A comprehensive evaluation of career trajectories of the American Association of Neurological Surgeons William P. Van Wagenen fellows.

Objective: To elucidate the current academic, demographic, and professional factors influencing the career trajectories of the American Association of Neurological Surgeons (AANS) William P. Van Wagenen (VW) fellows while also identifying trends that may influence future fellow selection. Methods: Fifty-five VW fellows were identified from 1968 to 2022 from the AANS website, along with corresponding institutions, countries, and continents of study. Additional variables such as age at selection, accruing additional degrees, neurosurgical subspecialty, the number of publications at the time of selection, funding, and h-index were collected from various publicly available sources. Results: Eighty-five percent of VW fellows were male and had a mean age of 34 ± 2.4 years. Ninety-one percent of fellows chose to study in Europe, and 40% had earned additional degrees. Univariate linear regression demonstrated a positive relationship between the year of selection and both age at selection (p = 0.0094) and the number of publications at hire (p < 0.001), while logistic regression revealed that more recently selected fellows were less likely to study in Europe (p = 0.037) and be of the white race (p = 0.0047). Logistic regression also exhibited a positive trend between the year of selection and both the likelihood that the VW fellow was currently enrolled in another fellowship (p = 0.019) and possessed additional degrees (p = 0.0019). Females were shown to have fewer publications at hire compared to males (p = 0.04). Conclusions: Most Van Wagenen fellows are academically productive members of the neurosurgical community. Increased attention is likely to be placed on both academic, research, and individualized factors when selecting future fellows.

Intratumoral Hemorrhage in Vestibular Schwannomas After Stereotactic Radiosurgery: Multi-Institutional Study.

BACKGROUND AND OBJECTIVES: Intratumoral hemorrhage (ITH) in vestibular schwannoma (VS) after stereotactic radiosurgery (SRS) is exceedingly rare. The aim of this study was to define its incidence and describe its management and outcomes in this subset of patients. METHODS: A retrospective multi-institutional study was conducted, screening 9565 patients with VS managed with SRS at 10 centers affiliated with the International Radiosurgery Research Foundation. RESULTS: A total of 25 patients developed ITH (cumulative incidence of 0.26%) after SRS management, with a median ITH size of 1.2 cm 3 . Most of the patients had Koos grade II-IV VS, and the median age was 62 years. After ITH development, 21 patients were observed, 2 had urgent surgical intervention, and 2 were initially observed and had late resection because of delayed hemorrhagic expansion and/or clinical deterioration. The histopathology of the resected tumors showed typical, benign VS histology without sclerosis, along with chronic inflammatory cells and multiple fragments of hemorrhage. At the last follow-up, 17 patients improved and 8 remained clinically stable. CONCLUSION: ITH after SRS for VS is extremely rare but has various clinical manifestations and severity. The management paradigm should be individualized based on patient-specific factors, rapidity of clinical and/or radiographic progression, ITH expansion, and overall patient condition.

Intraoperative Imaging and Optical Visualization Techniques for Brain Tumor Resection: A Narrative Review.

Advancements in intraoperative visualization and imaging techniques are increasingly central to the success and safety of brain tumor surgery, leading to transformative improvements in patient outcomes. This comprehensive review intricately describes the evolution of conventional and emerging technologies for intraoperative imaging, encompassing the surgical microscope, exoscope, Raman spectroscopy, confocal microscopy, fluorescence-guided surgery, intraoperative ultrasound, magnetic resonance imaging, and computed tomography. We detail how each of these imaging modalities contributes uniquely to the precision, safety, and efficacy of neurosurgical procedures. Despite their substantial benefits, these technologies share common challenges, including difficulties in image interpretation and steep learning curves. Looking forward, innovations in this field are poised to incorporate artificial intelligence, integrated multimodal imaging approaches, and augmented and virtual reality technologies. This rapidly evolving landscape represents fertile ground for future research and technological development, aiming to further elevate surgical precision, safety, and, most critically, patient outcomes in the management of brain tumors.

Using a Guided Machine Learning Ensemble Model to Predict Discharge Disposition following Meningioma Resection.

Objective Machine learning (ML) algorithms are powerful tools for predicting patient outcomes. This study pilots a novel approach to algorithm selection and model creation using prediction of discharge disposition following meningioma resection as a proof of concept. Materials and Methods A diversity of ML algorithms were trained on a single-institution database of meningioma patients to predict discharge disposition. Algorithms were ranked by predictive power and top performers were combined to create an ensemble model. The final ensemble was internally validated on never-before-seen data to demonstrate generalizability. The predictive power of the ensemble was compared with a logistic regression. Further analyses were performed to identify how important variables impact the ensemble. Results Our ensemble model predicted disposition significantly better than a logistic regression (area under the curve of 0.78 and 0.71, respectively, p = 0.01). Tumor size, presentation at the emergency department, body mass index, convexity location, and preoperative motor deficit most strongly influence the model, though the independent impact of individual variables is nuanced. Conclusion Using a novel ML technique, we built a guided ML ensemble model that predicts discharge destination following meningioma resection with greater predictive power than a logistic regression, and that provides greater clinical insight than a univariate analysis. These techniques can be extended to predict many other patient outcomes of interest.

The Effect of a Student-Run Free Clinic on Hospital Utilization.

Student-run free clinics (SRFCs) serve uninsured patients and offer unique educational opportunities. However, the impact of these clinics on hospital utilization is unclear. In this pre-post observational study, we used multivariable modeling to test the hypothesis that patients of Shade Tree Clinic, the SRFC affiliated with Vanderbilt University Medical Center (VUMC), would have decreased hospital utilization after joining the clinic. To evaluate the relationship between STC and VUMC, we conducted a sub-analysis of patients referred to Shade Tree from VUMC using univariate Wilcoxon signed-rank tests. Multivariable analysis showed patients were less likely to be hospitalized after joining Shade Tree (p=.04). Univariate analysis showed differences in hospitalizations among patients referred from VUMC (p=.02). These results suggest that Shade Tree does not result in an additional burden on the health care system and may reduce hospital utilization. Additional research with control populations may further highlight the effect of SRFCs on health care utilization.

Upper Extremity Pulse Pressure Predicts Amputation-Free Survival after Lower Extremity Bypass.

Increased pulse pressure reflects pathologic arterial stiffening and predicts cardiovascular events and mortality. The effect of pulse pressure on outcomes in lower extremity bypass patients remains unknown. We thus investigated whether preoperative pulse pressure could predict amputation-free survival in patients undergoing lower extremity bypass for atherosclerotic occlusive disease. An institutional database identified 240 included patients undergoing lower extremity bypass from 2005 to 2014. Preoperative demographics, cardiovascular risk factors, operative factors, and systolic and diastolic blood pressures were recorded, and compared between patients with pulse pressures above and below 80 mm Hg. Factors were analyzed in bi- and multivariable models to assess independent predictors of amputation-free survival. Kaplan-Meier analysis was performed to evaluate the temporal effect of pulse pressure ≥80 mm Hg on amputation-free survival. Patients with a pulse pressure ≥80 mm Hg were older, male, and had higher systolic and lower diastolic pressures. Patients with pulse pressure <80 mm Hg demonstrated a survival advantage on Kaplan-Meier analysis at six months (log-rank P = 0.003) and one year (P = 0.005) postoperatively. In multivariable analysis, independent risk factors for decreased amputation-free survival at six months included nonwhite race, tissue loss, infrapopliteal target, and preoperative pulse pressure ≥80 mm Hg (hazard ratio 2.60; P = 0.02), while only tissue loss and pulse pressure ≥80 mm Hg (hazard ratio 2.30, P = 0.02) remained predictive at one year. Increased pulse pressure is independently associated with decreased amputation-free survival in patients undergoing lower extremity bypass. Further efforts to understand the relationship between increased arterial stiffness and poor outcomes in these patients are needed.

Prediction of Prolonged Ventilation after Coronary Artery Bypass Grafting: Data from an Artificial Neural Network.

OBJECTIVES: The need for mechanical ventilation 24 hours after coronary artery bypass grafting (CABG) is considered a morbidity by the Society of Thoracic Surgeons. The purpose of this investigation was twofold: to identify simple preoperative patient factors independently associated with prolonged ventilation and to optimize prediction and early identification of patients prone to prolonged ventilation using an artificial neural network (ANN). METHODS: Using the institutional Adult Cardiac Database, 738 patients who underwent CABG since 2005 were reviewed for preoperative factors independently associated with prolonged postoperative ventilation. Prediction of prolonged ventilation from the identified variables was modeled using both "traditional" multiple logistic regression and an ANN. The two models were compared using Pearson r2 and area under the curve (AUC) parameters. RESULTS: Of 738 included patients, 14% (104/738) required mechanical ventilation ≥ 24 hours postoperatively. Upon multivariate analysis, higher body-mass index (BMI; odds ratio [OR] 1.10 per unit, P < 0.001), lower ejection fraction (OR 0.97 per %, P = 0.01) and use of cardiopulmonary bypass (OR 2.59, P = 0.02) were independently predictive of prolonged ventilation. The Pearson r2 and AUC of the multivariate nominal logistic regression model were 0.086 and 0.698 ± 0.05, respectively; analogous statistics of the ANN model were 0.159 and 0.732 ± 0.05, respectively.BMI, ejection fraction and cardiopulmonary bypass represent three simple factors that may predict prolonged ventilation after CABG. Early identification of these patients can be optimized using an ANN, an emerging paradigm for clinical outcomes modeling that may consider complex relationships among these variables.