDMs, with superior diagnostic performance compared to traditional Doppler-based measures methods
Background: Neonatal hypoglycemia is a common and potentially serious metabolic disorder that can result in permanent neurodevelopmental impairment. Objective: To investigate the independent maternal and neonatal risk factors associated with the occurrence of neonatal hypoglycemia in the immediate postnatal period and to quantify their predictive strength using multivariate modeling. Methods: In this cohort study, 100 neonates at high risk for hypoglycemia were enrolled within the first 24 hours of life at a tertiary neonatal intensive care unit. Risk factors assessed included intrauterine growth restriction, infant of a diabetic mother, large for gestational age, gestational age, birth weight, mode of delivery, and maternal medical history. Blood glucose levels were measured via standardized capillary sampling at multiple time points during the first 12 hours of life. Logistic regression analysis was used to identify independent predictors of hypoglycemia, and model performance was evaluated using area under the receiver operating characteristic curve (AUROC) and the Hosmer–Lemeshow goodness-of-fit test. Results: about 38% of neonates developed hypoglycemia within the first 12 hours of life. IUGR (adjusted odds ratio [aOR] 3.52, 95% CI: 1.47–8.44), maternal diabetes (aOR 2.89, 95% CI: 1.19–7.02), and cesarean delivery (aOR 2.21, 95% CI: 1.01–4.89) were identified as significant independent predictors. The final multivariate model demonstrated good discriminative ability (AUROC = 0.79) and calibration (Hosmer–Lemeshow p = 0.62), indicating reliable predictive performance. Conclusion: Specific maternal and neonatal factors—including IUGR, maternal diabetes, and mode of delivery—are strong independent predictors of neonatal hypoglycemia. These findings underscore the need for structured risk-based screening protocols to identify at-risk neonates early and allocate monitoring resources effectively.
