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Introduction: Intracranial hemorrhages present across a spectrum of clinical phenotypes, with many patients transferred across hospitals to access higher levels of neurocritical care. We sought to characterize patient dispositions following intracranial hemorrhage and examine disparities associated with interhospital transfers. Methods: Using the Healthcare Cost and Utilization Project database, we mapped and identified factors influencing the likelihood of patient transfers and receipt of specialist interventional procedures following intracranial hemorrhage. Results: Of 11,660 patients with intracranial hemorrhage, 59.4% had non-traumatic and 87.5% single compartment bleeds. After presentation, about a quarter of patients were transferred to another facility either directly from the ED (23.0%) or after inpatient admission (1.8%). On unadjusted analysis, patients who were white, in the upper income quartiles, with private insurance, or resided in suburban areas were more frequently transferred. After adjusting for patient and hospital-level variables, younger and non-white patients had higher odds of transfer. Hospital capabilities, residence location, insurance status, and prior therapeutic relationship remained as transfer predictors. Transferred patients had a similar hospital length of stay compared to admitted patients, with 43.1% having no recorded surgical or specialist interventional procedure after transfer. Discussion: Our analysis reveals opportunities for improvement in risk stratification guiding transfers, as well as structural challenges likely impacting transfer decisions. [ABSTRACT FROM AUTHOR]

Rett syndrome is an X-linked neurodevelopmental disorder caused by mutation of Mecp2 gene and primarily affects females. Glial cell dysfunction has been implicated in in Rett syndrome (RTT) both in patients and in mouse models of this disorder and can affect synaptogenesis, glial metabolism and inflammation. Here we assessed whether treatment of adult (5–6 months old) symptomatic Mecp2- heterozygous female mice with N-acetyl cysteine conjugated to dendrimer (D-NAC), which is known to target glia and modulate inflammation and oxidative injury, results in improved behavioral phenotype, sleep and glial inflammatory profile. We show that unbiased global metabolomic analysis of the hippocampus and striatum in adult Mecp2-heterozygous mice demonstrates significant differences in lipid metabolism associated with neuroinflammation, providing the rationale for targeting glial inflammation in this model. Our results demonstrate that treatment with D-NAC (10 mg/kg NAC) once weekly is more efficacious than equivalently dosed free NAC in improving the gross neurobehavioral phenotype in symptomatic Mecp2-heterozygous female mice. We also show that D-NAC therapy is significantly better than saline in ameliorating several aspects of the abnormal phenotype including paw clench, mobility, fear memory, REM sleep and epileptiform activity burden. Systemic D-NAC significantly improves microglial proinflammatory cytokine production and is associated with improvements in several aspects of the phenotype including paw clench, mobility, fear memory, and REM sleep, and epileptiform activity burden in comparison to saline-treated Mecp2-hetereozygous mice. Systemic glial-targeted delivery of D-NAC after symptom onset in an older clinically relevant Rett syndrome model shows promise in improving neurobehavioral impairments along with sleep pattern and epileptiform activity burden. These findings argue for the translational value of this approach for treatment of patients with Rett Syndrome. [ABSTRACT FROM AUTHOR]

This article explores the impact of chromosome copy number thresholds on the molecular classification of meningioma, a type of brain tumor. The study analyzes data from 1,042 patients who underwent surgery for meningioma and examines the relationship between chromosome arm copy number variants (CNVs) and tumor behavior. The researchers find significant variability in the fraction of chromosomes lost or gained in meningiomas, with certain chromosomes showing higher rates of loss or gain. They also observe that altering CNV thresholds can affect the molecular classification of meningiomas and potentially impact predictions of tumor recurrence. The study highlights the importance of determining optimal CNV call thresholds to ensure accurate and reproducible molecular classification of meningiomas. [Extracted from the article]

Background: Surgical intervention is a critical tool to address adult spinal deformity (ASD). Given the evolution of spinal surgical techniques, we sought to characterize developments in ASD correction and barriers impacting clinical outcomes. Methods: We conducted a literature review utilizing PubMed, Embase, Web of Science, and Google Scholar to examine advances in ASD surgical correction and ongoing challenges from patient and clinician perspectives. ASD procedures were examined across pre-, intra-, and post-operative phases. Results: Several factors influence the effectiveness of ASD correction. Standardized radiographic parameters and three-dimensional modeling have been used to guide operative planning. Complex minimally invasive procedures, targeted corrections, and staged procedures can tailor surgical approaches while minimizing operative time. Further, improvements in osteotomy technique, intraoperative navigation, and enhanced hardware have increased patient safety. However, challenges remain. Variability in patient selection and deformity undercorrection have resulted in heterogenous clinical responses. Surgical complications, including blood loss, infection, hardware failure, proximal junction kyphosis/failure, and pseudarthroses, pose barriers. Although minimally invasive approaches are being utilized more often, clinical validation is needed. Conclusions: The growing prevalence of ASD requires surgical solutions that can lead to sustained symptom resolution. Leveraging computational and imaging advances will be necessary as we seek to provide comprehensive treatment plans for patients. [ABSTRACT FROM AUTHOR]

Simple Summary: Diagnosis of posterior fossa tumors is challenging yet proper classification is imperative given that treatment decisions diverge based on tumor type. The aim of this systematic review is to summarize the current state of machine learning methods developed as diagnostic tools for these pediatric brain tumors. We found that, while individual algorithms were quite efficacious, the field is limited by its heterogeneity in methods, outcome reporting, and study populations. We identify common limitations in the study and development of these algorithms and make recommendations as to how they can be overcome. If incorporated into algorithm design, the practical guidelines outlined in this review could help to bridge the gap between theoretical algorithm diagnostic testing and practical clinical application for a wide variety of pathologies. Background: Posterior fossa tumors (PFTs) are a morbid group of central nervous system tumors that most often present in childhood. While early diagnosis is critical to drive appropriate treatment, definitive diagnosis is currently only achievable through invasive tissue collection and histopathological analyses. Machine learning has been investigated as an alternative means of diagnosis. In this systematic review and meta-analysis, we evaluated the primary literature to identify all machine learning algorithms developed to classify and diagnose pediatric PFTs using imaging or molecular data. Methods: Of the 433 primary papers identified in PubMed, EMBASE, and Web of Science, 25 ultimately met the inclusion criteria. The included papers were extracted for algorithm architecture, study parameters, performance, strengths, and limitations. Results: The algorithms exhibited variable performance based on sample size, classifier(s) used, and individual tumor types being investigated. Ependymoma, medulloblastoma, and pilocytic astrocytoma were the most studied tumors with algorithm accuracies ranging from 37.5% to 94.5%. A minority of studies compared the developed algorithm to a trained neuroradiologist, with three imaging-based algorithms yielding superior performance. Common algorithm and study limitations included small sample sizes, uneven representation of individual tumor types, inconsistent performance reporting, and a lack of application in the clinical environment. Conclusions: Artificial intelligence has the potential to improve the speed and accuracy of diagnosis in this field if the right algorithm is applied to the right scenario. Work is needed to standardize outcome reporting and facilitate additional trials to allow for clinical uptake. [ABSTRACT FROM AUTHOR]

Introduction: We aimed to investigate facilitators and barriers that impact the sustainability of an interprofessional situation awareness bundle., Methods: This is a single-center qualitative study at a tertiary care pediatric center examining the sustainability of an interprofessional situation awareness bundle to reduce in-hospital cardiac arrests. The bundle includes an automated clinical decision support tool, twice-daily safety huddles, and a bedside mitigation plan. A trained research staff member interviewed participants in October 2022. Interviews were audio recorded and transcribed verbatim, and recruitment continued until data saturation. Inductive and deductive analyses were used here., Results: The authors interviewed twelve staff members via individual semistructured interviews: registered nurses (RN, n = 2) and clinicians [(advanced practice providers, n = 2), pediatric critical care fellows, n = 4 and attendings, n = 4)]. Five main themes were identified: (1) the situation awareness bundle is ingrained into daily practice and culture, (2) the bundle has strengthened communication, decision-making, and improved outcomes, (3) standardized processes, stakeholder buy-in, and support of team members are key to adoption and sustainability , (4) variation in processes and fast-changing clinical context remains a challenge for reliable use, and (5) the situation awareness bundle excluded families., Conclusions: The situation awareness bundle has become ingrained, strengthened, and sustained over the last 5 years through integration into daily practice and culture and leveraging standardized processes, tools and technology. It is associated with improved communication and shared decision-making. Understanding the key components for implementation and sustainability is necessary for ongoing spread and improvement in the future., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)

Background and Objectives: The size and anatomic location of meningiomas have been shown to correlate with distinct clinical manifestations, histopathological subtypes, and surgical risk. However, meningioma anatomic origin sites can be obscured in large tumors and those crossing compartments. We therefore sought to apply unbiased lesion mapping to localize intracranial meningioma distributions and their association with biology and grade., Methods: MRI scans, World Health Organization (WHO) grade, and a molecularly Integrated Grade (IG) derived from cytogenetics were analyzed from adult patients with intracranial meningiomas. Semi-automated tumor segmentation was performed on T1-weighted contrast-enhanced MRI. We used the voxel-based lesion mapping technique to generate a meningioma atlas, mapping spatial frequency and correlating with tumor grades., Results: Of 881 patients with meningioma (median age: 57 years, 68.8% female), 589 were WHO grade 1 (66.8%), 265 WHO grade 2 (30.1%), and 27 WHO grade 3 (3.1%) with a median tumor volume of 14.6 cm3. After molecular reclassification, 585 were IG-1 (66.4%), 160 IG-2 (18.2%), and 136 IG-3 (15.4%). Benign tumors were concentrated in and around the midline anterior skull base while malignant meningiomas were enriched in the falcine/parasagittal region and the sphenoid wing, similar to the distribution when stratified by chromosome 1p loss. Meningiomas exhibited sharper spatial clustering when stratified by the molecular IG than by WHO grade. WHO grade 2 meningiomas divided equally across IG 1-3, with corresponding partition of spatial distribution in the midline anterior skull base (in WHO grade 2, IG-1) and falcine/parasagittal and sphenoid regions (WHO grade 2, IG-3). Meningioma volumes significantly varied across age, sex, and WHO/IG grades., Conclusion: We demonstrate the utility of voxel-based lesion mapping for intracranial tumors, characterizing distinct meningioma distribution patterns across histopathological and molecularly defined grades. Molecular grading associated with sharper tumor spatial clusters, supporting a phenotype-genotype association in meningiomas., (Copyright © Congress of Neurological Surgeons 2024. All rights reserved.)

Viral vector mediated gene therapies for neurodegenerative and neurodevelopmental conditions that require neurosurgical administration continue to expand. We systematically reviewed the National Institutes of Health (NIH) ClinicalTrials.gov database to identify all clinical trials studying in-vivo viral vector mediated gene therapies targeted to the CNS for neurodegenerative and neurodevelopmental diseases. We isolated studies which delivered therapies using neurosurgical approaches: intracisternal, intraventricular, and/or intraparenchymal. Clinical trials primarily registered in international countries were included if they were referenced by an NIH registered clinical trial. We performed a scoping review to identify the preclinical studies that supported each human clinical trial. Key preclinical and clinical data were aggregated to characterize vector capsid design, delivery methods, gene expression profile, and clinical benefit. A total of 64 clinical trials were identified in active, completed, terminated, and long-term follow-up stages. A range of CNS conditions across pediatric and adult populations are being studied with CNS targeted viral vector gene therapy, including Alzheimer's disease, Parkinson's disease, AADC deficiency, sphingolipidoses, mucopolysaccharidoses, neuronal ceroid lipofuscinoses, spinal muscular atrophy, adrenoleukodystrophy, Canavan disease, frontotemporal dementia, Huntington's disease, Rett syndrome, Dravet syndrome, mesial temporal lobe epilepsy, and glutaric acidemia. Adeno-associated viral vectors (AAVs) were utilized by the majority of tested therapies, with vector serotypes, regulatory elements, delivery methods, and vector monitoring varying based on the disease being studied. Intraparenchymal delivery has evolved significantly, with MRI-guided convection-enhanced delivery established as a gold standard method for pioneering novel gene targets., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Mark Richardson reports a relationship with uniQure biopharma BV that includes: consulting or advisory. Mark Richardson reports a relationship with AviadoBio that includes: consulting or advisory. Mark Richardson reports a relationship with ClearPoint Neuro that includes: consulting or advisory. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: With the significant shift in the classification, risk stratification, and standards of care for gliomas, we sought to understand how the overall survival of patients with these tumors is impacted by molecular features, clinical metrics, and treatment received., Methods: We assembled a cohort of patients with a histopathologically diagnosed glioma from The Cancer Genome Atlas, Project Genomics Evidence Neoplasia Information Exchange, and Dana-Farber Cancer Institute/Brigham and Women's Hospital. This incorporated retrospective clinical, histological, and molecular data alongside prospective assessment of patient survival., Results: 4,400 gliomas were identified: 2,195 glioblastoma, 1,198 IDH1/2-mutant astrocytoma, 531 oligodendroglioma, 271 other IDH1/2-wildtype glioma, and 205 pediatric-type glioma. Molecular classification updated 27.2% of gliomas from their original histopathologic diagnosis. Examining the distribution of molecular alterations across glioma subtypes revealed mutually exclusive alterations within tumorigenic pathways. Non-TCGA patients had significantly improved overall survival compared to TCGA patients, with 26.7%, 55.6%, and 127.8% longer survival for glioblastoma, IDH1/2-mutant astrocytoma, and oligodendroglioma respectively (all p<0.01). Several prognostic features were characterized, including NF1 alteration and 21q loss in glioblastoma, and EGFR amplification and 22q loss in IDH1/2-mutant astrocytoma. Leveraging the size of this cohort, nomograms were generated to assess the probability of overall survival based on patient age, the molecular features of a tumor, and the treatment received., Conclusions: By applying modern molecular criteria, we characterize the genomic diversity across glioma subtypes, identify clinically applicable prognostic features, and provide a contemporary update on patient survival to serve as a reference for ongoing investigations., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society for Neuro-Oncology.)

Objective: 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., Methods: Preoperative paired CT and contrast-enhanced MR images were collected for patients with meningioma, pituitary tumor, vestibular schwannoma, and cerebrovascular disease. CT and MR images were denoised, field corrected, and coregistered. MR images were fed to a cGAN that exported a "synthetic" CT scan. The accuracy of synthetic CT images was assessed objectively using the quantitative similarity metrics as well as by clinical features such as sella and internal auditory canal (IAC) dimensions and mastoid/clinoid/sphenoid aeration., Results: A total of 92,981 paired CT/MR images obtained in 80 patients were used for training/testing, and 10,068 paired images from 10 patients were used for external validation. Synthetic CT images reconstructed the bony skull base and convexity with relatively high accuracy. Measurements of the sella and IAC showed a median relative error between synthetic CT scans and ground truth images of 6%, with greater variability in IAC reconstruction compared with the sella. Aerations in the mastoid, clinoid, and sphenoid regions were generally captured, although there was heterogeneity in finer air cell septations. Performance varied based on pathology studied, with the highest limitation observed in evaluating meningiomas with intratumoral calcifications or calvarial invasion., Conclusions: 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.