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

Huq, Sakibul; Khalafallah, Adham M.; Patel, Palak; Sharma, Paarth; Dux, Hayden; White, Taija; Jimenez, Adrian E.; Mukherjee, Debraj

doi:10.1016/j.wneu.2020.11.018

Cited by 22 publications

(9 citation statements)

References 43 publications

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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, the fact that Medicare or Medicaid insurance, non-elective admission, greater tumor volume, and less surgeon years of experience were all associated with higher odds in high hospital charges according to our predictive model is also in line with previous research. 16,30,[36][37][38][39][40] Interestingly, several associations that were significant in bivariate analysis were excluded from the final predictive models during the training process (such as the significant association between older patient age and higher odds of prolonged LOS), and some associations that were not significant in bivariate analysis were included as inputs in the final elastic net models (such as the association between patient sex and discharge disposition).…”

Section: Discussion Prior Researchsupporting

confidence: 89%

“…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, the fact that Medicare or Medicaid insurance, non-elective admission, greater tumor volume, and less surgeon years of experience were all associated with higher odds in high hospital charges according to our predictive model is also in line with previous research. 16 30 36 37 38 39 40…”

Section: Discussionsupporting

confidence: 89%

Section: Present Studymentioning

confidence: 99%

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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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Section: Discussion Prior Researchsupporting

confidence: 89%

Section: Discussionsupporting

confidence: 89%

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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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“…Further practice and policy considerations are discussed in Supplemental Digital Content 7, http://links.lww.com/NEU/ D112. [40][41][42][43]…”

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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“…Specific protocols for the multidisciplinary management of patients classified as high frailty after AVM-5 scoring could prevent adverse events through proper precautions, such as prophylaxis for cardiovascular and respiratory complications. 43 The AVM-5 may also guide preoperative counseling and discussion with patients over topics such as risks of operation and postoperative adverse events. Finally, the AVM-5 may provide more accurate risk profiles for proactively identifying patients who may require nonroutine discharge to a postacute care or skilled nursing facility.…”

Section: Discussionmentioning

confidence: 99%

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

Tang

Bajaj

Zhao

et al. 2022

Neurosurgical Focus

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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.

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

Cited by 22 publications

References 43 publications

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

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

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

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

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