Integrating genomic features for non-invasive early lung cancer detection.

Radiologic screening of high-risk adults reduces lung-cancer-related mortality1,2; however, a small minority of eligible individuals undergo such screening in the United States3,4. The availability of blood-based tests could increase screening uptake. Here we introduce improvements to cancer personalized profiling by deep sequencing (CAPP-Seq)5, a method for the analysis of circulating tumour DNA (ctDNA), to better facilitate screening applications. We show that, although levels are very low in early-stage lung cancers, ctDNA is present prior to treatment in most patients and its presence is strongly prognostic. We also find that the majority of somatic mutations in the cell-free DNA (cfDNA) of patients with lung cancer and of risk-matched controls reflect clonal haematopoiesis and are non-recurrent. Compared with tumour-derived mutations, clonal haematopoiesis mutations occur on longer cfDNA fragments and lack mutational signatures that are associated with tobacco smoking. Integrating these findings with other molecular features, we develop and prospectively validate a machine-learning method termed 'lung cancer likelihood in plasma' (Lung-CLiP), which can robustly discriminate early-stage lung cancer patients from risk-matched controls. This approach achieves performance similar to that of tumour-informed ctDNA detection and enables tuning of assay specificity in order to facilitate distinct clinical applications. Our findings establish the potential of cfDNA for lung cancer screening and highlight the importance of risk-matching cases and controls in cfDNA-based screening studies.

Early hypocoagulable state in traumatic brain injury patients: incidence, predisposing factors, and outcomes in a retrospective cohort study.

Coagulopathy development in traumatic brain injury (TBI) is among the significant complications that can negatively affect the clinical course and outcome of TBI patients. Timely identification of this complication is of utmost importance in the acute clinical setting. We reviewed TBI patients admitted to our trauma center from 2015 to 2021. Demographic data, mechanism of injury, findings on admission, imaging studies, procedures during hospitalization, and functional outcomes were gathered. INR with a cutoff of 1.3, platelet count less than 100 × 109/L, or partial thromboplastin time greater than 40s were utilized as the markers of coagulopathy. A total of 4002 patients were included. Coagulopathy occurred in 38.1% of the patients. Age of the patients (Odds Ratio (OR) = 0.993, 95% Confidence Interval (CI) = 0.986-0.999, p = 0.028), systolic blood pressure (OR = 0.993, 95% CI = 0.989-0.998, p = 0.005), fibrinogen level (OR = 0.998, 95% CI = 0.996-0.999, p < 0.001), and hemoglobin level (OR = 0.886, 95% CI = 0.839-0.936, p < 0.001) were independently associated with coagulopathy. Furthermore, coagulopathy was independently associated with higher mortality rates and longer ICU stays. Coagulopathy had the most substantial effect on mortality of TBI patients (OR = 2.6, 95% CI = 2.1-3.3, p < 0.001), compared to other admission clinical characteristics independently associated with mortality such as fixed pupillary light reflex (OR = 1.8, 95% CI = 1.5-2.4, p < 0.001), GCS (OR = 0.91, 95% CI = 0.88-0.94, p < 0.001), and hemoglobin level (OR = 0.93, 95% CI = 0.88-0.98, p = 0.004). Early coagulopathy in TBI patients can lead to higher mortality rates. Future studies are needed to prove that early detection and correction of coagulopathy and modifiable risk factors may help improve outcomes of TBI patients.

Motor vehicle collision characteristics and hospitalization outcomes associated with mild traumatic brain injury and concomitant whiplash injury.

OBJECTIVE: Few large studies have investigated the factors and outcomes related to concomitant injuries occurring alongside mild traumatic brain injury (mTBI) after motor vehicle collisions (MVCs). Thus, the objective of this study was to assess whether MVC characteristics predict which patients with mTBI will have concomitant whiplash injury, and whether concomitant whiplash injury affects care utilization for these patients. METHODS: This retrospective cohort study included 22,213 patients with mTBI after MVC identified from the American College of Surgeons Trauma Quality Programs dataset. A hierarchical logistic regression model was constructed to investigate patient and MVC factors associated with concomitant whiplash injury. Propensity score matching on whiplash status, in conjunction with a multivariable logistic regression model, assessed if concomitant whiplash affected odds of hospitalization. In the subgroup of patients who were hospitalized, associations with hospital length of stay (LOS) and discharge disposition were investigated. RESULTS: The median (IQR) age was 34 (24-51) years, with a median Glasgow Coma Scale score at presentation of 15 (15-15). Patients with concomitant whiplash were older (median 36 years vs 34 years, p = 0.03) and had higher rates of hospitalization (75% vs 64%, p < 0.001). In the hierarchical model for associations with concomitant whiplash injury, patients with blood alcohol content (BAC) greater than the federal driving limit had lower odds of concomitant whiplash (OR 0.63, 95% CI 0.49-0.81) along with those who had airbag deployment (OR 0.80, 95% CI 0.68-0.95), but seatbelt use was associated with greater odds (OR 1.41, 95% CI 1.16-1.71). After matching, concomitant whiplash was independently associated with increased odds of hospitalization (OR 1.67, 95% CI 1.40-1.99) while seatbelt use was associated with decreased odds (OR 0.88, 95% CI 0.81-0.95). Among hospitalized patients, concomitant whiplash was not associated with hospital LOS or discharge disposition. CONCLUSIONS: MVC characteristics such as alcohol consumption and airbag deployment were protective toward development of concomitant whiplash for mTBI patients, while seatbelt use was associated with higher risk. Concomitant whiplash increases the odds of hospitalization for mTBI patients but does not affect hospital LOS or discharge disposition, while seatbelt use is associated with lower rates of hospitalization and a more favorable hospital course. These findings provide context to injury patterns and care provision after a common mechanism of injury.

A future blood test for acute traumatic spinal cord injury.

Acute spinal cord injury (SCI) requires prompt diagnosis and intervention to minimize the risk of permanent neurologic deficit. Presently, SCI diagnosis and interventional planning rely on magnetic resonance imaging (MRI), which is not always available or feasible for severely injured patients. Detection of disease-specific biomarkers in biofluids via liquid biopsy may provide a more accessible and objective means of evaluating patients with suspected SCI. Cell-free DNA, which has been used for diagnosing and monitoring oncologic disease, may detect damage to spinal cord neurons via tissue-specific methylation patterns. Other types of biomarkers, including proteins and RNA species, have also been found to reflect neuronal injury and may be included as part of a multi-analyte assay to improve liquid biopsy performance. The feasibility of implementing liquid biopsy into current practices of SCI management is supported by the relative ease of blood sample collection as well as recent advancements in droplet digital polymerase chain reaction technology. In this review, we detail the current landscape of biofluid biomarkers for acute SCI and propose a framework for the incorporation of a putative blood test into the clinical management of SCI.

H3 K27-altered diffuse midline glioma presenting as massive cerebellopontine hemorrhage.

We report the case of a 14-year-old boy who presented with extensive cerebellar and brainstem hemorrhage. Our presumptive diagnosis was a ruptured arteriovenous malformation (AVM), but two cerebral angiograms showed no significant vascular abnormalities. The patient underwent posterior fossa craniotomy and microsurgical evacuation of the hematoma. Pathological analysis of the hemorrhagic tissue made the diagnosis of diffuse midline glioma, H3 K27-altered (WHO grade 4), based on immunohistochemistry. He subsequently developed diffuse craniospinal leptomeningeal disease and progressed rapidly, with respiratory failure followed by severe neurologic decline without further hemorrhage. He was compassionately extubated at the request of the family and died before initiation of adjuvant therapy. This unusual case of a diffuse midline glioma presenting with massive hemorrhage underscores the importance of searching for an underlying etiology of hemorrhage in a child when a vascular lesion cannot be identified.

Machine learning predictive models in neurosurgery: an appraisal based on the TRIPOD guidelines. Systematic review.

OBJECTIVE: In recent years, machine learning models for clinical prediction have become increasingly prevalent in the neurosurgical literature. However, little is known about the quality of these models, and their translation to clinical care has been limited. The aim of this systematic review was to empirically determine the adherence of machine learning models in neurosurgery with standard reporting guidelines specific to clinical prediction models. METHODS: Studies describing the development or validation of machine learning predictive models published between January 1, 2020, and January 10, 2023, across five neurosurgery journals (Journal of Neurosurgery, Journal of Neurosurgery: Spine, Journal of Neurosurgery: Pediatrics, Neurosurgery, and World Neurosurgery) were included. Studies where the TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) guidelines were not applicable, radiomic studies, and natural language processing studies were excluded. RESULTS: Forty-seven studies featuring a machine learning-based predictive model in neurosurgery were included. The majority (53%) of studies were single-center studies, and only 15% of studies externally validated the model in an independent cohort of patients. The median compliance across all 47 studies was 82.1% (IQR 75.9%-85.7%). Giving details of treatment (n = 17 [36%]), including the number of patients with missing data (n = 11 [23%]), and explaining the use of the prediction model (n = 23 [49%]) were identified as the TRIPOD criteria with the lowest rates of compliance. CONCLUSIONS: Improved adherence to TRIPOD guidelines will increase transparency in neurosurgical machine learning predictive models and streamline their translation into clinical care.

Epidemiology and survival outcomes of synchronous and metachronous brain metastases: a retrospective population-based study.

OBJECTIVE: Brain metastases (BMs) are the most common CNS tumors, yet their prevalence is difficult to determine. Most studies only report synchronous metastases, which make up a fraction of all BMs. The authors report the incidence and prognosis of patients with synchronous and metachronous BMs over a decade. METHODS: Study data were obtained from the TriNetX Research Network. Patients were included if they had a primary cancer diagnosis and a BM diagnosis, with primary cancer occurring between January 1, 2013, and January 1, 2023. Metachronous BM was defined as BM diagnosed more than 2 months after the primary cancer. Cohorts were balanced by propensity score matching for age, extracranial metastasis, and antineoplastic or radiation therapy. Kaplan-Meier plots were used to evaluate survival differences between synchronous and metachronous BMs and associations with clinical conditions. A log-rank test was used to evaluate BM-free survival for metachronous BM and overall survival (OS) for all BMs. Hazard ratios and 95% CIs were calculated. RESULTS: Of the 11,497,663 patients with 15 primary cancers identified, 300,863 (2.6%) developed BMs. BMs most commonly arose from lung and breast cancers and melanoma. Of all BMs, 113,827 (37.8%) presented synchronously and 187,036 (62.2%) presented metachronously. Lung and bronchial cancer had the highest metastasis rate (11.0%) and the highest synchronous presentation (51.0%). For metachronous presentations, the time from primary diagnosis to metastasis ranged from 1.3 to 2.5 years, averaging 1.8 years. Metachronous BM diagnosis was associated with longer survival over synchronous BM from primary diagnosis (11.54 vs 37.41 months, p < 0.0001), but shorter survival than extracranial metastases without BM (38.75 vs 69.18 months, p < 0.0001). Antineoplastic therapy prior to BM was associated with improved BM-free survival (4.46 vs 17.80 months, p < 0.0001) and OS (25.15 vs 42.26 months, p < 0.0001). Radiotherapy showed a similar effect that was statistically significant but modest for BM-free survival (5.25 vs 11.44 months, p < 0.0001) and OS (30.13 vs 32.82 months, p < 0.0001). CONCLUSIONS: The majority of BMs present metachronously and arise within 2 years of primary cancer diagnosis. The substantial rate of BMs presenting within 6 months of primary cancer, especially liver, lung, and pancreatic cancer, may guide future recommendations on intracranial staging. Antineoplastic therapy prior to the development of BM may prolong the time before metastasis and improve survival. Further characterization of this population can better inform screening, prevention, and treatment efforts.

Reexamining the Role of Postoperative ICU Admission for Patients Undergoing Elective Craniotomy: A Systematic Review.

OBJECTIVES: The standard-of-care for postoperative care following elective craniotomy has historically been ICU admission. However, recent literature interrogating complications and interventions during this postoperative ICU stay suggests that all patients may not require this level of care. Thus, hospitals began implementing non-ICU postoperative care pathways for elective craniotomy. This systematic review aims to summarize and evaluate the existing literature regarding outcomes and costs for patients receiving non-ICU care after elective craniotomy. DATA SOURCES: A systematic review of the PubMed database was performed following PRISMA guidelines from database inception to August 2021. STUDY SELECTION: Included studies were published in peer-reviewed journals, in English, and described outcomes for patients undergoing elective craniotomies without postoperative ICU care. DATA EXTRACTION: Data regarding study design, patient characteristics, and postoperative care pathways were extracted independently by two authors. Quality and risk of bias were evaluated using the Oxford Centre for Evidence-Based Medicine Levels of Evidence tool and Risk Of Bias In Non-Randomized Studies-of Interventions tool, respectively. DATA SYNTHESIS: In total, 1,131 unique articles were identified through the database search, with 27 meeting inclusion criteria. Included articles were published from 2001 to 2021 and included non-ICU inpatient care and same-day discharge pathways. Overall, the studies demonstrated that postoperative non-ICU care for elective craniotomies led to length of stay reduction ranging from 6 hours to 4 days and notable cost reductions. Across 13 studies, 53 of the 2,469 patients (2.1%) intended for postoperative management in a non-ICU setting required subsequent care escalation. CONCLUSIONS: Overall, these studies suggest that non-ICU care pathways for appropriately selected postcraniotomy patients may represent a meaningful opportunity to improve care value. However, included studies varied greatly in patient selection, postoperative care protocol, and outcomes reporting. Standardization and multi-institutional collaboration are needed to draw definitive conclusions regarding non-ICU postoperative care for elective craniotomy.

Deployment of Clinical Prediction Models: A Practical Guide to Nomograms and Online Calculators.

The use of predictive models within neurosurgery is increasing and many models described in published journal articles are made available to readers in formats such as nomograms and online calculators. The present chapter details a step-by-step methodology with accompanying R code that may be used to implement models both in the form of traditional nomograms and as open-access, online calculators through RStudio's Shinyapps. The chapter assumes a basic understanding of predictive modeling in R and utilizes open-access files created by the Machine Intelligence in Clinical Neuroscience (MICN) Lab (Department of Neurosurgery and the Clinical Neuroscience Center of the University Hospital Zurich). When implemented correctly, tools such as nomograms and predictive calculators have the potential to improve user understanding of the underlying statistical models, facilitate broader adoption, and to streamline the eventual use of such models in clinical settings.

Overview of Algorithms for Natural Language Processing and Time Series Analyses.

A host of machine learning algorithms have been used to perform several different tasks in NLP and TSA. Prior to implementing these algorithms, some degree of data preprocessing is required. Deep learning approaches utilizing multilayer perceptrons, recurrent neural networks (RNNs), and convolutional neural networks (CNNs) represent commonly used techniques. In supervised learning applications, all these models map inputs into a predicted output and then model the discrepancy between predicted values and the real output according to a loss function. The parameters of the mapping function are then optimized through the process of gradient descent and backward propagation in order to minimize this loss. This is the main premise behind many supervised learning algorithms. As experience with these algorithms grows, increased applications in the fields of medicine and neuroscience are anticipated.

A Brief History of Machine Learning in Neurosurgery.

The history of machine learning in neurosurgery spans three decades and continues to develop at a rapid pace. The earliest applications of machine learning within neurosurgery were first published in the 1990s as researchers began developing artificial neural networks to analyze structured datasets and supervised tasks. By the turn of the millennium, machine learning had evolved beyond proof-of-concept; algorithms had success detecting tumors in unstructured clinical imaging, and unsupervised learning showed promise for tumor segmentation. Throughout the 2000s, the role of machine learning in neurosurgery was further refined. Well-trained models began to consistently best expert clinicians at brain tumor diagnosis. Additionally, the digitization of the healthcare industry provided ample data for analysis, both structured and unstructured. By the 2010s, the use of machine learning within neurosurgery had exploded. The rapid deployment of an exciting new toolset also led to the growing realization that it may offer marginal benefit at best over conventional logistical regression models for analyzing tabular datasets. Additionally, the widespread adoption of machine learning in neurosurgical clinical practice continues to lag until additional validation can ensure generalizability. Many exciting contemporary applications nonetheless continue to demonstrate the unprecedented potential of machine learning to revolutionize neurosurgery when applied to appropriate clinical challenges.

Microglia are effector cells of CD47-SIRPα antiphagocytic axis disruption against glioblastoma.

Glioblastoma multiforme (GBM) is a highly aggressive malignant brain tumor with fatal outcome. Tumor-associated macrophages and microglia (TAMs) have been found to be major tumor-promoting immune cells in the tumor microenvironment. Hence, modulation and reeducation of tumor-associated macrophages and microglia in GBM is considered a promising antitumor strategy. Resident microglia and invading macrophages have been shown to have distinct origin and function. Whereas yolk sac-derived microglia reside in the brain, blood-derived monocytes invade the central nervous system only under pathological conditions like tumor formation. We recently showed that disruption of the SIRPα-CD47 signaling axis is efficacious against various brain tumors including GBM primarily by inducing tumor phagocytosis. However, most effects are attributed to macrophages recruited from the periphery but the role of the brain resident microglia is unknown. Here, we sought to utilize a model to distinguish resident microglia and peripheral macrophages within the GBM-TAM pool, using orthotopically xenografted, immunodeficient, and syngeneic mouse models with genetically color-coded macrophages (Ccr2RFP) and microglia (Cx3cr1GFP). We show that even in the absence of phagocytizing macrophages (Ccr2RFP/RFP), microglia are effector cells of tumor cell phagocytosis in response to anti-CD47 blockade. Additionally, macrophages and microglia show distinct morphological and transcriptional changes. Importantly, the transcriptional profile of microglia shows less of an inflammatory response which makes them a promising target for clinical applications.

Endotypes and the Path to Precision in Moderate and Severe Traumatic Brain Injury.

Heterogeneity is recognized as a major barrier in efforts to improve the care and outcomes of patients with traumatic brain injury (TBI). Even within the narrower stratum of moderate and severe TBI, current management approaches do not capture the complexity of this condition characterized by manifold clinical, anatomical, and pathophysiologic features. One approach to heterogeneity may be to resolve undifferentiated TBI populations into endotypes, subclasses that are distinguished by shared biological characteristics. The endotype paradigm has been explored in a range of medical domains, including psychiatry, oncology, immunology, and pulmonology. In intensive care, endotypes are being investigated for syndromes such as sepsis and acute respiratory distress syndrome. This review provides an overview of the endotype paradigm as well as some of its methods and use cases. A conceptual framework is proposed for endotype research in moderate and severe TBI, together with a scientific road map for endotype discovery and validation in this population.

Circulating Tumor DNA Analysis for Detection of Minimal Residual Disease After Chemoradiotherapy for Localized Esophageal Cancer.

BACKGROUND & AIMS: Biomarkers are needed to risk stratify after chemoradiotherapy for localized esophageal cancer. These could improve identification of patients at risk for cancer progression and selection of additional therapy. METHODS: We performed deep sequencing (CAncer Personalized Profiling by deep Sequencing, [CAPP-Seq]) analyses of plasma cell-free DNA collected from 45 patients before and after chemoradiotherapy for esophageal cancer, as well as DNA from leukocytes and fixed esophageal tumor biopsy samples collected during esophagogastroduodenoscopy. Patients were treated from May 2010 through October 2015; 23 patients subsequently underwent esophagectomy, and 22 did not undergo surgery. We also sequenced DNA from blood samples from 40 healthy control individuals. We analyzed 802 regions of 607 genes for single-nucleotide variants previously associated with esophageal adenocarcinoma or squamous cell carcinoma. Patients underwent imaging analyses 6-8 weeks after chemoradiotherapy and were followed for 5 years. Our primary aim was to determine whether detection of circulating tumor DNA (ctDNA) after chemoradiotherapy is associated with risk of tumor progression (growth of local, regional, or distant tumors, detected by imaging or biopsy). RESULTS: The median proportion of tumor-derived DNA in total cell-free DNA before treatment was 0.07%, indicating that ultrasensitive assays are needed for quantification and analysis of ctDNA from localized esophageal tumors. Detection of ctDNA after chemoradiotherapy was associated with tumor progression (hazard ratio, 18.7; P < .0001), formation of distant metastases (hazard ratio, 32.1; P < .0001), and shorter disease-specific survival times (hazard ratio, 23.1; P < .0001). A higher proportion of patients with tumor progression had new mutations detected in plasma samples collected after chemoradiotherapy than patients without progression (P = .03). Detection of ctDNA after chemoradiotherapy preceded radiographic evidence of tumor progression by an average of 2.8 months. Among patients who received chemoradiotherapy without surgery, combined ctDNA and metabolic imaging analysis predicted progression in 100% of patients with tumor progression, compared with 71% for only ctDNA detection and 57% for only metabolic imaging analysis (P < .001 for comparison of either technique to combined analysis). CONCLUSIONS: In an analysis of cell-free DNA in blood samples from patients who underwent chemoradiotherapy for esophageal cancer, detection of ctDNA was associated with tumor progression, metastasis, and disease-specific survival. Analysis of ctDNA might be used to identify patients at highest risk for tumor progression.

Coronavirus Disease 2019 Policy Restricting Family Presence May Have Delayed End-of-Life Decisions for Critically Ill Patients.

OBJECTIVES: To determine if a restrictive visitor policy inadvertently lengthened the decision-making process for dying inpatients without coronavirus disease 2019. DESIGN: Regression discontinuity and time-to-event analysis. SETTING: Two large academic hospitals in a unified health system. PATIENTS OR SUBJECTS: Adult decedents who received greater than or equal to 1 day of ICU care during their terminal admission over a 12-month period. INTERVENTIONS: Implementation of a visit restriction policy. MEASUREMENTS AND MAIN RESULTS: We identified 940 adult decedents without coronavirus disease 2019 during the study period. For these patients, ICU length of stay was 0.8 days longer following policy implementation, although this effect was not statistically significant (95% CI, -2.3 to 3.8; p = 0.63). After excluding patients admitted before the policy but who died after implementation, we observed that ICU length of stay was 2.9 days longer post-policy (95% CI, 0.27-5.6; p = 0.03). A time-to-event analysis revealed that admission after policy implementation was associated with a significantly longer time to first do not resuscitate/do not intubate/comfort care order (adjusted hazard ratio, 2.2; 95% CI, 1.6-3.1; p < 0.0001). CONCLUSIONS: Policies restricting family presence may lead to longer ICU stays and delay decisions to limit treatment prior to death. Further policy evaluation and programs enabling access to family-centered care and palliative care during the ongoing coronavirus disease 2019 pandemic are imperative.

Single-suture craniosynostosis and the epigenome: current evidence and a review of epigenetic principles.

Craniosynostosis (CS) is a congenital disease that arises due to premature ossification of single or multiple sutures, which results in skull deformities. The surgical management of single-suture CS continues to evolve and is driven by a robust body of clinical research; however, the molecular underpinnings of CS remain poorly understood. Despite long-standing hypotheses regarding the interaction of genetic predisposition and environmental factors, formal investigation of the epigenetic underpinnings of CS has been limited. In an effort to catalyze further investigation into the epigenetic basis of CS, the authors review the fundamentals of epigenetics, discuss recent studies that shed light on this emerging field, and offer hypotheses regarding the role of epigenetic mechanisms in the development of single-suture CS.

Patterns of Opioid and Benzodiazepine Use in Opioid-Naïve Patients with Newly Diagnosed Low Back and Lower Extremity Pain.

BACKGROUND: The morbidity and mortality associated with opioid and benzodiazepine co-prescription is a pressing national concern. Little is known about patterns of opioid and benzodiazepine use in patients with acute low back pain or lower extremity pain. OBJECTIVE: To characterize patterns of opioid and benzodiazepine prescribing among opioid-naïve, newly diagnosed low back pain (LBP) or lower extremity pain (LEP) patients and to investigate the relationship between benzodiazepine prescribing and long-term opioid use. DESIGN/SETTING: We performed a retrospective analysis of a commercial database containing claims for more than 75 million enrollees in the USA. PARTICIPANTS: Participants were adult patients newly diagnosed with LBP or LEP between 2008 and 2015 who did not have a red flag diagnosis, had not received an opioid prescription in the 6 months prior to diagnosis, and had 12 months of continuous enrollment after diagnosis. MAIN OUTCOMES AND MEASURES: Among patients receiving at least one opioid prescription within 12 months of diagnosis, we defined discrete patterns of benzodiazepine prescribing-continued use, new use, stopped use, and never use. We tested the association of these prescription patterns with long-term opioid use, defined as six or more fills within 12 months. RESULTS: We identified 2,497,653 opioid-naïve patients with newly diagnosed LBP or LEP. Between 2008 and 2015, 31.9% and 11.5% of these patients received opioid and benzodiazepine prescriptions, respectively, within 12 months of diagnosis. Rates of opioid prescription decreased from 34.8% in 2008 to 27.0% in 2015 (P < 0.001); however, prescribing of benzodiazepines only decreased from 11.6% in 2008 to 10.8% in 2015. Patients with continued or new benzodiazepine use consistently used more opioids than patients who never used or stopped using benzodiazepines during the study period (one-way ANOVA, P < 0.001). For patients with continued and new benzodiazepine use, the odds ratio of long-term opioid use compared with those never prescribed a benzodiazepine was 2.99 (95% CI, 2.89-3.08) and 2.68 (95% CI, 2.62-2.75), respectively. LIMITATIONS: This study used administrative claims analyses, which rely on accuracy and completeness of diagnostic, procedural, and prescription codes. CONCLUSION: Overall opioid prescribing for low back pain or lower extremity pain decreased substantially during the study period, indicating a shift in management within the medical community. Rates of benzodiazepine prescribing, however, remained at approximately 11%. Concurrent prescriptions of benzodiazepines and opioids after LBP or LEP diagnosis were associated with increased risk of long-term opioid use.

Limitations of functional neuroimaging for patient selection and surgical planning in glioma surgery.

The optimal surgical management of gliomas requires a balance between surgical cytoreduction and preservation of neurological function. Preoperative functional neuroimaging, such as functional MRI (fMRI) and diffusion tensor imaging (DTI), has emerged as a possible tool to inform patient selection and surgical planning. However, evidence that preoperative fMRI or DTI improves extent of resection, limits neurological morbidity, and broadens surgical indications in classically eloquent areas is lacking. In this review, the authors describe facets of functional neuroimaging techniques that may limit their impact on neurosurgical oncology and critically evaluate the evidence supporting fMRI and DTI for patient selection and operative planning in glioma surgery. The authors also propose alternative applications for functional neuroimaging in the care of glioma patients.

Liquid biopsy for pediatric diffuse midline glioma: a review of circulating tumor DNA and cerebrospinal fluid tumor DNA.

Diffuse midline glioma (DMG) is a highly malignant childhood tumor with an exceedingly poor prognosis and limited treatment options. The majority of these tumors harbor somatic mutations in genes encoding histone variants. These recurrent mutations correlate with treatment response and are forming the basis for molecularly guided clinical trials. The ability to detect these mutations, either in circulating tumor DNA (ctDNA) or cerebrospinal fluid tumor DNA (CSF-tDNA), may enable noninvasive molecular profiling and earlier prediction of treatment response. Here, the authors review ctDNA and CSF-tDNA detection methods, detail recent studies that have explored detection of ctDNA and CSF-tDNA in patients with DMG, and discuss the implications of liquid biopsies for patients with DMG.

Randomized Controlled Trials in Functional Neurosurgery-Association of Device Approval Status and Trial Quality.

INTRODUCTION: Randomized controlled trials (RCTs) have been critical in evaluating the safety and efficacy of functional neurosurgery interventions. Given this, we sought to systematically assess the quality of functional neurosurgery RCTs. METHODS: We used a database of neurosurgical RCTs (trials published from 1961 to 2016) to identify studies of functional neurosurgical procedures (N = 48). We extracted data on the design and quality of these RCTs and quantified the quality of trials using Jadad scores. We categorized RCTs based on the device approval status at the time of the trial and tested the association of device approval status with trial design and quality parameters. RESULTS: Of the 48 analyzed functional neurosurgery RCTs, the median trial size was 34.5 patients with a median age of 51. The most common indications were Parkinson's disease (N = 20), epilepsy (N = 10), obsessive-compulsive disorder (N = 4), and pain (N = 4). Most trials reported inclusion and exclusion criteria (95.8%), sample size per arm (97.9%), and baseline characteristics of the patients being studied (97.9%). However, reporting of allocation concealment (29.2%), randomization mode (66.7%), and power calculations (54.2%) were markedly less common. We observed that trial quality has improved over time (Spearman r, 0.49). We observed that trials studying devices with humanitarian device exemption (HDE) and experimental indications (EI) tended to be of higher quality than trials of FDA-approved devices (p = 0.011). A key distinguishing quality characteristic was the proportion of HDE and EI trials that were double-blinded, compared to trials of FDA-approved devices (HDE, 83.3%; EI, 69.2%; FDA-approved, 35.3%). Although more than one-third of functional neurosurgery RCTs reported funding from industry, no significant association was identified between funding source and trial quality or outcome. CONCLUSION: The quality of RCTs in functional neurosurgery has improved over time but reporting of specific metrics such as power calculations and allocation concealment requires further improvement. Device approval status but not funding source was associated with trial quality.