Neurosurgery practice最新文献

Background and objectives: Traumatic brain injury (TBI) affects over 69 million people worldwide, most of whom suffer a so-called "mild" injury. Patients with mild TBI, defined as a Glasgow Coma Scale (GCS) of 13-15 on presentation, often have a significant symptom burden as detected by the Rivermead Post-Concussion Questionnaire (RPQ). In this study, we aimed to determine whether social determinants of health (SDOH) may influence patient self-report of symptoms within a month of injury.

Methods: Patients presenting to an academic Level I trauma center with GCS 13-15 were included in the study with data collected as part of a prospectively maintained neurotrauma registry. Overall, 451 individuals completed the RPQ at a follow-up clinic visit. Demographic variables, injury characteristics, comorbidities, and geocoded SDOH information were captured from the electronic medical record. Multivariable regression analysis was performed.

Results: Variables contributing to increased symptom burden on the RPQ included sex, living in an area with a high fraction of poverty, history of depression or anxiety, initial GCS score, history of illicit drug use, obesity, and positive head CT.

Conclusion: In addition to the expected patient and injury characteristics, fraction living in poverty was a significant variable contributing to scores on the RPQ-3 and RPQ-13. Efforts to incorporate screening for SDOH factors should be considered to identify patients at risk of poor recovery after mild TBI.

Background and objectives: We developed a series of machine learning models to predict early mortality after chronic subdural hematoma (cSDH) evacuation.

Methods: We retrospectively collected patients treated surgically for cSDH at 4 level 1 trauma centers (2009-2021). Previously, we developed a deep learning segmentation tool to automatically calculate preoperative and postoperative cSDH volumes. Using cSDH volumes and clinical information, we developed 6 machine learning models including logistic regression (LR), support vector machine, neural network (NN), decision tree (DT), Naïve Bayes, and XGBoost to predict 30-day mortality after surgery. We applied least absolute shrinkage and selection operator regression to select a subset of predictors for consistent model input. To account for class imbalance, we used synthetic minority oversampling technique. We used 10-fold cross validation to evaluate model performance.

Results: We included 731 patients. Our final models included age, admission Glasgow Coma Scale, unilateral/bilateral hematoma, antiplatelet status, platelet count, preoperative volume, and method of surgical evacuation. The 30-day mortality rate was 7.5%. Overall, our models demonstrated moderate discriminative ability with area under the receiver operating characteristics curves (AUCs) ranging from 0.64 for DT (95% CI: 0.56-0.72) to 0.75 for LR (95% CI: 0.69-0.81). AUC for DT was significantly lower than LR (P < .03). AUCs for support vector machine (AUC = 0.73; 95% CI: 0.67-0.79), NN (0.69; 95% CI: 0.62-0.76), Naïve Bayes (0.70; 95% CI: 0.63-0.78), and XGBoost (0.73; 95% CI: 0.66-0.80) were not significantly different from LR. LR achieved the highest balanced accuracy (0.69) whereas DT and NN had the lowest (0.61). Age, craniotomy, Glasgow Coma Scale, larger preoperative volumes, unilateral cSDH, and lower platelet count were associated with increased risk of mortality on multivariate analysis.

Conclusion: The LR model demonstrated the best performance of discriminative ability, balanced accuracy, and recall, whereas DT modeling performed worst. Using an automated segmentation software, our models demonstrate an ability to identify patients at high risk of mortality after treatment for cSDH.

Background and objectives: CyberKnife (CK) is advanced stereotactic radiosurgery (SRS) technology indicated for brain metastases, arteriovenous malformations, functional disease, and a number of other neurosurgical conditions that combines a linear accelerator with a highly maneuverable robotic arm and real-time imaging, allowing for several advantages including the ability to deliver radiation from a wide variety of angles and frameless delivery of therapy. We seek to explore the current trend and evolution of publications related to CK SRS using bibliometric approach, with a particular focus on neurosurgical disease applications.

Methods: The Web of Science database was queried for data collection, using keyword "CyberKnife". Network visualization figures representing exported Web of Science data were created using visualization of similarities viewer. Statistics were completed in R.

Results: In total, 3980 articles from 12 077 authors were identified for analysis. Annual publication number has expressed growth from 1 article in 1996 to 263 articles in 2023. Most frequently reported conditions were brain metastases, followed by vestibular schwannoma and meningiomas. The top 5 most prolific authors in the field are Andrew Muacevic, Steven D. Chang, Oliver Blanck, Christian Baues, and John R. Adler. The top 3 most prolific institutions are the University of Cologne, Stanford University, and the University of Munich. Institutional collaboration is strongest between institutions within countries. Through authorial key word analysis, we identified that articles related to pituitary adenoma, brain metastases, meningioma, hemangioma, and cavernous sinus have increased. Through the same analysis, we noticed an increase in key words potentially associated with interdisciplinary applications of CK, such as immunotherapy, machine learning, and deep learning.

Conclusion: CK SRS is an emergent technology with increasingly more neurological conditions and diseases being researched for treatment options. The rise in academic focus on CK SRS has been spearheaded by international effort from the United States, Japan, and Italy. There remains significant potential for future international collaboration.

Background and objectives: The incidence of subdural hematomas (SDH) is expected to climb precipitously in the coming decades. Aphasia is one of the most common operative neurological symptoms of left-sided SDH. However, the rates of aphasia recovery after SDH evacuation have not been reported as neurological outcomes have been limited to mostly functional assessment scores and mortality. This study represents the first detailed analysis on aphasia and recovery in patients undergoing chronic SDH (cSDH) evacuation.

Methods: Adult patients who underwent evacuation of subacute or cSDH at a tertiary academic medical center between November 2013 and December 2021 were retrospectively identified using ICD 9 and 10 billing codes. Patients were categorized by the presence or absence of aphasia at initial presentation. Other clinical and demographic variables were also collected. After surgical evacuation, improvement and resolution of aphasia was recorded at the time of discharge, along with several outcome metrics.

Results: Of the 311 patients requiring cSDH evacuation who met inclusion criteria, 10% presented with aphasia. Risk factors for the development of aphasia were evaluated, including age, sex, hypertension, SDH size, location laterality, and midline shift size. Only left-sided SDH laterality was associated with a significantly greater risk of aphasia compared with right-sided and bilateral SDH (odds ratio 4.89, P < .001) while adjusting for age and sex. No difference in the rate of postoperative complications, neurological outcome, or mortality was found between patient cohorts. After surgical evacuation, 90% of patients had improvement of aphasia by the time of discharge, and 73% had complete resolution. At most recent follow-up (<180 days), aphasia had resolved in 83% of patients.

Conclusion: This study represents one of the first detailed investigations into patients presenting with aphasia in the setting of cSDH. These findings provide unique insights to aid in management and rehabilitation planning of patients with cSDH presenting with aphasia.

Background and objectives: High-grade glioma has a poor prognosis despite advancements in histopathological classifications and treatments. Various intraoperative modalities are used to maximize extent of resection (EoR) and intraoperative detection of residual tumor, including 5-aminolevulinic acid (5-ALA) and intraoperative MRI (iMRI). We conducted a systematic review with meta-analysis investigating the efficacy of iMRI vs 5-ALA in maximizing EoR and improving survival outcomes.

Methods: A systematic review with meta-analysis was performed using Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, PubMed, Embase, Scopus, and ClinicalTrials.gov databases, identifying randomized clinical trials (RCTs) and prospective studies comparing the use of 5-ALA and iMRI in high-grade glioma resection. The primary end points were EoR and survival outcomes. Quality assessment was conducted using the ROBINS-I risk of bias assessment and Jadad scale. Meta-analysis was performed using gross total resection rates, tumor detection sensitivity, and specificity.

Results: Five RCTs and 5 prospective studies were identified. Five RCTs lacked published data, thus only 5 prospective studies were included in the data extraction. Combined 5-ALA with iMRI (100%) was superior to 5-ALA alone (61.7%; P < .002) in maximizing EoR. Gross total resection did not differ significantly between 5-ALA alone (78%) and iMRI alone (81%; P = .79). One study showed that specificity was higher with iMRI alone (0.70) than with 5-ALA alone (0.43; P < .001); however, this was not replicated by 2 other studies (iMRI vs 5-ALA: 0.60 vs 0.80, P < .001; 1.00 vs 1.00, P not significant). Two studies reported sensitivity; only 1 found lower sensitivity with iMRI vs 5-ALA with a significant difference (iMRI vs 5-ALA: 0.66 vs 0.90, P < .001).

Conclusion: There is no clear evidence to suggest iMRI is superior to 5-ALA in maximizing EoR and improving survival. However, combined use of 5-ALA and iMRI may be more effective compared with either modality alone. Larger RCTs are needed to confirm any differences in efficacy between the 2 modalities.

[This corrects the article DOI: 10.1227/neuprac.0000000000000113.].

Background and importance: Marfan syndrome's association with intracranial aneurysms, though controversial, poses unique challenges for surgical intervention because of the inherent vascular fragility. Open surgical approaches carry considerable risk. Endovascular flow diversion with the Pipeline embolization device (PED) offers a less invasive alternative, and adjunctive coiling can decrease occlusion time for complex aneurysms. We report the first combined use of PED and coiling to treat a Marfan-associated intracranial aneurysm, detailing our operative approach and reviewing the literature.

Clinical presentation: A 53-year-old patient with a pathological FBN1 mutation was incidentally found to have an unruptured paraophthalmic aneurysm during stroke workup. Diagnostic angiography revealed an irregular superior hypophyseal aneurysm along with tortuous and irregular internal carotid arteries suggesting previous dissections. The aneurysm was successfully treated with a PED and adjunctive coil embolization. The patient was discharged on aspirin and clopidogrel. Six-month follow-up angiography confirmed aneurysm occlusion. Although there was no residual or recurrent aneurysm at 2-year follow-up, imaging revealed 2 de novo intracranial aneurysms. The patient remains asymptomatic and under observation.

Conclusion: This is the first case of combined PED and coiling for an intracranial aneurysm in Marfan syndrome. This dual approach may offer safer and more effective treatment than traditional methods. In addition, the formation of de novo aneurysms underscores the importance of long-term angiographic follow-up in patients with FBN1 mutations to monitor for evolving vascular pathology. These findings warrant further investigation into identifying a genetic link between FBN1 mutations and cerebrovascular pathology.

Background and objectives: The clinical characteristics and natural history of post-traumatic cerebral venous outflow compromise (VOC) are poorly characterized. We aimed to determine the prevalence of VOC in at-risk traumatic brain injury patients, assess its effect on long-term mortality, and describe our management practices.

Methods: We conducted a retrospective single-center case-control study. We included patients admitted to the neurocritical care unit after traumatic brain injury who had been investigated with computed tomography venography because of clinical suspicion of VOC between 2008 and 2015.

Results: A total of 89 patients underwent computed tomography venography during their neurocritical care unit admission. A total of 43 patients (48%, 32 male [74%], mean age 40 years [SD 16.2]) had evidence of VOC. Of these, 28 (65%) were due to an intraluminal dural venous sinus thrombosis (DVST) and 15 (35%) due to an extraluminal cause. Twelve (43%) of the DVSTs were occlusive, and 16 (57%) were nonocclusive. A total of 24 patients (27%) underwent decompressive craniectomy, which was more commonly performed for patients with an occlusive thrombosis than those with partial or no VOC (67% vs 23% vs 20%, P < .01). A total of 4 patients (14%) with an intraluminal thrombosis received antithrombotic therapy. Mortality rate of those with VOC may have been higher compared with those without VOC at 14 days (14% vs 7%, P = .31) but was similar at 5 years (21% vs 22%, P = .93). Patients with a midline or bilateral thrombosis had higher overall mortality (83% vs 18%, P = .01) than those with a thrombosis located elsewhere.

Conclusion: Among those at risk, patients with evidence of post-traumatic VOC may have had higher short-term mortality, but VOCs did not increase long-term mortality rates compared with those without VOC. Patients with an occlusive thrombosis were more likely to undergo decompressive craniectomy. Most patients with a DVST received prophylactic rather than treatment-dose antithrombosis. Further studies are required to determine the optimal management of post-traumatic VOC.

Background and objectives: Infection with COVID-19 may be linked to both higher rates of severe strokes and strokes in younger patients through distinct pathophysiological processes. This study aims to investigate characteristics and outcomes in COVID-19-related acute ischemic stroke (AIS) and endovascular thrombectomy (EVT) included in the National Inpatient Sample database.

Methods: This is a retrospective population-based observational study that includes all patients with COVID-19 and patients with AIS with COVID-19 who underwent EVT and were admitted to hospitals included in the US National Inpatient Sample between 2020 and 2021. Primary outcomes include age difference and difference in cardiovascular risk factors between patients with AIS with and without COVID-19 and EVT patients with and without COVID-19. Secondary outcomes include rates of favorable hospital outcome (defined as discharge to home or acute rehabilitation facility), in-hospital mortality, and the length of hospital stay.

Results: A total of 1 381 039 patients with AIS were included. A total of 58 525 had a COVID-19 diagnosis. A total of 83 650 patients underwent EVT, and 3075 of these patients were diagnosed with COVID-19. The mean age for patients with COVID-19 with AIS was lower (67.55 vs 69.40 years; P < .001). There was a higher proportion of Black (21.1% vs 17.9%) and Hispanic patients (15.0% vs 8.5%) among the COVID-19 group of patients with AIS. In adjusted multivariable analyses, the presence of COVID-19 in patients with AIS was significantly associated with a higher rate of inpatient mortality (adjusted odds ratio (AOR): 2.79; 95% CI 2.61-2.98; P < .001) and a lower rate of favorable hospital outcome (AOR: 0.66; 95% CI 0.62-0.70; P < .001). Trend analysis shows that COVID-19-positive AIS cases decreased overall with the rise of the delta variant, but the number of COVID-19-positive AIS cases with the National Institutes of Health Stroke Scale equal to or greater than 10 rose drastically.

Conclusion: This analysis demonstrates that patients with AIS and concomitant COVID-19 infection were younger, with fewer cardiovascular risk factors, and disproportionately Black and Hispanic, with worse neurological deficit and worse hospital outcome.

Background and objectives: Degenerative cervical myelopathy (DCM) is a progressive and disabling condition resulting from chronic compression of the spinal cord, leading to functional impairments that can severely affect quality of life. Traditional methods for assessing spinal cord injury and morphometrics rely on subjective visualization of contrast changes and manual segmentation, which are nonstandardized, time-consuming, and inconsistent across patients. This variability limits understanding of DCM pathology and hampers timely clinical intervention.

Methods: We introduce a semiautomated pipeline using the Spinal Cord Toolbox, an open-source platform that uses advanced algorithms, including optimization and computational efficiency algorithms, support vector machine, and convolutional neural networks, to streamline the assessment of spinal cord shape, microstructural changes, and gray and white matter integrity. By integrating spinal cord segmentation, anatomical labeling, and registration to a standardized template, the pipeline extracts normalized morphometric measures, providing efficient and reliable analysis of spinal cord pathology in DCM.

Results: We extracted normalized spinal cord morphometrics, including cross-sectional area (CSA), anterior-posterior diameter, right-left diameter, eccentricity, solidity, gray matter CSA, white matter CSA, and regional and tract-based magnetization transfer ratio measures. Our analysis demonstrates that DCM patients exhibit significant reductions in these morphometrics compared with healthy controls, even in regions without visible compression. Furthermore, CSA reductions across the spinal cord highlight areas of severe compression, including at the intervertebral disks, which may not be apparent on standard imaging.

Conclusion: These quantitative measures give clinicians easily interpretable data on the extent of spinal cord injury, even in regions without obvious compression. This enables a comprehensive understanding of DCM pathophysiology. By eliminating the subjectivity of manual segmentation and accounting for intersubject and intrasubject variability, this approach supports consistent cross-subject comparisons and is poised to reshape how clinicians assess and manage DCM.