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The field of spine research is rapidly evolving, with new research topics continually emerging. Analyzing topics and trends in the literature can provide insights into the shifting research landscape. This study aimed to elucidate prevalent and emerging research topics and trends within The Spine Journal using a natural language processing technique called topic modeling. We utilized BERTopic, a topic modeling technique rooted in natural language processing (NLP), to examine articles from The Spine Journal. Through this approach, we discerned topics from distinct keyword clusters and representative documents that represented the main concepts of each topic. We then used linear regression models on these topic likelihoods to trace trends over time, pinpointing both "hot" (growing in prominence) and "cold" (decreasing in prominence) topics. Additionally, we conducted an in-depth review of the trending topics in the present decade. Our analysis led to the categorization of 3358 documents into 30 distinct topics. These topics spanned a wide range of themes, with the most commonly identified topics being "Outcome Measures," "Scoliosis," and "Intradural Lesions." Throughout the history of the journal, the three hottest topics were "Degenerative Cervical Myelopathy," "Osteoporosis," and "Opioid Use." Conversely, the coldest topics were "Intradural Lesions," "Extradural Tumors," and "Vertebral Augmentation." Within the current decade, the hottest topics were "Screw Biomechanics," "Paraspinal Muscles," and "Biologics for Fusion," whereas the cold topics were "Intraoperative Blood Loss," "Construct Biomechanics," and "Material Science." This study accentuates the dynamic nature of spine research and the changing focus within the field. The insights gleaned from our analysis can steer future research directions, inform policy decisions, and spotlight emerging areas of interest. The implementation of NLP to synthesize and analyze vast amounts of academic literature exhibits the potential of advanced analytical techniques in comprehending the research landscape, setting a precedent for similar analyses across other medical disciplines. [ABSTRACT FROM AUTHOR]

A traumatic spinal cord injury (SCI) can cause temporary or permanent motor and sensory impairment, leading to serious short and long-term consequences that can result in significant morbidity and mortality. The cervical spine is the most commonly affected area, accounting for about 60% of all traumatic SCI cases. This study aims to employ machine learning (ML) algorithms to predict various outcomes, such as in-hospital mortality, nonhome discharges, extended length of stay (LOS), extended length of intensive care unit stay (ICU-LOS), and major complications in patients diagnosed with cervical SCI (cSCI). Our study was a retrospective machine learning classification study aiming to predict the outcomes of interest, which were binary categorical variables, in patients diagnosed with cSCI. The data for this study were obtained from the American College of Surgeons (ACS) Trauma Quality Program (TQP) database, which was queried to identify patients who suffered from cSCI between 2019 and 2021. The outcomes of interest of our study were in-hospital mortality, nonhome discharges, prolonged LOS, prolonged ICU-LOS, and major complications. The study evaluated the models' performance using both graphical and numerical methods. The receiver operating characteristic (ROC) and precision-recall curves (PRC) were used to assess model performance graphically. Numerical evaluation metrics included AUROC, balanced accuracy, weighted area under PRC (AUPRC), weighted precision, and weighted recall. The study employed data from the American College of Surgeons (ACS) Trauma Quality Program (TQP) database to identify patients with cSCI. Four ML algorithms, namely XGBoost, LightGBM, CatBoost, and Random Forest, were utilized to develop predictive models. The most effective models were then incorporated into a publicly available web application designed to forecast the outcomes of interest. There were 71,661 patients included in the analysis for the outcome mortality, 67,331 for the outcome nonhome discharges, 76,782 for the outcome prolonged LOS, 26,615 for the outcome prolonged ICU-LOS, and 72,132 for the outcome major complications. The algorithms exhibited an AUROC value range of 0.78 to 0.839 for in-hospital mortality, 0.806 to 0.815 for nonhome discharges, 0.679 to 0.742 for prolonged LOS, 0.666 to 0.682 for prolonged ICU-LOS, and 0.637 to 0.704 for major complications. An open access web application was developed as part of the study, which can generate predictions for individual patients based on their characteristics. Our study suggests that ML models can be valuable in assessing risk for patients with cervical cSCI and may have considerable potential for predicting outcomes during hospitalization. ML models demonstrated good predictive ability for in-hospital mortality and nonhome discharges, fair predictive ability for prolonged LOS, but poor predictive ability for prolonged ICU-LOS and major complications. Along with these promising results, the development of a user-friendly web application that facilitates the integration of these models into clinical practice is a significant contribution of this study. The product of this study may have significant implications in clinical settings to personalize care, anticipate outcomes, facilitate shared decision making and informed consent processes for cSCI patients. [ABSTRACT FROM AUTHOR]

This study aimed to assess the effectiveness of machine learning (ML) algorithms in predicting short-term adverse postoperative outcomes after cervical disc arthroplasty (CDA) and to create a user-friendly and accessible tool for this purpose. The American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) database was used to identify patients who underwent CDA. The outcome of interest was the combined occurrence of adverse events in the short-term postoperative period, including prolonged stay, major complications, nonhome discharges, and 30-day readmissions. To predict the combined outcome of interest, short-term adverse postoperative outcomes, 4 different ML algorithms were utilized to develop predictive models, and these models were incorporated into an open access web application. A total of 6,604 patients that underwent CDA were included in the analysis. The mean area under the receiver operating characteristic curve (AUROC) and accuracy were 0.814 and 87.8% for all algorithms. SHapley Additive exPlanations (SHAP) analyses revealed that white race was the most important predictor variable for all 4 algorithms. The following URL will take users to the open access web application created to provide predictions for individual patients based on their characteristics: huggingface.co/spaces/MSHS-Neurosurgery-Research/NSQIP-CDA. ML approaches have the potential to predict postoperative outcomes after CDA surgery. As the amount of data in spinal surgery grows, the development of predictive models as clinically useful decision-making tools may significantly improve risk assessment and prognosis. We present and make publicly available predictive models for CDA intended to achieve the goals mentioned above. [ABSTRACT FROM AUTHOR]

With increasing life expectancies and population aging, the incidence of elderly patients with grade 2 and 3 gliomas is increasing. However, there is a paucity of knowledge on factors affecting their treatment selection and overall survival (OS). Geriatric patients aged between 60 and 89 years with histologically proven grade 2 and 3 intracranial gliomas were identified from the National Cancer Database between 2010 and 2017. We analyzed patients' demographic data, tumor characteristics, treatment modality, and outcomes. The Kaplan-Meier method was used to analyze OS. Univariate and multivariate analyses were performed to assess the predictive factors of mortality and treatment selection. A total of 6257 patients were identified: 3533 (56.3 %) hexagenerians, 2063 (32.9 %) septuagenarians, and 679 (10.8 %) octogenarians. We identified predictors of lower OS in patients, including demographic factors (older age, non-zero Charlson-Deyo score, non-Hispanic ethnicity), socioeconomic factors (low income, treatment at non-academic centers, government insurance), and tumor-specific factors (higher grade, astrocytoma histology, multifocality). Receiving surgery and chemotherapy were associated with a lower risk of mortality, whereas receiving radiotherapy was not associated with better OS. Our findings provide valuable insights into the complex interplay of demographic, socioeconomic, and tumor-specific factors that influence treatment selection and OS in geriatric grade 2 and 3 gliomas. We found that advancing age correlates with a decrease in OS and a reduced likelihood of undergoing surgery, chemotherapy, or radiotherapy. While receiving surgery and chemotherapy were associated with improved OS, radiotherapy did not exhibit a similar association. [ABSTRACT FROM AUTHOR]

This study aims to demonstrate the capacity of natural language processing and topic modeling to manage and interpret the vast quantities of scholarly publications in the landscape of stroke research. These tools can expedite the literature review process, reveal hidden themes, and track rising research areas. Our study involved reviewing and analyzing articles published in five prestigious stroke journals, namely Stroke, International Journal of Stroke, European Stroke Journal, Translational Stroke Research, and Journal of Stroke and Cerebrovascular Diseases. The team extracted document titles, abstracts, publication years, and citation counts from the Scopus database. BERTopic was chosen as the topic modeling technique. Using linear regression models, current stroke research trends were identified. Python 3.1 was used to analyze and visualize data. Out of the 35,779 documents collected, 26,732 were classified into 30 categories and used for analysis. "Animal Models," "Rehabilitation," and "Reperfusion Therapy" were identified as the three most prevalent topics. Linear regression models identified "Emboli," "Medullary and Cerebellar Infarcts," and "Glucose Metabolism" as trending topics, whereas "Cerebral Venous Thrombosis," "Statins," and "Intracerebral Hemorrhage" demonstrated a weaker trend. The methodology can assist researchers, funders, and publishers by documenting the evolution and specialization of topics. The findings illustrate the significance of animal models, the expansion of rehabilitation research, and the centrality of reperfusion therapy. Limitations include a five-journal cap and a reliance on high-quality metadata. [ABSTRACT FROM AUTHOR]

Acute traumatic cervical spinal cord injury (cSCI) leads in temporary or permanent impairment of motor function and sensation, and has devastating short- and long-term consequences, including a high morbidity and mortality rate. cSCI has major effects on lifespan, functional capacity, mental health, and socioeconomic stability. The prevalence of chronic spinal cord injuries in the United States is believed to be close to 300,000. Given the significance of fast diagnosis and therapy in the management of SCI, machine learning (ML) applications have the potential to improve best practices and the standard of care. The aim of the study is to use machine learning ML algorithms to predict in-hospital mortality, non-home discharges, prolonged length of stay (LOS), prolonged length of intensive care unit stay (ICU-LOS), and major complications in patients with cSCI and incorporate the resulting ML models into a user-friendly web application for use in the clinical setting. This was a retrospective machine learning classification study (outcomes were binary categorical) for prognostication in patients with cSCI. The American College of Surgeons (ACS) Trauma Quality Program (TQP) database was used to identify patients with cSCI. Adult patients (aged 18 and over) with isolated cSCI were identified by the International Classification of Diseases, Tenth Revision (ICD-10) codes S12.X, S13.X, S14.X. In this study, in-hospital mortality, nonhome discharges, prolonged LOS, prolonged ICU-LOS, and major complications were the outcomes of interest. The performance of the models was both graphically and numerically tested. Graphically, the receiver operating characteristic (ROC) curve and precision-recall curve (PRC) were used, and numerically, the evaluation metrics used were: the area under the ROC (AUROC), accuracy, the area under PRC (AUPRC), precision, and recall. In addition, SHapley Additive exPlanations (SHAP) were used to establish the relative significance of predictor variables. In a ratio of 60:20:20, data from 2019 to 2021 was divided into training, validation, and test sets. Four ML algorithms (XGBoost, LightGBM, CatBoost, and Random Forest) were used and integrated into an open-access web application to predict the outcomes of interest for individual patients depending on their characteristics. The study found that the ML algorithms had high area under the receiver operating characteristic curve (AUROC) values in predicting outcomes for patients with at EDH. In particular, the algorithms had an AUROC value range of between 0.850 to 0.886 for in-hospital mortality, 0.838 to 0.845 for nonhome discharges, 0.756 to 0.804 for prolonged LOS, 0.741 to 0.759 for prolonged ICU-LOS, and 0.719 to 0.768 for major complications. This study aimed to improve the prognosis of patients with cSCI using ML techniques and developed a web application for convenient clinical integration. It was shown that machine learning algorithms could aid in risk stratification and have considerable potential for predicting in-hospital outcomes in cSCI patients. Results demonstrated good performance for predicting in-hospital mortality, and non-home discharges, fair performance for prolonged LOS, prolonged ICU-LOS, and major complications. This abstract does not discuss or include any applicable devices or drugs. [ABSTRACT FROM AUTHOR]

The goal of this study is to implement machine learning (ML) algorithms to predict mortality, non-home discharge, prolonged length of stay (LOS), prolonged length of intensive care unit stay (ICU-LOS), and major complications in patients diagnosed with thoracolumbar spinal cord injury, while creating a publicly accessible online tool. The American College of Surgeons Trauma Quality Program database was used to identify patients with thoracolumbar spinal cord injury. Feature selection was performed with the Least Absolute Shrinkage and Selection Operator algorithm. Five ML algorithms, including TabPFN, TabNet, XGBoost, LightGBM, and Random Forest, were used along with the Optuna optimization library for hyperparameter tuning. A total of 147,819 patients were included in the analysis. For each outcome, we determined the best model for deployment in our web application based on the area under the receiver operating characteristic (AUROC) values. The top performing algorithms were as follows: LightGBM for mortality with an AUROC of 0.885, TabPFN for non-home discharge with an AUROC of 0.801, LightGBM for prolonged LOS with an AUROC of 0.673, Random Forest for prolonged ICU-LOS with an AUROC of 0.664, and LightGBM for major complications with an AUROC of 0.73. ML models demonstrate good predictive ability for in-hospital mortality and non-home discharge, fair predictive ability for major complications and prolonged ICU-LOS, but poor predictive ability for prolonged LOS. We have developed a web application that allows these models to be accessed. [ABSTRACT FROM AUTHOR]

Objective: We study the effectiveness and safety of intrathecal baclofen therapy for the treatment of spasticity and gait improvement in patients suffering from hereditary spastic paraplegia. Methods: Sixteen patients diagnosed with hereditary spastic paraplegia (mean age: 43 years) were enrolled in this open prospective study. The main inclusion criteria were: spastic paraparesis with a negative laboratory and imaging work-up (apart from spinal cord atrophy), unsuccessful trial of oral antispasticity drugs. An intrathecal baclofen trial was initially performed and a pump for the intrathecal administration of baclofen was implanted to the patients who responded favorably to the baclofen trial. The patients were followed for lower limbs' spasticity, walking performance and complications. Results: Fourteen patients had a positive baclofen trial and were submitted to the implantation of the baclofen pump. The average follow-up period was 25.8 months. All patients had a reduction in lower limbs' spasticity measured in the modified Ashworth scale from 2.6 (±0.8) to 0.7 (±0.9) (p = .000). Walking ability was improved in a modified version of the functional walking scale of the Gillette Functional Assessment Questionnaire from 5.9 (±1.7) to 7.4 (±2.0) (p = .001). Two patients had to be reoperated due to a catheter fracture. Conclusions: Intrathecal baclofen can offer an improvement in spasticity and in the walking performance in patients suffering from hereditary spastic paraplegia. The underlying residual motor function and the patient's adherence to the rehabilitation program might contribute to the post-operative improvement of gait. [ABSTRACT FROM AUTHOR]

The current research on geriatric patients with spinal chondrosarcoma is limited. This study aimed to investigate the demographics, patterns of care, and survival of geriatric patients with chondrosarcoma of the mobile spine. The National Cancer Database was queried from 2008 to 2018 for geriatric patients (60–89 years) with chondrosarcoma of the mobile spine. The primary outcome of this study was overall survival. The secondary outcome was treatment utilization patterns. Survival analyses were conducted using log-rank tests and Cox proportional hazards regressions. Logistic regression models were utilized to assess correlations between baseline variables and treatment utilization. The database retrieved 122 patients. While 43.7% of the patients presented with tumors exceeding 5 cm in size, the incidence of regional lymph node involvement or distant metastases was relatively low, affecting only 5% of the patients. Furthermore, 22.3% of the patients had tumors graded as 3–4. The 5-year overall survival rate was 52.9% (95% confidence interval: 42–66.6). The mortality risk was significantly associated with age, tumor grade and stage, and treatment plan. Most patients (79.5%) underwent surgery, while 35.9% and 4.2% were treated with radiotherapy and chemotherapy, respectively. Age, race, comorbidities, geographical region, tumor stage, and healthcare facility type significantly correlated with treatment utilization. Surgical resection significantly lowered the mortality risk in geriatric patients with spinal chondrosarcomas. Demographic and geographical factors significantly dictated treatment plans. Further studies are required to assess the role of radiotherapy and chemotherapy in treating these patients in the modern era. [ABSTRACT FROM AUTHOR]

Numerous factors have been associated with the survival outcomes in patients with spinal cord gliomas (SCG). Recognizing these specific determinants is crucial, yet it is also vital to establish a reliable and precise prognostic model for estimating individual survival outcomes. The objectives of this study are twofold: first, to create an array of interpretable machine learning (ML) models developed for predicting survival outcomes among SCG patients; and second, to integrate these models into an easily navigable online calculator to showcase their prospective clinical applicability. This was a retrospective, population-based cohort study aiming to predict the outcomes of interest, which were binary categorical variables, in SCG patients with ML models. The National Cancer Database (NCDB) was utilized to identify adults aged 18 years or older who were diagnosed with histologically confirmed SCGs between 2010 and 2019. The outcomes of interest were survival outcomes at three specific time points postdiagnosis: 1, 3, and 5 years. These outcomes were formed by combining the "Vital Status" and "Last Contact or Death (Months from Diagnosis)" variables. Model performance was evaluated visually and numerically. The visual evaluation utilized receiver operating characteristic (ROC) curves, precision-recall curves (PRCs), and calibration curves. The numerical evaluation involved metrics such as sensitivity, specificity, accuracy, area under the PRC (AUPRC), area under the ROC curve (AUROC), and Brier Score. We employed five ML algorithms—TabPFN, CatBoost, XGBoost, LightGBM, and Random Forest—along with the Optuna library for hyperparameter optimization. The models that yielded the highest AUROC values were chosen for integration into the online calculator. To enhance the explicability of our models, we utilized SHapley Additive exPlanations (SHAP) for assessing the relative significance of predictor variables and incorporated partial dependence plots (PDPs) to delineate the influence of singular variables on the predictions made by the top performing models. For the 1-year survival analysis, 4,913 patients [5.6% with 1-year mortality]; for the 3-year survival analysis, 4,027 patients (11.5% with 3-year mortality]; and for the 5-year survival analysis, 2,854 patients (20.4% with 5-year mortality) were included. The top models achieved AUROCs of 0.938 for 1-year mortality (TabPFN), 0.907 for 3-year mortality (LightGBM), and 0.902 for 5-year mortality (Random Forest). Global SHAP analyses across survival outcomes at different time points identified histology, tumor grade, age, surgery, radiotherapy, and tumor size as the most significant predictor variables for the top-performing models. This study demonstrates ML techniques can develop highly accurate prognostic models for SCG patients with excellent discriminatory ability. The interactive online calculator provides a tool for assessment by physicians (https://huggingface.co/spaces/MSHS-Neurosurgery-Research/NCDB-SCG). Local interpretability informs prediction influences for a given individual. External validation across diverse datasets could further substantiate potential utility and generalizability. This robust, interpretable methodology aligns with the goals of precision medicine, establishing a foundation for continued research leveraging ML's predictive power to enhance patient counseling. [ABSTRACT FROM AUTHOR]

Temporal lobe epilepsy is the most common reason behind drug-resistant seizures and temporal lobectomy (TL) is performed after all other efforts have been taken for a Temporal lobe epilepsy. Our study aims to develop multiple machine learning (ML) models capable of predicting postoperative outcomes following TL surgery. Data from the American College of Surgeons National Surgical Quality Improvement Program database identified patients who underwent TL surgery. We focused on 3 outcomes: prolonged length of stay (LOS), nonhome discharges, and 30-day readmissions. Six ML algorithms, TabPFN, XGBoost, LightGBM, Support Vector Machine, Random Forest, and Logistic Regression, coupled with the Optuna optimization library for hyperparameter tuning, were tested. Models with the highest area under the receiver operating characteristic (AUROC) values were included in the web application. SHapley Additive exPlanations was used to evaluate importance of predictor variables. Our analysis included 423 patients. Of these patients, 111 (26.2%) experienced prolonged LOS, 33 (7.8%) had nonhome discharges, and 29 (6.9%) encountered 30-day readmissions. The top-performing models for each outcome were those built with the Random Forest algorithm. The Random Forest models yielded AUROCs of 0.868, 0.804, and 0.742 in predicting prolonged LOS, nonhome discharges, and 30-day readmissions, respectively. Our study uses ML to forecast adverse postoperative outcomes following TL. We developed accessible predictive models that enhance prognosis prediction for TL surgery. Making ML models available for this purpose represents a significant advancement in shifting toward a more patient-centric, data-driven paradigm. [ABSTRACT FROM AUTHOR]

Meningiomas display diverse biological traits and clinical behaviors, complicating patient outcome prediction. This heterogeneity, along with varying prognoses, underscores the need for a precise, personalized evaluation of postoperative outcomes. Data from the American College of Surgeons National Surgical Quality Improvement Program database identified patients who underwent intracranial meningioma resections from 2014 to 2020. We focused on 5 outcomes: prolonged LOS, nonhome discharges, 30-day readmissions, unplanned reoperations, and major complications. Six machine learning algorithms, including TabPFN, TabNet, XGBoost, LightGBM, Random Forest, and Logistic Regression, coupled with the Optuna optimization library for hyperparameter tuning, were tested. Models with the highest area under the receiver operating characteristic (AUROC) values were included in the web application. SHapley Additive exPlanations were used to evaluate the importance of predictor variables. Our analysis included 7000 patients. Of these patients, 1658 (23.7%) had prolonged LOS, 1266 (18.1%) had nonhome discharges, 573 (8.2%) had 30-day readmission, 253 (3.6%) had unplanned reoperation, and 888 (12.7%) had major complications. Performance evaluation indicated that the top-performing models for each outcome were the models built with LightGBM and Random Forest algorithms. The LightGBM models yielded AUROCs of 0.842 and 0.846 in predicting prolonged LOS and nonhome discharges, respectively. The Random Forest models yielded AUROCs of 0.717, 0.76, and 0.805 in predicting 30-day readmissions, unplanned reoperations, and major complications, respectively. The study successfully demonstrated the potential of machine learning models in predicting short-term adverse postoperative outcomes after meningioma resections. This approach represents a significant step forward in personalizing the information provided to meningioma patients. [ABSTRACT FROM AUTHOR]

Spinal epidural abscess may require prompt surgical intervention. Ventral cervical abscesses pose a particular challenge regarding the approach for surgical evacuation. The aim of this article was to describe the technical nuances of a posterior transpedicular cervical approach for evacuation of a ventral epidural abscess. After a standard laminectomy, a foraminotomy was performed to identify the exiting nerve root. Then the medial aspect of the pedicle below the nerve was drilled. This allowed the insertion of a dissector to reach the ventral epidural space and drain the contents in conjunction with suction and irrigation. The posterolateral aspect of the superior endplate of the respective vertebra could be further drilled at this point, allowing access to the disc space with minimal retraction of the exiting nerve root. Two patients underwent emergent evacuation of a ventral epidural abscess in the cervical spine using this technique. Radiographic and clinical improvement was evident after evacuation of the abscesses in both cases. Access to the ventral epidural space is feasible using a transpedicular approach in the cervical spine for evacuation of an epidural abscess. [ABSTRACT FROM AUTHOR]

Pneumorrhachis is the presence of air within the spinal canal and is most often traumatic or iatrogenic in etiology. Rarely, a small amount of pneumorrhachis can be seen with spontaneous pneumomediastinum. Here we describe a case of asymptomatic longitudinally extensive pneumorrhachis associated with spontaneous pneumomediastinum. A man in his mid-20s presented to the hospital with subcutaneous emphysema after a choking episode. On imaging of his neck and chest he was noted to have extensive pneumorrhachis with anterior displacement of the spinal cord. Out of concern for further accumulation of air, he was monitored in an intensive care setting for 48 hours but remained asymptomatic. He was discharged home after ruling out esophageal rupture as a cause for his pneumomediastinum. On follow-up 1 month after discharge he was doing well without symptoms. In cases of spontaneous pneumomediastinum, air can be entrained within the spinal canal. Special attention should be paid to any patient with pneumomediastinum with neurologic symptoms, as this could be due to pneumorrhachis. [ABSTRACT FROM AUTHOR]

Thoracic spinal deformities may reduce chest wall compliance, leading to respiratory complications. The first SARS-CoV-2 (L-variant) strain caused critical respiratory illness, especially in vulnerable patients. This study investigates the association between scoliosis and SARS-CoV-2 (COVID-19) disease course severity. Clinical data of 129 patients treated between March 2020 to June 2021 who received a positive COVID-19 polymerase chain reaction result from Mount Sinai and had a scoliosis ICD-10 code (M41.0–M41.9) was retrospectively analyzed. Degree of coronal plane scoliosis on imaging was confirmed by 2 independent measurers and grouped into no scoliosis (Cobb angle <10°), mild (10°–24°), moderate (25°–39°), and severe (>40°) cohorts. Baseline characteristics were compared, and a multivariable logistic regression controlling for clinically significant comorbidities examined the significance of scoliosis as an independent risk factor for hospitalization, intensive care unit (ICU) admission, acute respiratory distress syndrome (ARDS), mechanical ventilation, and mortality. The no (n = 42), mild (n = 14), moderate (n = 44), and severe scoliosis (n = 29) cohorts differed significantly only in age (P = 0.026). The percentage of patients hospitalized (P = 0.59), admitted to the ICU (P = 0.33), developing ARDS (P = 0.77), requiring mechanical ventilation (P = 1.0), or who expired (P = 0.77) did not significantly differ between cohorts. The scoliosis cohorts did not have a significantly higher likelihood of hospital admission (mild P = 0.19, moderate P = 0.67, severe P = 0.98), ICU admission (P = 0.97, P = 0.94, P = 0.22), ARDS (P = 0.87, P = 0.74, P = 0.94), mechanical ventilation (P = 0.73, P = 0.69, P = 0.70), or mortality (P = 0.74, P = 0.87, P = 0.66) than the no scoliosis cohort. Scoliosis was not an independent risk factor for critical COVID-19 illness. No trends indicated any consistent effect of degree of scoliosis on increased adverse outcome likelihood. [ABSTRACT FROM AUTHOR]

Objective: Intraventricular cystic tumors constitute a surgical challenge, because of their deep location and the histologically benign nature of most of them. We aim to present concisely, yet comprehensively, the role of neuroendoscopy in the treatment of intraventricular cystic tumors. Methods: A literature review searching for applications of endoscopy in the treatment of intraventricular cystic tumors is presented. Our experience is added to the presented data. In controversial issues, a comparison is made with traditional treatment methods. Results: Intraventricular endoscopy has been successfully used in the treatment of the whole range of intraventricular cystic tumors. The most common indication is the treatment of colloid cysts. In the treatment of colloid cysts, a comparison with microsurgical techniques showed that endoscopy is advantageous in regard to operative morbidity and postoperative shunt dependency but is associated with a slightly higher recurrence rate. Conclusion: Intraventricular endoscopy has emerged as a viable option in the treatment of intraventricular cystic tumors. [Copyright &y& Elsevier]

Seizures and epilepsy after traumatic brain injury (TBI) negatively affect quality of life and longevity. Antiseizure medication (ASM) prophylaxis after severe TBI is associated with improved outcomes; these medications are rarely used in mild TBI. However, a paucity of research is available to inform ASM use in complicated mild TBI (cmTBI) and no empirically based clinical care guidelines for ASM use in cmTBI exist. We aim to identify seizure prevention and management strategies used by clinicians experienced in treating patients with cmTBI to characterize standard care and inform a systematic approach to clinical decision making regarding ASM prophylaxis. We recruited a multidisciplinary international cohort through professional organizational listservs and social media platforms. Our questionnaire assessed factors influencing ASM prophylaxis after cmTBI at the individual, institutional, and health system–wide levels. Ninety-two providers with experience managing cmTBI completed the survey. We found a striking diversity of ASM use in cmTBI, with 30% of respondents reporting no/infrequent use and 42% reporting frequent use; these tendencies did not differ by provider or institutional characteristics. Certain conditions universally increased or decreased the likelihood of ASM use and represent consensus. Based on survey results, ASMs are commonly used in patients with cmTBI who experience acute secondary seizure or select positive neuroimaging findings; we advise caution in elderly patients and those with concomitant neuropsychiatric illness. This study is the first to characterize factors influencing clinical decision making in ASM prophylaxis after cmTBI based on multidisciplinary multicenter provider practices. Prospective controlled studies are necessary to inform standardized guideline development. [ABSTRACT FROM AUTHOR]

Background Context: Nearly 70% of patients with multiple myeloma (MM) experience vertebral fracture. As a consequence, these patients suffer significantly poorer quality of life. However, no studies have characterized the natural progression of these fractures.Purpose: The purpose of this study was to characterize the progression of MM-associated vertebral fractures.Study Design/setting: A consecutive retrospective chart review at a single tertiary-care center was carried out.Patient Sample: Patients with MM and pathologic vertebral fracture with at least one follow-up between January 2007 and December 2013 were included. Radiographic measurements were recorded until last follow-up (LFU) or until surgical intervention or patient death. Patients with a history of vertebral fracture not associated with MM were excluded.Outcome Measures: The primary outcome measure was change in height of the fractured vertebrae. Fractures were characterized by Genant grade and morphology.Methods: At baseline and each follow-up, anterior, middle, and posterior vertebral body heights were measured from midline sagittal T1-weighted magnetic resonance imaging. Student t tests and Fisher exact tests were performed to identify variables associated with fracture progression.Results: Among 33 patients, 67 fractures were followed. Sixty-four percent of patients were female, with a mean age of 66. Baseline mean anterior, middle, and posterior vertebral body height losses were 30%, 36%, and 15%, respectively. Forty-three percent of fractures were Genant grade 3, and 57% were biconcave. Mean time to LFU was 40 months. At LFU, mean anterior, middle, and posterior vertebral body height losses increased to 47% (p<.01), 49% (p<.01), and 28% (p<.01), respectively. More fractures became Genant grade 3 (75%, p<.01) and wedge (54%, p=.03). On average, patients lost 0.83% in vertebral body height per month, with initial Genant grade 1 fractures progressing most rapidly (1.69%/month, p<.01). Patients treated with bisphosphonates suffered less additional height loss compared with untreated patients (14% vs. 24%, p=.07).Conclusions: We observed significant fracture progression despite high utilization of bisphosphonates. Patients lost nearly 1% of additional vertebral body height per month, with the least severe presenting fractures progressing most rapidly, highlighting the necessity for early referral to spine specialists and evidence-based guidelines for surveillance and treatment in the myeloma population. [ABSTRACT FROM AUTHOR]

Background Context: Patients with multiple myeloma (MM) incur significant degradation in quality of life because of progressive osteolytic vertebral fractures. No studies have investigated predictors of fracture progression, and limited data are available for predicting the development of future fractures.Purpose: The purpose of this study was to identify independent predictors of vertebral fracture progression and the development of future vertebral fracture.Study Design/setting: This is a consecutive retrospective chart review at a single tertiary-care center.Patient Sample: Patients with MM and pathologic vertebral fracture with radiographic follow-up between January 2007 and December 2013 were included. Radiographic measurements were recorded at presentation with fracture and first follow-up (FFU) after at least three months. Patients with a history of vertebral fracture not associated with MM were excluded.Outcome Measures: The primary outcome measure was the rate of vertebral body height loss. The development of future vertebral fracture was secondary.Methods: Anterior, middle, and posterior vertebral body heights were measured from midline sagittal T1-weighted magnetic resonance imaging (MRI). Future fracture-free survival was calculated using Kaplan-Meier analysis. Multivariable regression was used to identify independent predictors of the rate of vertebral height loss. Multivariable Cox proportional hazards modeling was used to identify predictors of developing future vertebral fracture.Results: Thirty-three patients with 67 fractures were followed for a median of 10.8 months to FFU. Sixty-four percent of the patients were female and the median age was 66. The median additional vertebral height loss between presentation and FFU was 15%, whereas the median rate of vertebral height loss was 1.01%/month. More rapid vertebral height loss was predicted by dyslipidemia (β=0.36, p=.05), previous non-vertebral pathologic fracture related to MM (β=0.51, p=.01), and Durie-Salmon Stage III (β=0.66, p=.06). The median time to future fracture was 25.1 months; the 5-year future fracture-free survival rate was 34%. Osteopenia/osteoporosis (hazard ratio [HR]: 9.28, p<.01), serum light chains (HR: 1.37, p=.06), and serum calcium (HR: 1.62, p=.05) predicted the development of future vertebral fracture.Conclusions: We observed significant fracture progression over a short follow-up period. Several comorbidities and laboratory measures predicted more rapid vertebral height loss and the development of future fracture. Identifying risk factors for increased fracture burden may allow spine specialists to pursue earlier and appropriate intervention to optimize function and minimize morbidity. [ABSTRACT FROM AUTHOR]

The New York Neurotrauma Consortium (NYNC) is a nascent multidisciplinary research and advocacy organization based in the New York Metropolitan Area (NYMA). It aims to advance health equity and optimize outcomes for traumatic brain and spine injury patients. Given the extensive racial, ethnic, and socioeconomic diversity of the NYMA, global health frameworks aimed at eliminating disparities in neurotrauma may provide a relevant and useful model for the informing research agendas of consortia like the NYNC. In this review, we present a comparative analysis of key health disparities in traumatic brain injury (TBI) that persists in the NYMA as well as in low- and middle-income countries (LMICs). Examples include (a) inequitable access to quality care due to fragmentation of healthcare systems, (b) barriers to effective prehospital care for TBI, and (c) socioeconomic challenges faced by patients and their families during the subacute and chronic postinjury phases of TBI care. This review presents strategies to address each area of health disparity based on previous studies conducted in both LMIC and high-income country settings. Increased awareness of healthcare disparities, education of healthcare professionals, effective policy advocacy for systemic changes, and fostering racial diversity of the trauma care workforce can guide the development of trauma care systems in the NYMA that are free of racial and related healthcare disparities. [ABSTRACT FROM AUTHOR]

Both unfractionated heparin (UH) and low-molecular-weight heparin (LMWH) are routinely used prophylactically after traumatic brain injury (TBI) to prevent deep vein thrombosis (DVT). Their comparative risk for development or worsening of intracranial hemorrhage necessitating cranial decompression is unclear. Furthermore, the absence of a specific antidote for LMWH may lead to UH being used more often for high-risk patients. This study aims to compare the incidence of delayed cranial decompression occurring after initiation of prophylactic UH versus LMWH using the National Trauma Data Bank. Cranial decompression procedures included craniotomy and craniectomy. Multiple imputation was used for missing data. Propensity score matching was used to account for selection bias between UH and LMWH. The 1:1 matched groups were compared using logistic regression for the primary outcome of postprophylaxis cranial decompression. A total of 218,594 patients with TBI were included, with 61,998 (28.3%) receiving UH and 156,596 (71.7%) receiving LMWH as DVT prophylaxis. The UH group had higher patient age, body mass index, comorbidity rates, Injury Severity Score, and worse motor Glasgow Coma Scale score. After the UH and LMWH groups were matched for these factors, logistic regression showed lower rates of postprophylaxis cranial decompression for the LMWH group (odds ratio, 0.13; 95% confidence interval, 0.11–0.16; P < 0.001). Despite the absence of a specific antidote, LMWH was associated with lower rates of need for post-DVT-prophylaxis in craniotomy/craniectomy. This finding questions the notion of UH being safer for patients with TBI because it can be readily reversed. Randomized studies are needed to elucidate causality. [ABSTRACT FROM AUTHOR]

Stage 3 acute kidney injury (AKI) has been observed to develop after serious traumatic brain injury (TBI) and is associated with worse outcomes, though its incidence is not consistently established. This study aims to report the incidence of stage 3 AKI in serious isolated TBI in a large, national trauma database and explore associated predictive factors. This was a retrospective cohort study using 2015–2018 data from the American College of Surgeons Trauma Quality Improvement Program, a national database of trauma patients. Adult trauma patients admitted to the hospital with isolated serious TBI were included. Variables relating to demographics, comorbidities, vitals, hospital presentation, and course of stay were assessed. Imputed multivariable logistic regression assessed factors predictive of stage 3 AKI development. A total of 342,675 patients with isolated serious TBI were included, 1585 (0.5%) of whom developed stage 3 AKI. Variables associated with stage 3 AKI in multivariable analysis were older age, male sex, Black race, higher body mass index, history of hypertension, diabetes, peripheral artery disease, chronic kidney disease, higher injury severity score, higher heart rate on arrival, lower oxygen saturation and motor Glasgow Coma Scale, admission to the intensive care unit or operating room, development of catheter-associated urinary tract infections or acute respiratory distress syndrome, longer intensive care unit stay, and ventilation duration. Stage 3 AKI occurred in 0.5% of serious TBI cases. Complications of acute respiratory distress syndrome and catheter-associated urinary tract infections are more likely to co-occur with stage 3 AKI in patients with serious TBI. [ABSTRACT FROM AUTHOR]

New York City is the epicenter of the novel coronavirus disease 2019 (COVID-19) pandemic in the United States. Traumatic brain injury accounts for a significant proportion of admissions to our trauma center. We sought to characterize the effect of the pandemic on neurotraumas, given the cancellation of nonessential activities during the crisis. Retrospective and prospective reviews were performed from November 2019 to April 2020. General demographics, clinical status, mechanism of trauma, diagnosis, and treatment instituted were recorded. We dichotomized the data between pre−COVID-19 (before 1 March) and COVID-19 periods and compared the differences between the 2 groups. We present the timeline of events since the beginning of the crisis in relation to the number of neurotraumas. A total of 150 patients composed our cohort with a mean age of 66.2 years (standard deviation ±18.9), and 66% were male. More males sustained neurotrauma in the COVID-19 period compared with the pre−COVID-19 (60.4% vs. 77.6%, P = 0.03). The most common mechanism of trauma was mechanical fall, but it was observed less frequently compared with the pre−COVID-19 period (61.4% vs. 40.8; P = 0.03). Subdural hematoma, traumatic subarachnoid hemorrhage, and intracerebral contusion accounted for the most common pathologies in both periods. Nonoperative management was selected for most patients (79.2 vs. 87.8%, P = 0.201) in both periods. A decrease in the frequency of neurotraumas was observed during the COVID-19 crisis concomitant with the increase in COVID-19 patients in the city. This trend began after the cancellation of nonessential activities and implementation of social distancing recommendations. [ABSTRACT FROM AUTHOR]

Background Context: Nearly 80% of patients with newly diagnosed multiple myeloma (MM) have bony lesions on magnetic resonance imaging (MRI). These lesions may progress to debilitating vertebral fractures. No studies have quantitatively characterized these fractures or identified predictors of fracture burden and severity.Purpose: The purpose of this study was to characterize the clinical and radiologic features of these fractures and to identify independent predictors of fracture burden and severity.Study Design/setting: A consecutive retrospective chart review was conducted from January 2007 to December 2013 at a single tertiary-care institution.Patient Sample: Patients with diagnoses of both MM and vertebral fracture were included in this study. Those with a history of non-MM vertebral fracture were excluded.Outcome Measures: The primary outcome measure was height loss of the fractured vertebral body, whereas secondary outcome measures included number of fractures and morphology.Methods: Data were collected at fracture presentation. Radiologic data were obtained from T1-weighted MRI. Anterior, middle, and posterior vertebral body height losses were recorded, and a Genant grading was made. Multivariable Poisson and logistic regression were performed to identify predictors of fracture burden and severity.Results: Among 50 patients presenting with vertebral fracture, 124 fractures were observed. The majority (76%) of these patients did not have a previous MM diagnosis. The most common presenting symptom was back pain (84%), followed by neurologic (54%) and constitutional (50%) symptoms. The mean anterior, middle, and posterior height losses of the fractured vertebral body were 30%, 37%, and 16%, respectively. Twenty percent of fractures were Genant Grade 1 (mild), whereas 32% and 48% were grades 2 (moderate) and 3 (severe). Fifty-five percent of fractures were biconcave, whereas 32% and 13% were wedge and crush fractures. Lower body mass index and albumin and increased myeloma protein, light chains, and creatinine predicted an increased number of fractures at presentation. Increased β2-microglobulin and creatinine predicted more severe vertebral fractures.Conclusions: In the present study, 124 fractures were observed among 50 patients. These fractures were generally severe, biconcave, and in the thoracic spine. Laboratory signs of advanced MM predict greater fracture burden and severity. In the future, monitoring of these predictors may raise suspicion for an MM-associated vertebral fracture. [ABSTRACT FROM AUTHOR]