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

View More View Less
  • 1 Department of Neurosurgery, Warren Alpert Medical School of Brown University, Providence, Rhode Island;
  • | 2 Center for Skull Base and Pituitary Surgery, Neurological Institute of New Jersey, Newark, New Jersey;
  • | 3 Department of Neurological Surgery, New Jersey Medical School, Newark, New Jersey;
  • | 4 Department of Otolaryngology–Head and Neck Surgery, New Jersey Medical School, Newark, New Jersey; and
  • | 5 Saint Barnabas Medical Center, RWJ Barnabas Health, Livingston, New Jersey
Free access

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.

ABBREVIATIONS

AUC = area under the curve; AVICH = AVM intracerebral hemorrhage; AVM = arteriovenous malformation; CCI = Charlson Comorbidity Index; DVT = deep vein thrombosis; LOS = length of stay; mFI-5 = 5-factor modified frailty index; mFI-11 = 11-factor modified frailty index; NIS = National (Nationwide) Inpatient Sample; PE = pulmonary embolism; ROC = receiver operating characteristic.

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.

Risk stratification of neurosurgical candidates is essential to optimizing perioperative outcomes.1,2 As growing evidence demonstrates that advanced age alone is insufficiently predictive of worse outcomes after neurosurgery, it is important to evaluate other risk factors that may guide surgical selection and postoperative management.3–5 Recent studies have demonstrated the clinical value of frailty—diminishing physiological reserve associated with comorbidity burden—as a predictor of morbidity and mortality across various skull base procedures.6–8 However, this frailty-outcome relationship is poorly characterized for patients undergoing cerebrovascular surgery. This gap in understanding is critical because patients with neurovascular pathologies often have significant comorbidity profiles leading to postoperative complications like intracranial hemorrhage and reoperation.9,10

Among hundreds of frailty and risk stratification measures documented in the literature, the 5-factor modified frailty index (mFI-5), 11-factor modified frailty index (mFI-11), and Charlson Comorbidity Index (CCI) have been assessed extensively across the neurosurgical literature.8,11–13 However, the use of these scores in the setting of cerebrovascular surgery has three limitations. First, it remains unclear which frailty score most accurately predicts patient outcomes following neurosurgery, as only the CCI has been studied in relation to microsurgical outcomes for aneurysm repair and arteriovenous malformation (AVM) resection.14 Second, earlier studies have focused on a single preselected score and the comparative accuracy between different metrics is poorly understood. Third, these scores have poor specificity to the neurosurgical setting and may not track the clinical variables that best inform neurosurgical candidate selection.

The present study assessed the comparative accuracy of the mFI-5, mFI-11, and CCI in predicting cerebral AVM microsurgical outcomes in a nationwide cohort. Additionally, we created a custom risk score tailored to AVM microsurgery, combining comorbidities from frailty scales with clinical variables reflecting disease severity.

Methods

Data Source and Inclusion Criteria

From 2002 to 2017, we analyzed all adult AVM admissions undergoing microsurgery in the National (Nationwide) Inpatient Sample (NIS). The NIS represents a 20% stratified random sample of all US community hospital discharges, providing weighted estimates for more than 35 million hospitalizations nationwide annually. Admissions were selected using relevant diagnosis and procedure codes classified by the International Classification of Diseases, Ninth Revision (ICD-9) and Tenth Revision (ICD-10) (Supplementary Table 1). Identical criteria were used to identify a pseudoprospective cohort of 2005 admissions from 2018 to 2019. We chose to study microsurgical admissions specifically because microsurgical resection is the definitive treatment modality for AVM, but we used endovascular embolization procedure codes to identify a subset of patients receiving “combined” treatment.15 Because of the high prevalence of missing race data in the NIS (> 20%), we followed Healthcare Cost and Utilization Project (HCUP) statistical recommendations to impute missing data on race using age, sex, insurance source, hospital location, Census region, and household income. This study was exempt from institutional review board approval because it used publicly available and anonymized data.

Frailty Scores

This study evaluated 3 distinct frailty measures in association with AVM microsurgical outcomes: the mFI-5, mFI-11, and CCI. Table 1 shows the comorbidity variables represented by each frailty scoring system. The CCI evaluates comorbidity burden on a scale of 0–29 but allots different values to comorbid diseases based on severity.16 Each comorbidity featured in the mFI-5 and MFI-11 is assigned a value of 1, and all are summed to calculate a total score ranging from 0 to 5 (mFI-5) or from 0 to 11 (mFI-11).17 Per the methodology of Dicpinigaitis et al., we determined functional health status in the NIS using codes corresponding to impairments in activities of daily living, bedridden status, and fall risk.8 Receiver operating characteristic (ROC) curves were created for each frailty score with reference to their classification of discharge disposition to discriminate between low-frailty and high-frailty patients in the study population. We calculated the high frailty threshold for each score by determining the point on its corresponding ROC curve at which Youden’s index (sum of sensitivity and specificity) was maximized.8 This threshold was ≥ 2 for all 3 frailty scores. Previous studies have validated the association between preoperative frailty and hospital disposition in the setting of neurosurgery, supporting the utility of hospital discharge status as a proxy for functional status and outcome for frailty stratification.18,19 In-hospital mortality was not chosen as the primary endpoint for this study due to being present in a small frequency of the study population (2.2%, n = 365), creating the risk of model overfitting.20

TABLE 1.

Characteristics for AVM resection admissions from 2002 to 2017

CharacteristicValue
Total admissions16,721 (100.0)
Age, yrs43.7 ± 15.3 [32–75]
Sex
 Male8135 (48.7)
 Female8586 (51.3)
Race
 White10,792 (64.5)
 Non-White5929 (35.5)
Insurance status
 Private insurance10,227 (61.2)
 Medicare2253 (13.5)
 Medicaid2433 (14.6)
 Other1808 (10.8)
Income quartile of patient’s zip code
 0–25% (lowest)3635 (21.7)
 25–50%3936 (23.5)
 50–75%4294 (25.7)
 75–100% (highest)4856 (29.0)
Weekend admission1163 (7.0)
Ruptured AVM3515 (21.0)
Treatment modality
 Microsurgery only13,658 (81.7)
 Combined microsurgery & endovascular3063 (18.3)
Hospital ownership
 Government3149 (18.8)
 Private nonprofit12,751 (76.3)
 Private for-profit821 (4.9)
Hospital location & teaching status
 Rural164 (1.0)
 Urban nonteaching1517 (9.1)
 Urban teaching15,040 (90.0)
Hospital bedsize
 Small752 (4.5)
 Medium2088 (12.5)
 Large13,881 (83.0)

Values are given as number of patients (%) or mean ± SD [IQR]. Percentages may not add up to 100% due to rounding.

Study Outcomes

Outcomes included in-hospital mortality, routine discharge status, inpatient complications, postoperative and total length of stay (LOS), and hospitalization costs. Discharge status was categorized as routine if patients were discharged to home or a short-term facility, consistent with prior NIS neurosurgical studies.18,20 Complications were identified using ICD-9 and ICD-10 codes extracted from validated Clinical Classifications Software groupings, as reported in prior NIS neurosurgical studies.19,21 Inpatient complications were classified as medical or surgical. Medical complications consisted of 5 subtypes (cardiovascular complications, respiratory complications, deep vein thrombosis [DVT] or pulmonary embolism [PE], pneumonia, and other postoperative infection). Surgical complications were composed of intra- or postoperative hemorrhage and neurological complications.

Statistical Analysis

Stata 14 (StataCorp) was used for all data aggregation and regression analysis, using Stata’s svy command suite to apply NIS survey weights and make stable national estimates. To evaluate associations between high-frailty status and AVM microsurgical outcomes, we created a multivariate model adjusting for 13 confounding variables: hospital characteristics (ownership, teaching status, bedsize), patient demographics (age, sex, race, insurance status, income quartile), disease severity on admission (presence of hydrocephalus, presence of epilepsy), weekend admission, treatment modality, and rupture status. Institutional studies have employed both hydrocephalus and epilepsy for risk stratification in AVM microsurgery.22–24 We analyzed associations between patient frailty status and binary outcomes using multivariate logistic regression and reported odds ratios. To evaluate LOS and hospitalization costs, we used gamma regression with a logistic link function, idealized for modeling right-skewed, continuous outcomes, and reported β-coefficients (percentage change in the outcome relative to a reference category).20

Machine Learning Development of Custom AVM-5 Risk Score

Finally, following earlier methods of neurosurgical risk stratification, we generated a custom AVM risk score using k-fold cross-validation (k = 3 partitions) and Akaike information criterion–based model selection in R version 3.5.3 (R Foundation for Statistical Computing). The study population was first partitioned into 3 random subsamples of approximately 5000 admissions (Fig. 1). In each unique combination of 2 subsamples (n = 3), we used the admissions in these partitions (67%) to generate a 5-variable severity score that we subsequently validated in the remaining partition (33%). The variables used in model selection for this risk score included age (threshold ≥ 50 chosen by Youden’s index), all disease severity models in our multivariate model, and all comorbidities used by at least one of the 3 frailty scores present in at least 1% of the study population. This cutoff was chosen to minimize the risk of model overfitting.

FIG. 1.
FIG. 1.

Analysis overview for development of custom AVM risk stratification score. Overview of analysis plan for developing a custom AVM risk stratification score (AVM-5), including selection of study population, k-fold cross-validation, and machine learning–based model selection of variables for the AVM-5.

Following the selection of our custom score’s 5 variables, we followed the methodology of Washington et al.25 and Newman et al.26 to develop a weighted score by constructing a multivariate regression model with the 5 variables and routine discharge as the outcome, using the regression coefficients as each variable’s score weight. We evaluated associations between our custom AVM risk score and perioperative outcomes via the multivariate analysis paradigm used for the mFI-5, mFI-11, and CCI. ROC analysis was used to calculate area under the curve (AUC) for each score’s ROC curve, with routine discharge as the primary endpoint. The DeLong test was used to compare each score’s performance in predicting routine discharge. For all analyses, statistical significance was maintained at p < 0.05.

Results

Study Population

We analyzed 16,271 adult admissions undergoing AVM microsurgery in the NIS from 2002 to 2017 (Fig. 2). Patients were an average age of 43.7 years (SD 15.3), and the study population primarily consisted of female (51.3%), White (64.5%), and privately insured (61.2%) patients (Table 1). Patients were treated through microsurgery alone (81.7%) or a combination of microsurgical and endovascular management (18.3%). Most admissions were treated at urban teaching (90.0%) and private nonprofit (76.3%) hospitals. Twenty-one percent of patients admitted presented with ruptured AVMs (Supplementary Fig. 1).

FIG. 2.
FIG. 2.

Nationwide admissions for AVM microsurgery. Admissions in the NIS for AVM microsurgery from 2002 to 2017. Annual admissions were stratified by frailty status, as quantified by the mFI-5, mFI-11, and CCI.

Supplementary Table 2 features summary statistics for the 3 risk stratification metrics, which all had a high frailty threshold ≥ 2. The CCI determined the greatest number of frail admissions (20.8%), followed by the mFI-11 (14.3%) and mFI-5 (8.3%). Moreover, 14.8% and 7.0% of patients presented with epilepsy and hydrocephalus on admission, respectively.

Frailty and AVM Microsurgical Outcomes

High-frailty patients as determined by the mFI-5 (OR 0.65, p = 0.002), mFI-11 (OR 0.54, p < 0.001), and CCI (OR 0.25, p < 0.001) exhibited significantly lower rates of routine hospital discharge among AVM admissions (Fig. 3). However, no relationship was observed between any of the frailty scores and in-hospital mortality. All 3 frailty scores were further associated with higher postoperative and total LOS (all p ≤ 0.001), although only mFI-11 (+8%, p = 0.011) and CCI (+30%, p < 0.001) predicted increases in hospitalization costs.

FIG. 3.
FIG. 3.

Association between risk stratification scores and AVM microsurgical outcomes. All scores were dichotomized into low frailty and high frailty, with reported results corresponding to high-frailty patients. Odds ratios were reported for binary outcomes and β-coefficients, representing percent changes, were reported for continuous outcomes. Values in brackets are 95% CIs. A: Associations between mFI-5 and outcomes. B: Associations between mFI-11 and outcomes. C: Associations between CCI and outcomes. D: Associations between the AVM-5 and outcomes. The AVM-5 was scored as follows: 2.24 for age > 50 years, 8.06 for presence of hydrocephalus, 10.31 for presence of paralysis, 1.52 for presence of diabetes mellitus, and 1.65 for presence of hypertension present. Then the scores were summed. High-frailty patients were defined as having a score ≥ 3.5.

Moreover, frail patients identified by the CCI were 2.35 times more likely to develop perioperative complications relative to low-frailty patients (p < 0.001). This relationship was also observed for the mFI-5 (OR 1.47, p = 0.008) and mFI-11 (OR 1.88, p < 0.001). Across complication subtypes, pneumonia and other postoperative infection were associated with high frailty as determined by all 3 scores (all p < 0.05), although the CCI alone predicted both respiratory (OR 2.27, p < 0.001) and neurological (OR 2.03, p < 0.001) complications (Table 2).

TABLE 2.

TABLE 2. Association between frailty scores and perioperative complications

ComplicationmFI-5mFI-11CCIAVM-5
OR (95% CI)p ValueOR (95% CI)p ValueOR (95% CI)p ValueOR (95% CI)p Value
Intra- or postop hemorrhage1.67 (0.73–3.84)0.2241.08 (0.50–2.33)0.8391.57 (0.88–2.79)0.1252.20 (1.23–3.91)0.007
Neurological1.21 (0.68–2.15)0.5081.30 (0.80–2.12)0.2962.03 (1.42–2.91)<0.0012.23 (1.46–3.39)<0.001
Cardiovascular1.06 (0.49–2.30)0.8821.28 (0.69–2.38)0.4351.39 (0.77–2.51)0.2742.26 (1.26–4.07)0.007
Respiratory1.24 (0.84–1.85)0.2821.35 (0.98–1.85)0.0682.27 (1.74–2.96)<0.0012.01 (1.47–2.75)<0.001
DVT or PE1.57 (0.61–4.08)0.3541.65 (0.72–3.79)0.2380.65 (0.24–1.75)0.3881.89 (0.71–5.06)0.204
Pneumonia2.52 (1.24–5.09)0.0101.96 (1.02–3.79)0.0441.69 (1.00–2.86)0.0482.80 (1.56–5.02)0.001
Other postop infection1.70 (1.10–2.62)0.0162.38 (1.67–3.41)<0.0012.38 (1.73–3.27)<0.0012.88 (1.98–4.19)<0.001

The association of mFI-5, mFI-11, CCI, or AVM-5 risk score with complication subtypes after multivariate adjustment is shown. Boldface type indicates statistical significance.

On ROC analysis, the CCI (AUC 0.76) significantly outperformed age (AUC 0.67), mFI-5 (AUC 0.61), and mFI-11 (AUC 0.63) in predicting discharge disposition (p < 0.001) for all AVM resection admissions (Fig. 4). After stratifying the study population by treatment modality and AVM rupture status, CCI remained the best predictor of routine discharge for microsurgical (AUC 0.78), combined (AUC 0.75), and unruptured (AUC 0.75) AVM admissions (p < 0.01). However, age and CCI had comparable predictive ability for patients with ruptured AVMs (p = 0.449).

FIG. 4.
FIG. 4.

ROC analysis of risk stratification scores. ROC curves for prediction of routine hospital discharge using age, mFI-5, mFI-11, CCI, and AVM-5. AUCs for different ROC curves were compared using the DeLong test. A: Comparison of scores for all AVM admissions. B: Comparison of scores for AVM admissions in pseudoprospective cohort (2018–2019).

Custom AVM Risk Score

Each of the three k-fold cross-validation groups identified the same 5 variables as most predictive of discharge disposition following AVM resection and were successfully validated with the testing partition (Supplementary Fig. 2). Accordingly, in our custom VS risk score (AVM-5 at https://skullbaseresearch.shinyapps.io/avm-5_calculator/), the following scores were assigned: 2.24 for age > 50 years, 8.06 for presence of hydrocephalus, 10.31 for presence of paralysis, 1.52 for presence of diabetes mellitus, and 1.65 for presence of hypertension present. These were then summed to obtain the AVM-5 risk score.

Youden’s index set the AVM-5 high frailty threshold at ≥ 3.5, with possible values ranging from 0 to 23.78. While 32.0% of patients were identified as frail by the AVM-5, these admissions accounted for 67.9% of patients who died in-hospital. Our custom score predicted decreased rates of routine hospital discharge (OR 0.26, p < 0.001) and higher odds of perioperative complications (OR 2.42, p < 0.001; Fig. 3D). Additional factors associated with perioperative complications included weekend admission (OR 1.40, p = 0.044), rupture status (OR 3.34, p < 0.001), presence of hydrocephalus (OR 2.65, p < 0.001), and combined microsurgical and endovascular treatment (OR 1.94, p < 0.001; Supplementary Table 3). The AVM-5 predicted higher complication rates for both unruptured (OR 2.81, p < 0.001) and ruptured (OR 1.79, p < 0.001) AVMs. High-frailty patients identified by the score also had increased postoperative LOS (+49%, p < 0.001), total LOS (+47%, p < 0.001), and hospitalization costs (+22%, p < 0.001), However, the AVM-5 was not significantly associated with in-hospital mortality.

Unlike the earlier evaluated frailty scores, the AVM-5 was predictive of both surgical complications, namely intra- or postoperative hemorrhage (OR 2.20, p = 0.007), and neurological complications (OR 2.23, p < 0.001). Moreover, the AVM-5 was associated with all medical complications except for DVT or PE.

ROC analysis demonstrated that the AVM-5 outperformed age and all other risk stratification scores for the overall study population (AUC 0.78, all p < 0.002; Fig. 4A), patients receiving combined treatment (AUC 0.75, all p < 0.002), patients with unruptured AVMs (AUC 0.75, all p < 0.001), and patients with ruptured AVMs (AUC 0.76, all p < 0.001; Supplementary Fig. 3). Additionally, the AVM-5 performed comparably to CCI in predicting discharge disposition for microsurgical patients (AUC 0.79, p = 0.059).

Validation of AVM-5 With Pseudoprospective Cohort

In a pseudoprospective cohort of 2005 AVM microsurgical admissions from 2018 to 2019, the AVM-5 remained significantly associated with decreased rates of routine discharge disposition (OR 0.20, p < 0.001) as well as increased complications (OR 3.92, p < 0.001), postoperative LOS (+90%, p < 0.001), total LOS (+92%, p < 0.001), and hospitalization costs (+40%, p < 0.001). Frail patients identified by the AVM-5 also had elevated mortality, but this did not reach significance (OR 3.02, p = 0.198). On ROC analysis, the AVM-5 (AUC 0.79) continued to outperform age (AUC 0.69, p < 0.001), mFI-5 (AUC 0.58, p < 0.001), mFI-11 (AUC 0.59, p < 0.001), and CCI (AUC 0.67, p < 0.001; Fig. 4B). Moreover, when the cohort was stratified by treatment modality (microsurgical vs combined) or rupture status (unruptured vs ruptured), the AVM-5 significantly outperformed all 3 earlier scores (all p < 0.001; Supplementary Fig. 3).

Discussion

As comorbidity burden trends upward in the aging neurosurgical population, the utility of patient frailty for preoperative risk assessment has been increasingly studied.27,28 Although frailty metrics have been evaluated as prognostic tools across spinal and oncological neurosurgery, there is a paucity of literature concerning the relationship between frailty and perioperative outcomes in cerebrovascular surgery.11,29,30 In our analysis of nationwide admissions for AVM microsurgery over 16 years, we demonstrated that the CCI, mFI-5, and mFI-11 all predicted nonroutine hospital discharge, elevated perioperative complications, and extended LOS. These frailty-outcome associations affirm and extend the findings of Davies and Lawton, which correlated CCI with lower routine discharge percentage and higher LOS following AVM resection in the NIS from 2000 to 2009.19 Our results further validate this frailty-outcome relationship for the mFI-5 and mFI-11 while providing a novel comparison of all 3 scores’ accuracy. The CCI significantly outperformed the mFI-5 and mFI-11 in predicting discharge disposition for all patients undergoing AVM resection but requires more than three times as many comorbidities for calculation as the mFI-5, which may negatively impact its ease of use in preoperative decision-making.12

On analysis of complication subtypes, high-frailty status across the mFI-5, mFI-11, and CCI was significantly associated with medical complications, such as pneumonia and other postoperative infection. Furthermore, the CCI was uniquely associated with a greater risk of respiratory complications. These findings concur with prior neurosurgical studies that have correlated high frailty with medical complications.8 However, these scores were less predictive of adverse surgical events, with only the CCI being associated with neurological complications and none predicting perioperative hemorrhage. Consequently, frailty status as denoted by these metrics may be a more accurate measure of risk assessment for postoperative recovery than for surgically related morbidity.

While the association between frailty and patient outcomes has been characterized extensively in the neurosurgical literature, the role of frailty in operative decision-making for AVM resection remains poorly elucidated.7,13,31 The clinical standard for AVM classification is the Spetzler-Martin grading system, which assigns a grade from I to V based on characteristics including AVM size, adjacency to eloquent cortex, and venous drainage to estimate patients’ risk of neurological deficit and inform surgical candidate selection.32,33 Additional risk stratification systems like the Lawton-Young grade and AVM intracerebral hemorrhage (AVICH) score have also been developed and validated.34–37 In the present study, we present a custom, 5-variable AVM-5 risk score (https://skullbaseresearch.shinyapps.io/avm-5_calculator/) that had superior performance in predicting discharge disposition for AVM admissions relative to age and all 3 existing frailty indices. The AVM-5’s superior performance was additionally validated in a pseudoprospective cohort of 2018–2019 admissions not used for initial score development. Notably, the AVM-5 outperformed the CCI despite featuring three times less variables. Additionally, the AVM-5 was uniquely predictive of both perioperative hemorrhage and neurological complications, suggesting that it has utility in predicting both intraoperative morbidity and postoperative recovery. One potential explanation of this metric’s strength is its dual incorporation of comorbidity burden as well as two variables indicative of neurological morbidity (hydrocephalus and history of paralysis). Both variables have been shown to influence patient outcomes in retrospective cohort analyses of AVM resection.38,39 These 2 variables notably also had the highest weights among AVM-5 variables, and presentation with either variable in isolation qualified a patient as “high frailty.” This finding may be secondary to paralysis and hydrocephalus reflecting AVM disease severity: paralysis can result from motor nerve compression due to AVM size or eloquent cortex involvement, whereas obstructive hydrocephalus has been demonstrated to occur disproportionately in AVMs exhibiting deep venous drainage.40,41

The AVM-5 importantly continued to outperform the mFI-5, mFI-11, and CCI after stratification by rupture status or treatment modality. While the presence of hematomas may injure surrounding brain tissue and is associated with higher rates of rehemorrhage, other studies have suggested that the presence of a hematoma may facilitate surgical resection by expanding the dead space around AVMs and obliterating AVM arterial supply.34,35 In our cohort, rupture status predicted poorer outcomes, but the AVM-5 successfully risk stratified patients regardless of AVM rupture. Moreover, it is possible that the higher odds of complications observed among patients receiving combined treatment, rather than microsurgery alone, may represent higher case complexity among AVMs requiring preoperative embolization.

In comparison with other validated AVM-specific grading systems, age is notably a variable jointly used by the AVM-5, Lawton-Young grade, and AVICH.34–37 Unlike the Lawton-Young grade, the AVM-5 does not incorporate rupture status or diffuse nidus.34,35 Moreover, while the AVICH also incorporates 6 variables involving AVM or hemorrhage imaging characteristics, the AVM-5 only uses 1 variable requiring imaging to confirm (hydrocephalus) and includes 3 variables representing patient comorbidities or clinical presentation (paralysis, diabetes mellitus, and hypertension).36,37 Importantly, the AVM-5 is not intended to supplant these existing scores for AVM classification but rather serves as an additional, complementary clinical tool for risk stratification from the alternative perspective of comorbidity burden. While the primary endpoint used to validate the Lawton-Young grade and AVICH was the modified Rankin Scale (mRS) at discharge and follow-up,34–37,42 our AVM-5 score was correlated to several additional outcomes at the inpatient level, including medical complications, LOS, and costs. Each of the AVM-5’s variables, excluding hydrocephalus, also has the advantage of being a binary variable easily obtained from chart review and physical examination. Importantly, the NIS lacks data on mRS or postdischarge outcomes, so it was not possible to compare predictive ability for long-term functional status between scores in this study. It is conceivable that due to lacking the AVM imaging characteristics captured by the Lawton-Young grade and AVICH, the AVM-5 may not have the same utility for prognosticating long-term neurological function. For example, the predictive ability of the AVM-5 for discharge disposition among ruptured AVM admissions (AUC 0.76), a proxy for functional status,18,19 was slightly lower than the performance of the AVICH in earlier studies (AUC 0.77–0.84).36,37 The association between the AVM-5 and long-term mRS in multi-institutional cohorts will be the subject of future study.

The AVM-5 may facilitate the identification of patients at risk for hospital-acquired conditions who may benefit from interventions like advanced postoperative discharge planning and integrated care pathways for complication prevention. 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.44 By enhancing postoperative resource utilization through integrated care, risk stratification in the AVM-5 carries a potential benefit to microsurgical outcomes and hospitalization costs.45

Limitations

The present study has several limitations. First, our analysis did not incorporate hospital AVM volume, which has been demonstrated to influence morbidity and mortality following AVM microsurgery,19 due to the sampling redesign of the NIS in 2012, making it impossible to accurately calculate hospital case volumes. Second, our results may be subject to residual confounding by disease severity and case complexity because the NIS does not include disease process–specific variables like AVM size and adjacency to eloquent cortex.33 Third, variations and errors in billing codes used in large healthcare databases like the NIS have been well documented.46,47 Finally, analysis of postdischarge outcomes like readmission was not possible because the NIS only recording data from the index admission.48

Conclusions

Patient frailty was predictive of higher mortality, nonroutine discharge, complications, and resource utilization for AVM patients undergoing microsurgery, with our custom AVM-5 risk stratification score outperforming age, mFI-5, mFI-11, and CCI in identifying at-risk patients. This score may guide and be applied toward preoperative counseling, intraoperative decision-making, and postoperative integrated care pathways and discharge planning.

Disclosures

The authors report no conflict of interest concerning the materials or methods used in this study or the findings specified in this paper.

Author Contributions

Conception and design: Liu, Tang. Acquisition of data: Liu, Tang. Analysis and interpretation of data: all authors. Drafting the article: all authors. Critically revising the article: all authors. Reviewed submitted version of manuscript: all authors. Approved the final version of the manuscript on behalf of all authors: Liu. Statistical analysis: Tang, Bajaj, Zhao. Administrative/technical/material support: Liu. Study supervision: Liu.

Supplemental Information

Online-Only Content

Supplemental material is available online.

Previous Presentations

Preliminary data for this study were presented as a podium presentation at the 2021 Congress of Neurological Surgeons Annual Meeting, Austin, Texas, October 16–20, 2021.

References

  • 1

    Ehlers LD, Pistone T, Haller SJ, Will Robbins J, Surdell D. Perioperative risk factors associated with ICU intervention following select neurosurgical procedures. Clin Neurol Neurosurg. 2020;192:105716.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 2

    Lakomkin N, Zuckerman SL, Stannard B, et al. Preoperative risk stratification in spine tumor surgery: a comparison of the modified Charlson Index, Frailty Index, and ASA score. Spine (Phila Pa 1976).2019;44(13):E782E787.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 3

    Maslink C, Cheng K, Smith TR, Das S. Advanced age is not a universal predictor of poorer outcome in patients undergoing neurosurgery. World Neurosurg.2019;130:e375e382.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 4

    Seicean A, Seicean S, Schiltz NK, et al. Short-term outcomes of craniotomy for malignant brain tumors in the elderly. Cancer. 2013;119(5):10581064.

  • 5

    Burkhardt JK, Lasker GF, Winkler EA, Kim H, Lawton MT. Microsurgical resection of brain arteriovenous malformations in the elderly: outcomes analysis and risk stratification. J Neurosurg. 2018;129(5):11071113.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 6

    Henry RK, Reeves RA, Wackym PA, Ahmed OH, Hanft SJ, Kwong KM. Frailty as a predictor of postoperative complications following skull base surgery. Laryngoscope. 2021;131(9):19771984.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 7

    Khalafallah AM, Shah PP, Huq S, et al. The 5-factor modified frailty index predicts health burden following surgery for pituitary adenomas. Pituitary. 2020;23(6):630640.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 8

    Dicpinigaitis AJ, Kalakoti P, Schmidt M, et al. Associations of baseline frailty status and age with outcomes in patients undergoing vestibular schwannoma resection. JAMA Otolaryngol Head Neck Surg. 2021;147(7):608614.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 9

    Cipolla MJ, Liebeskind DS, Chan SL. The importance of comorbidities in ischemic stroke: Impact of hypertension on the cerebral circulation. J Cereb Blood Flow Metab. 2018;38(12):21292149.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 10

    Michalak SM, Rolston JD, Lawton MT. Incidence and predictors of complications and mortality in cerebrovascular surgery: national trends from 2007 to 2012. Neurosurgery. 2016;79(2):182193.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 11

    Ali R, Schwalb JM, Nerenz DR, Antoine HJ, Rubinfeld I. Use of the modified frailty index to predict 30-day morbidity and mortality from spine surgery. J Neurosurg Spine. 2016;25(4):537541.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 12

    Khalafallah AM, Huq S, Jimenez AE, Brem H, Mukherjee D. The 5-factor modified frailty index: an effective predictor of mortality in brain tumor patients. J Neurosurg. 2021;135(1):7886.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 13

    McIntyre MK, Rawanduzy C, Afridi A, et al. The effect of frailty versus initial Glasgow Coma Score in predicting outcomes following chronic subdural hemorrhage: a preliminary analysis. Cureus. 2020;12(8):e10048.

    • Search Google Scholar
    • Export Citation
  • 14

    Newman WC, Kubilis PS, Hoh BL. Validation of a neurovascular comorbidities index for retrospective database analysis. J Neurosurg. 2018;130(1):273277.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 15

    Grüter BE, Mendelowitsch I, Diepers M, Remonda L, Fandino J, Marbacher S. Combined endovascular and microsurgical treatment of arteriovenous malformations in the hybrid operating room. World Neurosurg.2018;117:e204e214.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 16

    Charlson M, Szatrowski TP, Peterson J, Gold J. Validation of a combined comorbidity index. J Clin Epidemiol. 1994;47(11):12451251.

  • 17

    Subramaniam S, Aalberg JJ, Soriano RP, Divino CM. New 5-factor modified frailty index using American College of Surgeons NSQIP data. J Am Coll Surg. 2018;226(2):173181.e8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 18

    Clement RC, Carr BG, Kallan MJ, Wolff C, Reilly PM, Malhotra NR. Volume-outcome relationship in neurotrauma care. J Neurosurg. 2013;118(3):687693.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 19

    Davies JM, Lawton MT. Improved outcomes for patients with cerebrovascular malformations at high-volume centers: the impact of surgeon and hospital volume in the United States, 2000-2009. J Neurosurg. 2017;127(1):6980.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 20

    Tang OY, Yoon JS, Kimata AR, Lawton MT. Volume-outcome relationship in pediatric neurotrauma care: analysis of two national databases. Neurosurg Focus. 2019;47(5):E9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 21

    Boze H, Marlin T, Durand D, et al. Proline-rich salivary proteins have extended conformations. Biophys J. 2010;99(2):656665.

  • 22

    Soldozy S, Norat P, Yağmurlu K, et al. Arteriovenous malformation presenting with epilepsy: a multimodal approach to diagnosis and treatment. Neurosurg Focus. 2020;48(4):E17.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 23

    Ding D, Starke RM, Quigg M, et al. Cerebral arteriovenous malformations and epilepsy, Part 1: predictors of seizure presentation. World Neurosurg. 2015;84(3):645652.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 24

    Hafez A, Oulasvirta E, Koroknay-Pál P, Niemelä M, Hernesniemi J, Laakso A. Timing of surgery for ruptured supratentorial arteriovenous malformations. Acta Neurochir (Wien). 2017;159(11):21032112.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 25

    Washington CW, Derdeyn CP, Dacey RG Jr, Dhar R, Zipfel GJ. Analysis of subarachnoid hemorrhage using the Nationwide Inpatient Sample: the NIS-SAH Severity Score and Outcome Measure. J Neurosurg. 2014;121(2):482489.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 26

    Newman WC, Neal DW, Hoh BL. A new comorbidities index for risk stratification for treatment of unruptured cerebral aneurysms. J Neurosurg. 2016;125(3):713719.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 27

    Kim S, Brooks AK, Groban L. Preoperative assessment of the older surgical patient: honing in on geriatric syndromes. Clin Interv Aging. 2014;10:1327.

    • Search Google Scholar
    • Export Citation
  • 28

    Chibbaro S, Di Rocco F, Makiese O, et al. Neurosurgery and elderly: analysis through the years. Neurosurg Rev. 2010;34(2):229234.

  • 29

    Pazniokas J, Gandhi C, Theriault B, et al. The immense heterogeneity of frailty in neurosurgery: a systematic literature review. Neurosurg Rev. 2021;44(1):189201.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 30

    Youngerman BE, Neugut AI, Yang J, Hershman DL, Wright JD, Bruce JN. The modified frailty index and 30-day adverse events in oncologic neurosurgery. J Neurooncol. 2018;136(1):197206.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 31

    Sastry RA, Pertsch N, Tang O, Shao B, Toms SA, Weil RJ. Frailty and outcomes after craniotomy or craniectomy for atraumatic chronic subdural hematoma. World Neurosurg.2021;145:e242e251.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 32

    Feghali J, Huang J. Updates in arteriovenous malformation management: the post-ARUBA era. Stroke Vasc Neurol. 2019;5(1):3439.

  • 33

    Spetzler RF, Martin NA. A proposed grading system for arteriovenous malformations. J Neurosurg. 1986;65(4):476483.

  • 34

    Hafez A, Koroknay-Pál P, Oulasvirta E, et al. The application of the novel grading scale (Lawton-Young grading system) to predict the outcome of brain arteriovenous malformation. Neurosurgery. 2019;84(2):529536.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 35

    Lawton MT, Kim H, McCulloch CE, Mikhak B, Young WL. A supplementary grading scale for selecting patients with brain arteriovenous malformations for surgery. Neurosurgery. 2010;66(4):702713.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 36

    Neidert MC, Lawton MT, Mader M, et al. The AVICH score: a novel grading system to predict clinical outcome in arteriovenous malformation-related intracerebral hemorrhage. World Neurosurg.2016;92:292297.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 37

    Neidert MC, Lawton MT, Kim LJ, et al. International multicentre validation of the arteriovenous malformation-related intracerebral haemorrhage (AVICH) score. J Neurol Neurosurg Psychiatry. 2018;89(11):11631166.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 38

    Ye Z, Ai X, Hu X, Fang F, You C. Clinical features and prognostic factors in patients with intraventricular hemorrhage caused by ruptured arteriovenous malformations. Medicine (Baltimore). 2017;96(45):e8544.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 39

    Ravindra VM, Bollo RJ, Eli IM, et al. A study of pediatric cerebral arteriovenous malformations: clinical presentation, radiological features, and long-term functional and educational outcomes with predictors of sustained neurological deficits. J Neurosurg Pediatr. 2019;24(1):18.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 40

    Geibprasert S, Pereira V, Krings T, Jiarakongmun P, Lasjaunias P, Pongpech S. Hydrocephalus in unruptured brain arteriovenous malformations: pathomechanical considerations, therapeutic implications, and clinical course. J Neurosurg. 2009;110(3):500507.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 41

    Kikuchi M, Funabiki K, Hasebe S, Takahashi H. Cerebellar arteriovenous malformation with facial paralysis, hearing loss, and tinnitus: a case report. Otol Neurotol. 2002;23(5):723726.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 42

    Frisoli FA, Catapano JS, Farhadi DS, et al. Spetzler-Martin Grade III arteriovenous malformations: a comparison of modified and supplemented Spetzler-Martin grading systems. Neurosurgery. 2021;88(6):11031110.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 43

    Huq S, Khalafallah AM, Patel P, et al. Predictive model and online calculator for discharge disposition in brain tumor patients. World Neurosurg.2021;146:e786e798.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 44

    Berger I, Piazza M, Sharma N, et al. Evaluation of the risk assessment and prediction tool for postoperative disposition needs after cervical spine surgery. Neurosurgery. 2019;85(5):E902E909.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 45

    Tang OY, Rivera Perla KM, Lim RK, Yoon JS, Weil RJ, Toms SA. Interhospital competition and hospital charges and costs for patients undergoing cranial neurosurgery. J Neurosurg. 2021;135(2):361372.

    • Search Google Scholar
    • Export Citation
  • 46

    Nouraei SA, Hudovsky A, Frampton AE, et al. A study of clinical coding accuracy in surgery: implications for the use of administrative big data for outcomes management. Ann Surg. 2015;261(6):10961107.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 47

    Gologorsky Y, Knightly JJ, Chi JH, Groff MW. The Nationwide Inpatient Sample database does not accurately reflect surgical indications for fusion. J Neurosurg Spine. 2014;21(6):984993.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 48

    Durand WM, Johnson JR, Li NY, et al. Hospital competitive intensity and perioperative outcomes following lumbar spinal fusion. Spine J. 2018;18(4):626631.

    • Crossref
    • Search Google Scholar
    • Export Citation

Illustration from Agosti et al. (E5). Used with permission of Mayo Foundation for Medical Education and Research. All rights reserved.

  • View in gallery

    Analysis overview for development of custom AVM risk stratification score. Overview of analysis plan for developing a custom AVM risk stratification score (AVM-5), including selection of study population, k-fold cross-validation, and machine learning–based model selection of variables for the AVM-5.

  • View in gallery

    Nationwide admissions for AVM microsurgery. Admissions in the NIS for AVM microsurgery from 2002 to 2017. Annual admissions were stratified by frailty status, as quantified by the mFI-5, mFI-11, and CCI.

  • View in gallery

    Association between risk stratification scores and AVM microsurgical outcomes. All scores were dichotomized into low frailty and high frailty, with reported results corresponding to high-frailty patients. Odds ratios were reported for binary outcomes and β-coefficients, representing percent changes, were reported for continuous outcomes. Values in brackets are 95% CIs. A: Associations between mFI-5 and outcomes. B: Associations between mFI-11 and outcomes. C: Associations between CCI and outcomes. D: Associations between the AVM-5 and outcomes. The AVM-5 was scored as follows: 2.24 for age > 50 years, 8.06 for presence of hydrocephalus, 10.31 for presence of paralysis, 1.52 for presence of diabetes mellitus, and 1.65 for presence of hypertension present. Then the scores were summed. High-frailty patients were defined as having a score ≥ 3.5.

  • View in gallery

    ROC analysis of risk stratification scores. ROC curves for prediction of routine hospital discharge using age, mFI-5, mFI-11, CCI, and AVM-5. AUCs for different ROC curves were compared using the DeLong test. A: Comparison of scores for all AVM admissions. B: Comparison of scores for AVM admissions in pseudoprospective cohort (2018–2019).

  • 1

    Ehlers LD, Pistone T, Haller SJ, Will Robbins J, Surdell D. Perioperative risk factors associated with ICU intervention following select neurosurgical procedures. Clin Neurol Neurosurg. 2020;192:105716.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 2

    Lakomkin N, Zuckerman SL, Stannard B, et al. Preoperative risk stratification in spine tumor surgery: a comparison of the modified Charlson Index, Frailty Index, and ASA score. Spine (Phila Pa 1976).2019;44(13):E782E787.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 3

    Maslink C, Cheng K, Smith TR, Das S. Advanced age is not a universal predictor of poorer outcome in patients undergoing neurosurgery. World Neurosurg.2019;130:e375e382.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 4

    Seicean A, Seicean S, Schiltz NK, et al. Short-term outcomes of craniotomy for malignant brain tumors in the elderly. Cancer. 2013;119(5):10581064.

  • 5

    Burkhardt JK, Lasker GF, Winkler EA, Kim H, Lawton MT. Microsurgical resection of brain arteriovenous malformations in the elderly: outcomes analysis and risk stratification. J Neurosurg. 2018;129(5):11071113.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 6

    Henry RK, Reeves RA, Wackym PA, Ahmed OH, Hanft SJ, Kwong KM. Frailty as a predictor of postoperative complications following skull base surgery. Laryngoscope. 2021;131(9):19771984.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 7

    Khalafallah AM, Shah PP, Huq S, et al. The 5-factor modified frailty index predicts health burden following surgery for pituitary adenomas. Pituitary. 2020;23(6):630640.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 8

    Dicpinigaitis AJ, Kalakoti P, Schmidt M, et al. Associations of baseline frailty status and age with outcomes in patients undergoing vestibular schwannoma resection. JAMA Otolaryngol Head Neck Surg. 2021;147(7):608614.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 9

    Cipolla MJ, Liebeskind DS, Chan SL. The importance of comorbidities in ischemic stroke: Impact of hypertension on the cerebral circulation. J Cereb Blood Flow Metab. 2018;38(12):21292149.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 10

    Michalak SM, Rolston JD, Lawton MT. Incidence and predictors of complications and mortality in cerebrovascular surgery: national trends from 2007 to 2012. Neurosurgery. 2016;79(2):182193.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 11

    Ali R, Schwalb JM, Nerenz DR, Antoine HJ, Rubinfeld I. Use of the modified frailty index to predict 30-day morbidity and mortality from spine surgery. J Neurosurg Spine. 2016;25(4):537541.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 12

    Khalafallah AM, Huq S, Jimenez AE, Brem H, Mukherjee D. The 5-factor modified frailty index: an effective predictor of mortality in brain tumor patients. J Neurosurg. 2021;135(1):7886.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 13

    McIntyre MK, Rawanduzy C, Afridi A, et al. The effect of frailty versus initial Glasgow Coma Score in predicting outcomes following chronic subdural hemorrhage: a preliminary analysis. Cureus. 2020;12(8):e10048.

    • Search Google Scholar
    • Export Citation
  • 14

    Newman WC, Kubilis PS, Hoh BL. Validation of a neurovascular comorbidities index for retrospective database analysis. J Neurosurg. 2018;130(1):273277.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 15

    Grüter BE, Mendelowitsch I, Diepers M, Remonda L, Fandino J, Marbacher S. Combined endovascular and microsurgical treatment of arteriovenous malformations in the hybrid operating room. World Neurosurg.2018;117:e204e214.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 16

    Charlson M, Szatrowski TP, Peterson J, Gold J. Validation of a combined comorbidity index. J Clin Epidemiol. 1994;47(11):12451251.

  • 17

    Subramaniam S, Aalberg JJ, Soriano RP, Divino CM. New 5-factor modified frailty index using American College of Surgeons NSQIP data. J Am Coll Surg. 2018;226(2):173181.e8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 18

    Clement RC, Carr BG, Kallan MJ, Wolff C, Reilly PM, Malhotra NR. Volume-outcome relationship in neurotrauma care. J Neurosurg. 2013;118(3):687693.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 19

    Davies JM, Lawton MT. Improved outcomes for patients with cerebrovascular malformations at high-volume centers: the impact of surgeon and hospital volume in the United States, 2000-2009. J Neurosurg. 2017;127(1):6980.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 20

    Tang OY, Yoon JS, Kimata AR, Lawton MT. Volume-outcome relationship in pediatric neurotrauma care: analysis of two national databases. Neurosurg Focus. 2019;47(5):E9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 21

    Boze H, Marlin T, Durand D, et al. Proline-rich salivary proteins have extended conformations. Biophys J. 2010;99(2):656665.

  • 22

    Soldozy S, Norat P, Yağmurlu K, et al. Arteriovenous malformation presenting with epilepsy: a multimodal approach to diagnosis and treatment. Neurosurg Focus. 2020;48(4):E17.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 23

    Ding D, Starke RM, Quigg M, et al. Cerebral arteriovenous malformations and epilepsy, Part 1: predictors of seizure presentation. World Neurosurg. 2015;84(3):645652.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 24

    Hafez A, Oulasvirta E, Koroknay-Pál P, Niemelä M, Hernesniemi J, Laakso A. Timing of surgery for ruptured supratentorial arteriovenous malformations. Acta Neurochir (Wien). 2017;159(11):21032112.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 25

    Washington CW, Derdeyn CP, Dacey RG Jr, Dhar R, Zipfel GJ. Analysis of subarachnoid hemorrhage using the Nationwide Inpatient Sample: the NIS-SAH Severity Score and Outcome Measure. J Neurosurg. 2014;121(2):482489.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 26

    Newman WC, Neal DW, Hoh BL. A new comorbidities index for risk stratification for treatment of unruptured cerebral aneurysms. J Neurosurg. 2016;125(3):713719.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 27

    Kim S, Brooks AK, Groban L. Preoperative assessment of the older surgical patient: honing in on geriatric syndromes. Clin Interv Aging. 2014;10:1327.

    • Search Google Scholar
    • Export Citation
  • 28

    Chibbaro S, Di Rocco F, Makiese O, et al. Neurosurgery and elderly: analysis through the years. Neurosurg Rev. 2010;34(2):229234.

  • 29

    Pazniokas J, Gandhi C, Theriault B, et al. The immense heterogeneity of frailty in neurosurgery: a systematic literature review. Neurosurg Rev. 2021;44(1):189201.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 30

    Youngerman BE, Neugut AI, Yang J, Hershman DL, Wright JD, Bruce JN. The modified frailty index and 30-day adverse events in oncologic neurosurgery. J Neurooncol. 2018;136(1):197206.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 31

    Sastry RA, Pertsch N, Tang O, Shao B, Toms SA, Weil RJ. Frailty and outcomes after craniotomy or craniectomy for atraumatic chronic subdural hematoma. World Neurosurg.2021;145:e242e251.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 32

    Feghali J, Huang J. Updates in arteriovenous malformation management: the post-ARUBA era. Stroke Vasc Neurol. 2019;5(1):3439.

  • 33

    Spetzler RF, Martin NA. A proposed grading system for arteriovenous malformations. J Neurosurg. 1986;65(4):476483.

  • 34

    Hafez A, Koroknay-Pál P, Oulasvirta E, et al. The application of the novel grading scale (Lawton-Young grading system) to predict the outcome of brain arteriovenous malformation. Neurosurgery. 2019;84(2):529536.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 35

    Lawton MT, Kim H, McCulloch CE, Mikhak B, Young WL. A supplementary grading scale for selecting patients with brain arteriovenous malformations for surgery. Neurosurgery. 2010;66(4):702713.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 36

    Neidert MC, Lawton MT, Mader M, et al. The AVICH score: a novel grading system to predict clinical outcome in arteriovenous malformation-related intracerebral hemorrhage. World Neurosurg.2016;92:292297.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 37

    Neidert MC, Lawton MT, Kim LJ, et al. International multicentre validation of the arteriovenous malformation-related intracerebral haemorrhage (AVICH) score. J Neurol Neurosurg Psychiatry. 2018;89(11):11631166.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 38

    Ye Z, Ai X, Hu X, Fang F, You C. Clinical features and prognostic factors in patients with intraventricular hemorrhage caused by ruptured arteriovenous malformations. Medicine (Baltimore). 2017;96(45):e8544.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 39

    Ravindra VM, Bollo RJ, Eli IM, et al. A study of pediatric cerebral arteriovenous malformations: clinical presentation, radiological features, and long-term functional and educational outcomes with predictors of sustained neurological deficits. J Neurosurg Pediatr. 2019;24(1):18.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 40

    Geibprasert S, Pereira V, Krings T, Jiarakongmun P, Lasjaunias P, Pongpech S. Hydrocephalus in unruptured brain arteriovenous malformations: pathomechanical considerations, therapeutic implications, and clinical course. J Neurosurg. 2009;110(3):500507.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 41

    Kikuchi M, Funabiki K, Hasebe S, Takahashi H. Cerebellar arteriovenous malformation with facial paralysis, hearing loss, and tinnitus: a case report. Otol Neurotol. 2002;23(5):723726.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 42

    Frisoli FA, Catapano JS, Farhadi DS, et al. Spetzler-Martin Grade III arteriovenous malformations: a comparison of modified and supplemented Spetzler-Martin grading systems. Neurosurgery. 2021;88(6):11031110.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 43

    Huq S, Khalafallah AM, Patel P, et al. Predictive model and online calculator for discharge disposition in brain tumor patients. World Neurosurg.2021;146:e786e798.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 44

    Berger I, Piazza M, Sharma N, et al. Evaluation of the risk assessment and prediction tool for postoperative disposition needs after cervical spine surgery. Neurosurgery. 2019;85(5):E902E909.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 45

    Tang OY, Rivera Perla KM, Lim RK, Yoon JS, Weil RJ, Toms SA. Interhospital competition and hospital charges and costs for patients undergoing cranial neurosurgery. J Neurosurg. 2021;135(2):361372.

    • Search Google Scholar
    • Export Citation
  • 46

    Nouraei SA, Hudovsky A, Frampton AE, et al. A study of clinical coding accuracy in surgery: implications for the use of administrative big data for outcomes management. Ann Surg. 2015;261(6):10961107.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 47

    Gologorsky Y, Knightly JJ, Chi JH, Groff MW. The Nationwide Inpatient Sample database does not accurately reflect surgical indications for fusion. J Neurosurg Spine. 2014;21(6):984993.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 48

    Durand WM, Johnson JR, Li NY, et al. Hospital competitive intensity and perioperative outcomes following lumbar spinal fusion. Spine J. 2018;18(4):626631.

    • Crossref
    • Search Google Scholar
    • Export Citation

Metrics

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 458 458 371
PDF Downloads 339 339 287
EPUB Downloads 0 0 0