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David J. Cote, Jacob J. Ruzevick, Keiko M. Kang, Dhiraj J. Pangal, Ilaria Bove, John D. Carmichael, Mark S. Shiroishi, Ben A. Strickland, and Gabriel Zada

OBJECTIVE

The aim of this study was to evaluate the association between zip code–level socioeconomic status (SES) and presenting characteristics and short-term clinical outcomes in patients with nonfunctioning pituitary adenoma (NFPA).

METHODS

A retrospective review of prospectively collected data from the University of Southern California Pituitary Center was conducted to identify all patients undergoing surgery for pituitary adenoma (PA) from 2000 to 2021 and included all patients with NFPA with recorded zip codes at the time of surgery. A normalized socioeconomic metric by zip code was then constructed using data from the American Community Survey estimates, which was categorized into tertiles. Multiple imputation was used for missing data, and multivariable linear and logistic regression models were constructed to estimate mean differences and multivariable-adjusted odds ratios for the association between zip code–level SES and presenting characteristics and outcomes.

RESULTS

A total of 637 patients were included in the overall analysis. Compared with patients in the lowest SES tertile, those in the highest tertile were more likely to be treated at a private (rather than safety net) hospital, and were less likely to present with headache, vision loss, and apoplexy. After multivariable adjustment for age, sex, and prior surgery, SES in the highest compared with lowest tertile was inversely associated with tumor size at diagnosis (−4.9 mm, 95% CI −7.2 to −2.6 mm, p < 0.001) and was positively associated with incidental diagnosis (multivariable-adjusted OR 1.72, 95% CI 1.02–2.91). Adjustment for hospital (private vs safety net) attenuated the observed associations, but disparities by SES remained statistically significant for tumor size. Despite substantial differences at presentation, there were no significant differences in length of stay or odds of an uncomplicated procedure by zip code–level SES. Patients from lower-SES zip codes were more likely to require postoperative steroid replacement and less likely to achieve gross-total resection.

CONCLUSIONS

In this series, lower zip code–level SES was associated with more severe disease at the time of diagnosis for NFPA patients, including larger tumor size and lower rates of incidental diagnosis. Despite these differences at presentation, no significant differences were observed in short-term postoperative complications, although patients with higher zip code–level SES had higher rates of gross-total resection.

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Dhiraj J. Pangal, David J. Cote, Jacob Ruzevick, Benjamin Yarovinsky, Guillaume Kugener, Bozena Wrobel, Elisabeth H. Ference, Mark Swanson, Andrew J. Hung, Daniel A. Donoho, Steven Giannotta, and Gabriel Zada

OBJECTIVE

The utility of robotic instrumentation is expanding in neurosurgery. Despite this, successful examples of robotic implementation for endoscopic endonasal or skull base neurosurgery remain limited. Therefore, the authors performed a systematic review of the literature to identify all articles that used robotic systems to access the sella or anterior, middle, or posterior cranial fossae.

METHODS

A systematic review of MEDLINE and PubMed in accordance with PRISMA guidelines performed for articles published between January 1, 1990, and August 1, 2021, was conducted to identify all robotic systems (autonomous, semiautonomous, or surgeon-controlled) used for skull base neurosurgical procedures. Cadaveric and human clinical studies were included. Studies with exclusively otorhinolaryngological applications or using robotic microscopes were excluded.

RESULTS

A total of 561 studies were identified from the initial search, of which 22 were included following full-text review. Transoral robotic surgery (TORS) using the da Vinci Surgical System was the most widely reported system (4 studies) utilized for skull base and pituitary fossa procedures; additionally, it has been reported for resection of sellar masses in 4 patients. Seven cadaveric studies used the da Vinci Surgical System to access the skull base using alternative, non–TORS approaches (e.g., transnasal, transmaxillary, and supraorbital). Five cadaveric studies investigated alternative systems to access the skull base. Six studies investigated the use of robotic endoscope holders. Advantages to robotic applications in skull base neurosurgery included improved lighting and 3D visualization, replication of more traditional gesture-based movements, and the ability for dexterous movements ordinarily constrained by small operative corridors. Limitations included the size and angulation capacity of the robot, lack of drilling components preventing fully robotic procedures, and cost. Robotic endoscope holders may have been particularly advantageous when the use of a surgical assistant or second surgeon was limited.

CONCLUSIONS

Robotic skull base neurosurgery has been growing in popularity and feasibility, but significant limitations remain. While robotic systems seem to have allowed for greater maneuverability and 3D visualization, their size and lack of neurosurgery-specific tools have continued to prevent widespread adoption into current practice. The next generation of robotic technologies should prioritize overcoming these limitations.

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Shane Shahrestani, Nolan J. Brown, Tasha S. Nasrollahi, Ben A. Strickland, Joshua Bakhsheshian, Jacob J. Ruzevick, Ilaria Bove, Ariel Lee, Ugochi A. Emeh, John D. Carmichael, and Gabriel Zada

OBJECTIVE

Although pituitary adenomas (PAs) are common intracranial tumors, literature evaluating the utility of comorbidity indices for predicting postoperative complications in patients undergoing pituitary surgery remains limited, thereby hindering the development of complex models that aim to identify high-risk patient populations. We utilized comparative modeling strategies to evaluate the predictive validity of various comorbidity indices and combinations thereof in predicting key pituitary surgery outcomes.

METHODS

The Nationwide Readmissions Database was used to identify patients who underwent pituitary tumor operations (n = 19,653) in 2016–2017. Patient frailty was assessed using the Johns Hopkins Adjusted Clinical Groups (ACG) System. The Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Index (ECI) were calculated for each patient. Five sets of generalized linear mixed-effects models were developed, using as the primary predictors 1) frailty, 2) CCI, 3) ECI, 4) frailty + CCI, or 5) frailty + ECI. Complications of interest investigated included inpatient mortality, nonroutine discharge (e.g., to locations other than home), length of stay (LOS) within the top quartile (Q1), cost within Q1, and 1-year readmission rates.

RESULTS

Postoperative mortality occurred in 73 patients (0.4%), 1-year readmission was reported in 2994 patients (15.2%), and nonroutine discharge occurred in 2176 patients (11.1%). The mean adjusted all-payer cost for the procedure was USD $25,553.85 ± $26,518.91 (Q1 $28,261.20), and the mean LOS was 4.8 ± 7.4 days (Q1 5.0 days). The model using frailty + ECI as the primary predictor consistently outperformed other models, with statistically significant p values as determined by comparing areas under the curve (AUCs) for most complications. For prediction of mortality, however, the frailty + ECI model (AUC 0.831) was not better than the ECI model alone (AUC 0.831; p = 0.95). For prediction of readmission, the frailty + ECI model (AUC 0.617) was not better than the frailty model alone (AUC 0.606; p = 0.10) or the frailty + CCI model (AUC 0.610; p = 0.29).

CONCLUSIONS

This investigation is to the authors’ knowledge the first to implement mixed-effects modeling to study the utility of common comorbidity indices in a large, nationwide cohort of patients undergoing pituitary surgery. Knowledge gained from these models may help neurosurgeons identify high-risk patients who require additional clinical attention or resource utilization prior to surgical planning.

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Shane Shahrestani, Nolan J. Brown, Ben A. Strickland, Joshua Bakhsheshian, Seyed Mohammadreza Ghodsi, Tasha Nasrollahi, Michela Borrelli, Julian Gendreau, Jacob J. Ruzevick, and Gabriel Zada

OBJECTIVE

Frailty embodies a state of increased medical vulnerability that is most often secondary to age-associated decline. Recent literature has highlighted the role of frailty and its association with significantly higher rates of morbidity and mortality in patients with CNS neoplasms. There is a paucity of research regarding the effects of frailty as it relates to neurocutaneous disorders, namely, neurofibromatosis type 1 (NF1). In this study, the authors evaluated the role of frailty in patients with NF1 and compared its predictive usefulness against the Elixhauser Comorbidity Index (ECI).

METHODS

Publicly available 2016–2017 data from the Nationwide Readmissions Database was used to identify patients with a diagnosis of NF1 who underwent neurosurgical resection of an intracranial tumor. Patient frailty was queried using the Johns Hopkins Adjusted Clinical Groups frailty-defining indicator. ECI scores were collected in patients for quantitative measurement of comorbidities. Propensity score matching was performed for age, sex, ECI, insurance type, and median income by zip code, which yielded 60 frail and 60 nonfrail patients. Receiver operating characteristic (ROC) curves were created for complications, including mortality, nonroutine discharge, financial costs, length of stay (LOS), and readmissions while using comorbidity indices as predictor values. The area under the curve (AUC) of each ROC served as a proxy for model performance.

RESULTS

After propensity matching of the groups, frail patients had an increased mean ± SD hospital cost ($85,441.67 ± $59,201.09) compared with nonfrail patients ($49,321.77 ± $50,705.80) (p = 0.010). Similar trends were also found in LOS between frail (23.1 ± 14.2 days) and nonfrail (10.7 ± 10.5 days) patients (p = 0.0020). For each complication of interest, ROC curves revealed that frailty scores, ECI scores, and a combination of frailty+ECI were similarly accurate predictors of variables (p > 0.05). Frailty+ECI (AUC 0.929) outperformed using only ECI for the variable of increased LOS (AUC 0.833) (p = 0.013). When considering 1-year readmission, frailty (AUC 0.642) was outperformed by both models using ECI (AUC 0.725, p = 0.039) and frailty+ECI (AUC 0.734, p = 0.038).

CONCLUSIONS

These findings suggest that frailty and ECI are useful in predicting key complications, including mortality, nonroutine discharge, readmission, LOS, and higher costs in NF1 patients undergoing intracranial tumor resection. Consideration of a patient’s frailty status is pertinent to guide appropriate inpatient management as well as resource allocation and discharge planning.