Preoperative Frailty Predicts Worse Outcomes after Microvascular Decompression for Trigeminal Neuralgia, Hemifacial Spasm, and Glossopharyngeal Neuralgia: A Multicenter Analysis of 1,473 Patients from a Prospective Surgical Registry.

INTRODUCTION: Microvascular decompression (MVD) is an efficacious neurosurgical intervention for patients with medically intractable neurovascular compression syndromes. However, MVD may occasionally cause life-threatening or altering complications, particularly in patients unfit for surgical operations. Recent literature suggests a lack of association between chronological age and surgical outcomes for MVD. The Risk Analysis Index (RAI) is a validated frailty tool for surgical populations (both clinical and large database). The present study sought to evaluate the prognostic ability of frailty, as measured by RAI, to predict outcomes for patients undergoing MVD from a large multicenter surgical registry. METHODS: The American College of Surgeons-National Surgical Quality Improvement Program (ACS-NSQIP) database (2011-2020) was queried using diagnosis/procedure codes for patients undergoing MVD procedures for trigeminal neuralgia (n = 1,211), hemifacial spasm (n = 236), or glossopharyngeal neuralgia (n = 26). The relationship between preoperative frailty (measured by RAI and 5-factor modified frailty index [mFI-5]) for primary endpoint of adverse discharge outcome (AD) was analyzed. AD was defined as discharge to a facility which was not home, hospice, or death within 30 days. Discriminatory accuracy for prediction of AD was assessed by computation of C-statistics (with 95% confidence interval) from receiver operating characteristic (ROC) curve analysis. RESULTS: Patients undergoing MVD (N = 1,473) were stratified by RAI frailty bins: 71% with RAI 0-20, 28% with RAI 21-30, and 1.2% with RAI 31+. Compared to RAI score 19 and below, RAI 20 and above had significantly higher rates of postoperative major complications (2.8% vs. 1.1%, p = 0.01), Clavien-Dindo grade IV complications (2.8% vs. 0.7%, p = 0.001), and AD (6.1% vs. 1.0%, p < 0.001). The rate of primary endpoint was 2.4% (N = 36) and was positively associated with increasing frailty tier: 1.5% in 0-20, 5.8% in 21-30, and 11.8% in 31+. RAI score demonstrated excellent discriminatory accuracy for primary endpoint in ROC analysis (C-statistic: 0.77, 95% CI: 0.74-0.79) and demonstrated superior discrimination compared to mFI-5 (C-statistic: 0.64, 95% CI: 0.61-0.66) (DeLong pairwise test, p = 0.003). CONCLUSIONS: This was the first study to link preoperative frailty to worse surgical outcomes after MVD surgery. RAI frailty score predicts AD after MVD with excellent discrimination and holds promise for preoperative counseling and risk stratification of surgical candidates. A risk assessment tool was developed and deployed with a user-friendly calculator: https://nsgyfrailtyoutcomeslab.shinyapps.io/microvascularDecompression.

Preoperative frailty and 30-day mortality after resection of brain metastases: a multicenter surgical registry analysis of 11,038 patients.

OBJECTIVE: Surgery plays a key role in the management of brain metastases. Stratifying surgical risk and individualizing treatment will help optimize outcomes because there is clinical equipoise between radiation and resection as treatment options for many patients. Here, the authors used a multicenter database to assess the prognostic utility of baseline frailty, calculated with the Risk Analysis Index (RAI), for prediction of mortality within 30 days after surgery for brain metastasis. METHODS: The authors pooled patients who had been surgically treated for brain metastasis from the American College of Surgeons National Surgical Quality Improvement Program database (2012-2020). The authors studied the relationship between preoperative calculated RAI score and 30-day mortality after surgery for brain metastasis by using linear-by-linear proportional trend tests and binary logistic regression. The authors calculated C-statistics (with 95% CIs) in receiver operating characteristic (ROC) curve analysis to assess discriminative accuracy. RESULTS: The authors identified 11,038 patients who underwent brain metastasis resection with a median (interquartile range) age of 62 (54-69) years. The authors categorized patients into four groups on the basis of RAI: robust (RAI 0-20), 8.1% of patients; normal (RAI 21-30), 9.2%; frail (RAI 31-40), 75%; and severely frail (RAI ≥ 41), 8.1%. The authors found a positive correlation between 30-day mortality and frailty. RAI demonstrated superior predictive discrimination for 30-day mortality as compared with the 5-factor modified frailty index (mFI-5) on ROC analysis (C-statistic 0.65, 95% CI 0.65-0.66). CONCLUSIONS: The RAI frailty score accurately estimates 30-day mortality after brain metastasis resection and can be calculated online with an open-access software tool: https://nsgyfrailtyoutcomeslab.shinyapps.io/BrainMetsResection/. Accordingly, RAI can be utilized to measure surgical risk, guide treatment options, and optimize outcomes for patients with brain metastases. RAI has superior discrimination for predicting 30-day mortality compared with mFI-5.

Gender concordance and publication productivity within Neurosurgical Focus: a 10-year review (2013-2022).

OBJECTIVE: The "leaky academic pipeline" describes how female representation in leadership positions has remained stagnant despite an increase in the number of female trainees. Female mentorship to female mentees, and female role models at higher academic positions have been shown to positively influence academic productivity. To the authors' knowledge, the impact of female editorial board representation on authorship trends in neurosurgical journals remains undescribed. This study aimed to analyze trends in the representation of female topic editors and its impact on female authorship within Neurosurgical Focus over a 10-year period. METHODS: Publicly available data were collected from the journal's website, inclusive from January 2013 to December 2022. The articles were grouped into technical and nontechnical themes based on their relevance to specific technical details regarding surgical techniques. Female gender-concordant publications were defined as publications having a female first author (or co-first author) and a female senior author. Linear regression analysis determined trends in publishing. Odds ratios and 95% CIs were calculated using logistic regression analysis. Pearson correlation and cross-correlation analyses were used to examine each pairwise comparison of time series. The statistical significance of associations was evaluated using t-tests and chi-square and Fisher's exact tests. RESULTS: The number of female topic editors and gender-concordant authors increased over time (p < 0.05). Women accounted for ≥ 50% of the topic editors on nontechnical themes relevant to education and gender diversity. Having a female senior author was associated with higher publication productivity for original research and review articles among female authors (OR 13.73, 95% CI 1.75-394.31; p < 0.05). Female authors had higher odds of publishing editorials with a female topic editor (OR 3.81, 95% CI 1.37-11.02; p < 0.01). Publications with female first and senior authors were significantly more likely to have female topic editors (OR 4.05, 95% CI 1.38-12.92; p < 0.01). A positive association was observed between female senior authors and female topic editors at lag -8, with a correlation coefficient of 0.19 (p = 0.03). CONCLUSIONS: Female attending-to-female trainee mentorship and female representation among editorial boards play a crucial role in enhancing academic productivity among women. Efforts to sustain academic productivity during the early-career period would presumably help increase female representation in neurosurgery.

Baseline Frailty Measured by the Risk Analysis Index and 30-Day Mortality After Surgery for Spinal Malignancy: Analysis of a Prospective Registry (2011-2020).

OBJECTIVE: To evaluate the prognostic utility of baseline frailty, measured by the Risk Analysis Index (RAI), for prediction of postoperative mortality among patients with spinal malignancy (SM) undergoing resection. METHODS: SM surgery cases were queried from the American College of Surgeons - National Surgical Quality Improvement Program database (2011-2020). The relationship between preoperative RAI frailty score and increasing rate of primary endpoint (mortality or discharge to hospice within 30 days, "mortality/hospice") were assessed. Discriminatory accuracy was assessed by computation of C-statistics (with 95% confidence interval [CI]) in receiver operating characteristic (ROC) curve analysis. RESULTS: A total of 2,235 cases were stratified by RAI score: 0-20, 22.7%; 21-30, 11.9%; 31-40, 54.7%; and ≥ 41, 10.7%. The rate of mortality/hospice was 6.5%, which increased linearly with increasing RAI score (p < 0.001). RAI was also associated with increasing rates of major complication, extended length of stay, and nonhome discharge (all p < 0.05). The RAI demonstrated acceptable discriminatory accuracy for prediction of primary endpoint (C-statistic, 0.717; 95% CI, 0.697-0.735). In pairwise ROC comparison, RAI demonstrated superiority versus modified frailty index-5 and chronological age (p < 0.001). CONCLUSION: Preoperative frailty, as measured by RAI, is a robust predictor of mortality/ hospice after SM surgery. The frailty score may be applied in clinical settings using a user-friendly calculator, deployed here: https://nsgyfrailtyoutcomeslab.shinyapps.io/spinalMalignancyRAI/.

The frailty-driven predictive model for failure to rescue among patients who experienced a major complication following cervical decompression and fusion: an ACS-NSQIP analysis of 3,632 cases (2011-2020).

BACKGROUND CONTEXT: Preoperative risk stratification for patients considering cervical decompression and fusion (CDF) relies on established independent risk factors to predict the probability of complications and outcomes in order to help guide pre and perioperative decision-making. PURPOSE: This study aims to determine frailty's impact on failure to rescue (FTR), or when a mortality occurs within 30 days following a major complication. STUDY DESIGN/SETTING: Cross-sectional retrospective analysis of retrospective and nationally-representative data. PATIENT SAMPLE: The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was queried for all CDF cases from 2011-2020. OUTCOME MEASURES: CDF patients who experienced a major complication were identified and FTR was calculated as death or hospice disposition within 30 days of a major complication. METHODS: Frailty was measured by the Risk Analysis Index-Revised (RAI-Rev). Baseline patient demographics and characteristics were compared for all FTR patients. Significant factors were assessed by univariate and multivariable regression for the development of a frailty-driven predictive model for FTR. The discriminative ability of the predictive model was assessed using a receiving operating characteristic (ROC) curve analysis. RESULTS: There were 3632 CDF patients who suffered a major complication and 7.6% (277 patients) subsequently expired or dispositioned to hospice, the definition of FTR. Independent predictors of FTR were nonelective surgery, frailty, preoperative intubation, thrombosis or embolic complication, unplanned intubation, on ventilator for >48 hours, cardiac arrest, and septic shock. Frailty, and a combination of preoperative and postoperative risk factors in a predictive model for FTR, achieved outstanding discriminatory accuracy (C-statistic = 0.901, CI: 0.883-0.919). CONCLUSION: Preoperative and postoperative risk factors, combined with frailty, yield a highly accurate predictive model for FTR in CDF patients. Our model may guide surgical management and/or prognostication regarding the likelihood of FTR after a major complication postoperatively with CDF patients. Future studies may determine the predictive ability of this model in other neurosurgical patient populations.

Risk Analysis Index and 30-Day Mortality after Brain Tumor Resection: A Multicenter Frailty Analysis of 31,776 Patients from 2012 to 2020.

Introduction The aim of this study was to evaluate the discriminative accuracy of the preoperative Risk Analysis Index (RAI) frailty score for prediction of mortality or transition to hospice within 30 days of brain tumor resection (BTR) in a large multicenter, international, prospective database. Methods Records of BTR patients were extracted from the American College of Surgeons National Surgical Quality Improvement Program (2012-2020) database. The relationship between the RAI frailty scale and the primary end point (mortality or discharge to hospice within 30 days of surgery) was assessed using linear-by-linear proportional trend tests, logistic regression, and receiver operating characteristic (ROC) curve analysis (area under the curve as C-statistic). Results Patients with BTR ( N = 31,776) were stratified by RAI frailty tier: 16,800 robust (52.8%), 7,646 normal (24.1%), 6,593 frail (20.7%), and 737 severely frail (2.3%). The mortality/hospice rate was 2.5% ( n = 803) and was positively associated with increasing RAI tier: robust (0.9%), normal (3.3%), frail (4.6%), and severely frail (14.2%) ( p < 0.001). Isolated RAI was a robust discriminatory of primary end point in ROC curve analysis in the overall BTR cohort (C-statistic: 0.74; 95% confidence interval [CI]: 0.72-0.76) as well as the malignant (C-statistic: 0.74; 95% CI: 0. 67-0.80) and benign (C-statistic: 0.71; 95% CI: 0.70-0.73) tumor subsets (all p < 0.001). RAI score had statistically significantly better performance compared with the 5-factor modified frailty index and chronological age (both p < 0.0001). Conclusions RAI frailty score predicts 30-day mortality after BTR and may be translated to the bedside with a user-friendly calculator ( https://nsgyfrailtyoutcomeslab.shinyapps.io/braintumormortalityRAIcalc/ ). The findings hope to augment the informed consent and surgical decision-making process in this patient population and provide an example for future study designs.

Frailty measured by risk analysis index and adverse discharge outcomes after adult spine deformity surgery: analysis of 3104 patients from a prospective surgical registry (2011-2020).

BACKGROUND CONTEXT: Measurement of frailty with the Risk Analysis Index (RAI) has demonstrated improved outcome prediction compared to other frailty indices across the surgical literature. However, the generalizability and clinical utility of preoperative RAI scoring for prediction of postoperative morbidity after adult spinal deformity surgery is presently unknown. Thus, recent studies have called for an RAI analysis of spine deformity outcomes. PURPOSE: The present study sought to evaluate the discriminatory accuracy of preoperative frailty, as measured by RAI, for predicting postoperative morbidity among adult spine deformity surgery patients using data queried from a large prospective surgical registry representing over 700 hospitals from 49 US states and 11 countries. STUDY DESIGN/SETTING: Secondary analysis of a prospective surgical registry. PATIENT SAMPLE: American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database (2011-2020). OUTCOME MEASURES: The primary endpoint was "adverse discharge outcome" (ADO) defined as discharge to a non-home, non-rehabilitation nursing/chronic care facility. METHODS: Adult spine deformity surgeries were queried from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database (2011-2020) using diagnosis and procedure codes. The relationship between increasing preoperative RAI frailty score and increasing rate of primary endpoint (ADO) was assessed with Cochran-Armitage linear trend tests. Discriminatory accuracy was tested by computation of concordance statistics (with 95% confidence interval [CI]) in receiver operating characteristic (ROC) curve analysis. RESULTS: A total of 3104 patients underwent spine deformity surgery and were stratified by RAI score: 0-10: 22%, 11-15: 11%, 16-20: 29%, 21-25: 26%, 26-30: 8.0%, 31-35: 2.4%, and 36+: 1.4%. The rate of ADO was 14% (N=439/3094). The rate of ADO increased significantly with increasing RAI score (p<.0001). RAI demonstrated robust discriminatory accuracy for prediction of ADO in ROC analysis (C-statistic: 0.71, 95% CI: 0.69-0.74, p<.001). In pairwise comparison of ROC curves (DeLong test), RAI demonstrates superior discriminatory accuracy compared to the 5-factor modified frailty index (mFI-5; p<.001). CONCLUSION: Preoperative frailty, as measured by RAI, is a robust predictor of postoperative morbidity (measured by ADO) after adult spine deformity surgery. The frailty score may be translated directly to the bedside with a user-friendly risk calculator, deployed here: https://nsgyfrailtyoutcomeslab.shinyapps.io/spineDeformity.

Endoscopic and Nonendoscopic Approaches to Single-Level Lumbar Spine Decompression: Propensity Score-Matched Comparative Analysis and Frailty-Driven Predictive Model.

OBJECTIVE: The endoscopic spine surgery (ESS) approach is associated with high levels of patient satisfaction, shorter recovery time, and reduced complications. The present study reports multicenter, international data, comparing ESS and non-ESS approaches for singlelevel lumbar decompression, and proposes a frailty-driven predictive model for nonhome discharge (NHD) disposition. METHODS: Cases of ESS and non-ESS lumbar spine decompression were queried from the American College of Surgeons National Surgical Quality Improvement Program database (2017-2020). Propensity score matching was performed on baseline characteristics frailty score (measured by risk analysis index [RAI] and modified frailty index-5 [mFI-5]). The primary outcome of interest was NHD disposition. A predictive model was built using logistic regression with RAI as the primary driver. RESULTS: Single-level nonfusion spine lumbar decompression surgery was performed in 38,686 patients. Frailty, as measured by RAI, was a reliable predictor of NHD with excellent discriminatory accuracy in receiver operating characteristic (ROC) curve analysis: C-statistic: 0.80 (95% confidence interval [CI], 0.65-0.94) in ESS cohort, C-statistic: 0.75 (95% CI, 0.73-0.76) overall cohort. After propensity score matching, there was a reduction in total operative time (89 minutes vs. 103 minutes, p = 0.049) and hospital length of stay (LOS) (0.82 days vs. 1.37 days, p < 0.001) in patients treated endoscopically. In ROC curve analysis, the frailty-driven predictive model performed with excellent diagnostic accuracy for the primary outcome of NHD (C-statistic: 0.87; 95% CI, 0.85-0.88). CONCLUSION: After frailty-based propensity matching, ESS is associated with reduced operative time, shorter hospital LOS, and decreased NHD. The RAI frailty-driven model predicts NHD with excellent diagnostic accuracy and may be applied to preoperative decisionmaking with a user-friendly calculator: nsgyfrailtyoutcomeslab.shinyapps.io/lumbar_decompression_dischargedispo.

A Predictive Model of Failure to Rescue After Thoracolumbar Fusion.

OBJECTIVE: Although failure to rescue (FTR) has been utilized as a quality-improvement metric in several surgical specialties, its current utilization in spine surgery is limited. Our study aims to identify the patient characteristics that are independent predictors of FTR among thoracolumbar fusion (TLF) patients. METHODS: Patients who underwent TLF were identified using relevant diagnostic and procedural codes from the National Surgical Quality Improvement Program (NSQIP) database from 2011-2020. Frailty was assessed using the risk analysis index (RAI). FTR was defined as death, within 30 days, following a major complication. Univariate and multivariable analyses were used to compare baseline characteristics and early postoperative sequelae across FTR and non-FTR cohorts. Receiver operating characteristic (ROC) curve analysis was used to assess the discriminatory accuracy of the frailty-driven predictive model for FTR. RESULTS: The study cohort (N = 15,749) had a median age of 66 years (interquartile range, 15 years). Increasing frailty, as measured by the RAI, was associated with an increased likelihood of FTR: odds ratio (95% confidence interval [CI]) is RAI 21-25, 1.3 [0.8-2.2]; RAI 26-30, 4.0 [2.4-6.6]; RAI 31-35, 7.0 [3.8-12.7]; RAI 36-40, 10.0 [4.9-20.2]; RAI 41- 45, 21.5 [9.1-50.6]; RAI ≥ 46, 45.8 [14.8-141.5]. The frailty-driven predictive model for FTR demonstrated outstanding discriminatory accuracy (C-statistic = 0.92; CI, 0.89-0.95). CONCLUSION: Baseline frailty, as stratified by type of postoperative complication, predicts FTR with outstanding discriminatory accuracy in TLF patients. This frailty-driven model may inform patients and clinicians of FTR risk following TLF and help guide postoperative care after a major complication.

Frailty Measured by the Risk Analysis Index Predicts Nonhome Discharge and Mortality After Resection in Refractory Epilepsy: Analysis of 1236 Patients From a Prospective Surgical Registry, 2012 to 2020.

BACKGROUND: Risk stratification of epilepsy surgery patients remains difficult. The Risk Analysis Index (RAI) is a frailty measurement that augments preoperative risk stratification. OBJECTIVE: To evaluate RAI's discriminative threshold for nonhome discharge disposition (NHD) and mortality (or discharge to hospice within 30 days of operation) in epilepsy surgery patients. METHODS: Patients were queried from the American College of Surgeons-National Surgical Quality Improvement Program database (2012-2020) using diagnosis/procedure codes. Linear-by-linear trend tests assessed RAI's relationship with NHD and mortality. Discriminatory accuracy was assessed by C-statistics (95% CI) in receiver operating characteristic curve analysis. RESULTS: Epilepsy resections (N = 1236) were grouped into temporal lobe (60.4%, N = 747) and nontemporal lobe (39.6%, N = 489) procedures. Patients were stratified by RAI tier: 76.5% robust (RAI 0-20), 16.2% normal (RAI 21-30), 6.6% frail (RAI 31-40), and 0.8% severely frail (RAI 41 and above). The NHD rate was 18.0% (N = 222) and positively associated with increasing RAI tier: 12.5% robust, 34.0% normal, 38.3% frail, and 50.0% severely frail ( P < .001). RAI had robust predictive discrimination for NHD in overall cohort (C-statistic 0.71), temporal lobe (C-statistic 0.70), and nontemporal lobe (C-statistic 0.71) cohorts. The mortality rate was 2.7% (N = 33) and significantly associated with RAI frailty: 1.1% robust, 8.0% normal, 6.2% frail, and 20.0% severely frail ( P < .001). RAI had excellent predictive discrimination for mortality in overall cohort (C-statistic 0.78), temporal lobe (C-statistic 0.80), and nontemporal lobe (C-statistic 0.74) cohorts. CONCLUSION: The RAI frailty score predicts mortality and NHD after epilepsy surgery. This is accomplished with a user-friendly calculator: https://nsgyfrailtyoutcomeslab.shinyapps.io/epilepsy/ .