Predictors of Intrahospital Mortality in Aneurysmal Subarachnoid Hemorrhage after Endovascular Embolization.

Background and Objectives: Aneurysmal subarachnoid hemorrhage (ASAH) is defined as bleeding in the subarachnoid space caused by the rupture of a cerebral aneurysm. About 11% of people who develop ASAH die before receiving medical treatment, and 40% of patients die within four weeks of being admitted to hospital. There are limited data on single-center experiences analyzing intrahospital mortality in ASAH patients treated with an endovascular approach. Given that, we wanted to share our experience and explore the risk factors that influence intrahospital mortality in patients with ruptured intracranial aneurysms treated with endovascular coil embolization. Materials and Methods: Our study was designed as a clinical, observational, retrospective cross-sectional study. It was performed at the Department for Radiology, University Clinical Center Kragujevac in Kragujevac, Serbia. The study inclusion criteria were ≥18 years, admitted within 24 h of symptoms onset, acute SAH diagnosed on CT, aneurysm on DSA, and treated by endovascular coil embolization from January 2014 to December 2018 at our institution. Results: A total of 66 patients were included in the study-48 (72.7%) women and 18 (27.3%) men, and 19.7% of the patients died during hospitalization. After adjustment, the following factors were associated with in-hospital mortality: a delayed ischemic neurological deficit, the presence of blood in the fourth cerebral ventricle, and an elevated urea value after endovascular intervention, increasing the chances of mortality by 16.3, 12, and 12.6 times. Conclusions: Delayed cerebral ischemia and intraventricular hemorrhage on initial head CT scan are strong predictors of intrahospital mortality in ASAH patients. Also, it is important to monitor kidney function and urea levels in ASAH patients, considering that elevated urea values after endovascular aneurysm embolization have been shown to be a significant risk factor for intrahospital mortality.

Neurosonographic Classification in Premature Infants Receiving Omega-3 Supplementation Using Convolutional Neural Networks.

This study focuses on developing a model for the precise determination of ultrasound image density and classification using convolutional neural networks (CNNs) for rapid, timely, and accurate identification of hypoxic-ischemic encephalopathy (HIE). Image density is measured by comparing two regions of interest on ultrasound images of the choroid plexus and brain parenchyma using the Delta E CIE76 value. These regions are then combined and serve as input to the CNN model for classification. The classification results of images into three groups (Normal, Moderate, and Intensive) demonstrate high model efficiency, with an overall accuracy of 88.56%, precision of 90% for Normal, 85% for Moderate, and 88% for Intensive. The overall F-measure is 88.40%, indicating a successful combination of accuracy and completeness in classification. This study is significant as it enables rapid and accurate identification of hypoxic-ischemic encephalopathy in newborns, which is crucial for the timely implementation of appropriate therapeutic measures and improving long-term outcomes for these patients. The application of such advanced techniques allows medical personnel to manage treatment more efficiently, reducing the risk of complications and improving the quality of care for newborns with HIE.

Advanced Diagnostics of Respiratory Distress Syndrome in Premature Infants Treated with Surfactant and Budesonide through Computer-Assisted Chest X-ray Analysis.

This research addresses the respiratory distress syndrome (RDS) in preterm newborns caused by insufficient surfactant synthesis, which can lead to serious complications, including pneumothorax, pulmonary hypertension, and pulmonary hemorrhage, increasing the risk of a fatal outcome. By analyzing chest radiographs and blood gases, we specifically focus on the significant contributions of these parameters to the diagnosis and analysis of the recovery of patients with RDS. The study involved 32 preterm newborns, and the analysis of gas parameters before and after the administration of surfactants and inhalation corticosteroid therapy revealed statistically significant changes in values of parameters such as FiO2, pH, pCO2, HCO3, and BE (Sig. < 0.05), while the pO2 parameter showed a potential change (Sig. = 0.061). Parallel to this, the research emphasizes the development of a lung segmentation algorithm implemented in the MATLAB programming environment. The key steps of the algorithm include preprocessing, segmentation, and visualization for a more detailed understanding of the recovery dynamics after RDS. These algorithms have achieved promising results, with a global accuracy of 0.93 ± 0.06, precision of 0.81 ± 0.16, and an F-score of 0.82 ± 0.14. These results highlight the potential application of algorithms in the analysis and monitoring of recovery in newborns with RDS, also underscoring the need for further development of software solutions in medicine, particularly in neonatology, to enhance the diagnosis and treatment of preterm newborns with respiratory distress syndrome.