Neurosurg Focus
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Comparative Study
A comparison of digital subtraction angiography and computed tomography angiography for the diagnosis of penetrating cerebrovascular injury.
Penetrating cerebrovascular injury (PCVI) is a subset of traumatic brain injury (TBI) comprising a broad spectrum of cerebrovascular pathology, including traumatic pseudoaneurysms, direct arterial injury, venous sinus stenosis or occlusion, and traumatic dural arteriovenous fistulas. These can result in immediate or delayed vascular injury and consequent neurological morbidity. Current TBI guidelines recommend cerebrovascular imaging for detection, but there is no consensus on the optimum modality. The aim of this retrospective cohort study was to compare CT angiography (CTA) and digital subtraction angiography (DSA) for the diagnosis of PCVI. ⋯ In this retrospective analysis of PCVI at two large trauma centers, CTA demonstrated low sensitivity, specificity, and positive and negative predictive values for the diagnosis of PCVI. These findings suggest that DSA provides better accuracy than CTA in the diagnosis of both immediate and delayed PCVI and should be considered for patients experiencing penetrating head or neck trauma.
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Patients with traumatic brain injury (TBI) often undergo repeat head CT scans to identify the possible progression of injury. The objective of this study is to evaluate the need for routine repeat head CT scans in patients with mild to moderate head injury and an initial positive abnormal CT scan. ⋯ The role of routine repeat head CT in medically managed patients with head injury is controversial. The authors have tried to study the various factors that are associated with neurological deterioration, radiological deterioration, and/or need for neurosurgical intervention. In this study the authors found lower GCS score at admission, abnormal INR, presence of midline shift, effaced basal cisterns, and multiple lesions on initial CT to be significantly associated with the above outcomes.
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Comparing prenatal and postnatal surgical repair techniques for myelomeningocele (MMC), in utero fetal surgery has increasingly gained acceptance and is considered by many specialized centers the first choice of treatment. Despite its benefits, as demonstrated in the Management of Myelomeningocele Study (MOMS), including reduced need for CSF shunting in neonates and improved motor outcomes at 30 months, there is still an ongoing debate on fetal and maternal risks associated with the procedure. Prenatal open hysterotomy, fetoscopic MMC repair techniques, and subsequent delivery by cesarean section are associated with maternal complications. The aim of this systematic review is to assess the available literature on maternal and obstetric complication rates and perinatal maternal outcomes related to fetal MMC repair. ⋯ Although the efforts of further advancement of intrauterine prenatal MMC repair aim to increase neonatal outcomes, maternal health hazard will continue to be an issue of crucial importance and further studies are required.
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Historical Article
History of awake mapping and speech and language localization: from modules to networks.
Lesion-symptom correlations shaped the early understanding of cortical localization. The classic Broca-Wernicke model of cortical speech and language organization underwent a paradigm shift in large part due to advances in brain mapping techniques. ⋯ The aim of this historical review is to highlight the essential role of direct electrical stimulation and cortical-subcortical mapping and the advancements it has made to our understanding of speech and language cortical organization. Specifically, using cortical and subcortical mapping, neurosurgeons shifted from a localist view in which the brain is composed of rigid functional modules to one of dynamic and integrative large-scale networks consisting of interconnected cortical subregions.
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Surgical site infection (SSI) following a neurosurgical operation is a complication that impacts morbidity, mortality, and economics. Currently, machine learning (ML) algorithms are used for outcome prediction in various neurosurgical aspects. The implementation of ML algorithms to learn from medical data may help in obtaining prognostic information on diseases, especially SSIs. The purpose of this study was to compare the performance of various ML models for predicting surgical infection after neurosurgical operations. ⋯ The naive Bayes algorithm is highlighted as an accurate ML method for predicting SSI after neurosurgical operations because of its reasonable accuracy. Thus, it can be used to effectively predict SSI in individual neurosurgical patients. Therefore, close monitoring and allocation of treatment strategies can be informed by ML predictions in general practice.