Frailty and outcomes after craniotomy for brain tumor

Sastry, Rahul; Pertsch, Nathan J.; Tang, Oliver Y.; Shao, Belinda; Toms, Steven; Weil, Robert J.

doi:10.1016/j.jocn.2020.09.002

Cited by 46 publications

(51 citation statements)

References 32 publications

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“…Although all 3 frailty scores analyzed in this study have been widely used as predictors of neurosurgical outcomes, a key drawback in using these frailty metrics for neurosurgery is that their scoring components are not specific to neurosurgical disease processes. 7,19,32,35,36 A notable example is mFI-5 because its 5 variables were chosen primarily on the basis of being available in the National Surgical Quality Improvement Program database, as opposed to any formal clinical rationale. 11 Our custom 5-variable vestibular schwannoma risk score (VS-5, https://skullbaseresearch.shinyapps.io/vs-5_calculator/), generated using machine learning–based model selection, outperformed patient age and all 3 frailty indices in predicting routine hospital discharge.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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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)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017). Three risk stratification scores were analyzed: modified Frailty Index-5, modified Frailty , 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.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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“…11,[29][30][31] Further, the fact that older patient age, Medicare or Medicaid insurance, greater tumor volume, higher ASA score, and higher mFI-5 score are all associated with higher odds of nonroutine discharge also validates prior research findings. [32][33][34][35] Finally,…”

Section: Present Studymentioning

confidence: 99%

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

Jimenez

Porras

Azad

et al. 2022

J Neurol Surg B Skull Base

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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 2016-2019 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 (AUROCs) 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 healthcare and optimizing clinical workflows.

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“…6,7 This study investigated the association between frailty and outcomes after elective craniotomy for the resection of brain tumors. 5 Frailty assessment was based on a 5-factor modified frailty index (mFI-5) and classified as nonfrail (mFI-5 = 0), low frailty (mFI = 0.2), or medium to high frailty (mFI > 0.2). Patient demographics, comorbidities, and operative factors were compared between nonfrail, low, and medium to high frailty groups.…”

Section: Frailty and Outcomes After Brain Tumor Surgerymentioning

confidence: 99%

Year in Review: Synopsis of Selected Articles in Neuroanesthesia and Neurocritical Care from 2020

Esmail

Subramaniam

Venkatraghavan

2021

Journal of Neuroanaesthesiology and Critical Care

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This review is a synopsis of selected articles from neuroscience, neuroanesthesia, and neurocritical care from the year 2020 (January–December 2020). The journals reviewed include anesthesia journals, critical care medicine journals, neurology and neurosurgical journals, as well as high-impact medical journals such as the Lancet, Journal of American Medical Association, New England Journal of Medicine, and Stroke. This summary of important articles will serve to update the knowledge of anesthesiologists and other perioperative physicians who provide care to neurosurgical and neurocritical care patients. In addition, some of the important narrative reviews that are of interest to neuroanesthesiologists are also listed.

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Frailty and outcomes after craniotomy for brain tumor

Cited by 46 publications

References 32 publications

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

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

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

Year in Review: Synopsis of Selected Articles in Neuroanesthesia and Neurocritical Care from 2020

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