Frailty predicts worse outcomes after intracranial meningioma surgery irrespective of existing prognostic factors

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  • 1 School of Medicine, New York Medical College, Valhalla, New York;
  • 2 Department of Neurosurgery, Westchester Medical Center, Valhalla, New York;
  • 3 Department of Neurosurgery, University of New Mexico, Albuquerque, New Mexico; and
  • 4 Department of Neurosurgery, University of Utah, Salt Lake City, Utah
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OBJECTIVE

Frailty has been recognized as a predictor of adverse surgical outcomes across multiple surgical disciplines, but until now the relationship between frailty and intracranial meningioma surgery has not been studied. The goal of the present study was to determine the relationship between increasing frailty (determined using the modified Frailty Index [mFI]) and intracranial meningioma resection outcomes (including hospital length of stay [LOS], discharge location, and reoperation and readmission rates).

METHODS

This is a single-center retrospective cohort study of patients who underwent intracranial meningioma resection between August 2012 and May 2018. Seventy-six patients met the inclusion criteria.

RESULTS

Frailty was associated with increased hospital LOS (p = 0.0218), increased reoperation rate (p = 0.029), and discharge to a higher level of care: an inpatient rehabilitation facility or a skilled nursing facility (p = 0.0002). After multivariable analysis, frailty was determined to be an independent risk factor for increased LOS, worse discharge disposition, and subsequent readmission.

CONCLUSIONS

Frailty is an independent risk factor for worse outcomes following intracranial meningioma resection, including increased LOS, reoperations, and worse discharge disposition. Frailty may help stratify preoperative surgical risk, and thus may provide important clinical information to help neurosurgeons and elderly patients weigh the risks and benefits of resection.

ABBREVIATIONS BMI = body mass index; DVT = deep vein thrombosis; LOS = length of stay; mFI = modified Frailty Index.

OBJECTIVE

Frailty has been recognized as a predictor of adverse surgical outcomes across multiple surgical disciplines, but until now the relationship between frailty and intracranial meningioma surgery has not been studied. The goal of the present study was to determine the relationship between increasing frailty (determined using the modified Frailty Index [mFI]) and intracranial meningioma resection outcomes (including hospital length of stay [LOS], discharge location, and reoperation and readmission rates).

METHODS

This is a single-center retrospective cohort study of patients who underwent intracranial meningioma resection between August 2012 and May 2018. Seventy-six patients met the inclusion criteria.

RESULTS

Frailty was associated with increased hospital LOS (p = 0.0218), increased reoperation rate (p = 0.029), and discharge to a higher level of care: an inpatient rehabilitation facility or a skilled nursing facility (p = 0.0002). After multivariable analysis, frailty was determined to be an independent risk factor for increased LOS, worse discharge disposition, and subsequent readmission.

CONCLUSIONS

Frailty is an independent risk factor for worse outcomes following intracranial meningioma resection, including increased LOS, reoperations, and worse discharge disposition. Frailty may help stratify preoperative surgical risk, and thus may provide important clinical information to help neurosurgeons and elderly patients weigh the risks and benefits of resection.

ABBREVIATIONS BMI = body mass index; DVT = deep vein thrombosis; LOS = length of stay; mFI = modified Frailty Index.

Meningiomas are the most common primary brain tumor; they are generally benign and slow-growing, and frequently may remain an incidental finding for prolonged periods.1,2 Additionally, meningiomas can be associated with significant morbidity and mortality, through their compression of critical neurovascular structures and/or the high associated comorbidity of resection. Resection of symptomatic or enlarging meningiomas is elective; thus, identifying independent risk factors associated with worse outcomes after intracranial meningioma surgery is crucial.

Frailty is broadly defined as a “decrease in physiological reserve,” which represents patients’ biological age rather than their chronological age, and has recently been used to predict outcomes and complication risk after surgical interventions.3 While the medical literature often uses functional frailty measurements, the surgical literature more commonly uses comorbidity-based frailty indices such as the modified Frailty Index (mFI). The mFI was developed by Velanovich et al. by matching factors identified in the Canadian Study of Health and Aging to variables recorded in the National Surgical Quality Improvement Program (NSQIP), which allows for retrospective studies (Table 1).4 A large body of neurosurgery literature has recently demonstrated the value of the mFI in predicting complications among neurosurgical patients. A recent review of the literature on frailty in neurosurgery revealed that the mFI was used to quantify frailty in 13 of 25 published studies.5 However, the overwhelming majority of this literature involves spine procedures.6–10 In fact, only 3 published studies demonstrated the predictive value of frailty for patients harboring cranial tumors.11–13 These studies demonstrated an increase in mortality,11–13 complications,11–13 length of stay (LOS),12,13 and discharge to a higher level of care13 in frailer patients following tumor resection.

TABLE 1.

Variables covered in the modified Frailty Index

1Functional status 2 (not independent)
2History of diabetes mellitus
3History of COPD or pneumonia
4History of myocardial infarction
5History of PCI, PCS, or angina
6History of congestive heart failure
7History of hypertension requiring medication
8History of either peripheral vascular disease or rest pain
9History of either transient ischemic attack or CVA
10History of CVA w/ neurological deficit
11History of impaired sensorium

COPD = chronic obstructive pulmonary disease; CVA = cerebrovascular accident; PCI = percutaneous coronary intervention; PCS = prior cardiac surgery.

Based on information from Velanovich et al.,4 each variable is weighted equally (1 point each), and the mFI is determined by tallying how many variables were identified in each patient. Patients with scores of 1 or greater were designated as “frail.” Those with 0 variables were designated as “non-frail.”

The goals of the present study were to examine the effect of increasing frailty, that is, poorer functional reserve, on outcomes after meningioma resection.

Methods

Study Design and Participant Selection

This paper describes a retrospective cohort study conducted at Westchester Medical Center in Valhalla, New York; the patients had been treated between August 2012 and May 2018. The study was approved by the center’s institutional review board. As this was a retrospective chart review, we received a waiver for patient consent. Eighty-eight patients were identified as having “meningioma” according to pathology reports, with 6 patients excluded for having a spinal meningioma and 6 other patients excluded because definitive surgery was not performed at our institution. Therefore, the final cohort sample contained 76 patients.

Measures and Outcomes

Patient age, sex, body mass index (BMI), smoking status, and tumor size (largest diameter in centimeters) were recorded. The World Health Organization (WHO) tumor grade (per pathology report) and tumor location (per radiology report) were recorded but were not used in the statistical analysis. The incidence of medical complications (deep vein thrombosis [DVT], pulmonary embolism, pneumonia, hyponatremia, and acute kidney injury) as well as surgical complications (new postoperative neurological deficit, postoperative seizures, and wound infections) were recorded. The modified Frailty Index (mFI) was used to measure frailty in this study. Because the required components of this index are routinely included in patient charts, the mFI is conducive to a retrospective chart review, whereas other frailty measurements requiring specific assessments were not available for this study. The patients’ mFIs were determined as previously described by reviewing patient charts and tallying how many mFI variables were identified in each patient (Table 1).4 Each mFI variable is equally weighted and counted as 1. The patient cohort had an overall low mFI (0.86), and thus patients were categorized as either non-frail (mFI = 0) or frail (mFI ≥ 1). The primary outcomes were hospital LOS, discharge location, readmission rates, and reoperation rates. We examined frailty’s effect on these primary outcomes. Secondary analyses examining the effect of patient age (≥ 65 vs < 65 years), sex (male vs female), BMI (≥ 30 vs < 30), tumor size (> 3.5 vs < 3.5 cm), or tumor location (skull base vs non–skull base) were conducted on the same outcomes.

Statistics

All statistical analyses were performed using GraphPad Prism 8.1.2 (GraphPad Software, Inc.) and SPSS (version 25, IBM Corp.). For all tests, significance was defined as p < 0.05. For the univariate analysis, continuous variables (LOS) were compared between groups using a two-sample t-test. Data are shown as the mean ± standard error of the mean (SEM). For categorical (binary) variables, groups were compared using Fisher’s exact test. Data are shown as odds ratios (ORs) with 95% confidence intervals (CIs).

Multivariable linear regression was conducted using age, sex, BMI, tumor size, and mFI as independent variables and LOS as the dependent variable. The results are reported as unstandardized beta coefficients with corresponding p values. Unstandardized beta coefficients represent the strength of the effect of each individual variable on the dependent variable whereby for every unit change in the independent variable, it is expected that the dependent variable will change by a factor of the beta coefficient.

Multivariable logistic regression was used to predict the effect of the independent variables listed above on categorical dependent variables (e.g., discharge location, readmission, and reoperation rates). Results are represented as ORs with corresponding p values.

For all statistical tests, significance is defined as p < 0.05.

Results

Demographics and Descriptive Statistics

Seventy-six patients, who had undergone craniotomy for resection of an intracranial meningioma between August 2012 and May 2018, met the study’s inclusion criteria (Fig. 1). Table 2 displays patient characteristics for the overall sample as well as for each frailty group separately.

FIG. 1.
FIG. 1.

Flow diagram showing patient selection.

TABLE 2.

Cohort demographics and tumor characteristics

All Patients (n = 76)Non-Frail (n = 34)Frail (n = 42)p Value
Mean age (SD), yrs55.8 (15.3)45.6 (11.2)64.1 (13.1)<0.0001
Sex0.31
 Male23 (30.2%)8 (23.5%)15 (35.7%)
 Female53 (72.6%)26 (76.5%)27 (64.3%)
Mean mFI0.8601.57
Mean BMI27.926.5290.046
Tumor size, cm4.153.674.630.009
Tumor grade0.0015
 Grade I39 (51.3%)11 (32.3%)28 (66.6%)
 Grade II35 (46.1%)23 (67.6%)12 (28.6%)
 Grade III2 (2.6%)0 (0%)2 (4.8%)
Tumor location0.2439
 Skull base48 (63.2%)24 (70.6%)24 (57.1%)
 Non–skull base28 (36.8%)10 (29.4%)18 (42.9%)
Complications*
 Medical1 (1.3%)01
 Surgical000

Patients designated as non-frail had an mFI equivalent to 0; patients designated as frail had an mFI ≥ 1. Values are presented as the number of patients (%) unless otherwise indicated. Boldface type indicates statistical significance.

Medical complications include DVT, pulmonary embolism, pneumonia, hyponatremia, and acute kidney injury. Surgical complications include a new postoperative neurological deficit, postoperative seizures, and wound infection.

The cohort was 72.6% female, with a mean age of 55.8 ± 1.8 years and a mean BMI of 27.9 ± 0.64. The mean mFI was 0.86. Thirty-four patients had no mFI variables and were therefore given a score of mFI = 0 and categorized as “non-frail.” Forty-two patients had one or more mFI variables and were categorized as “frail.” Frail patients were more likely to be older (mean age 64.1 ± 2 vs 45.6 ± 1.92 years, p < 0.0001) and more likely to have a greater BMI (29.0 ± 0.86 vs 26.5 ± 0.91, p = 0.046). There was no difference in sex between the two groups (p = 0.31) (Table 2). The most common mFI variables were a history of hypertension requiring medications (31.5%) and a history of diabetes (23%) (Table 3). Only 5.3% of patients had mFI scores greater than 2 (Table 4).

TABLE 3.

Distribution of mFI variables in sample

mFI VariableNo. of Patients%
History of HTN requiring medication2431.5
History of diabetes2023
Functional status 21315
History of impaired sensorium89.3
History of COPD or pneumonia67
History of PCI, PCS, or angina44.7
History of stroke w/ deficits22.3
History of CHF11.2
History of MI11.2
History of PVD11.2
History of TIA or stroke00

CHF = congestive heart failure; HTN = hypertension; MI = myocardial infarction; PVD = peripheral vascular disease; TIA = transient ischemic attack.

TABLE 4.

Modified Frailty Index value distribution

No. of Patients (%)
FrequencyCumulative Frequency
034 (44.7)34 (44.7)
126 (34.2)60 (78.9)
212 (15.7)72 (94.6)
32 (2.6)74 (97.3)
41 (1.3)75 (98.6)
50 (0)75 (98.6)
61 (1.3)76 (100)

Tumor characteristics between the groups are displayed in Table 2. Frail patients had larger (4.63 vs 3.67 cm at the largest diameter, p = 0.009) and lower-grade (p = 0.0015) tumors than non-frail patients. However, although not statistically different, frail patients proportionally had fewer tumors located in a skull base location, while non-frail patients overwhelmingly had skull base tumors (p = 0.2439) (Table 2).

There were no surgical complications in either group. Only one medical complication (DVT) occurred in a single patient in the frail group (Table 2).

Postoperative Outcomes—Univariate Analysis

Frail patients (mFI ≥ 1) had increased LOSs (p = 0.0218) (Fig. 2). The LOS trended upwards with increasing mFI scores. Additionally, frail patients were 6 times more likely to be discharged to a higher level of care (skilled nursing facility or inpatient rehabilitation) (OR 6.713, 95% CI 2.498–19.27, p = 0.0002) (Table 5). Frail patients were also more likely to require reoperation (OR ∞, 95% CI 1.205–∞, p = 0.029). Readmission rates between frail and non-frail patients were not significantly different (OR 5.161, CI 1.196–24.61, p = 0.0545). Critically, older age alone (≥ 65 years) did not increase patients’ LOS (p = 0.2672), readmission (p = 0.3182) or reoperation (p = 0.3505) rate, or discharge to an inpatient rehabilitation or a skilled nursing facility (p = 0.0754) (Table 5).

FIG. 2.
FIG. 2.

A bar graph demonstrating LOS in the two patient groups (left) and a line graph showing LOS stratified by patients’ mFI (right).

TABLE 5.

Results of the univariate analyses

Dependent VariableIndependent VariableT Statistic (Tcrit 1.922)p ValueUnivariate OR (95% CI)p Value
mFI2.3440.0218
Age−1.1180.2672
LOSBMI0.680.4967
Tumor size−0.4280.1573
Sex0.02760.978
mFI6.713 (2.498–19.27)0.0002
Age2.795 (0.9634–7.286)0.0754
Discharge locationBMI1.719 (0.6795–4.418)0.3378
Tumor size1.987 (0.7992–5.093)0.2324
Sex0.6868 (0.2722–1.874)0.6181
mFI5.161 (1.196–24.61)0.0545
Age1.933 (0.5802–6.907)0.3182
Readmission rateBMI0.7407 (0.2305–2.488)0.7557
Tumor size0.7854 (0.2285–2.531)0.7509
Sex0.5727 (0.1580–2.356)0.5318
mFI∞ (1.205–∞)0.029
Age2.632 (0.5668–11.88)0.3505
Reoperation rateBMI1.2 (0.2020–6.181)0.9999
Tumor size0.556 (0.1235–2.540)0.6636
Sex0.4364 (0.3559–3.657)0.6611

For continuous dependent variables, a two-sample t-test was used and T statistics are reported. For categorical dependent variables, Fisher’s exact test was used and ORs are reported. Boldface type indicates statistical significance.

Multivariable Linear and Logistic Regression

Multivariable linear regression was used to examine the effect of independent variables (e.g., age, BMI, sex, tumor size, mFI) on the continuous dependent variable (e.g., LOS) (Table 6). Assumptions for conducting multivariable linear regression analyses were met, including multivariate normality, absence of outliers, and absence of multicollinearity. Increased mFI was the only independent risk factor associated with an increased LOS that was independent of age, BMI, sex, and tumor size. For every unit increase in the mFI, the expected LOS increased by 1.678 days on average, holding other variables constant (p = 0.046). On multivariate logistic regression, an increased mFI was also associated with a significantly increased likelihood of discharge to a higher level of care (p = 0.001) and to a higher readmission rate (p = 0.025) (Table 6).

TABLE 6.

Results of the multivariate analyses

Dependent VariableIndependent VariableUnstandardized β Coefficientp ValueMultivariate OR (95% CI)p Value
mFI1.678 (0.034–3.322)0.046
Age−0.029 (−0.141 to 0.083)0.606
LOSBMI−0.201 (−0.467 to 0.066)0.138
Tumor size0.769 (−0.185 to 1.724)0.112
Sex−0.493 (−2.785 to 3.772)0.765
mFI0.077 (0.018–0.331)0.001
Age1.465 (0.347–6.179)0.603
Discharge locationBMI3.196 (0.885–11.538)0.076
Tumor size0.540 (0.173–1.689)0.29
Sex0.449 (0.131–1.542)0.203
mFI0.131 (0.022–0.773)0.025
Age1.300 (0.311–5.445)0.719
Readmission rateBMI1.174 (0.299–4.610)0.819
Tumor size1.379 (0.364–5.228)0.636
Sex0.552 (0.123–2.474)0.437
mFI0 (0–∞)0.998
Age1.052 (0.177–6.239)0.956
Reoperation rateBMI0.360 (0.055–2.364)0.287
Tumor size2.238 (0.378–13.266)0.375
Sex0.194 (0.018–2.041)0.172

For continuous dependent variables (LOS), a multivariable linear regression was carried out and unstandardized beta coefficients are reported. Results are to be interpreted as follows: for every 1 unit increase in the independent variable, the dependent variable changed by a factor of the unstandardized beta coefficient (e.g., a 1-unit increase in the mFI correlated with a 1.678-fold increase in LOS) holding all other variables constant. For categorical dependent variables, a multivariable logistic regression was carried out and ORs are reported. Boldface type indicates statistical significance.

Discussion

This is the first reported study of the effect of frailty on intracranial meningioma surgery. Frailty was found to be an independent risk factor for increased LOS, discharge to a higher level of care, and readmission rate for patients of all ages. On the univariate analysis, frail patients were also found to be more likely to require reoperation. Importantly, increased age (≥ 65 years) alone was not an independent risk factor associated with worse outcomes.

As an independent risk factor for worse outcomes following intracranial meningioma surgery, frailty has tremendous potential for risk stratification and outcome prediction. These surgeries are almost exclusively elective. Although frail patients were more likely to have larger tumors, frailty remained predictive of outcomes after the multivariate analysis, even after multivariate regression was performed to account for increased tumor size and patient age.

The difference in tumor size in our sample can be explained by the fact that as patients age and become frailer, they also become worse surgical candidates. A surgical selection bias, commonplace in retrospective chart reviews, is demonstrated in this cohort. Older frail patients with small tumors tend to be observed with serial imaging, due to the higher risk of surgical intervention in this age group. Younger, less frail patients with small tumors tend to be offered surgical intervention, given the lower risk of operative intervention in that age group. For this reason, there is a significant difference in tumor size between the frail and non-frail groups: as older and more frail patients harbor meningiomas approaching the size of 5 cm (4.63 cm), surgeons and patients believe that surgery becomes less optional and more indicated, because of the neurological symptoms that frequently accompany 5-cm meningiomas.

At first consideration, the 50% grade II meningioma rate is shockingly high, since it is a 10-fold increase from the normal average 5% rate. However, our institution’s neuropathological practice is adamant about labeling the hint of possible brain invasion as a grade II. Most of these “grade II” meningiomas act like typical grade I tumors, so we tend to observe them with serial imaging after resection.

Tumor location (skull base vs non–skull base) did not confound these results, since the proportional percentage of non–skull base tumors was higher in frail patients. Typically, a meningioma located more peripherally or along the convexities is more surgically accessible and would, therefore, be associated with lower postoperative morbidity. Therefore, these results strengthen the evidence of frailty’s influence, since frail patients showed worse outcomes despite the more ideal location of their tumors.

The lower overall mean mFI in this patient cohort was almost 1 (0.86) and, although the neurosurgery literature on frailty has a variety of methods of frailty definition and stratification for analysis, one key objective of this paper was to closely examine how healthy older patients fare after meningioma surgery. This helped us stratify frailty into frail (mFI ≥ 1) and non-frail (mFI < 1). Therefore, our study’s conclusion that non-frail patients of all ages obtain better outcomes after meningioma resection can be reframed in the context of “robustness,” or a distinct lack of frailty. Our results indicate that patients who are robust (mFI = 0) have a high physiological reserve and are able to tolerate meningioma resection better than their non-robust, or frail, peers. This conclusion may be particularly important not only for the elderly, but also for young patients diagnosed with meningioma. For elderly patients, robustness may merit consideration of surgical intervention in a patient who traditionally may not otherwise have been considered a candidate for surgery based on advanced age alone.

Additionally, the benign natural history of meningioma is the reason that identification of patients at particularly high or low risk for a poor postoperative course is critical. Increasing use of imaging modalities results in higher rates of incidentally discovered meningiomas.2 The slow- or nongrowing nature of these tumors makes the majority of surgeries elective in nature or at least nonurgent. This allows for the possibility of preoperative interventions, or “pre-hab” to optimize surgical readiness.14 Although there are no studies demonstrating a benefit of prehabilitation for neurosurgical patients, it is possible that presurgical interventions aimed at decreasing frailty could improve outcomes, and this area merits further investigation.

This study adds to the body of literature on frailty in neurosurgery by demonstrating that frailty is predictive of outcomes following elective surgery for a benign entity. No previous studies have demonstrated the effect of frailty specific to meningioma resection, despite the fact that meningiomas are the most common primary brain tumor.

Limitations

There are two primary limitations to this study: its retrospective design and its small sample size.

Any retrospective chart review is subject to several limitations. Importantly, retrospective designs can only establish association rather than causation. Additionally, because the data were not collected in a controlled, prospective manner, it is possible that study variables may have been recorded incorrectly or even be absent from the record. Fortunately, in the present study, there were no missing data. This is likely due to the fact that mFI variables are simply elements of a patient history that are found documented in several areas of the patient chart.

In many tumor outcomes studies, authors prospectively examine outcomes related to medical or surgical complications, neurological or functional status, and wound complications. Measures for these variables were collected in the chart, but fortunately, except for one case of DVT, we did not observe such complications in our cohort. Further studies examining these variables prospectively are warranted and would bolster the frailty and outcomes literature.

An additional limitation is our small sample size. Studying a benign disease that has a very slow growth process is challenging, because very frail patients with very mild symptoms or incidental lesions most likely would not have undergone surgery, thus lowering the overall mean mFI in this series when compared to other cohorts. Furthermore, non-frail patients with very mild symptoms may have been offered surgery, and some of these patients may have elected resection over observation. Therefore, while we provide statistically significant evidence that the mFI can have clinical utility in determining preoperative risk, prospective studies that compare postoperative outcomes in frail and non-frail patients and that document both the frailty of patients and the tumor characteristics of meningiomas that were not resected are warranted. Furthermore, additional studies identifying modifiable frailty variables that may benefit from preoperative interventions are needed.

Conclusions

The present study is the first to examine the relationship between frailty and intracranial meningioma resection. We have demonstrated that increased frailty was an independent risk factor for worse clinical outcomes after meningioma resection, including increased LOS, discharge to a higher level of care, and readmission rates for patients of all ages. These findings were irrespective of patient age, sex, BMI, or tumor size. This finding, once validated prospectively, may provide vital preoperative clinical information to help prognosticate and guide treatment recommendations for older patients considering surgery for intracranial meningioma.

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: Bowers. Acquisition of data: Theriault, Pazniokas, Adkoli, Cho, Rao. Analysis and interpretation of data: Bowers, Theriault, Pazniokas. Drafting the article: Theriault. Reviewed submitted version of manuscript: Schmidt, Cole, Gandhi, Couldwell, Al-Mufti. Approved the final version of the manuscript on behalf of all authors: Bowers. Administrative/technical/material support: Schmidt. Study supervision: Bowers.

References

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Contributor Notes

Correspondence Christian A. Bowers: University of New Mexico School of Medicine, Albuquerque, NM. cabowers@salud.unm.edu.

INCLUDE WHEN CITING DOI: 10.3171/2020.7.FOCUS20324.

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

  • View in gallery

    Flow diagram showing patient selection.

  • View in gallery

    A bar graph demonstrating LOS in the two patient groups (left) and a line graph showing LOS stratified by patients’ mFI (right).

  • 1

    Islim AI, Mohan M, Moon RDC, Incidental intracranial meningiomas: a systematic review and meta-analysis of prognostic factors and outcomes. J Neurooncol. 2019;142(2):211221.

    • Search Google Scholar
    • Export Citation
  • 2

    Wiemels J, Wrensch M, Claus EB. Epidemiology and etiology of meningioma. J Neurooncol. 2010;99(3):307314.

  • 3

    Fried LP, Tangen CM, Walston J, Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56(3):M146M156.

    • Search Google Scholar
    • Export Citation
  • 4

    Velanovich V, Antoine H, Swartz A, Accumulating deficits model of frailty and postoperative mortality and morbidity: its application to a national database. J Surg Res. 2013;183(1):104110.

    • Search Google Scholar
    • Export Citation
  • 5

    Pazniokas J, Gandhi C, Theriault B, The immense heterogeneity of frailty in neurosurgery: a systematic literature review. Neurosurg Rev. Published online January 17, 2020. doi:10.1007/s10143-020-01241-2

    • Search Google Scholar
    • Export Citation
  • 6

    Miller EK, Neuman BJ, Jain A, An assessment of frailty as a tool for risk stratification in adult spinal deformity surgery. Neurosurg Focus. 2017;43(6):E3.

    • Search Google Scholar
    • Export Citation
  • 7

    Leven DM, Lee NJ, Kothari P, Frailty index is a significant predictor of complications and mortality after surgery for adult spinal deformity. Spine (Phila Pa 1976). 2016;41(23):E1394E1401.

    • Search Google Scholar
    • Export Citation
  • 8

    Phan K, Kim JS, Lee NJ, Frailty is associated with morbidity in adults undergoing elective anterior lumbar interbody fusion (ALIF) surgery. Spine J. 2017;17(4):538544.

    • Search Google Scholar
    • Export Citation
  • 9

    Yagi M, Fujita N, Okada E, Impact of frailty and comorbidities on surgical outcomes and complications in adult spinal disorders. Spine (Phila Pa 1976). 2018;43(18):12591267.

    • Search Google Scholar
    • Export Citation
  • 10

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