Articles: traumatic-brain-injuries.
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Journal of neurotrauma · Jun 2024
Predicting Hematoma Expansion and Prognosis in Cerebral Contusions: A Radiomics-Clinical Approach.
Hemorrhagic progression of contusion (HPC) often occurs early in cerebral contusions (CC) patients, significantly impacting their prognosis. It is vital to promptly assess HPC and predict outcomes for effective tailored interventions, thereby enhancing prognosis in CC patients. We utilized the Attention-3DUNet neural network to semi-automatically segment hematomas from computed tomography (CT) images of 452 CC patients, incorporating 695 hematomas. ⋯ Selected radiomic features indicated that irregularly shaped and highly heterogeneous hematomas increased the likelihood of HPC, while larger weighted axial lengths and lower densities of hematomas were associated with a higher risk of poor prognosis. Predictive models that combine radiomic and clinical features exhibit robust performance in forecasting HPC and the risk of poor prognosis in CC patients. Radiomic features complement clinical features in predicting HPC, although their ability to enhance the predictive accuracy of the clinical model for adverse prognosis is limited.
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Anasthesiol Intensivmed Notfallmed Schmerzther · Jun 2024
Review[Specialized Concepts for the Management of Severe Neurotrauma].
Neurotrauma results from violence on structures of the central or peripheral nervous system and is a clinically common disease entity with high relevance for patients' long-term outcome. The application of evidence-based diagnostic and therapeutic concepts aims to minimize secondary injury and thus to improve treatment outcome. This article describes the current management of the two main injury patterns of neurotrauma - traumatic brain and spinal cord injury.
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Journal of neurotrauma · Jun 2024
Development of a Multimodal Machine Learning-Based Prognostication Model for Traumatic Brain Injury Using Clinical Data and Computed Tomography Scans: A CENTER-TBI and CINTER-TBI Study.
Computed tomography (CT) is an important imaging modality for guiding prognostication in patients with traumatic brain injury (TBI). However, because of the specialized expertise necessary, timely and dependable TBI prognostication based on CT imaging remains challenging. This study aimed to enhance the efficiency and reliability of TBI prognostication by employing machine learning (ML) techniques on CT images. ⋯ The developed model achieved superior performance without the necessity for manual CT assessments (AUC = 0.846 [95% CI: 0.843-0.849]) compared with the model based on the clinical and laboratory variables (AUC = 0.817 [95% CI: 0.814-0.820]) and established CT scoring systems requiring manual interpretations (AUC = 0.829 [95% CI: 0.826-0.832] for Marshall and 0.838 [95% CI: 0.835-0.841] for International Mission for Prognosis and Analysis of Clinical Trials in TBI [IMPACT]). The external validation demonstrated the prognostic capacity of the developed model to be significantly better (AUC = 0.859 [95% CI: 0.857-0.862]) than the model using clinical variables (AUC = 0.809 [95% CI: 0.798-0.820]). This study established an ML-based model that provides efficient and reliable TBI prognosis based on CT scans, with potential implications for earlier intervention and improved patient outcomes.
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Brain contusion is a prevalent traumatic brain injury (TBI) in low-age children, bearing the potential for coma and fatality. Hence, it is imperative to undertake comprehensive research in this field. ⋯ These findings not only facilitate a comprehensive understanding of brain contusion dynamics in pediatric TBIs, but also contribute to the validation of theories and finite element models for piglet heads, which are commonly employed as surrogates for children.
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Journal of neurosurgery · Jun 2024
Evaluating mortality and 6-month functional outcomes of patients with dural venous sinus thrombosis in traumatic brain injury.
Patients with dural venous sinus thrombosis (DVST) in select populations following traumatic brain injury (TBI), including those with blunt mechanism or depressed skull fractures, have been shown to have an increased risk of mortality. The purpose of this study was to assess these findings in a mixed population of head trauma patients. ⋯ The authors observed a prevalence of traumatic DVST of 1.64% in a mixed population of head-injured patients, with 23.5% of patients with DVST having concurrent BCVI. Traumatic DVST alone was not associated with a significantly increased risk of inpatient mortality.