Journal of neurosurgery
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Journal of neurosurgery · Apr 2024
Efficacy and safety of tranexamic acid in the management of chronic subdural hematoma: a systematic review and meta-analysis.
Chronic subdural hematoma (CSDH) is a prevalent neurosurgical condition, particularly among the elderly. Various treatment options exist, but recurrence rates remain high. This systematic review and meta-analysis aims to assess the efficacy and safety of tranexamic acid (TXA) in the management of CSDH. ⋯ The findings suggest that TXA might be a promising agent for reducing the risk of CSDH recurrence without elevating the risk of complications. However, these results should be interpreted cautiously due to the limited number of studies included and the methodological heterogeneity. Further large-scale randomized controlled trials are needed to confirm these findings.
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Journal of neurosurgery · Apr 2024
Surgical intervention for cerebral amyloid angiopathy-related lobar intracerebral hemorrhage: a systematic review.
The risks and benefits of surgery for cerebral amyloid angiopathy (CAA)-related lobar intracerebral hemorrhage (ICH) are unclear. The aim of this study was to systematically review the literature on this topic. ⋯ Surgery in CAA-related ICH is safe with no substantial IOH, POH, and early recurrent hemorrhage risk. Outcome appears to be poor, however, especially in older patients, although good quality of evidence is lacking. Patients with CAA should not be excluded from ongoing surgery RCTs in ICH to enable future subgroup analysis of this specific patient population.
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Journal of neurosurgery · Apr 2024
Generation and applications of synthetic computed tomography images for neurosurgical planning.
CT and MRI are synergistic in the information provided for neurosurgical planning. While obtaining both types of images lends unique data from each, doing so adds to cost and exposes patients to additional ionizing radiation after MRI has been performed. Cross-modal synthesis of high-resolution CT images from MRI sequences offers an appealing solution. The authors therefore sought to develop a deep learning conditional generative adversarial network (cGAN) which performs this synthesis. ⋯ The generation of high-resolution CT scans from MR images through cGAN offers promise for a wide range of applications in cranial and spinal neurosurgery, especially as an adjunct for preoperative evaluation. Optimizing cGAN performance on specific anatomical regions may increase its clinical viability.
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Journal of neurosurgery · Apr 2024
The intersection of race and social determinants of health on clinical outcome of glioblastoma patients.
Resection, chemotherapy, radiation therapy, and tumor treating fields significantly increase the overall survival (OS) of glioblastoma (GBM) patients. Yet, cost and healthcare disparities might limit access. Multiple studies have attributed more than 80% of the GBM disease burden to White patients. The aim of this study was to explore the intersections of race and social determinants of health (SDoH) with healthcare access and outcomes of GBM patients in a large metropolitan area. ⋯ This study is the first to report on race and SDoH of a cohort using the latest WHO criteria for GBM classification. In contrast to previous literature, the study cohort exhibits a higher proportion of non-White patients with GBM, similar to the representation of non-White individuals in the general US population. This study corroborates the impact of SDoH and not race on LOS and discharge location. Initiatives to identify and address these barriers are crucial for enhancing the care of all GBM patients.
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Journal of neurosurgery · Apr 2024
Development and validation of machine learning models to predict postoperative infarction in moyamoya disease.
Cerebral infarction is a common complication in patients undergoing revascularization surgery for moyamoya disease (MMD). Although previous statistical evaluations have identified several risk factors for postoperative brain ischemia, the ability to predict its occurrence based on these limited predictors remains inadequately explored. This study aimed to assess the feasibility of machine learning algorithms for predicting cerebral infarction after revascularization surgery in patients with MMD. ⋯ This study indicates the usefulness of employing machine learning techniques with routine perioperative data to predict the occurrence of cerebral infarction after revascularization procedures in patients with MMD.