Comparative analysis of perioperative characteristics and early outcomes in transforaminal endoscopic lumbar diskectomy: general anesthesia versus conscious sedation.

PURPOSE: To better understand how anesthesia type impacts patient selection and recovery in TELD, we conducted a multicenter prospective study which evaluates the differences in perioperative characteristics and outcomes between patients who underwent TELD with either general anesthesia (GA) or conscious sedation (CS). METHODS: We prospectively collected data from all TELD performed by five neurosurgeons at five different medical centers between February and October of 2022. The study population was dichotomized by anesthesia scheme, creating CS and GA cohorts. This study's primary outcomes were the Oswetry Disability Index (ODI) and the Visual Analog Scale (VAS) for back and leg pain, assessed preoperatively and at 2-week follow-up. RESULTS: A total of 52 patients underwent TELD for symptomatic lumbar disk herniation. Twenty-three patients received conscious sedation with local anesthesia, and 29 patients were operated on under general anesthesia. Patients who received CS were significantly older (60.0 vs. 46.7, p < 0.001) and had lower BMI (28.2 vs. 33.4, p = 0.005) than patients under GA. No intraoperative or anesthetic complications occurred in the CS and GA cohorts. Improvement at 2-week follow-up in ODI, VAS-back, and VAS-leg was greater in patients receiving CS relative to patients under GA, but these differences were not statistically significant. CONCLUSION: In our multicenter prospective analysis of 52 patients undergoing TELD, we found that patients receiving CS were significantly older and had significantly lower BMI compared to patients under GA. On subgroup analysis, no statistically significant differences were found in the improvement of PROMs between patients in the CS and GA group.

Patient frailty association with cerebral arteriovenous malformation microsurgical outcomes and development of custom risk stratification score: an analysis of 16,721 nationwide admissions.

OBJECTIVE: Patient frailty is associated with poorer perioperative outcomes for several neurosurgical procedures. However, comparative accuracy between different frailty metrics for cerebral arteriovenous malformation (AVM) outcomes is poorly understood and existing frailty metrics studied in the literature are constrained by poor specificity to neurosurgery. This aim of this paper was to compare the predictive ability of 3 frailty scores for AVM microsurgical admissions and generate a custom risk stratification score. METHODS: All adult AVM microsurgical admissions in the National (Nationwide) Inpatient Sample (2002-2017) were identified. Three frailty measures were analyzed: 5-factor modified frailty index (mFI-5; range 0-5), 11-factor modified frailty index (mFI-11; range 0-11), and Charlson Comorbidity Index (CCI) (range 0-29). Receiver operating characteristic curves were used to compare accuracy between metrics. The analyzed endpoints included in-hospital mortality, routine discharge, complications, length of stay (LOS), and hospitalization costs. Survey-weighted multivariate regression assessed frailty-outcome associations, adjusting for 13 confounders, including patient demographics, hospital characteristics, rupture status, hydrocephalus, epilepsy, and treatment modality. Subsequently, k-fold cross-validation and Akaike information criterion-based model selection were used to generate a custom 5-variable risk stratification score called the AVM-5. This score was validated in the main study population and a pseudoprospective cohort (2018-2019). RESULTS: The authors analyzed 16,271 total AVM microsurgical admissions nationwide, with 21.0% being ruptured. The mFI-5, mFI-11, and CCI were all predictive of lower rates of routine discharge disposition, increased perioperative complications, and longer LOS (all p < 0.001). Their AVM-5 risk stratification score was calculated from 5 variables: age, hydrocephalus, paralysis, diabetes, and hypertension. The AVM-5 was predictive of decreased rates of routine hospital discharge (OR 0.26, p < 0.001) and increased perioperative complications (OR 2.42, p < 0.001), postoperative LOS (+49%, p < 0.001), total LOS (+47%, p < 0.001), and hospitalization costs (+22%, p < 0.001). This score outperformed age, mFI-5, mFI-11, and CCI for both ruptured and unruptured AVMs (area under the curve [AUC] 0.78, all p < 0.001). In a pseudoprospective cohort of 2005 admissions from 2018 to 2019, the AVM-5 remained significantly associated with all outcomes except for mortality and exhibited higher accuracy than all 3 earlier scores (AUC 0.79, all p < 0.001). CONCLUSIONS: Patient frailty is predictive of poorer disposition and elevated complications, LOS, and costs for AVM microsurgical admissions. The authors' custom AVM-5 risk score outperformed age, mFI-5, mFI-11, and CCI while using threefold less variables than the CCI. This score may complement existing AVM grading scales for optimization of surgical candidates and identification of patients at risk of postoperative medical and surgical morbidity.

Association of Patient Frailty With Vestibular Schwannoma Resection Outcomes and Machine Learning Development of a Vestibular Schwannoma Risk Stratification Score.

BACKGROUND: Patient frailty is predictive of higher neurosurgical morbidity and mortality. However, existing frailty measures are hindered by lack of specificity to neurosurgery. OBJECTIVE: To analyze the association between 3 risk stratification scores and outcomes for nationwide vestibular schwannoma (VS) resection admissions and develop a custom VS risk stratification score. METHODS: We identified all VS resection admissions in the National Inpatient Sample (2002-2017). Three risk stratification scores were analyzed: modified Frailty Index-5, modified Frailty Index-11(mFI-11), and Charlson Comorbidity Index (CCI). Survey-weighted multivariate regression evaluated associations between frailty and inpatient outcomes, adjusting for patient demographics, hospital characteristics, and disease severity. Subsequently, we used k -fold cross validation and Akaike Information Criterion-based model selection to create a custom risk stratification score. RESULTS: We analyzed 32 465 VS resection admissions. High frailty, as identified by the mFI-11 (odds ratio [OR] = 1.27, P = .021) and CCI (OR = 1.72, P < .001), predicted higher odds of perioperative complications. All 3 scores were also associated with lower routine discharge rates and elevated length of stay (LOS) and costs (all P < .05). Our custom VS-5 score ( https://skullbaseresearch.shinyapps.io/vs-5_calculator/ ) featured 5 variables (age ≥60 years, hydrocephalus, preoperative cranial nerve palsies, diabetes mellitus, and hypertension) and was predictive of higher mortality (OR = 6.40, P = .001), decreased routine hospital discharge (OR = 0.28, P < .001), and elevated complications (OR = 1.59, P < .001), LOS (+48%, P < .001), and costs (+23%, P = .001). The VS-5 outperformed the modified Frailty Index-5, mFI-11, and CCI in predicting routine discharge (all P < .001), including in a pseudoprospective cohort (2018-2019) of 3885 admissions. CONCLUSION: Patient frailty predicted poorer inpatient outcomes after VS surgery. Our custom VS-5 score outperformed earlier risk stratification scores.

The National Inpatient Sample: A Primer for Neurosurgical Big Data Research and Systematic Review.

OBJECTIVE: The National Inpatient Sample (NIS) (the largest all-payer inpatient database in the United States) is an important instrument for big data analysis of neurosurgical inquiries. However, earlier research has determined that many NIS studies are limited by common methodological pitfalls. In this study, we provide the first primer of NIS methodological procedures in the setting of neurosurgical research and review all reported neurosurgical studies using the NIS. METHODS: We designed a protocol for neurosurgical big data research using the NIS, based on our subject matter expertise, NIS documentation, and input and verification from the Healthcare Cost and Utilization Project. We subsequently used a comprehensive search strategy to identify all neurosurgical studies using the NIS in the PubMed and MEDLINE, Embase, and Web of Science databases from inception to August 2021. Studies underwent qualitative categorization (years of NIS studied, neurosurgical subspecialty, age group, and thematic focus of study objective) and analysis of longitudinal trends. RESULTS: We identified a canonical, 4-step protocol for NIS analysis: study population selection; defining additional clinical variables; identification and coding of outcomes; and statistical analysis. Methodological nuances discussed include identifying neurosurgery-specific admissions, addressing missing data, calculating additional severity and hospital-specific metrics, coding perioperative complications, and applying survey weights to make nationwide estimates. Inherent database limitations and common pitfalls of NIS studies discussed include lack of disease process-specific variables and data after the index admission, inability to calculate certain hospital-specific variables after 2011, performing state-level analyses, conflating hospitalization charges and costs, and not following proper statistical methodology for performing survey-weighted regression. In a systematic review, we identified 647 neurosurgical studies using the NIS. Although almost 60% of studies were reported after 2015, <10% of studies analyzed NIS data after 2015. The average sample size of studies was 507,352 patients (standard deviation = 2,739,900). Most studies analyzed cranial procedures (58.1%) and adults (68.1%). The most prevalent topic areas analyzed were surgical outcome trends (35.7%) and health policy and economics (17.8%), whereas patient disparities (9.4%) and surgeon or hospital volume (6.6%) were the least studied. CONCLUSIONS: We present a standardized methodology to analyze the NIS, systematically review the state of the NIS neurosurgical literature, suggest potential future directions for neurosurgical big data inquiries, and outline recommendations to improve the design of future neurosurgical data instruments.

Analysis of Abdominal Radiology fellowship website content and comprehensiveness.

PURPOSE: Fellowship programs' online content plays a key role in prospective Abdominal Radiology applicants' evaluation of programs. The purpose of this study is to examine the online accessibility of Abdominal Radiology fellowships, the comprehensiveness of the program websites' content, and evaluate whether specific program characteristics are associated with differentiated website comprehensiveness. METHODS: A list of 67 Abdominal Radiology fellowship programs was obtained from the Society of Abdominal Radiology (SAR) website. Each of the 65 publicly-available fellowship websites was scored for the presence of 19 binary variables related to the program's attributes and curriculum to assess informational comprehensiveness. Comprehensiveness scores were compared by program characteristics (accreditation status, region, and size) using Kruskal-Wallis and two-tailed t tests. RESULTS: Mean comprehensiveness score of Abdominal Radiology fellowship websites as measured by online criteria met was 52.6% (10.0 ± 3.0/19). Application requirements and information, rotation scheduling, and program director contact were found on more than 87.5% of the 65 websites, whereas salary and benefits, social information, and alumni were listed on fewer than 33.8% (22/65) of websites. Program accreditation status, region, and size were not associated with difference in mean comprehensiveness scores. CONCLUSIONS: There is a discrepancy between information commonly sought by prospective Abdominal Radiology fellowship applicants and what is available on fellowship program websites. Programs and applicants alike may benefit from programs strengthening their online material.