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BACKGROUND: Promoting workplace diversity leads to a variety of benefits related to a broader range of perspectives and insights. Underrepresented in medicine (URiM), including African Americans, Latinx, and Natives (Americans/Alaskan/Hawaiians/Pacific Islanders), are currently accounting for approximately 40% of the US population. OBJECTIVE: To establish a snapshot of current URiM representation within academic neurosurgery (NS) programs and trends within NS residency. METHODS: All 115 NS residencies and academic programs accredited by the Accreditation Council for Graduate Medical Education in 2020 were included in this study. The National Residency Matching Program database was reviewed from 2011 to 2020 to analyze URiM representation trends over time within the NS resident workforce. The academic rank, academic and clinical title(s), subspecialty, sex, and race of URiM NS faculty (NSF) were obtained from publicly available data. RESULTS: The Black and Latinx NS resident workforce currently accounts for 4.8% and 5.8% of the total workforce, respectively. URiM NSF are present in 71% of the Accreditation Council for Graduate Medical Education-accredited NS programs and account for 8% (148 of 1776) of the workforce. Black and Latinx women comprise 10% of URiM NSF. Latinx NSFs are the majority within the URiM cohort for both men and women. URiM comprise 5% of all department chairs. All are men. Spine (26%), tumor (26%), and trauma (17%) were the top 3 subspecialties among URiM NSF. CONCLUSION: NS has evolved, expanded, and diversified in numerous directions, including race and gender representation. Our data show that ample opportunities remain to improve URiM representation within NS.

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BACKGROUND: Extended postoperative hospital stays are associated with numerous clinical risks and increased economic cost. Accurate preoperative prediction of extended length of stay (LOS) can facilitate targeted interventions to mitigate clinical harm and resource utilization. OBJECTIVE: To develop a machine learning algorithm aimed at predicting extended LOS after cervical spine surgery on a national level and elucidate drivers of prediction. METHODS: Electronic medical records from a large, urban academic medical center were retrospectively examined to identify patients who underwent cervical spine fusion surgeries between 2008 and 2019 for machine learning algorithm development and insample validation. The National Inpatient Sample database was queried to identify cervical spine fusion surgeries between 2009 and 2017 for out-of-sample validation of algorithm performance. Gradient-boosted trees predicted LOS and efficacy was assessed using the area under the receiver operating characteristic curve (AUROC). Shapley values were calculated to characterize preoperative risk factors for extended LOS and explain algorithm predictions. RESULTS: Gradient-boosted trees accurately predicted extended LOS across cohorts, achieving an AUROC of 0.87 (SD = 0.01) on the single-center validation set and an AUROC of 0.84 (SD = 0.00) on the nationwide National Inpatient Sample data set. Anterior approach only, elective admission status, age, and total number of Elixhauser comorbidities were important predictors that affected the likelihood of prolonged LOS. CONCLUSION: Machine learning algorithms accurately predict extended LOS across single-center and national patient cohorts and characterize key preoperative drivers of increased LOS after cervical spine surgery.

BACKGROUND: The Knosp criteria have been the historical standard for predicting cavernous sinus invasion, and therefore extent of surgical resection, of pituitary macroadenomas. Few studies have sought to reappraise the utility of this tool after recent advances in visualization and modeling of tumors in complex endoscopic surgery. OBJECTIVE: To evaluate our proposed alternative method, using 3-dimensional (3D) volumetric imaging, and whether it can better predict extent of resection in nonfunctional pituitary adenomas. METHODS: Patients who underwent endoscopic transsphenoidal resection of pituitary macroadenomas at our institution were reviewed. Information was collected on neurological, endocrine, and visual function. Volumetric segmentation was performed using 3D Slicer software. Relationship of tumor volume, clinical features, and Knosp grade on extent of resection was examined. RESULTS: One hundred forty patients were identified who had transsphenoidal resection of nonfunctional pituitary adenomas. Macroadenomas had a median volume of 6 cm 3 (IQR 3.4-8.7), and 17% had a unilateral Knosp grade of at least 3B. On multiple logistic regression, only smaller log-transformed preoperative tumor volume was independently associated with increased odds of gross total resection (GTR; odds ratio: 0.27, 95% CI: 0.07-0.89, P < .05) when controlling for tumor proliferative status, age, and sex (area under the curve 0.67). The Knosp criteria did not independently predict GTR in this cohort (P > .05, area under the curve 0.46). CONCLUSION: Increasing use of volumetric 3D imaging may better anticipate extent of resection compared with the Knosp grade metric and may have a greater positive predictive value for GTR. More research is needed to validate these findings and implement them using automated methods.