Delays in the diagnosis and surgical treatment of drug-resistant epilepsy: A cohort study.

OBJECTIVE: Delay in referral for epilepsy surgery of patients with drug-resistant epilepsy (DRE) is associated with decreased quality of life, worse surgical outcomes, and increased risk of sudden unexplained death in epilepsy (SUDEP). Understanding the potential causes of delays in referral and treatment is crucial for optimizing the referral and treatment process. We evaluated the treatment intervals, demographics, and clinical characteristics of patients referred for surgical evaluation at our level 4 epilepsy center in the U.S. Intermountain West. METHODS: We retrospectively reviewed the records of patients who underwent surgery for DRE between 2012 and 2022. Data collected included patient demographics, DRE diagnosis date, clinical characteristics, insurance status, distance from epilepsy center, date of surgical evaluation, surgical procedure, and intervals between different stages of evaluation. RESULTS: Within our cohort of 185 patients with epilepsy (99 female, 53.5%), the mean ± standard deviation (SD) age at surgery was 38.4 ± 11.9 years. In this cohort, 95.7% of patients had received definitive epilepsy surgery (most frequently neuromodulation procedures) and 4.3% had participated in phase 2 intracranial monitoring but had not yet received definitive surgery. The median (1st-3rd quartile) intervals observed were 10.1 (3.8-21.5) years from epilepsy diagnosis to DRE diagnosis, 16.7 (6.5-28.4) years from epilepsy diagnosis to surgery, and 1.4 (0.6-4.0) years from DRE diagnosis to surgery. We observed significantly shorter median times from epilepsy diagnosis to DRE diagnosis (p < .01) and epilepsy diagnosis to surgery (p < .05) in patients who traveled further for treatment. Patients with public health insurance had a significantly longer time from DRE diagnosis to surgery (p < .001). SIGNIFICANCE: Both shorter distance traveled to our epilepsy center and public health insurance were predictive of delays in diagnosis and treatment intervals. Timely referral of patients with DRE to specialized epilepsy centers for surgery evaluation is crucial, and identifying key factors that may delay referral is paramount to optimizing surgical outcomes.

Hemorrhagic Safety of Magnetic Resonance-Guided Focused Ultrasound Thalamotomy for Tremor without Interruption of Antiplatelet or Anticoagulant Therapy.

INTRODUCTION: Magnetic resonance-guided focused ultrasound (MRgFUS) thalamotomy is an incision-less ablative technique used to treat medically refractory tremor. Although intracerebral hemorrhage has not been reported with MRgFUS thalamotomy for the treatment of movement disorders, clinicians commonly interrupt active blood thinning medications prior to the procedure or offer gamma knife radiosurgery instead. However, MRgFUS uses focal thermoablation, and bleeding risk is likely minimal. This study aimed to evaluate the safety of MRgFUS thalamotomy in patients with essential tremor (ET) and tremor-dominant Parkinson's disease (PD) without interrupting anticoagulant or antiplatelet therapies. METHODS: This was a single-center retrospective case series of all patients with ET or PD undergoing MRgFUS from February 2019 through December 2022 (n = 96). Demographic variables and medications taken at the time of surgery were obtained. Our primary outcome was the type and frequency of hemorrhagic complications noted on the operative report or postoperative imaging. RESULTS: The mean age of patients was 74.2 years, and 26% were female. Forty patients were taking ≥1 antiplatelet or anticoagulant medications. No patient actively taking anticoagulant or antiplatelet therapies had a hemorrhagic complication during or <48 h after the procedure. CONCLUSION: The frequency of intra- or postoperative complications from MRgFUS was not higher in patients actively taking anticoagulant or antiplatelet therapies relative to those who were not. Our findings suggest that MRgFUS thalamotomy does not necessitate interrupting anticoagulant or antiplatelet therapies. However, given the limited number of patients actively taking these therapies in our cohort (n = 40), additional testing in large, prospective studies should be conducted to further establish safety.

Ethical Considerations in Surgical Decompression for Stroke.

Stroke is a major cause of morbidity and mortality. Neurosurgical decompression is often considered for the treatment of malignant infarcts and intraparenchymal hemorrhages, but this treatment can be frought with ethical dilemmas. In this article, the authors outline the primary principles of bioethics and their application to stroke care, provide an overview of key ethical issues and special situations in the neurosurgical management of stroke, and highlight methods to improve ethical decision-making for patients with stroke. Understanding these ethical principles is essential for stroke care teams to deliver appropriate, timely, and ethical care to patients with stroke.

Ethical considerations in the surgical and neuromodulatory treatment of epilepsy.

Surgical resection and neuromodulation are well-established treatments for those with medically refractory epilepsy. These treatments entail important ethical considerations beyond those which extend to the treatment of epilepsy generally. In this paper, the authors explore these unique considerations through a framework that relates foundational principles of bioethics to features of resective epilepsy surgery and neuromodulation. The authors conducted a literature review to identify ethical considerations for a variety of epilepsy surgery procedures and to examine how foundational principles in bioethics may inform treatment decisions. Healthcare providers should be cognizant of how an increased prevalence of somatic and psychiatric comorbidities, the dynamic nature of symptom burden over time, the individual and systemic barriers to treatment, and variable sociocultural contexts constitute important ethical considerations regarding the use of surgery or neuromodulation for the treatment of epilepsy. Moreover, careful attention should be paid to how resective epilepsy surgery and neuromodulation relate to notions of patient autonomy, safety and privacy, and the shared responsibility for device management and maintenance. A three-tiered approach-(1) gathering information and assessing the risks and benefits of different treatment options, (2) clear communication with patient or proxy with awareness of patient values and barriers to treatment, and (3) long-term decision maintenance through continued identification of gaps in understanding and provision of information-allows for optimal treatment of the individual person with epilepsy while minimizing disparities in epilepsy care.

Evaluating the utility and quality of large administrative databases in pediatric spinal neurosurgery research.

PURPOSE: The purpose of this study was to assess the quality of articles utilizing large administrative databases to answer questions related to pediatric spinal neurosurgery by quantifying their adherence to standard reporting guidelines. METHODS: A systematic literature search was conducted with search terms including "pediatric" and "neurosurgery," associated neurosurgical diagnoses, and the names of known databases. Study abstracts were reviewed to identify clinical studies involving pediatric populations, spine-related pathology or procedures, and large administrative databases. Included studies were graded using the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) criteria. RESULTS: A total of 28 papers of the initial 1496 identified met inclusion criteria. These papers involved 10 databases and had a mean study period of 11.46 ± 12.27 years. The subjects of these research papers were undergoing treatment of scoliosis (n = 5), spinal cord injury (n = 5), spinal cord tumors (n = 9), and spine surgery in general (n = 9). The mean STROBE score was 19.41 ± 2.02 (out of 22). CONCLUSION: Large administrative databases are commonly used within pediatric spine-related neurosurgical research to cover a broad spectrum of research questions and study topics. The heterogeneity of research to this point encourages the continued use of large databases to better understand treatment and diagnostic trends, perioperative and long-term outcomes, and rare pathologies within pediatric spinal neurosurgery.

Pharmacologically informed machine learning approach for identifying pathological states of unconsciousness via resting-state fMRI.

Determining the level of consciousness in patients with disorders of consciousness (DOC) remains challenging. To address this challenge, resting-state fMRI (rs-fMRI) has been widely used for detecting the local, regional, and network activity differences between DOC patients and healthy controls. Although substantial progress has been made towards this endeavor, the identification of robust rs-fMRI-based biomarkers for level of consciousness is still lacking. Recent developments in machine learning show promise as a tool to augment the discrimination between different states of consciousness in clinical practice. Here, we investigated whether machine learning models trained to make a binary distinction between conscious wakefulness and anesthetic-induced unconsciousness would then be capable of reliably identifying pathologically induced unconsciousness. We did so by extracting rs-fMRI-based features associated with local activity, regional homogeneity, and interregional functional activity in 44 subjects during wakefulness, light sedation, and unresponsiveness (deep sedation and general anesthesia), and subsequently using those features to train three distinct candidate machine learning classifiers: support vector machine, Extra Trees, artificial neural network. First, we show that all three classifiers achieve reliable performance within-dataset (via nested cross-validation), with a mean area under the receiver operating characteristic curve (AUC) of 0.95, 0.92, and 0.94, respectively. Additionally, we observed comparable cross-dataset performance (making predictions on the DOC data) as the anesthesia-trained classifiers demonstrated a consistent ability to discriminate between unresponsive wakefulness syndrome (UWS/VS) patients and healthy controls with mean AUC's of 0.99, 0.94, 0.98, respectively. Lastly, we explored the potential of applying the aforementioned classifiers towards discriminating intermediate states of consciousness, specifically, subjects under light anesthetic sedation and patients diagnosed as having a minimally conscious state (MCS). Our findings demonstrate that machine learning classifiers trained on rs-fMRI features derived from participants under anesthesia have potential to aid the discrimination between degrees of pathological unconsciousness in clinical patients.

Evaluating the concordance of functional MRI-based language lateralization and Wada testing in epilepsy patients: A single-center analysis.

BACKGROUND: For patients with drug-resistant epilepsy, surgery may be effective in controlling their disease. Surgical evaluation may involve localization of the language areas using functional magnetic resonance imaging (fMRI) or Wada testing. We evaluated the accuracy of task-based fMRI versus Wada-based language lateralization in a cohort of our epilepsy patients. METHODS: In a single-center, retrospective analysis, we identified patients with medically intractable epilepsy who participated in presurgical language mapping (n = 35) with fMRI and Wada testing. Demographic variables and imaging metrics were obtained. We calculated the laterality index (LI) from task-evoked fMRI activation maps across language areas during auditory and reading tasks to determine lateralization. Possible scores for LI range from -1 (strongly left-hemisphere dominant) to 1 (strongly right-hemisphere dominant). Concordance between fMRI and Wada was estimated using Cohen's Kappa coefficient. Association between the LI scores from the auditory and reading tasks was tested using Spearman's rank correlation coefficient. RESULTS: The fMRI-based laterality indices were concordant with results from Wada testing in 91.4% of patients during the reading task (κ = .55) and 96.9% of patients during the auditory task (κ = .79). The mean LIs for the reading and auditory tasks were -0.52 ± 0.43 and -0.68 ± 0.42, respectively. The LI scores for the language and reading tasks were strongly correlated, r(30) = 0.57 (p = 0.001). CONCLUSION: Our findings suggest that fMRI is generally an accurate, low-risk alternative to Wada testing for language lateralization. However, when fMRI indicates atypical language lateralization (e.g., bilateral dominance), patients may benefit from subsequent Wada testing or intraoperative language mapping.

Subsets of cortico-cortical evoked potentials propagate as traveling waves.

Emerging evidence suggests that the temporal dynamics of cortico-cortical evoked potentials (CCEPs) may be used to characterize the patterns of information flow between and within brain networks. At present, however, the spatiotemporal dynamics of CCEP propagation cortically and subcortically are incompletely understood. We hypothesized that CCEPs propagate as an evoked traveling wave emanating from the site of stimulation. To elicit CCEPs, we applied single-pulse stimulation to stereoelectroencephalography (SEEG) electrodes implanted in 21 adult patients with intractable epilepsy. For each robust CCEP, we measured the timing of the maximal descent in evoked local field potentials and broadband high-gamma power (70-150 Hz) envelopes relative to the distance between the recording and stimulation contacts using three different metrics (i.e., Euclidean distance, path length, geodesic distance), representing direct, subcortical, and transcortical propagation, respectively. Many evoked responses to single-pulse electrical stimulation appear to propagate as traveling waves (~17-30%), even in the sparsely sampled, three-dimensional SEEG space. These results provide new insights into the spatiotemporal dynamics of CCEP propagation.

Discovering how the amygdala shapes human behavior: From lesion studies to neuromodulation.

Case studies of patients with amygdala damage or those receiving direct amygdala stimulation have informed our understanding of the amygdala's role in emotion and cognition. These foundational studies illustrate how the human amygdala influences our present behavior and prioritizes memories of our past in service of future experiences. This broad influence makes the amygdala a novel target for clinical neuromodulation.

Balance and cognitive impairments are prevalent and correlated with age in presurgical patients with essential tremor.

INTRODUCTION: Essential tremor (ET) was long considered a monosymptomatic disorder, but this view has given way to a more comprehensive clinical picture that involves consideration of non-tremor symptoms (e.g., balance impairment, cognitive impairment). Recently, the novel designation of "ET-plus" was proposed to reclassify ET patients who demonstrate these non-tremor clinical features, but the prevalence of ET-plus remains poorly defined. The primary aim of our study was to estimate the prevalence of ET-plus among presurgical patients with ET by applying this reclassification scheme. METHODS: We performed a retrospective review of patients with ET being considered for deep brain stimulation or focused ultrasound thalamotomy. Patient demographics and data from their clinical workups were collected. As part of their clinical workup, patients were screened for preexisting balance and cognitive impairment. Patients with ET were designated as ET-plus if they had balance impairment, cognitive impairment, or tremor at rest. We performed a series of Pearson correlations to examine how individual clinical and demographic variables were related. RESULTS: We identified 92 patients who met the study criteria. Our results indicate that 87% of the presurgical patients in our cohort met the criteria for reclassification as ET-plus. In addition, we observed robust correlations between patient age and balance impairment, cognitive impairment, history of falls, family history of tremor, and ET-plus reclassification. CONCLUSION: We propose that balance and gait impairment should be assessed preoperatively alongside neuropsychological evaluation to improve the counseling and treatment of patients with ET-plus.

Reevaluating Innovations in Medical Student Neurosurgery Education: Lessons Learned Today from Data Collected Before COVID-19.

OBJECTIVE: Virtual learning has made neurosurgery education more available to medical students (MSs) of all stages than ever before. We aimed to evaluate the impact of on-site learning in order to assess whether a return to this method of teaching, when safe, is warranted for MSs of all years. METHODS: Registrants of the 2019 MS Neurosurgery Training Camps were sent precourse and postcourse surveys to assess changes in self-assessed confidence in concrete neurosurgical skills. Data were analyzed using a 2-tailed paired Student's t-test for continuous variables. A P value <0.05 was considered significant. RESULTS: The 2019 Training Camp had 105 attendees, of whom 94 (89.5%) completed both surveys. Students reported statistically significant improvements in every surveyed skill area, except for understanding what is and is not sterile in an operating room. The cohort of MS 3/4 students indicated a postcourse decrease in confidence in their ability to understand what is and is not sterile in an operating room (93.69 ± 16.41 vs. 86.20 ± 21.18; P < 0.05). MS 3/4 students did not benefit in their ability to perform a neurologic examination or tie knots using a 1-handed technique. CONCLUSIONS: Neurosurgical education initiatives for MSs should continue to be developed. Hands-on neurosurgical training experiences for MSs serve as a valuable educational experience. Improvement in training models will lead to capitalizing on MS education to better improve readiness for neurosurgical residency without concern for patient safety.

Machine Learning-Based Analysis and Prediction of Unplanned 30-Day Readmissions After Pituitary Adenoma Resection: A Multi-Institutional Retrospective Study With External Validation.

BACKGROUND: Unplanned readmission after transsphenoidal resection of pituitary adenoma can occur in up to 10% of patients but is unpredictable. OBJECTIVE: To develop a reliable system for predicting unplanned readmission and create a validated method for stratifying patients by risk. METHODS: Data sets were retrospectively collected from the National Surgical Quality Improvement Program and 2 tertiary academic medical centers. Eight machine learning classifiers were fit to the National Surgical Quality Improvement Program data, optimized using Bayesian parameter optimization and evaluated on the external data. Permutation analysis identified the relative importance of predictive variables, and a risk stratification system was built using the trained machine learning models. RESULTS: Readmissions were accurately predicted by several classification models with an area under the receiving operator characteristic curve of 0.76 (95% CI 0.68-0.83) on the external data set. Permutation analysis identified the most important variables for predicting readmission as preoperative sodium level, returning to the operating room, and total operation time. High-risk and medium-risk patients, as identified by the proposed risk stratification system, were more likely to be readmitted than low-risk patients, with relative risks of 12.2 (95% CI 5.9-26.5) and 4.2 (95% CI 2.3-8.7), respectively. Overall risk stratification showed high discriminative capability with a C-statistic of 0.73. CONCLUSION: In this multi-institutional study with outside validation, unplanned readmissions after pituitary adenoma resection were accurately predicted using machine learning techniques. The features identified in this study and the risk stratification system developed could guide clinical and surgical decision making, reduce healthcare costs, and improve the quality of patient care by better identifying high-risk patients for closer perioperative management.