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Seizures are the second most common presenting symptom of cerebral arteriovenous malformations (AVMs). Evidence supporting different treatment modalities is continuously evolving and it remains unclear which modality offers better seizure outcomes.To compare various interventional treatment modalities (i.e., microsurgery, radiosurgery, endovascular embolization, or multimodality treatment), regarding outcomes in AVM-associated epilepsy.PubMed, Embase, and Web of Science were searched on December 31, 2020 for studies that evaluated outcomes in patients with AVM-associated epilepsy after undergoing different treatment modalities. Pooled analysis was performed using a random-effects model and stratified by different modalities.Forty-nine studies including 2668 patients were included. Interventional management was associated with a 56.0% probability of seizure freedom and a 73.0% probability of seizure improvement. The probability of discontinuing antiepileptic drugs was estimated at 38.0%. The stratified analysis showed that microsurgery was associated with a higher probability of seizure freedom and seizure improvement than was radiosurgery, endovascular, or multimodality treatment. The probability of antiepileptic drug cessation was also higher after microsurgery compared with radiation therapy; however, only clinical but not statistical significance could be inferred because of the lack of comparative analyses.Interventional management of AVM-related epilepsy was associated with seizure freedom and seizure improvement in 56% and 73% of cases. Microsurgery seemed to be associated with a higher incidence of seizure freedom and seizure improvement than did other modalities. Future well-designed comparative studies are needed to draw definitive conclusions regarding each modality.

Background Neurosurgical training has been traditionally based on an apprenticeship model. However, restrictions on clinical exposure reduce trainees’ operative experience. Simulation models may allow for a more efficient, feasible, and time-effective acquisition of skills. Our objectives were to use face, content, and construct validity to review the use of simulation models in neurosurgical education. Methods PubMed, Web of Science, and Scopus were queried for eligible studies. After excluding duplicates, 1204 studies were screened. Eighteen studies were included in the final review. Results Neurosurgical skills assessed included aneurysm clipping (n = 6), craniotomy and burr hole drilling (n = 2), tumour resection (n = 4), and vessel suturing (n = 3). All studies assessed face validity, 11 assessed content, and 6 assessed construct validity. Animal models (n = 5), synthetic models (n = 7), and VR models (n = 6) were assessed. In face validation, all studies rated visual realism favourably, but haptic realism was key limitation. The synthetic models ranked a high median tactile realism (4 out of 5) compared to other models. Assessment of content validity showed positive findings for anatomical and procedural education, but the models provided more benefit to the novice than the experienced group. The cadaver models were perceived to be the most anatomically realistic by study participants. Construct validity showed a statistically significant proficiency increase among the junior group compared to the senior group across all modalities. Conclusion Our review highlights evidence on the feasibility of implementing simulation models in neurosurgical training. Studies should include predictive validity to assess future skill on an individual on whom the same procedure will be administered. This study shows that future neurosurgical training systems call for surgical simulation and objectively validated models. Supplementary Information The online version contains supplementary material available at 10.1007/s00701-021-05003-x.

Objective Spontaneous subarachnoid hemorrhage is often due to rupture of an intracranial aneurysm, but some patients present with no identifiable source. Increased incidence of nonaneurysmal subarachnoid hemorrhage (naSAH) has been reported over time. Methods We performed a retrospective analysis of naSAH from 2008−2017 to determine the rate of naSAH change over time and its association with cannabis use. Univariable and multivariable regression analyses were performed to study the trend over time, radiographic patterns of hemorrhage, and clinical outcome at the time of discharge. In addition, we compared the rate of naSAH with the rate of aneurysmal SAH (aSAH) to adjust for changes in hospital volume and prevalence/reporting of cannabis use in the population over time. Results A total of 86 naSAH and 328 aSAH patients were identified, with an increase in naSAH over time compared with aSAH (P = 0.0034). Increased cannabis use was associated with naSAH (odds ratio [OR] 2.1, 95% confidence interval 1.1, 4.1, P = 0.035) but not aSAH over time. Cannabis use was also associated with different subarachnoid hemorrhage patterns (P = 0.0065) in naSAH. Multivariable analysis demonstrated good neurologic outcome after naSAH to be inversely associated with cocaine use (OR 0.008 [0.002−0.4]), ventriculostomy placement (OR 0.004 [0.03−0.50]), and anticoagulant use (OR 0.016 [0.003−0.54]) but not with cannabis use. Conclusions As cannabis use becomes more prevalent with legalization, it is important to further investigate this association with spontaneous SAH.

BACKGROUND Growing evidence associates traumatic brain injury (TBI) with increased risk of dementia, but few studies have evaluated associations in patients younger than 55 yr using non-TBI orthopedic trauma (NTOT) patients as controls to investigate the influence of age and TBI severity, and to identify predictors of dementia after trauma. OBJECTIVE To investigate the relationship between TBI and dementia in an institutional group. METHODS Retrospective cohort study (2000-2018) of TBI patients aged 45 to 100 yr vs NTOT controls. Primary outcome was dementia after TBI (followed ≤10 yr). Cox proportional hazards models were used to assess risk of dementia; logistic regression models assessed predictors of dementia. RESULTS Among 24 846 patients, TBI patients developed dementia (7.5% vs 4.6%) at a younger age (78.6 vs 82.7 yr) and demonstrated higher 10-yr mortality than controls (27% vs 14%; P

Background Expandable cages for interbody fusion allow for in situ expansion optimizing fit while mitigating endplate damage. Studies comparing outcomes after using expandable or static cages have been conflicting. Methods This was a meta-analysis A systematic search was performed in accordance with the Preferred Reporting Items for Systemic Reviews and Meta-Analyses (PRISMA) guidelines identifying studies reporting outcomes among patients who underwent minimally invasive lumbar interbody fusion (MIS-LIF). Results Fourteen articles with 1129 patients met inclusion criteria. Compared with MIS-LIFs performed with static cages, those with expandable cages had a significantly lower incidence of graft subsidence (expandable: incidence 0.03, I2 22.50%; static: incidence 0.27, I2 51.03%, P interaction Conclusions Expandable interbody cages in MIS-LIF were associated with a decrease in subsidence rate, operative time and greater in increase in disc height.

Decompressive craniectomy is recommended to reduce mortality in severe traumatic brain injury (TBI). Disparities exist in TBI treatment outcomes; however, data on disparities pertaining to decompressive craniectomy utilization is lacking. We investigated these disparities, focusing on race, insurance, sex, and age.Hospitalizations (2004-2014) were retrospectively extracted from the Nationwide Inpatient Sample. The criteria included are as follows: age ≥18 years and indicators of severe TBI diagnosis. Poor outcomes were defined as discharge to institutional care and death. Multivariable logistic regression models were used to assess the effects of race, insurance, age, and sex, on craniectomy utilization and outcomes.Of 349,164 hospitalized patients, 6.8% (n = 23,743) underwent craniectomy. White (odds ratio [OR] = 0.50, 95% confidence interval [CI] = 0.44-0.57; P0.001) and Black (OR = 0.45, 95% CI = 0.32-0.64; P = 0.003) Medicare beneficiaries were less likely to undergo craniectomy. Medicare (P0.0001) and Medicaid beneficiaries (P 0.0001) of all race categories had poorer outcomes than privately insured White patients. Black (OR = 1.2, 95% CI = 1.08-2.34; P = 0.001) patients with private insurance and Black (OR = 1.39, 95% CI = 1.22-1.58; P 0.0001) Medicaid beneficiaries had poorer outcomes than privately insured White patients (P 0.0001). Older patients (OR = 0.74, 95%, CI = 0.71-0.76; P 0.001) were less likely to undergo craniectomy and were more likely to have poorer outcomes. Females (OR = 0.82, 95% CI = 0.76-0.88; P 0.001) were less likely to undergo craniectomy.There are disparities in race, insurance status, sex, and age in craniectomy utilization and outcome. This data highlights the necessity to appropriately address these disparities, especially race and sex, and actively incorporate these factors in clinical trial design and enrollment.

OBJECTIVE A major obstacle to improving bedside neurosurgical procedure safety and accuracy with image guidance technologies is the lack of a rapidly deployable, real-time registration and tracking system for a moving patient. This deficiency explains the persistence of freehand placement of external ventricular drains, which has an inherent risk of inaccurate positioning, multiple passes, tract hemorrhage, and injury to adjacent brain parenchyma. Here, the authors introduce and validate a novel image registration and real-time tracking system for frameless stereotactic neuronavigation and catheter placement in the nonimmobilized patient. METHODS Computer vision technology was used to develop an algorithm that performed near-continuous, automatic, and marker-less image registration. The program fuses a subject’s preprocedure CT scans to live 3D camera images (Snap-Surface), and patient movement is incorporated by artificial intelligence–driven recalibration (Real-Track). The surface registration error (SRE) and target registration error (TRE) were calculated for 5 cadaveric heads that underwent serial movements (fast and slow velocity roll, pitch, and yaw motions) and several test conditions, such as surgical draping with limited anatomical exposure and differential subject lighting. Six catheters were placed in each cadaveric head (30 total placements) with a simulated sterile technique. Postprocedure CT scans allowed comparison of planned and actual catheter positions for user error calculation. RESULTS Registration was successful for all 5 cadaveric specimens, with an overall mean (± standard deviation) SRE of 0.429 ± 0.108 mm for the catheter placements. Accuracy of TRE was maintained under 1.2 mm throughout specimen movements of low and high velocities of roll, pitch, and yaw, with the slowest recalibration time of 0.23 seconds. There were no statistically significant differences in SRE when the specimens were draped or fully undraped (p = 0.336). Performing registration in a bright versus a dimly lit environment had no statistically significant effect on SRE (p = 0.742 and 0.859, respectively). For the catheter placements, mean TRE was 0.862 ± 0.322 mm and mean user error (difference between target and actual catheter tip) was 1.674 ± 1.195 mm. CONCLUSIONS This computer vision–based registration system provided real-time tracking of cadaveric heads with a recalibration time of less than one-quarter of a second with submillimetric accuracy and enabled catheter placements with millimetric accuracy. Using this approach to guide bedside ventriculostomy could reduce complications, improve safety, and be extrapolated to other frameless stereotactic applications in awake, nonimmobilized patients.

Cellular prion protein (PrPC) is a lipid raft protein abundant within CNS. It is regulated by a disintegrin and metalloproteinase domain containing protein 10 (ADAM10). PrPC has previously been implicated as a biomarker for TBI. ADAM10 has not been investigated as a TBI biomarker.We evaluated PrPC and ADAM10 as candidate biomarkers for TBI.We performed ELISA for ADAM10 and PrPC on plasma samples of patients with TBI admitted to Brigham and Women's Hospital. Plasma samples from 20 patients admitted for isolated TBI were acquired from a biobank with clinical information. Control plasma (37 samples) was acquired from a commercial source. GraphPad was used to conduct statistical analysis.37 controls and 20 TBI samples were collected. Of the patients with TBI, eight were mild, three were moderate, and nine were severe. Both PrPC and ADAM10 were elevated in patients with TBI compared with control (Our results indicate that PrPC and ADAM10 appear to be useful potential tools for screening of TBI. ADAM10 is closely associated with clinical grade.

OBJECTIVETraumatic brain injury (TBI) is a leading cause of morbidity and mortality in the US, but the true incidence of TBI is unknown.METHODSThe National Trauma Data Bank National Sample Program (NTDB NSP) was queried for 2007 and 2013, and population-based weighted estimates of TBI-related emergency department (ED) visits, hospitalizations, and deaths were calculated. These data were compared to the 2017 Centers for Disease Control and Prevention (CDC) report on TBI, which used the Healthcare Cost and Utilization Project’s National (“Nationwide” before 2012) Inpatient Sample and National Emergency Department Sample.RESULTSIn the NTDB NSP the incidence of TBI-related ED visits was 59/100,000 in 2007 and 62/100,000 in 2013. However, in the CDC report there were 534/100,000 in 2007 and 787/100,000 in 2013. The CDC estimate for ED visits was 805% higher in 2007 and 1169% higher in 2013. In the NTDB NSP, the incidence of TBI-related deaths was 5/100,000 in 2007 and 4/100,000 in 2013. In the CDC report, the incidence was 18/100,000 in both years. The CDC estimate for deaths was 260% higher in 2007 and 325% higher in 2013.CONCLUSIONSThe databases disagreed widely in their weighted estimates of TBI incidence: CDC estimates were consistently higher than NTDB NSP estimates, by an average of 448%. Although such a discrepancy may be intuitive, this is the first study to quantify the magnitude of disagreement between these databases. Given that research, funding, and policy decisions are made based on these estimates, there is a need for a more accurate estimate of the true national incidence of TBI.

Objectives Cerebrospinal fluid (CSF) leaks are a possible complication in patients with skull base fractures (SBFs). The widely cited incidence of CSF leaks is 10 to 30% in SBF patients; however, this estimate is based only on a few outdated studies. A recent report found CSF leaks in Design This study is a retrospective chart review. Setting The study was conducted at two major academic medical centers (2000–2018). Participants Adult patients with SBF were included in this study. Main Outcome Measures Variables included age, gender, CSF leak within 90 days, management regimen, meningitis within 90 days, and 1-year mortality. Results Among 4,944 patients with SBF, 199 (4%) developed a CSF leak. SBF incidence was positively correlated with year of clinical presentation (r-squared 0.78, p Conclusion In the largest institutional review of SBF patients in the 21st century, we found CSF leak incidence to be 4%. This is lower than the widely cited range of 10 to 30%. Nevertheless, morbidity and mortality associated with this complication remains clinically significant, and SBF patients should continue to be monitored for CSF leaks. We provide here our institutional treatment algorithm for these patients that may help to inform the treatment strategy at other institutions.

IntroductionAlthough clinically derived information could improve patient care, its full potential remains unrealised because most of it is stored in a format unsuitable for traditional methods of analysis, free-text clinical reports. Various studies have already demonstrated the utility of natural language processing algorithms for medical text analysis. Yet, evidence on their learning efficiency is still lacking. This study aimed to compare the learning curves of various algorithms and develop an open-source framework for text mining in healthcare.MethodsDeep learning and regressions-based models were developed to determine the histopathological diagnosis of patients with brain tumour based on free-text pathology reports. For each model, we characterised the learning curve and the minimal required training examples to reach the area under the curve (AUC) performance thresholds of 0.95 and 0.98.ResultsIn total, we retrieved 7000 reports on 5242 patients with brain tumour (2316 with glioma, 1412 with meningioma and 1514 with cerebral metastasis). Conventional regression and deep learning-based models required 200–400 and 800–1500 training examples to reach the AUC performance thresholds of 0.95 and 0.98, respectively. The deep learning architecture utilised in the current study required 100 and 200 examples, respectively, corresponding to a learning capacity that is two to eight times more efficient.ConclusionsThis open-source framework enables the development of high-performing and fast learning natural language processing models. The steep learning curve can be valuable for contexts with limited training examples (eg, rare diseases and events or institutions with lower patient volumes). The resultant models could accelerate retrospective chart review, assemble clinical registries and facilitate a rapid learning healthcare system.

Background Lumbar puncture is a common procedure that can be safely performed in most patients. Certain populations may have increased risk for complications following lumbar puncture, but the significance of basilar invagination is often underappreciated. Case Description A 45-year-old woman with basilar invagination received multiple lumbar punctures in the workup of acute meningitis. Preprocedural computed tomography was obtained. Following lumbar puncture, the patient developed locked-in syndrome. Magnetic resonance imaging obtained several days later demonstrated severe compression and infarction of the medulla and inferior cerebellum by the odontoid process and ectopic cerebellar tonsils. The patient was transferred but at this point, surgical decompression was not possible. She did not regain significant neurologic function. Conclusions Basilar invagination is a risk factor for devastating neurologic complications following lumbar puncture. Awareness of this complication and prompt recognition of its occurrence may prevent future morbidity of lumbar puncture in patients with basilar invagination.

PURPOSE Although the bulk of patient-generated health data are increasing exponentially, their use is impeded because most data come in unstructured format, namely as free-text clinical reports. A variety of natural language processing (NLP) methods have emerged to automate the processing of free text ranging from statistical to deep learning–based models; however, the optimal approach for medical text analysis remains to be determined. The aim of this study was to provide a head-to-head comparison of novel NLP techniques and inform future studies about their utility for automated medical text analysis. PATIENTS AND METHODS Magnetic resonance imaging reports of patients with brain metastases treated in two tertiary centers were retrieved and manually annotated using a binary classification (single metastasis v two or more metastases). Multiple bag-of-words and sequence-based NLP models were developed and compared after randomly splitting the annotated reports into training and test sets in an 80:20 ratio. RESULTS A total of 1,479 radiology reports of patients diagnosed with brain metastases were retrieved. The least absolute shrinkage and selection operator (LASSO) regression model demonstrated the best overall performance on the hold-out test set with an area under the receiver operating characteristic curve of 0.92 (95% CI, 0.89 to 0.94), accuracy of 83% (95% CI, 80% to 87%), calibration intercept of –0.06 (95% CI, –0.14 to 0.01), and calibration slope of 1.06 (95% CI, 0.95 to 1.17). CONCLUSION Among various NLP techniques, the bag-of-words approach combined with a LASSO regression model demonstrated the best overall performance in extracting binary outcomes from free-text clinical reports. This study provides a framework for the development of machine learning-based NLP models as well as a clinical vignette of patients diagnosed with brain metastases.

Machine learning (ML) has been increasingly used in medicine and neurosurgery. We sought to determine whether ML models can distinguish ruptured from unruptured aneurysms and identify features associated with rupture.We performed a retrospective review of patients with intracranial aneurysms detected on vascular imaging at our institution between 2002 and 2018. The dataset was used to train 3 ML models (random forest, linear support vector machine [SVM], and radial basis function kernel SVM). Relative contributions of individual predictors were derived from the linear SVM model.Complete data were available for 845 aneurysms in 615 patients. Ruptured aneurysms (n = 309, 37%) were larger (mean 6.51 mm vs. 5.73 mm; P = 0.02) and more likely to be in the posterior circulation (20% vs. 11%; P0.001) than unruptured aneurysms. Area under the receiver operating curve was 0.77 for the linear SVM, 0.78 for the radial basis function kernel SVM models, and 0.81 for the random forest model. Aneurysm location and size were the 2 features that contributed most significantly to the model. Posterior communicating artery, anterior communicating artery, and posterior inferior cerebellar artery locations were most highly associated with rupture, whereas paraclinoid and middle cerebral artery locations had the strongest association with unruptured status.ML models are capable of accurately distinguishing ruptured from unruptured aneurysms and identifying features associated with rupture. Consistent with prior studies, location and size show the strongest association with aneurysm rupture.

OBJECTIVENonroutine discharge after elective spine surgery increases healthcare costs, negatively impacts patient satisfaction, and exposes patients to additional hospital-acquired complications. Therefore, prediction of nonroutine discharge in this population may improve clinical management. The authors previously developed a machine learning algorithm from national data that predicts risk of nonhome discharge for patients undergoing surgery for lumbar disc disorders. In this paper the authors externally validate their algorithm in an independent institutional population of neurosurgical spine patients.METHODSMedical records from elective inpatient surgery for lumbar disc herniation or degeneration in the Transitional Care Program at Brigham and Women’s Hospital (2013–2015) were retrospectively reviewed. Variables included age, sex, BMI, American Society of Anesthesiologists (ASA) class, preoperative functional status, number of fusion levels, comorbidities, preoperative laboratory values, and discharge disposition. Nonroutine discharge was defined as postoperative discharge to any setting other than home. The discrimination (c-statistic), calibration, and positive and negative predictive values (PPVs and NPVs) of the algorithm were assessed in the institutional sample.RESULTSOverall, 144 patients underwent elective inpatient surgery for lumbar disc disorders with a nonroutine discharge rate of 6.9% (n = 10). The median patient age was 50 years and 45.1% of patients were female. Most patients were ASA class II (66.0%), had 1 or 2 levels fused (80.6%), and had no diabetes (91.7%). The median hematocrit level was 41.2%. The neural network algorithm generalized well to the institutional data, with a c-statistic (area under the receiver operating characteristic curve) of 0.89, calibration slope of 1.09, and calibration intercept of −0.08. At a threshold of 0.25, the PPV was 0.50 and the NPV was 0.97.CONCLUSIONSThis institutional external validation of a previously developed machine learning algorithm suggests a reliable method for identifying patients with lumbar disc disorder at risk for nonroutine discharge. Performance in the institutional cohort was comparable to performance in the derivation cohort and represents an improved predictive value over clinician intuition. This finding substantiates initial use of this algorithm in clinical practice. This tool may be used by multidisciplinary teams of case managers and spine surgeons to strategically invest additional time and resources into postoperative plans for this population.

Background Appropriate drug distribution is needed for effective treatment of brain metastases and leptomeningeal carcinomatosis. Lack of proper drug bioavailability in the CNS contributes to the poor outcome in these patients. EnClear Therapies has developed a device delivering intrathecal therapeutics utilizing the active control of CSF flow through an external pump. Herein, we look at intrathecal drug delivery of methotrexate with EnClear’s system and its impact of drug concentration in sheep and non-human primates. METHODS The EnClear device utilizes two implantable catheters (intracranial in the ventricular system and lumbar thecal sac) and an extracorporeal pump that controls the speed and direction of CSF flow. Software monitors the output from sensor arrays for pressure, respiration, and heart rate. Gadolinium and MRI were used to determine the CSF flow characteristics. Administration of Methotrexate via the EnClear system was compared to current two standards of delivery, intracerebroventricular or lumbar intrathecal administration. Methotrexate levels were measured with Liquid Chromatography/Mass Spectrometry in CSF, blood, and multiple CNS and peripheral tissues. RESULTS There was a two-fold increase in methotrexate levels in multiple regions of the brain including deeper brain structures including the striatum using the EnClear's system, compared to traditional lumbar intrathecal and intracerebroventricular delivery. Higher peak levels of methotrexate were reached in the CSF with the use of EnClear’s system, with simultaneous reduction in the peripheral nerves and systemic tissues – a potential source of toxicity. Conclusions In large animal studies, the EnClear device has for the first time, demonstrated increased brain parenchymal drug levels via intrathecal delivery. In addition to the drug parenchyma, drug delivery with this novel device has shown improved distribution throughout CNS and leptomeninges as compared to traditional intrathecal drug delivery and decreased peripheral distribution.

Purpose Although standard-of-care has been defined for the treatment of glioblastoma patients, substantial practice variation exists in the day-to-day clinical management. This study aims to compare the use of laboratory tests in the perioperative care of glioblastoma patients between two tertiary academic centers—Brigham and Women’s Hospital (BWH), Boston, USA, and University Medical Center Utrecht (UMCU), Utrecht, the Netherlands. Methods All glioblastoma patients treated according to standard-of-care between 2005 and 2013 were included. We compared the number of blood drawings and laboratory tests performed during the 70-day perioperative period using a Poisson regression model, as well as the estimated laboratory costs per patient. Additionally, we compared the likelihood of an abnormal test result using a generalized linear mixed effects model. Results After correction for age, sex, IDH1 status, postoperative KPS score, length of stay, and survival status, the number of blood drawings and laboratory tests during the perioperative period were 3.7-fold (p p p p Conclusions Our results suggest a substantially lower clinical threshold for ordering laboratory tests in BWH compared to UMCU. Further investigating the clinical consequences of laboratory testing could identify over and underuse, decrease healthcare costs, and reduce unnecessary discomfort that patients are exposed to.

Background: Ruptured intracranial dermoid cysts are extremely rare. Standard treatment consists of endonasal decompression or craniotomy with evacuation and copious irrigation of subarachnoid spaces to remove any disseminated cystic contents. Disseminated fat particles in the subarachnoid space may be the cause of further sequalae, including the subsequent development of chemical meningitis and hydrocephalus. Here, we present a case of ruptured suprasellar dermoid cyst treated with craniotomy for emergent optic nerve decompression, followed by postoperative hydrocephalus successfully treated with lumbar drain.Case description: We describe a 30-year-old man with a history of migraines who presented with acute onset of headache, photophobia, nausea, vomiting, and vision loss in the left eye. Head CT and brain MRI demonstrated a ruptured suprasellar dermoid cyst with associated mass effect on the optic nerves and frontal lobes as well as fat attenuation material within the subarachnoid spaces. The patient underwent left frontotemporal craniotomy for cyst resection and developed non-obstructive hydrocephalus on postoperative day 1, refractory to external ventricular drainage. Placement of a lumbar drain cleared the subarachnoid space of debris derived from the ruptured dermoid cyst, and the hydrocephalus resolved. The patient did not require permanent CSF diversion.Conclusions: Intracranial dermoid cysts are uncommon, and rupture is a rare event. Standard surgical treatment with craniotomy for evacuation may leave disseminated dermoid contents and fat particles throughout the subarachnoid spaces. We highlight a case of ruptured suprasellar dermoid cyst with postoperative communicating hydrocephalus treated with lumbar drain when external ventricular drain (EVD) was ineffective. Review of the current literature reveals inconsistent findings on the effects of remaining fat particles. In cases with clinical evidence of increased intracranial pressure due to non-obstructive hydrocephalus attributable to chemical meningitis, temporary lumbar drainage is an option to be considered before committing the patient to permanent shunting.

Robotic neurosurgery may improve the accuracy, speed, and availability of stereotactic procedures. We recently developed a computer vision and artificial intelligence-driven frameless stereotaxy for nonimmobilized patients, creating an opportunity to develop accurate and rapidly deployable robots for bedside cranial intervention.To validate a portable stereotactic surgical robot capable of frameless registration, real-time tracking, and accurate bedside catheter placement.Four human cadavers were used to evaluate the robot's ability to maintain low surface registration and targeting error for 72 intracranial targets during head motion, ie, without rigid cranial fixation. Twenty-four intracranial catheters were placed robotically at predetermined targets. Placement accuracy was verified by computed tomography imaging.Robotic tracking of the moving cadaver heads occurred with a program runtime of 0.111 ± 0.013 seconds, and the movement command latency was only 0.002 ± 0.003 seconds. For surface error tracking, the robot sustained a 0.588 ± 0.105 mm registration accuracy during dynamic head motions (velocity of 6.647 ± 2.360 cm/s). For the 24 robotic-assisted intracranial catheter placements, the target registration error was 0.848 ± 0.590 mm, providing a user error of 0.339 ± 0.179 mm.Robotic-assisted stereotactic procedures on mobile subjects were feasible with this robot and computer vision image guidance technology. Frameless robotic neurosurgery potentiates surgery on nonimmobilized and awake patients both in the operating room and at the bedside. It can affect the field through improving the safety and ability to perform procedures such as ventriculostomy, stereo electroencephalography, biopsy, and potentially other novel procedures. If we envision catheter misplacement as a "never event," robotics can facilitate that reality.

A subset of primary central nervous system lymphomas (PCNSL) are difficult to distinguish from glioblastoma multiforme (GBM) on magnetic resonance imaging (MRI). We developed a convolutional neural network (CNN) to distinguish these tumors on contrast-enhanced T1-weighted images. Preoperative brain tumor MRIs were retrospectively collected among 320 patients with either GBM (n = 160) and PCNSL (n = 160) from two academic institutions. The individual images from these MRIs consisted of a training set (n = 1894 GBM and 1245 PCNSL), a validation set (n = 339 GBM; 202 PCNSL), and a testing set (99 GBM and 108 PCNSL). Three CNNs using the EfficientNetB4 architecture were evaluated. To increase the size of the training set and minimize overfitting, random flips and changes to color were performed on the training set. Our transfer learning approach (with image augmentation and 292 epochs) yielded an AUC of 0.94 (95% CI: 0.91–0.97) for GBM and an AUC of 0.95 (95% CI: 0.92–0.98) for PCNL. In the second case (not augmented and 137 epochs), the images were augmented prior to training. The area under the curve for GBM was 0.92 (95% CI: 0.88–0.96) for GBM and an AUC of 0.94 (95% CI: 0.91–0.97) for PCNSL. For the last case (augmented, Gaussian noise and 238 epochs) the AUC for GBM was 0.93 (95% CI: 0.89–0.96) and an AUC 0.93 (95% CI = 0.89–0.96) for PCNSL. Even with a relatively small dataset, our transfer learning approach demonstrated CNNs may provide accurate diagnostic information to assist radiologists in distinguishing PCNSL and GBM.

OBJECTIVE Postoperative routine imaging is common after pediatric ventricular shunt revision, but the benefit of scanning in the absence of symptoms is questionable. In this study, the authors aimed to assess how often routine scanning results in a change in clinical management after shunt revision. METHODS The records of a large, tertiary pediatric hospital were retrospectively reviewed for all consecutive cases of pediatric shunt revision between July 2013 and July 2018. Postoperative imaging was classified as routine (i.e., in the absence of symptoms, complications, or other direct indications) or nonroutine. Reinterventions within 30 days were assessed in these groups. RESULTS Of 387 included shunt revisions performed in 232 patients, postoperative imaging was performed in 297 (77%), which was routine in 244 (63%) and nonroutine in 53 (14%). Ninety revisions (23%) underwent any shunt-related procedure after postoperative imaging, including shunt reprogramming (n = 35, 9%), shunt tap (n = 10, 3%), and a return to the operating room (OR; n = 58, 15%). Of the 244 cases receiving routine imaging, 241 did not undergo a change in clinical management solely based on routine imaging findings. The remaining 3 cases returned to the OR, accounting for 0.8% (95% CI 0.0%–1.7%) of all cases or 1.2% (95% CI 0.0%–2.6%) of cases that received routine imaging. Furthermore, 27 of 244 patients in this group returned to the OR for other reasons, namely complications (n = 12) or recurrent symptoms (n = 15); all arose after initial routine imaging. CONCLUSIONS The authors found a low yield to routine imaging after pediatric shunt revision, with only 0.8% of cases undergoing a change in management based on routine imaging findings without corresponding clinical findings. Moreover, routine imaging without abnormal findings was no guarantee of an uneventful postoperative course. Clinical monitoring can be considered as an alternative in asymptomatic, uncomplicated patients.

Intraventricular hemorrhage (IVH) is an independent poor prognostic factor in subarachnoid and intra-parenchymal hemorrhage. The use of intraventricular fibrinolytics (IVF) has long been debated, and its exact effects on outcomes are unknown. A systematic review and meta-analysis were performed in accordance with the PRISMA guidelines to assess the impact of IVF after non-traumatic IVH on mortality, functional outcome, intracranial bleeding, ventriculitis, time until clearance of third and fourth ventricles, obstruction of external ventricular drains (EVD), and shunt dependency. Nineteen studies were included in the meta-analysis, totaling 1020 patients. IVF was associated with lower mortality (relative risk [RR] 0.58; 95% confidence interval [CI] 0.47–0.72), fewer EVD obstructions (RR 0.41; 95% CI 0.22–0.74), and a shorter time until clearance of the ventricles (median difference [MD] − 4.05 days; 95% CI − 5.52 to − 2.57). There was no difference in good functional outcome, RR 1.41 (95% CI 0.98–2.03), or shunt dependency, RR 0.93 (95% CI 0.70–1.22). Correction for publication bias predicted an increased risk of intracranial bleeding, RR 1.67 (95% CI 1.01–2.74) and a lower risk of ventriculitis, RR 0.68 (95% CI 0.45–1.03) in IVH patients treated with IVF. IVF was associated with improved survival, faster clearance of blood from the ventricles and fewer drain obstructions, but further research is warranted to elucidate the effects on ventriculitis, long-term functional outcomes, and re-hemorrhage. Electronic supplementary material The online version of this article (10.1007/s12028-019-00786-5) contains supplementary material, which is available to authorized users.

Background Although microvascular decompression (MVD) is a durable treatment for medically refractory trigeminal neuralgia, hemifacial spasm, or glossopharyngeal neuralgia attributable to neurovascular conflict, few national studies have analyzed predictors of postoperative complications. Objective To determine the incidence and risk factors for adverse events after MVD. Methods Patients who underwent MVD were extracted from the prospectively collected National Surgical Quality Improvement Program registry (2006–2017). Multivariable logistic regression identified predictors of 30-day adverse events and unplanned readmission; multivariable linear regression analyzed predictors of a longer hospital stay. Results Among the 1005 patients evaluated, the mortality was 0.3%, major neurologic complication rate 0.4%, and 2.8% had a nonroutine hospital discharge. Patient age was not a predictor of any adverse events. Statistically significant independent predictors both of any adverse event (9.2%) and of a longer hospitalization were American Society of Anesthesiologists (ASA) classification III–IV designation and longer operative duration (P ≤ 0.03) The 30-day readmission rate was 6.8%, and the most common reasons were surgical site infections (22.4%) and cerebrospinal fluid leakage (14.3%). Higher ASA classification, diabetes mellitus, and operative time were predictors of readmission (P Conclusions In this National Surgical Quality Improvement Program analysis, postoperative morbidity and mortality after MVD was low. Patient age was not a predictor of postoperative complications, whereas higher ASA classification, diabetes mellitus, and longer operative duration were predictive of any adverse event and readmission. ASA classification provided superior risk stratification compared with the total number of patient comorbidities or laboratory values. These data can assist with preoperative patient counseling and risk stratification.

Objective Variance between providers in neurosurgery can lead to inefficiencies and poor patient outcomes. Evidence-based guidelines (EBGs) have been developed; however, they have not been well implemented into the clinician workflow. Therefore, clinicians have been left to make decisions with incomplete information. Equally underused are the electronic health records (EHRs), which house enormous amounts of health data, but the power of that “big data” has failed to be capitalized on. Methods Early attempts at EBGs were rigid and nonadaptive; however, with the current advances in data informatics and machine learning algorithms, it is now possible to integrate “big data” and rapid data processing into clinical decision support tools. We have presented an overview of the background of EHRs and EBGs in neurosurgery and explored the possibility of integrating them to reduce unwanted variance. Results As we strive toward variance reduction in healthcare, the integration of “big data” and EBGs for decision-making will be key. We have proposed that EHRs are an ideal platform for integrating EBGs into the clinician workflow and have presented as an example of a successful early generation model, Neurocore. With this approach, it will be possible to build EBGs into the EHR software, to continuously update and optimize EBGs according to the flow of patient data into the EHR, and to present data-driven clinical decision support at the point of care. Conclusions Variance reduction in neurosurgery through the integration of evidence-based decision support in EHRs will lead to improved patient safety, a reduction in medical errors, maximization of the use of the available data, and enhanced decision-making power for clinicians.

Purpose: An excessive amount of total hospitalization is caused by delays due to patients waiting to be placed in a rehabilitation facility or skilled nursing facility (RF/SNF). An accurate preoperative prediction of who would need a RF/SNF place after surgery could reduce costs and allow more efficient organizational planning. We aimed to develop a machine learning algorithm that predicts non-home discharge after elective surgery for lumbar spinal stenosis. Methods: We used the American College of Surgeons National Surgical Quality Improvement Program to select patient that underwent elective surgery for lumbar spinal stenosis between 2009 and 2016. The primary outcome measure for the algorithm was non-home discharge. Four machine learning algorithms were developed to predict non-home discharge. Performance of the algorithms was measured with discrimination, calibration, and an overall performance score. Results: We included 28,600 patients with a median age of 67 (interquartile range 58–74). The non-home discharge rate was 18.2%. Our final model consisted of the following variables: age, sex, body mass index, diabetes, functional status, ASA class, level, fusion, preoperative hematocrit, and preoperative serum creatinine. The neural network was the best model based on discrimination (c-statistic = 0.751), calibration (slope = 0.933; intercept = 0.037), and overall performance (Brier score = 0.131). Conclusions: A machine learning algorithm is able to predict discharge placement after surgery for lumbar spinal stenosis with both good discrimination and calibration. Implementing this type of algorithm in clinical practice could avert risks associated with delayed discharge and lower costs. Graphical abstract: These slides can be retrieved under Electronic Supplementary Material.[Figure not available: see fulltext.].

Visits to the emergency department by patients who have recently undergone neurosurgery are a common occurrence, and these patients frequently receive emergent cross-sectional head imaging in order to evaluate for complications. Different neurosurgical approaches may have typical postoperative imaging findings that can be confused with pathology. Furthermore, particular abnormal postoperative imaging findings may signal an evolving complication. It is essential for the radiologist to understand common neurosurgical procedures and their potential complications in order to provide proper diagnostic evaluation of the postoperative neurosurgical patient. The purpose of this review is to assist radiologists in the evaluation of the postoperative neurosurgical patient and educate them on associated complications. Familiarity with common neurosurgical techniques and postoperative complications will help radiologists make the correct diagnosis, communicate effectively with the neurosurgeon, and expedite patient care.

BACKGROUND Despite improved perioperative management, the rate of postoperative morbidity and mortality after brain tumor resection remains considerably high. OBJECTIVE To assess the rates, causes, timing, and predictors of major complication, extended length of stay (>10 d), reoperation, readmission, and death within 30 d after craniotomy for primary malignant brain tumors. METHODS Patients were extracted from the National Surgical Quality Improvement Program registry (2005-2015) and analyzed using multivariable logistic regression. RESULTS A total of 7376 patients were identified, of which 948 (12.9%) experienced a major complication. The most common major complications were reoperation (5.1%), venous thromboembolism (3.5%), and death (2.6%). Furthermore, 15.6% stayed longer than 10 d, and 11.5% were readmitted within 30 d after surgery. The most common reasons for reoperation and readmission were intracranial hemorrhage (18.5%) and wound-related complications (11.9%), respectively. Multivariable analysis identified older age, higher body mass index, higher American Society of Anesthesiologists (ASA) classification, dependent functional status, elevated preoperative white blood cell count (white blood cell count [WBC], >12 000 cells/mm3), and longer operative time as predictors of major complication (all P

Acute myocardial infarction (AMI) is the rarest and least studied cardiac complication of aneurysmal subarachnoid hemorrhage (aSAH). Precise estimates of the incidence of AMI after aSAH are unavailable. Our goal was to estimate the incidence of registry-based AMI (rb-AMI) after aSAH and determine its association with clinical outcomes.Adult patients with aSAH in the National Inpatient Samples from 2002 to 2014 were included in the study. We evaluated risk factors for rb-AMI using univariate and multivariate regression models. Clinical outcomes that were assessed included functional status at discharge, in-patient mortality, length of stay, and total hospitalization cost, adjusting for patient demographics and cardiovascular risk factors through an inverse probability weighted analysis. Subgroup analyses were further performed stratified by rb-AMI type (ST-segment elevation myocardial infarction [STEMI] vs. non-STEMI [NSTEMI]).A total of 139,734 patients with aSAH were identified, 3.6% of whom had rb-AMI. NSTEMI was the most common type of rb-AMI occurring after aSAH (71% vs. 29% for NSTEMI vs. STEMI, respectively). Patient characteristics associated with higher odds of rb-AMI included age, female sex, poor aSAH grade, and various cardiovascular risk factors. Rb-AMI was also associated with poor functional status at discharge, higher in-hospital mortality, and a longer and more costly hospital stay.Rb-AMI occurs in 3.6% of patients with aSAH and is associated with poor functional status at discharge, higher in-patient mortality, and a longer and more costly hospitalization. Differentiating between different types of rb-AMI would be important in optimizing the management of patients with aSAH. Our definition of rb-AMI likely includes patients with neurogenic stress cardiomyopathy, which may confound the results.

Extra-axial fluid collections (EACs) frequently develop after decompressive craniectomy. Management of EACs remains poorly understood, and information on how to predict their clinical course is inadequate. We aimed to better characterize EACs, understand predictors of their resolution, and delineate the best treatment paradigm for patients.We reviewed patients who developed EACs after undergoing decompressive craniectomy for treatment of refractory intracranial pressure elevations. We excluded patients who had an ischemic stroke, as EACs in these patients have a different clinical course. We performed univariate analysis and multiple linear regression to find variables associated with earlier resolution of EACs and stratified our analyses by EAC phenotype (complicated vs. uncomplicated). We conducted a systematic review to compare our findings with the literature.Of 96 included patients, 73% were male, and median age was 42.5 years. EACs resolved after a median of 60 days. Complicated EACs were common (62.5%) and required multiple drainage methods before cranioplasty. These were not associated with a protracted course or increased risk of death (P0.05). Early bone flap restoration with simultaneous drainage was independently associated with earlier resolution of EACs (β = 0.56, P 0.001). Systematic review confirmed lack of standardized direction with respect to EAC management.Our analyses reveal 2 clinically relevant phenotypes of EAC: complicated and uncomplicated. Our proposed treatment algorithm involves replacing the bone flap as soon as it is safe to do so and draining refractory EACs aggressively. Further studies to assess long-term clinical outcomes of EACs are warranted.

Background The US opioid epidemic continues to afflict patients and the healthcare system. Surgery remains a risk factor for opioid misuse, and treatment of low back pain in orthopedics and neurosurgery is one of the largest introductions of opioids into the community. The objective of the study is to understand how opioid prescribing practices for spinal surgery have evolved in two academic hospital in the last 18 years. Methods Data were obtained from the Research Patient Data Registry for Brigham and Women's and Massachusetts General Hospital from January 2000 to December 2018. Patients included had a primary diagnosis of degenerative diseases, trauma, spinal infection, spinal deformities, or spinal pain symptoms/syndromes; were aged > 18 years; and had an opioid prescription. Covariates included demographics, diagnoses, comorbidities, procedures, opioid type, number of prescriptions, route of administration, doses and length of prescription. Results A total of 38,250 patients with spine-related diagnoses received an opioid prescription. The median age was 63 years (18-107), 50% male and 86% white. A total of 32,304 patients (84.4%) received at least one opioid prescription during their hospitalization. The sum of opioid prescriptions filled (inpatient and outpatient) were 889,868 between 2000 and 2018 (55.2% oral, 41.7% intravenous). Oxycodone was the most prescribed. The dose of ≥ 50 morphine milligram equivalents MME/day was reduced from 65.0% in 2000 to 17.3% in 2018, and doses ≥ 90 MME/day dropped from 26.9% in 2000 to 6.4% in 2018. However, the duration of prescription has increased from 4.1% having an opioid prescription for >7 days in 2000, to 21.7% in 2018. Conclusions Opioid prescription rates for spinal surgery patients have increased since 2000, declined temporarily in 2016, but are rising again. Physicians are prescribing fewer MMEs per day but have increased longitudinal dosing, which still leaves patients at risk for misuse and opioid use disorder. Key messages Between 2000 and 2016 there was an increase of 140 times the number of opioid prescriptions for spine patients. More interventions and non-pharmacological solutions are needed to reduce this public health epidemic.

Glioblastoma is associated with a poor prognosis. Even though survival statistics are well-described at the population level, it remains challenging to predict the prognosis of an individual patient despite the increasing number of prognostic models. The aim of this study is to systematically review the literature on prognostic modeling in glioblastoma patients. A systematic literature search was performed to identify all relevant studies that developed a prognostic model for predicting overall survival in glioblastoma patients following the PRISMA guidelines. Participants, type of input, algorithm type, validation, and testing procedures were reviewed per prognostic model. Among 595 citations, 27 studies were included for qualitative review. The included studies developed and evaluated a total of 59 models, of which only seven were externally validated in a different patient cohort. The predictive performance among these studies varied widely according to the AUC (0.58–0.98), accuracy (0.69–0.98), and C-index (0.66–0.70). Three studies deployed their model as an online prediction tool, all of which were based on a statistical algorithm. The increasing performance of survival prediction models will aid personalized clinical decision-making in glioblastoma patients. The scientific realm is gravitating towards the use of machine learning models developed on high-dimensional data, often with promising results. However, none of these models has been implemented into clinical care. To facilitate the clinical implementation of high-performing survival prediction models, future efforts should focus on harmonizing data acquisition methods, improving model interpretability, and externally validating these models in multicentered, prospective fashion. Supplementary Information The online version contains supplementary material available at 10.1007/s10143-020-01430-z.

Objective Outcome prediction in severe traumatic brain injury (sTBI) has been studied using clinical and radiographic measurements and by using biomarkers such as glial fibrillary acidic protein, ubiquitin C-terminal hydrolase-L1, and tau. Routine blood tests are regularly performed in patients with sTBI and could be used to predict outcomes. This study aims to investigate whether routine blood tests on admission can be predictive of outcome in patients with sTBI. Methods Patients with sTBI were selected from 2 institutional databases based on International Classification of Diseases Ninth and Tenth Revision codes for traumatic brain injury (TBI), ventilatory assistance >24 hours, intracranial pressure monitoring, and Glasgow Coma Score (GCS) score ≤8. Laboratory parameters included blood urea nitrogen, creatinine, glucose, hematocrit, hemoglobin, red blood cells, white blood cells, monocytes, lymphocytes, neutrophils, neutrophil lymphocyte ratio, platelets, international normalized ratio, prothrombin time, sodium, and potassium. Clinical outcome was measured as hospital length of stay, 30-day mortality, and favorable versus unfavorable outcome based on Glasgow Outcome Scale at 3 months. Results A total of 255 adult patients were selected. Median Injury Severity Score was 14.00 (interquartile range, 9.00–22.00). Of patients, 25.9% died within 30 days and 56.1% had an unfavorable outcome at 3 months. On multivariate analysis, low sodium level was significant for 30-day mortality and high sodium level was significant for unfavorable outcome at 3 months. However, after correction for multiple testing, no routine blood test remained significant. Conclusions No routine blood tests measured on admission were significant predictors of outcome in patients with sTBI. Other clinical and radiologic factors may be better suited to predicting outcomes in this patient population.

PURPOSE: Surgical navigation has remained a challenge in emergent procedures with mobile targets. Recent advances in RGB-Depth (RGB-D) camera technology have expanded the opportunities for adopting computer vision solutions in computer-assisted surgery. The purpose of this study was to demonstrate the capacity of an RGB-D camera rigidly fixed to a video projector to perform optical marker tracking, depth-based patient registration, and projected target visualization using open-source software. METHODS: The accuracy of marker tracking and system calibration was tested by projecting points onto the corners of a geometrically patterned marker, which was imaged in several locations and orientations. The depth-based registration and practical projection accuracy of the system was evaluated by targeting specific locations on a simulated patient and measuring the distance of the projected points from their targets. RESULTS: The average Euclidean distance between the marker corners and projected points was 5.34 mm (SD 2.93 mm), and the target projection error in the manikin trial following depth-based registration was 4.01 mm (SD 1.51 mm). On average, the distance of the captured point cloud from the manikin model after registration was 1.83 mm (SD 0.15 mm), while the fiducial registration error associated with registering a commercial tracking system to the manikin was 2.47 mm (SD 0.63 mm). CONCLUSION: This study highlights the potential application of RGB-D cameras calibrated to portable video projectors as inexpensive, rapidly deployable navigation systems for use in a variety of procedures, and demonstrates that their accuracy in performing patient registration is suitable for many bedside interventions.

PURPOSE The aim of this study was to develop an open-source natural language processing (NLP) pipeline for text mining of medical information from clinical reports. We also aimed to provide insight into why certain variables or reports are more suitable for clinical text mining than others. MATERIALS AND METHODS Various NLP models were developed to extract 15 radiologic characteristics from free-text radiology reports for patients with glioblastoma. Ten-fold cross-validation was used to optimize the hyperparameter settings and estimate model performance. We examined how model performance was associated with quantitative attributes of the radiologic characteristics and reports. RESULTS In total, 562 unique brain magnetic resonance imaging reports were retrieved. NLP extracted 15 radiologic characteristics with high to excellent discrimination (area under the curve, 0.82 to 0.98) and accuracy (78.6% to 96.6%). Model performance was correlated with the inter-rater agreement of the manually provided labels (ρ = 0.904; P < .001) but not with the frequency distribution of the variables of interest (ρ = 0.179; P = .52). All variables labeled with a near perfect inter-rater agreement were classified with excellent performance (area under the curve > 0.95). Excellent performance could be achieved for variables with only 50 to 100 observations in the minority group and class imbalances up to a 9:1 ratio. Report-level classification accuracy was not associated with the number of words or the vocabulary size in the distinct text documents. CONCLUSION This study provides an open-source NLP pipeline that allows for text mining of narratively written clinical reports. Small sample sizes and class imbalance should not be considered as absolute contraindications for text mining in clinical research. However, future studies should report measures of inter-rater agreement whenever ground truth is based on a consensus label and use this measure to identify clinical variables eligible for text mining.