Predictive Model and Online Calculator for Discharge Disposition in Brain Tumor Patients

Activity Measure for Post-Acute care (AM-PAC) scores predict Short and Long-Term outcomes following glioblastoma resection

BACKGROUND

Glioblastoma patients may develop functional deficits post-operatively that affect activities of daily living and result in worse outcomes. The Activity Measure for Post-Acute Care (AM-PAC) instrument assigns patients basic mobility and daily activity scores, but it is unknown if these scores correlate with post-operative outcomes in glioblastoma patients.

METHODS

Adult (≥18 years) glioblastoma patients evaluated by physical/occupational therapy after resection at a single instution (June 2008-December 2020) were identified. Patient demographics, post-operative AM-PAC scores, and clinical outcomes were collected. Multivariate regression identified associations between AM-PAC scores and post-operative outcomes.

RESULTS

600 patients were included (mean age 59.3 years, 59.2 % male); 151 (25.3 %) and 246 (43.8 %) patients had low mobility (<42.9) and activity (<39.4) scores, respectively. 103 (17.2 %) and 177 (29.5 %) patients experienced extended lengths of stay (LOS) in the ICU (≥2 days) and overall (≥7 days), respectively. 154 (25.7 %) patients had non-home discharges. The 30-day readmission rate was 13.7 %. In multivariate analysis, low mobility scores correlated with increased odds of extended overall (p < 0.0001) and ICU (p = 0.0004) LOS, non-home discharge (p < 0.0001), and 30-day readmission (p = 0.0405). Low activity scores correlated with extended overall LOS (<0.0001) and non-home discharge (p < 0.0001). In log-rank analysis, median survival time was shorter for patients with low mobility (9.5 vs. 14.7 months, p < 0.0001) and activity (10.6 vs. 16.3 months, p < 0.0001) scores than for high-scoring patients.

CONCLUSION

AM-PAC basic mobility and daily activity scores are associated with outcomes after glioblastoma resection. These easily obtainable scores may be useful for prognosticating and guiding decision making in post-operative glioblastoma patients.

Frailty as a predictor of postoperative outcomes in neurosurgery: a systematic review

INTRODUCTION

Baseline frailty status has been utilized to predict a wide range of outcomes and guide preoperative decision making in neurosurgery. This systematic review aims to analyze existing literature on the utilization of frailty as a predictor of neurosurgical outcomes.

EVIDENCE ACQUISITION

We conducted a systematic review following PRISMA guidelines. Studies that utilized baseline frailty status to predict outcomes after a neurosurgical intervention were included in this systematic review. Studies that utilized sarcopenia as the sole measure of frailty were excluded. PubMed, EMBASE, and Cochrane library was searched from inception to March 1st, 2023, to identify relevant articles.

EVIDENCE SYNTHESIS

Overall, 244 studies met the inclusion criteria. The 11-factor modified frailty index (mFI-11) was the most utilized frailty measure (N.=91, 37.2%) followed by the five-factor modified Frailty Index (mFI-5) (N.=80, 32.7%). Spine surgery was the most common subspecialty (N.=131, 53.7%), followed by intracranial tumor resection (N.=57, 23.3%), and post-operative complications were the most reported outcome (N.=130, 53.2%) in neurosurgical frailty studies. The USA and the Bowers author group published the greatest number of articles within the study period (N.=176, 72.1% and N.=37, 15.2%, respectively).

CONCLUSIONS

Frailty literature has grown exponentially over the years and has been incorporated into neurosurgical decision making. Although a wide range of frailty indices exist, their utility may vary according to their ability to be incorporated in the outpatient clinical setting.

Strategies to Assess and Manage Frailty among Patients Diagnosed with Primary Malignant Brain Tumors

OPINION STATEMENT

Frailty refers to a biologic process that results in reduced physiologic and functional reserve. Patients diagnosed with primary malignant brain tumors experience high symptom burden from tumor and tumor-directed treatments that, coupled with previous comorbidities, may contribute to frailty. Within the primary malignant brain tumor population, frailty is known to associate with mortality, higher healthcare utilization, and increased risk of postoperative complications. As such, methods to assess and manage frailty are paramount. However, there is currently no clear consensus on how to best assess and manage frailty throughout the entirety of the disease trajectory. Given the association between frailty and health outcomes, more research is needed to determine best practice protocols for the assessment and management of frailty among patients diagnosed with primary malignant brain tumors.

Development of an online calculator for the prediction of seizure freedom following pediatric hemispherectomy using the Hemispherectomy Outcome Prediction Scale (HOPS)

OBJECTIVES

Although hemispheric surgeries are among the most effective procedures for drug-resistant epilepsy (DRE) in the pediatric population, there is a large variability in seizure outcomes at the group level. A recently developed HOPS score provides individualized estimation of likelihood of seizure freedom to complement clinical judgement. The objective of this study was to develop a freely accessible online calculator that accurately predicts the probability of seizure freedom for any patient at 1-, 2-, and 5-years post-hemispherectomy.

METHODS

Retrospective data of all pediatric patients with DRE and seizure outcome data from the original Hemispherectomy Outcome Prediction Scale (HOPS) study were included. The primary outcome of interest was time-to-seizure recurrence. A multivariate Cox proportional-hazards regression model was developed to predict the likelihood of post-hemispheric surgery seizure freedom at three time points (1-, 2- and 5- years) based on a combination of variables identified by clinical judgment and inferential statistics predictive of the primary outcome. The final model from this study was encoded in a publicly accessible online calculator on the International Network for Epilepsy Surgery and Treatment (iNEST) website (https://hops-calculator.com/).

RESULTS

The selected variables for inclusion in the final model included the five original HOPS variables (age at seizure onset, etiologic substrate, seizure semiology, prior non-hemispheric resective surgery, and contralateral fluorodeoxyglucose-positron emission tomography [FDG-PET] hypometabolism) and three additional variables (age at surgery, history of infantile spasms, and magnetic resonance imaging [MRI] lesion). Predictors of shorter time-to-seizure recurrence included younger age at seizure onset, prior resective surgery, generalized seizure semiology, FDG-PET hypometabolism contralateral to the side of surgery, contralateral MRI lesion, non-lesional MRI, non-stroke etiologies, and a history of infantile spasms. The area under the curve (AUC) of the final model was 73.0%.

SIGNIFICANCE

Online calculators are useful, cost-free tools that can assist physicians in risk estimation and inform joint decision-making processes with patients and families, potentially leading to greater satisfaction. Although the HOPS data was validated in the original analysis, the authors encourage external validation of this new calculator.

Frailty Indexes in Metastatic Spine Tumor Surgery: A Narrative Review

Quantification of preoperative frailty is an important prognostic tool in neurosurgical decision making. Metastatic spine tumor patients undergoing surgery are frail and have unfavorable outcomes that include an increased length of stay, unfavorable discharge disposition, and increased readmission rates. These undesirable outcomes result in higher treatment costs. A heterogeneous mixture of various frailty indexes is available with marked variance in their validation, leading to disparate clinical utility. The lack of a universally accepted definition for frailty, let alone in the method of creation or elements required in the formation of a frailty index, has resulted in a body of frailty literature lacking precision for predicting neurosurgical outcomes. In this review, we examine the role of reported frailty indexes in predicting postoperative outcomes after resection of metastatic spine tumors and aim to assist as a frailty guide for helping clinical decision making.

Nonhome Discharge in Patients Undergoing Pelvic Reconstructive Surgery: A National Analysis

IMPORTANCE

Discharge to home after surgery has been recognized as a determinant of long-term survival and is a common concern in the elderly population.

OBJECTIVE

The aim of the study was to determine the incidence and risk factors for nonhome discharge in patients undergoing major surgery for pelvic organ prolapse.

STUDY DESIGN

We performed a retrospective cohort study using the American College of Surgeons National Surgical Quality Improvement Program Database from 2010 to 2018. We included patients who underwent sacrocolpopexy, vaginal colpopexy, and colpocleisis. We compared perioperative characteristics in patients who were discharged home versus those who were discharged to a nonhome location. Stepwise backward multivariate logistic regression was then used to control for confounding variables and identify independent predictors of nonhome discharge.

RESULTS

A total of 38,012 patients were included in this study, 209 of whom experienced nonhome discharge (0.5%). Independent predictors of nonhome discharge included preoperative weight loss (adjusted odds ratio [aOR], 5.9; 95% confidence interval [CI], 1.3-27.5), dependent health care status (aOR, 5.0; 95% CI, 2.6-9.5), abdominal hysterectomy (aOR, 2.3; 95% CI, 1.4-3.7), American Society of Anesthesiologists class 3 or greater (aOR, 2.0; 95% CI, 1.5-2.7), age (aOR, 1.1; 95% CI, 1.05-1.09), operative time (aOR, 1.005; 95% CI, 1.003-1.006), laparoscopic hysterectomy (aOR, 0.6; 95% CI, 0.4-1.0), and laparoscopic sacrocolpopexy (aOR, 0.5; 95% CI, 0.3-0.8).

CONCLUSIONS

In patients undergoing surgery for pelvic organ prolapse, nonhome discharge is associated with various indicators of frailty, including age, health care dependence, and certain comorbidities. An open surgical approach increases the risk of nonhome discharge, while a laparoscopic approach is associated with lower risk.

Frailty and postoperative outcomes in brain tumor patients: a systematic review subdivided by tumor etiology

PURPOSE

Frailty has gained prominence in neurosurgical oncology, with more studies exploring its relationship to postoperative outcomes in brain tumor patients. As this body of literature continues to grow, concisely reviewing recent developments in the field is necessary. Here we provide a systematic review of frailty in brain tumor patients subdivided by tumor type, incorporating both modern frailty indices and traditional Karnofsky Performance Status (KPS) metrics.

METHODS

Systematic literature review was performed using PRISMA guidelines. PubMed and Google Scholar were queried for articles related to frailty, KPS, and brain tumor outcomes. Only articles describing novel associations between frailty or KPS and primary intracranial tumors were included.

RESULTS

After exclusion criteria, systematic review yielded 52 publications. Amongst malignant lesions, 16 studies focused on glioblastoma. Amongst benign tumors, 13 focused on meningiomas, and 6 focused on vestibular schwannomas. Seventeen studies grouped all brain tumor patients together. Seven studies incorporated both frailty indices and KPS into their analyses. Studies correlated frailty with various postoperative outcomes, including complications and mortality.

CONCLUSION

Our review identified several patterns of overall postsurgical outcomes reporting for patients with brain tumors and frailty. To date, reviews of frailty in patients with brain tumors have been largely limited to certain frailty indices, analyzing all patients together regardless of lesion etiology. Although this technique is beneficial in providing a general overview of frailty's use for brain tumor patients, given each tumor pathology has its own unique etiology, this combined approach potentially neglects key nuances governing frailty's use and prognostic value.

RAI-measured frailty predicts non-home discharge following metastatic brain tumor resection: national inpatient sample analysis of 20,185 patients

PURPOSE

Preoperative risk stratification for patients undergoing metastatic brain tumor resection (MBTR) is based on established tumor-, patient-, and disease-specific risk factors for outcome prognostication. Frailty, or decreased baseline physiologic reserve, is a demonstrated independent risk factor for adverse outcomes following MBTR. The present study sought to assess the impact of frailty, measured by the Risk Analysis Index (RAI), on MBTR outcomes.

METHODS

All MBTR were queried from the National Inpatient Sample (NIS) from 2019 to 2020 using diagnosis and procedural codes. The relationship between preoperative RAI frailty score and our primary outcome - non-home discharge (NHD) - and secondary outcomes - complication rates, extended length of stay (eLOS), and mortality - were analyzed via univariate and multivariable analyses. Discriminatory accuracy was tested by computation of concordance statistics in area under the receiver operating characteristic (AUROC) curve analysis.

RESULTS

There were 20,185 MBTR patients from the NIS database from 2019 to 2020. Each patient's frailty status was stratified by RAI score: 0-20 (robust): 34%, 21-30 (normal): 35.1%, 31-40 (very frail): 13.9%, 41+ (severely frail): 16.8%. Compared to robust patients, severely frail patients demonstrated increased complication rates (12.2% vs. 6.8%, p < 0.001), eLOS (37.6% vs. 13.2%, p < 0.001), NHD (52.0% vs. 20.6%, p < 0.001), and mortality (9.9% vs. 4.1%, p < 0.001). AUROC curve analysis demonstrated good discriminatory accuracy of RAI-measured frailty in predicting NHD after MBTR (C-statistic = 0.67).

CONCLUSION

Increasing RAI-measured frailty status is significantly associated with increased complication rates, eLOS, NHD, and mortality following MBTR. Preoperative frailty assessment using the RAI may aid in preoperative surgical planning and risk stratification for patient selection.

Machine Learning Models for Predicting Postoperative Outcomes following Skull Base Meningioma Surgery

Objective  While predictive analytic techniques have been used to analyze meningioma postoperative outcomes, to our knowledge, there have been no studies that have investigated the utility of machine learning (ML) models in prognosticating outcomes among skull base meningioma patients. The present study aimed to develop models for predicting postoperative outcomes among skull base meningioma patients, specifically prolonged hospital length of stay (LOS), nonroutine discharge disposition, and high hospital charges. We also validated the predictive performance of our models on out-of-sample testing data. Methods  Patients who underwent skull base meningioma surgery between 2016 and 2019 at an academic institution were included in our study. Prolonged hospital LOS and high hospital charges were defined as >4 days and >$47,887, respectively. Elastic net logistic regression algorithms were trained to predict postoperative outcomes using 70% of available data, and their predictive performance was evaluated on the remaining 30%. Results  A total of 265 patients were included in our final analysis. Our cohort was majority female (77.7%) and Caucasian (63.4%). Elastic net logistic regression algorithms predicting prolonged LOS, nonroutine discharge, and high hospital charges achieved areas under the receiver operating characteristic curve of 0.798, 0.752, and 0.592, respectively. Further, all models were adequately calibrated as determined by the Spiegelhalter Z -test ( p >0.05). Conclusion  Our study developed models predicting prolonged hospital LOS, nonroutine discharge disposition, and high hospital charges among skull base meningioma patients. Our models highlight the utility of ML as a tool to aid skull base surgeons in providing high-value health care and optimizing clinical workflows.

Predictors of surgical site infection in glioblastoma patients undergoing craniotomy for tumor resection

OBJECTIVE

Surgical site infections (SSIs) burden patients and healthcare systems, often requiring additional intervention. The objective of this study was to identify the relationship between preoperative predictors inclusive of scalp incision type and postoperative SSI following glioblastoma resection.

METHODS

The authors retrospectively reviewed cases of glioblastoma resection performed at their institution from December 2006 to December 2019 and noted preoperative demographic and clinical presentations, excluding patients missing these data. Preoperative nutritional indices were available for a subset of cases. Scalp incisions were categorized as linear/curvilinear, reverse question mark, trapdoor, or frontotemporal. Patients were dichotomized by SSI incidence. Multivariable logistic regression was used to determine predictors of SSI.

RESULTS

A total of 911 cases of glioblastoma resection were identified, 30 (3.3%) of which demonstrated postoperative SSI. There were no significant differences in preoperative malnutrition or number of surgeries between SSI and non-SSI cases. The SSI cases had a significantly lower preoperative Karnofsky Performance Status (KPS) than the non-SSI cases (63.0 vs 75.1, p < 0.0001), were more likely to have prior radiation history (43.3% vs 26.4%, p = 0.042), and were more likely to have received steroids both preoperatively and postoperatively (83.3% vs 54.5%, p = 0.002). Linear/curvilinear incisions were more common in non-SSI than in SSI cases (56.9% vs 30.0%, p = 0.004). Trapdoor scalp incisions were more frequent in SSI than non-SSI cases (43.3% vs 24.2%, p = 0.012). On multivariable analysis, a lower preoperative KPS (OR 1.04, 95% CI 1.02–1.06), a trapdoor scalp incision (OR 3.34, 95% CI 1.37–8.49), and combined preoperative and postoperative steroid administration (OR 3.52, 95% CI 1.41–10.7) were independently associated with an elevated risk of postoperative SSI.

CONCLUSIONS

The study findings indicated that SSI risk following craniotomy for glioblastoma resection may be elevated in patients with a low preoperative KPS, a trapdoor scalp incision during surgery, and steroid treatment both preoperatively and postoperatively. These data may help guide future operative decision-making for these patients.

Frailty in Patients Undergoing Surgery for Brain Tumors: A Systematic Review of the Literature

BACKGROUND

Emerging literature suggests that frailty may be an important driver of postoperative outcomes in patients undergoing surgery for brain tumors. We systematically reviewed the literature on frailty in patients with brain tumor with respect to 3 questions: What methods of frailty assessment have been applied to patients with brain tumor? What thresholds have been defined to distinguish between different levels of frailty? What clinical outcomes does frailty predict in patients with brain tumor?

METHODS

A literature search was conducted using PubMed, Embase, The Cochrane Library, Web of Science, Scopus, and ClinicalTrials.gov. Included studies were specific to patients with brain tumor, used a validated instrument to assess frailty, and measured the impact of frailty on postoperative outcomes.

RESULTS

Of 753 citations, 21 studies met our inclusion criteria. Frailty instruments were studied, in order of frequency reported, including the 5-factor modified frailty index, 11-factor modified frailty index, Johns Hopkins Adjusted Clinical Groups frailty-defining diagnosis indicator, and Hopkins Frailty Score. Multiple different conventions and thresholds were reported for distinguishing the levels of frailty. Clinical outcomes associated with frailty included mortality, survival, complications, length of stay, charges, costs, discharge disposition, readmissions, and operative time.

CONCLUSIONS

Frailty is an increasingly popular concept in patients with brain tumor that is associated with important clinical outcomes. However, the extant literature is largely comprised of retrospective studies with heterogeneous definitions of frailty, thresholds for defining levels of frailty, and patient populations. Further work is needed to understand best practices in assessing frailty in patients with brain tumor and applying these concepts to clinical practice.

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.

Novel Predictive Models for High-Value Care Outcomes Following Glioblastoma Resection

BACKGROUND

Treating patients with glioblastoma (GBM) requires extensive medical infrastructure. Individualized risk assessment for extended length of stay (LOS), nonroutine discharge disposition, and increased total hospital charges is critical to optimize delivery of care. Our study sought to develop predictive models identifying independent risk factors for these outcomes.

METHODS

We retrospectively reviewed patients undergoing GBM resection at our institution between January 2017 and September 2020. Extended LOS and elevated hospital charges were defined as values in the upper quartile of the cohort. Nonroutine discharge was defined as any disposition other than to home. Multivariate models for each outcome included covariates demonstrating P ≤ 0.10 on bivariate analysis.

RESULTS

We identified 265 patients undergoing GBM resection, with an average age of 58.2 years. 24.5% of patients experienced extended LOS, 22.6% underwent nonroutine discharge, and 24.9% incurred elevated total hospital charges. Decreasing Karnofsky Performance Status (KPS) (P = 0.004), increasing modified 5-factor frailty (mFI-5) index (P = 0.012), lower surgeon experience (P = 0.005), emergent surgery (P < 0.0001), and larger tumor volume (P < 0.0001) predicted extended LOS. Independent predictors of nonroutine discharge included older age (P = 0.02), decreasing KPS (P < 0.0001), and emergent surgery (P = 0.048). Nonprivate insurance (P = 0.011), decreasing KPS (P = 0.029), emergent surgery (P < 0.0001), and larger tumor volume (P = 0.004) predicted elevated hospital charges. These models were incorporated into an open-access online calculator (https://neurooncsurgery3.shinyapps.io/gbm_calculator/).

CONCLUSIONS

Several factors were independent predictors for at least 1 high-value care outcome, with lower KPS and emergent admission associated with each outcome. These models and our calculator may help clinicians provide individualized postoperative risk assessment to glioblastoma patients.

Sarcopenia Diagnosed Using Masseter Muscle Diameter as a Survival Correlate in Elderly Patients with Glioblastoma

BACKGROUND

Elderly patients with glioblastoma (GBM) have a worse prognosis than do younger patients. The present study aimed to identify the patient, treatment, and imaging features, including measures of sarcopenia, associated with worse survival and 90-day postoperative mortality for elderly patients with GBM.

METHODS

A single-center retrospective study was conducted of patients aged ≥79 years at surgery who had undergone biopsy or resection of a World Health Organization grade IV GBM at the initial diagnosis. Imaging features of sarcopenia were collected, including the masseter and temporalis muscle diameters. Multivariate analyses were performed to identify factors associated with survival and 30-day complications.

RESULTS

The cohort included 110 patients with a mean age of 82.8 years at surgery and a median preoperative Karnofsky performance scale score of 80. The majority of patients underwent a surgical resection (66.4%) while a minority underwent biopsy (33.6%). Adjuvant chemo- and/or radiation therapy were used in 72.5% of the cohort. On multivariate analysis, age (hazard ratio [HR], 7.97; 95% confidence interval [CI], 1.63-36.3), adjuvant therapy (RT or TMZ vs. none: HR, 0.12; 95% CI, 0.05-0.3; RT plus TMZ vs. none: HR, 0.05; 95% CI, 0.02-0.14), surgical resection (HR, 0.46; 95% CI, 0.24-0.9), multifocality (HR, 2.7; 95% CI, 1.14-6.4), and masseter diameter (HR, 0.12; 95% CI, 0.02-0.78) were associated with survival. Masseter diameter was the only factor associated with 90-day mortality after surgical resection (P = 0.044).

CONCLUSIONS

GBM patients over the age of 79 have acceptable outcomes after resection, followed by adjuvant chemotherapy and RT. In addition to the treatment factors that predicted for survival, a decreased masseter diameter on preoperative imaging, a marker of sarcopenia, was associated with shorter overall survival and 90-day mortality after surgical resection.

The role of frailty in the clinical management of neurofibromatosis type 1: a mixed-effects modeling study using the Nationwide Readmissions Database

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.

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.

Enhanced Recovery After Surgery (ERAS) for Cranial Tumor Resection: A Review

Enhanced Recovery After Surgery (ERAS) protocols describe a standardized method of preoperative, perioperative, and postoperative care to enhance outcomes and minimize complication risks surrounding elective surgical intervention. A growing body of evidence is being generated as we learn to apply principles of ERAS standardization to neurosurgical patients. First applied in spinal surgery, ERAS protocols have been extended to cranial neuro-oncological procedures. This review synthesizes recent findings to generate evidence-based guidelines to manage neurosurgical oncology patients with standardized systems and assess ability of these systems to coordinate multidisciplinary, patient-centric care efforts. Furthermore, we highlight the potential utility of multimedia, app-based communication platforms to facilitate patient education, autonomy, and team communication within each of the three settings.

Predicting High-Value Care Outcomes After Surgery for Non-Skull Base Meningiomas

OBJECTIVE

A need exists to better understand the prognostic factors that influence high-value care outcomes after meningioma surgery. The goal of the present study was to develop predictive models to determine the patients at risk of experiencing an extended hospital length of stay (LOS), nonroutine discharge disposition, and/or a 90-day hospital readmission after non-skull base meningioma resection.

METHODS

In the present study, we analyzed the data from 396 patients who had undergone surgical resection of non-skull base meningiomas at a single institution between January 1, 2005 and December 31, 2020. The Mann-Whitney U test was used for bivariate analysis of the continuous variables and the Fisher exact test for bivariate analysis of the categorical variables. A multivariate analysis was conducted using logistic regression models.

RESULTS

Most patients had had a falcine or parasagittal meningioma (66.2%), with the remainder having convexity (31.8%) or intraventricular (2.0%) tumors. Nonelective surgery (P < 0.0001) and an increased tumor volume (P = 0.0022) were significantly associated with a LOS >4 days on multivariate analysis. The independent predictors of a nonroutine discharge disposition included male sex (P = 0.0090), nonmarried status (P = 0.024), nonelective surgery (P = 0.0067), tumor location within the parasagittal or intraventricular region (P = 0.0084), and an increased modified frailty index score (P = 0.039). Hospital readmission within 90 days was independently associated with nonprivate insurance (P = 0.010) and nonmarried status (P = 0.0081). Three models predicting for a prolonged LOS, nonroutine discharge disposition, and 90-day readmission were implemented in the form of an open-access, online calculator (available at: https://neurooncsurgery3.shinyapps.io/non_skull_base_meningiomas/).

CONCLUSIONS

After external validation, our open-access, online calculator could be useful for assessing the likelihood of adverse postoperative outcomes for patients undergoing surgery of non-skull base meningioma.

Predictive Analytics in Clinical Practice: Advantages and Disadvantages

Predictive analytics are increasingly reported by clinicians. These tools aim to improve patient outcomes in terms of quality, safety, and efficiency. However, deploying predictive analytics in clinical practice remains challenging today. We highlight several advantages and disadvantages of the application of predictive analytics in clinical practice. To flourish and reach its potential, predictive analytics need data that is of adequate quantity and quality, ideally tailored to clinical scenarios in equipoise regarding optimal management. Adequate reporting of predictive analytic tools is incumbent for uptake into clinical workflows. At least for now, the clinicians' knowledge, experience, and vigilance remain imperative for applying predictive analytics in clinical practice.

Predicting High-Value Care Outcomes After Surgery for Skull Base Meningiomas

BACKGROUND

Although various predictors of adverse postoperative outcomes among patients with meningioma have been established, research has yet to develop a method for consolidating these findings to allow for predictions of adverse health care outcomes for patients diagnosed with skull base meningiomas. The objective of the present study was to develop 3 predictive algorithms that can be used to estimate an individual patient's probability of extended length of stay (LOS) in hospital, experiencing a nonroutine discharge disposition, or incurring high hospital charges after surgical resection of a skull base meningioma.

METHODS

The present study used data from patients who underwent surgical resection for skull base meningiomas at a single academic institution between 2017 and 2019. Multivariate logistic regression analysis was used to predict extended LOS, nonroutine discharge, and high hospital charges, and 2000 bootstrapped samples were used to calculate an optimism-corrected C-statistic. The Hosmer-Lemeshow test was used to assess model calibration, and P < 0.05 was considered statistically significant.

RESULTS

A total of 245 patients were included in our analysis. Our cohort was mostly female (77.6%) and white (62.4%). Our models predicting extended LOS, nonroutine discharge, and high hospital charges had optimism-corrected C-statistics of 0.768, 0.784, and 0.783, respectively. All models showed adequate calibration (P>0.05), and were deployed via an open-access, online calculator: https://neurooncsurgery3.shinyapps.io/high_value_skull_base_calc/.

CONCLUSIONS

After external validation, our predictive models have the potential to aid clinicians in providing patients with individualized risk estimation for health care outcomes after meningioma surgery.