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

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  • 1 Department of Neurosurgery, University of Southern California, Los Angeles, California;
  • | 2 Department of Medical Engineering, California Institute of Technology, Pasadena, California;
  • | 3 Department of Neurosurgery, UCI Medical Center, Irvine, California;
  • | 4 Cedars-Sinai Sinus Center of Excellence, Division of Otolaryngology, Cedars-Sinai Medical Center, Los Angeles, California; and
  • | 5 Department of Biomedical Engineering, Johns Hopkins Whiting School of Engineering, Baltimore, Maryland
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OBJECTIVE

Frailty embodies a state of increased medical vulnerability that is most often secondary to age-associated decline. Recent literature has highlighted the role of frailty and its association with significantly higher rates of morbidity and mortality in patients with CNS neoplasms. There is a paucity of research regarding the effects of frailty as it relates to neurocutaneous disorders, namely, neurofibromatosis type 1 (NF1). In this study, the authors evaluated the role of frailty in patients with NF1 and compared its predictive usefulness against the Elixhauser Comorbidity Index (ECI).

METHODS

Publicly available 2016–2017 data from the Nationwide Readmissions Database was used to identify patients with a diagnosis of NF1 who underwent neurosurgical resection of an intracranial tumor. Patient frailty was queried using the Johns Hopkins Adjusted Clinical Groups frailty-defining indicator. ECI scores were collected in patients for quantitative measurement of comorbidities. Propensity score matching was performed for age, sex, ECI, insurance type, and median income by zip code, which yielded 60 frail and 60 nonfrail patients. Receiver operating characteristic (ROC) curves were created for complications, including mortality, nonroutine discharge, financial costs, length of stay (LOS), and readmissions while using comorbidity indices as predictor values. The area under the curve (AUC) of each ROC served as a proxy for model performance.

RESULTS

After propensity matching of the groups, frail patients had an increased mean ± SD hospital cost ($85,441.67 ± $59,201.09) compared with nonfrail patients ($49,321.77 ± $50,705.80) (p = 0.010). Similar trends were also found in LOS between frail (23.1 ± 14.2 days) and nonfrail (10.7 ± 10.5 days) patients (p = 0.0020). For each complication of interest, ROC curves revealed that frailty scores, ECI scores, and a combination of frailty+ECI were similarly accurate predictors of variables (p > 0.05). Frailty+ECI (AUC 0.929) outperformed using only ECI for the variable of increased LOS (AUC 0.833) (p = 0.013). When considering 1-year readmission, frailty (AUC 0.642) was outperformed by both models using ECI (AUC 0.725, p = 0.039) and frailty+ECI (AUC 0.734, p = 0.038).

CONCLUSIONS

These findings suggest that frailty and ECI are useful in predicting key complications, including mortality, nonroutine discharge, readmission, LOS, and higher costs in NF1 patients undergoing intracranial tumor resection. Consideration of a patient’s frailty status is pertinent to guide appropriate inpatient management as well as resource allocation and discharge planning.

ABBREVIATIONS

AUC = area under the curve; ECI = Elixhauser Comorbidity Index; ICD-10 = International Classification of Diseases, Tenth Revision; JHACG = Johns Hopkins Adjusted Clinical Groups; LOS = length of stay; NF1 = neurofibromatosis type 1; NRD = Nationwide Readmissions Database; ROC = receiver operating characteristic.

OBJECTIVE

Frailty embodies a state of increased medical vulnerability that is most often secondary to age-associated decline. Recent literature has highlighted the role of frailty and its association with significantly higher rates of morbidity and mortality in patients with CNS neoplasms. There is a paucity of research regarding the effects of frailty as it relates to neurocutaneous disorders, namely, neurofibromatosis type 1 (NF1). In this study, the authors evaluated the role of frailty in patients with NF1 and compared its predictive usefulness against the Elixhauser Comorbidity Index (ECI).

METHODS

Publicly available 2016–2017 data from the Nationwide Readmissions Database was used to identify patients with a diagnosis of NF1 who underwent neurosurgical resection of an intracranial tumor. Patient frailty was queried using the Johns Hopkins Adjusted Clinical Groups frailty-defining indicator. ECI scores were collected in patients for quantitative measurement of comorbidities. Propensity score matching was performed for age, sex, ECI, insurance type, and median income by zip code, which yielded 60 frail and 60 nonfrail patients. Receiver operating characteristic (ROC) curves were created for complications, including mortality, nonroutine discharge, financial costs, length of stay (LOS), and readmissions while using comorbidity indices as predictor values. The area under the curve (AUC) of each ROC served as a proxy for model performance.

RESULTS

After propensity matching of the groups, frail patients had an increased mean ± SD hospital cost ($85,441.67 ± $59,201.09) compared with nonfrail patients ($49,321.77 ± $50,705.80) (p = 0.010). Similar trends were also found in LOS between frail (23.1 ± 14.2 days) and nonfrail (10.7 ± 10.5 days) patients (p = 0.0020). For each complication of interest, ROC curves revealed that frailty scores, ECI scores, and a combination of frailty+ECI were similarly accurate predictors of variables (p > 0.05). Frailty+ECI (AUC 0.929) outperformed using only ECI for the variable of increased LOS (AUC 0.833) (p = 0.013). When considering 1-year readmission, frailty (AUC 0.642) was outperformed by both models using ECI (AUC 0.725, p = 0.039) and frailty+ECI (AUC 0.734, p = 0.038).

CONCLUSIONS

These findings suggest that frailty and ECI are useful in predicting key complications, including mortality, nonroutine discharge, readmission, LOS, and higher costs in NF1 patients undergoing intracranial tumor resection. Consideration of a patient’s frailty status is pertinent to guide appropriate inpatient management as well as resource allocation and discharge planning.

The term "frailty" describes the clinical state of increased medical vulnerability due to various effects resulting from the natural aging process.1 These effects place patients at a higher risk for disability, perioperative complications, readmissions, morbidity, and, ultimately, mortality.17 With respect to neurological surgery specifically, patients who have brain tumors and meet criteria for frailty have been shown to have increased postoperative rates of complications due to their frail status.1 It is thus important to carefully consider and balance the surgical management of this select population with respect to frailty status.

Previously, the role of frailty had been examined with respect to the surgical treatment of patients with primary CNS neoplasms,8 acute subdural hematoma,9 chronic subdural hematoma,10 and spinal deformity surgery.11,12 However, to date, no study has yet examined the role of frailty in the management of neurofibromatosis type 1 (NF1), which is a common autosomal dominant neurocutaneous disorder that can lead to the development of both benign and malignant tumors of the nervous system. The disorder can also result in many other disfiguring manifestations, such as iris Lisch nodules, dermatological deformities, and bony dysplasia.13 NF1 results from a mutation in the neurofibromin tumor suppressor gene on chromosome 17, which leads to dysfunction in the control of adenylyl cyclase and aberrant modulation of mTOR activity.13 Additional features of the genetic disorder include vasculopathy, cardiovascular disease, cerebrovascular disease, and cognitive impairment.13 Patients with NF1 also experience an increased amount of CNS and non-CNS malignancies when compared with the general population.14 Therefore, many of these patients will undergo craniotomy for the purpose of tumor resection.

For this reason, it is important to examine the effects of frailty on short- and long-term complications in patients with NF1 undergoing intracranial tumor resection. Although most individuals with NF1 exhibit mild forms, the frequency of severe manifestations increases with age. These manifestations can potentially lead to weakness, visual loss, and lack of social support, all of which are aspects that can affect frailty.1519 In this study, we sampled the Nationwide Readmissions Database (NRD), using mixed modeling and predictive analytical techniques, to provide an analysis that can address current gaps in the literature with respect to the identification of high-risk patients while improving understanding of the clinical management of frail patients with NF1.

Methods

Data Source

In this study, we used the Healthcare Cost and Utilization Project (HCUP) NRD from the years 2016 and 2017. The NRD is a yearly nationally representative inpatient database from the Agency for Healthcare Research and Quality (AHRQ) with information regarding patient demographics, diagnoses, procedures, and readmissions. Patients are de-identified and represented with unique patient identification numbers to allow for accurate patient tracking throughout the calendar year. The NRD is publicly available for purchase and has been designed for the purpose of analyzing nationally representative readmission data. Among the years of NRD included in this study, we identified more than 35 million patient discharges, with all data regarding patient diagnoses and procedures being retrieved using International Classification of Diseases, Tenth Revision (ICD-10) codes. IRB approval and informed consent were not required, as we used a de-identified publicly available database.

Patient Selection

Between 2016 and 2017, which were the years that the NRD used ICD-10 coding, 7319 patients with NF1 were identified. The data were separated into both primary admission and readmission patient cohorts for the purpose of data extraction. For each cohort, demographics and hospital information were collected for each inpatient stay. Patients who received craniotomy for the resection of intracranial neoplasms during the primary admission were then retrieved, yielding 314 patients. Patient frailty was queried using the Johns Hopkins Adjusted Clinical Groups (JHACG) frailty-defining diagnosis indicator, which uses 10 categories of ICD-10 codes (malnutrition, dementia, vision impairment, decubitus ulcer, urine control, weight loss, fecal control, social support, difficulty walking, and history of a fall) to predict a patient’s frailty status.20 Several studies have confirmed the clinical validity of the JHACG frailty-defining diagnosis indicator.1,2023 Lastly, Elixhauser Comorbidity Index (ECI) scores were collected for all patients using the R "comorbid" package, which uses 30 groups of diagnoses to measure their burden of comorbidity.2429

To quantify the influence of frailty within our patient population, nearest-neighbor propensity score matching for age, sex, ECI, insurance type, median income by zip code, and NRD discharge weighting was implemented between frail and nonfrail patients (Fig. 1). The MatchIt algorithm selects the best-fit parametric models based on the minimum "distance" parameter, which is determined through logistic regression models that minimize the propensity score with no replacement. This yielded 60 frail and 60 nonfrail patients for analysis.

FIG. 1.
FIG. 1.

Distribution of propensity scores after matching. Frail patients are shown as the matched treatment units, and propensity-matched nonfrail patients are shown as the matched control units. The unmatched control units represent nonfrail patients who were not chosen by the propensity-matching algorithm. The comparable distribution of patients in both matched treatment and control units implies the achievement of excellent propensity score matching.

Statistical Analysis

All statistics were conducted in RStudio (version 1.2.5042), with p < 0.05 considered statistically significant. Following propensity score matching, binarized patient complication variables were analyzed using odds ratios with the "Epitools" package. All associated p values were calculated using the chi-square test. Generalized linear mixed-effects models were developed using the "lme4" package. Receiver operating characteristic (ROC) curves were created using the "pROC" package following the creation of models for relevant postoperative complications using one or more comorbidity indices as predictor variables. A fixed-effects model was used for patient age, sex, insurance type, income quartile by zip code, tumor type, and NRD discharge weighting. A random-effects model was used for hospital identifiers. The area under the curve (AUC) of each ROC was computed and served as a proxy for model performance. In general, an AUC of 0.50 demonstrates a random guess, and AUC values greater than 0.70 are defined as acceptable.30 DeLong’s test for two correlated ROC curves was used to compare metrics between ROC models. There was no evidence of statistically poor fit based on Hosmer-Lemeshow testing for all models constructed within this study.

Results

Demographics

Within the frail cohort, the mean ± SD patient age was 32.2 ± 17.0 years, with 49.1% of patients being female and a mean ± SD ECI of 4.0 ± 2.8. The ages were normally distributed with no statistically significant difference in the distributions between frail and nonfrail patients. Within the nonfrail cohort, the mean ± SD patient age was 37.3 ± 21.9 years, with 50.3% of patients being female and a mean ± SD ECI of 3.3 ± 2.5. After propensity matching of the groups, frail patients had an increased mean ± SD hospital cost ($85,441.67 ± $59,201.09) compared with nonfrail patients ($49,321.77 ± $50,705.80) (p = 0.010). Similar trends were also found in length of stay (LOS) between frail (mean 23.1 ± 14.2 days) and nonfrail (mean 10.7 ± 10.5 days) patients (p = 0.0020). In the frail group, 33.3% were diagnosed with a malignant brain neoplasm, and in the nonfrail group, 26.7% were diagnosed with a malignant brain neoplasm. No statistically significant differences were found between age, sex, ECI, insurance type, median income by zip code, or tumor type between the two cohorts following propensity score matching (Table 1).

TABLE 1.

Demographics and perioperative characteristics of all NF1, frail, and nonfrail propensity score–matched NF1 cohorts

All Patients, n = 314Propensity Matched
Frail, n = 60Nonfrail, n = 60p Value
Mean age, yrs28.3 ± 18.032.2 ± 17.037.3 ± 21.90.59
Female sex50.4%49.1%50.3%0.80
Mean ECI2.4 ± 2.34.0 ± 2.83.3 ± 2.50.23
Insurance, n (%)
 Medicare39 (12.4)13 (21.7)21 (35.0)0.34
 Medicaid90 (28.7)21 (35.0)20 (33.3)
 Private178 (56.7)26 (43.3)19 (31.7)
Median income by zip code, n (%)
 Quartile 176 (24.2)12 (20.0)16 (26.7)>0.99
 Quartile 282 (26.1)23 (38.3)13 (21.7)
 Quartile 393 (29.6)10 (16.7)16 (26.7)
 Quartile 456 (17.8)15 (25.0)15 (25.0)
Cranial tumor type, n (%)
 Malignant94 (29.9)20 (33.3)16 (26.7)0.79
Mean all-payer cost, USD48,072.56 ± 57,171.9285,441.67 ± 59,201.0949,321.77 ± 50,705.800.010
Mean LOS, days10.7 ± 15.323.1 ± 14.210.7 ± 10.50.0020

USD = US dollars.

Mean values are presented as mean ± SD. Boldface type indicates statistical significance (p < 0.05).

Complications of Interest

For all patients, the mean ± SD ECI was 2.4 ± 2.3. The breakdown of comorbidities contributing toward ECI and frailty was as follows: cardiac (n = 34, 10.8%), vascular (n = 66, 21.0%), neurological (n = 99, 31.5%), pulmonary (n = 45, 14.3%), diabetes (n = 8, 2.5%), endocrine/rheumatological (n = 11, 3.5%), renal (n = 2, 0.6%), cancer (n = 94, 29.9%), hematological/fluid status (n = 47, 15.0%), BMI gain or loss (n = 15, 4.8%), psychiatric/drug use (n = 61, 19.4%), and frailty/physiological reserve (n = 60, 19.1%) (Fig. 2).

FIG. 2.
FIG. 2.

Breakdown of comorbidities seen in patients with NF1 that contribute toward increased ECI and frailty scores. Endo/Rheum = endocrine/rheumatological; Psych/Drug = psychiatric/drug use. Created with Biorender.com.

Overall, 10 patients (3.2%) died during or after surgery. Furthermore, 93 patients (29.6%) were discharged nonroutinely to a place other than their home. The mean ± SD cost for all patients receiving surgery was $48,072.56 ± $57,171.92, and the mean ± SD LOS for the same cohort of patients was 10.7 ± 15.3 days. The top quartile for cost was $55,418.00, and the top quartile for LOS was 15.0 days. Lastly, 63 patients (20.1%) were readmitted within 1 year of discharge for primary cranial surgery for NF1 (Supplementary Table 1).

Predictive Models and ROC Analysis

Three sets of generalized linear mixed-effects models were developed, using 1) frailty, 2) ECI, or 3) frailty+ECI as the primary predictor. Complications of interest investigated through mixed-effects modeling included inpatient mortality, nonroutine discharge, LOS within the top quartile, cost within the top quartile, and 1-year readmission. For each complication, ROC curves were plotted for all models (Figs. 37). As seen in the figures, frailty performed as well as ECI and the combination of frailty+ECI upon statistical testing (p > 0.05) in nearly every model. However, for the prediction of LOS in the top quartile, the model using frailty+ECI (AUC 0.929) significantly outperformed the model using frailty alone (AUC 0.833) (p = 0.013). Frailty was found to be similar to ECI as a predictor of LOS (p = 0.15). Finally, when considering 1-year readmission, frailty (AUC 0.642) was significantly outperformed by both models using ECI (AUC 0.725, p = 0.039) and frailty+ECI (AUC 0.734, p = 0.038).

FIG. 3.
FIG. 3.

ROC plot for the prediction of mortality. The black ROC (cannot be seen because the red ROC is the same) represents the model using ECI alone as the primary predictor, the blue ROC represents the model using frailty alone as the primary predictor, and the red ROC represents the model using frailty status and ECI as the primary predictors. There was no statistically significant difference between any of the model ROCs for mortality.

FIG. 4.
FIG. 4.

ROC plot for the prediction of nonroutine discharge. The black ROC represents the model using ECI alone as the primary predictor, the blue ROC represents the model using frailty alone as the primary predictor, and the red ROC represents the model using frailty status and ECI as the primary predictors. There was no statistically significant difference between any of the model ROCs for nonroutine discharge.

FIG. 5.
FIG. 5.

ROC plot for the prediction of cost in the top quartile. The black ROC represents the model using ECI alone as the primary predictor, the blue ROC represents the model using frailty alone as the primary predictor, and the red ROC represents the model using frailty status and ECI as the primary predictors. There was no statistically significant difference between any of the model ROCs for cost.

FIG. 6.
FIG. 6.

ROC plot for the prediction of LOS in the top quartile. The black ROC represents the model using ECI alone as the primary predictor, the blue ROC represents the model using frailty alone as the primary predictor, and the red ROC represents the model using frailty status and ECI as the primary predictors. The model utilizing frailty+ECI outperformed the model using frailty alone for the prediction of LOS (p = 0.013).

FIG. 7.
FIG. 7.

ROC plot for the prediction of readmission within 1 year. The black ROC represents the model using ECI alone as the primary predictor, the blue ROC represents the model using frailty alone as the primary predictor, and the red ROC represents the model using frailty status and ECI as the primary predictors. There was no statistically significant difference between any of the model ROCs for 1-year readmission. The model using frailty was outperformed by the models using ECI (p = 0.039) and frailty+ECI (p = 0.038).

Discussion

We report the first contemporary analysis of the influence of frailty in NF1 patients and develop mixed-effects models to compare the predictive utility of frailty against a traditional comorbidity index such as the ECI. Our findings suggest that frailty is as useful as ECI in predicting key complications, including mortality, nonroutine discharge, readmission, LOS, and high cost. Additionally, a combination of frailty+ECI was found to be predictive of these complications. However, frailty+ECI, when used together, performed better than frailty alone in the case of predicting increased hospital LOS in NF1 patients. The findings of our study suggest that frailty may serve as an accurate predictor for future studies that aim to evaluate comorbidity indices in NF1 patients or studies that aim to evaluate outcomes in NF1 patients while controlling for potential confounders. Furthermore, the propensity score–matched arm of our analysis demonstrated that frail NF1 patients have worse outcomes compared with nonfrail NF1 patients who received cranial neurosurgery. These findings may potentially be used for optimizing perioperative outcomes in frail NF1 patients who require neurosurgical intervention.31

With respect to frailty, there are many variables of the JHACG scoring system that could be potential comorbidities of NF1 patients. It has been shown that individuals with NF1 have increased rates of vision loss due to the optic pathway gliomas, and this would increase the score for visual loss.17,18 NF1 patients could also have associated muscle weakness and fatigability, which could increase scores for muscle weakness and potentially the history of fall variable.15,16 It has been reported that NF1 patients may also exhibit cognitive deficits and thus lack certain characteristics necessary for effective communication. Thus, this could potentially limit social skills, which would increase the frailty score for the lack of social support variable in the scoring system.19

While the influence of frailty has been poorly reported for neurocutaneous disorders like NF1, many other fields of neurosurgery have extensively analyzed frailty as a robust predictor of perioperative patient outcomes. In spine surgery, several studies have used both the JHACG frailty index and the 5-factor modified frailty index to demonstrate accurate prediction of postoperative patient status.32,33 In both of these contemporary studies, patient frailty status was directly correlated with discharge status, financial costs, and LOS. It also improved the accuracy of prediction models using ROC analysis of postoperative outcomes when combined with well-established perioperative predictors such as age and ECI.32,33 Furthermore, frailty has been well established as a robust predictor of patient outcomes in cranial surgery for neoplastic disease. Frailty has also been shown to be a well-established predictor of outcomes in pituitary and skull base surgery, with a recent study demonstrating that frail patients undergoing resection of a pituitary neoplasm had significantly worse outcomes compared with a propensity score–matched nonfrail cohort.34

When considering literature reporting on various types of intracranial tumors and their association with frailty, frailty in meningioma surgery has been reported to increase LOS, reoperation rates, and non–routine discharge dispositions.35,36 In patients with brain metastases, it has been reported that frailty is associated with increased LOS, postoperative complications, and mortality.37 A study by Huq et al. found that frailty was associated with worse discharge dispositions in patients when analyzing an institutional sample of a wide variety of tumors.38 This included patients with meningioma, high-grade glioma, low-grade glioma, pituitary tumor, metastatic brain tumor, and vestibular schwannoma. Additional data also report that frailty is associated with increased postoperative complications, discharge other than home, 30-day readmission, 30-day mortality, and increased financial charges in multiple different intracranial tumors.3941 In the current study, we found that frailty was a predictor of mortality, nonroutine discharge, increased costs, increased LOS, and readmissions in NF1 patients who underwent intracranial resection.

An understanding of frailty within the context of NF1 is critical for perioperative and follow-up planning within this patient population. Previous studies have demonstrated that patients with NF1 may require additional healthcare resource utilization compared with the rest of the population, and as such, computational models that account for and accurately predict perioperative complications may allow physicians to intervene more quickly and pursue preventative measures to prevent severe complications in high-risk populations.42 Specifically, because tumor recurrence often occurs in a majority of patients with NF1, accurate prediction of future complications may aid surgical planning and adjuvant chemotherapy and radiation therapy options.43,44 While our current study is the first to do so through the use of frailty measures, additional exploration using large multicenter databases is still necessary to develop the most robust models.

Lastly, while frailty has been shown to significantly increase complication rates, previous studies have demonstrated that appropriate perioperative physical therapy (PT), occupational therapy (OT), and nutritional rehabilitation may reduce the patient frailty burden. This would potentially result in lower rates of complications. Several studies have demonstrated that specific in-hospital interventions may prevent the development of frailty-associated illnesses in certain surgical populations.45 Furthermore, nutritional rehabilitation may prevent progression of frailty and even possibly treat frailty, as indicated by several recent studies.46,47 As such, NF1 patients meeting criteria for frailty using well-established frailty indices may benefit from appropriate PT/OT interventions and nutritional rehabilitation to further mitigate the risk for surgical complications.

Limitations

This study is not without limitations. First, as a retrospective cohort analysis, this study is limited by the quantity and depth of the provided patient data as supplied by ICD-10 coding. The years were chosen due to the implementation of mandatory ICD-10 coding in late 2015, which allowed for more detailed codes to be drawn for analysis. However, there is no existing ICD-10 code for plexiform neurofibromas, which are common in NF1. In addition, the ICD codes for resection of intracranial neoplasms did not specify the particular type of tumor resected in each patient, a variable we were unable to account for in this study. ICD-10 coding granularity also limited analysis of patients with concurrent spinal lesions.

Furthermore, the NRD itself is limited in its granularity, and while it is excellent at detecting correlation between data variables, it performs poorly when investigating causation. Additional research is warranted with databases that have more granular variables to fully answer these questions. In addition, while several frailty measures are available, the criteria for some are not included in the NRD. This is why we used only the JHACG frailty-defining diagnosis indicator, which includes the necessary ICD-10 codes to accurately define frailty in our patient cohort. Lastly, there was a risk of Berkson’s bias and selection bias because of our use of an inpatient hospital database, which we aimed to minimize by using random methods for selecting patients for subgroups as well as including large sample sizes.

Conclusions

The effects of frailty on patients with NF1 have not been previously studied. A better understanding of the role of comorbidity indices in patients with NF1 allows for optimization of patient management in patients undergoing neurosurgical intervention. Our findings suggest that frailty is an accurate predictor of a wide array of complications, including readmission, high cost, nonroutine discharge, and mortality in NF1 patients. Additionally, frailty+ECI scores used in conjunction were found to be a superior predictor of increased hospital stay and readmission. Further studies utilizing large multisystem databases should be performed with larger sample sizes to better understand the role of frailty on outcomes in patients with NF1.

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: Shahrestani, Brown, Strickland, Ghodsi, Nasrollahi, Borrelli, Ruzevick. Acquisition of data: Shahrestani. Analysis and interpretation of data: Shahrestani, Strickland, Nasrollahi, Borrelli. Drafting the article: Shahrestani, Brown, Strickland, Ghodsi, Nasrollahi, Borrelli, Ruzevick. Critically revising the article: all authors. Reviewed submitted version of manuscript: Shahrestani, Brown, Strickland, Bakhsheshian, Ghodsi, Nasrollahi, Borrelli, Gendreau, Ruzevick. Statistical analysis: Shahrestani. Administrative/technical/material support: Zada, Shahrestani, Borrelli. Study supervision: Zada, Shahrestani, Strickland, Bakhsheshian, Ghodsi, Nasrollahi, Ruzevick.

Supplemental Information

Online-Only Content

Supplemental material is available online.

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    Lieberman R, Abrams C, Weiner J. Development and evaluation of the Johns Hopkins University risk adjustment models for Medicare+ Choice plan payment. Johns Hopkins ACG System. Published June 6 2003.Accessed March 9, 2022. https://www.hopkinsacg.org/document/development-and-evaluation-of-the-johns-hopkins-university-risk-adjustment-models-for-medicarechoice-plan-payment/

    • Search Google Scholar
    • Export Citation
  • 23

    McIsaac DI, Bryson GL, van Walraven C. Association of frailty and 1-year postoperative mortality following major elective noncardiac surgery: a population-based cohort study. JAMA Surg. 2016;151(6):538545.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 24

    Chang HJ, Chen PC, Yang CC, Su YC, Lee CC. Comparison of Elixhauser and Charlson methods for predicting oral cancer survival. Medicine (Baltimore). 2016;95(7):e2861.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 25

    Quail JM, Lix LM, Osman BA, Teare GF. Comparing comorbidity measures for predicting mortality and hospitalization in three population-based cohorts. BMC Health Serv Res. 2011;11:146.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 26

    Menendez ME, Neuhaus V, van Dijk CN, Ring D. The Elixhauser comorbidity method outperforms the Charlson index in predicting inpatient death after orthopaedic surgery. Clin Orthop Relat Res. 2014;472(9):28782886.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 27

    Lieffers JR, Baracos VE, Winget M, Fassbender K. A comparison of Charlson and Elixhauser comorbidity measures to predict colorectal cancer survival using administrative health data. Cancer. 2011;117(9):19571965.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 28

    Quan H, Sundararajan V, Halfon P, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;43(11):11301139.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 29

    Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care. 1998;36(1):827.

  • 30

    Hosmer DW Jr, Lemeshow S, Sturdivant RX. Applied Logistic Regression. John Wiley & Sons;2013.

  • 31

    Feucht M, Griffiths B, Niemüller I, Haase W, Richard G, Mautner VF. Neurofibromatosis 2 leads to higher incidence of strabismological and neuro-ophthalmological disorders. Acta Ophthalmol. 2008;86(8):882886.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 32

    Bakhsheshian J, Shahrestani S, Buser Z, et al. The performance of frailty in predictive modeling of short-term outcomes in the surgical management of metastatic tumors to the spine. Spine J. 2022;22(4):605615.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 33

    Shahrestani S, Bakhsheshian J, Solaru S, et al. Inclusion of frailty improves predictive modeling for postoperative outcomes in surgical management of primary and secondary lumbar spine tumors. World Neurosurg. 2021;153:e454e463.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 34

    Shahrestani S, Ballatori AM, Chen XT, et al. Analysis of modifiable and nonmodifiable risk factors in patients undergoing pituitary surgery. J Neurosurg. 2020;134(6):18161823.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 35

    Theriault BC, Pazniokas J, Adkoli AS, et al. Frailty predicts worse outcomes after intracranial meningioma surgery irrespective of existing prognostic factors. Neurosurg Focus. 2020;49(4):E16.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 36

    Jimenez AE, Khalafallah AM, Huq S, et al. Predictors of nonroutine discharge disposition among patients with parasagittal/parafalcine meningioma. World Neurosurg. 2020;142:e344e349.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 37

    Dicpinigaitis AJ, Hanft S, Cooper JB, et al. Comparative associations of baseline frailty status and age with postoperative mortality and duration of hospital stay following metastatic brain tumor resection. Clin Exp Metastasis. Published online January 13, 2022. doi: 10.1007/s10585-021-10138-3

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 38

    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
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 39

    Sastry RA, Pertsch NJ, Tang O, Shao B, Toms SA, Weil RJ. Frailty and outcomes after craniotomy for brain tumor. J Clin Neurosci. 2020;81:95100.

  • 40

    Bonney PA, Chartrain AG, Briggs RG, et al. Frailty is associated with in-hospital morbidity and nonroutine disposition in brain tumor patients undergoing craniotomy. World Neurosurg. 2021;146:e1045e1053.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 41

    Torres-Perez P, Álvarez-Satta M, Arrazola M, et al. Frailty is associated with mortality in brain tumor patients. Am J Cancer Res. 2021;11(6):32943303.

  • 42

    Yang X, Desai K, Agrawal N, et al. Treatment, resource use and costs among pediatric patients with neurofibromatosis type 1 and plexiform neurofibromas. Pediatric Health Med Ther. 2020;11:421428.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 43

    Campian J, Gutmann DH. CNS tumors in neurofibromatosis. J Clin Oncol. 2017;35(21):23782385.

  • 44

    Costa AA, Gutmann DH. Brain tumors in neurofibromatosis type 1. Neurooncol Adv. 2019;1(1):vdz040.

  • 45

    Tsuchihashi J, Koya S, Hirota K, et al. Effects of in-hospital exercise on frailty in patients with hepatocellular carcinoma. Cancers (Basel). 2021;13(2):E194.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 46

    Lorenzo-López L, Maseda A, de Labra C, Regueiro-Folgueira L, Rodríguez-Villamil JL, Millán-Calenti JC. Nutritional determinants of frailty in older adults: a systematic review. BMC Geriatr. 2017;17(1):108.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 47

    Cruz-Jentoft AJ, Woo J. Nutritional interventions to prevent and treat frailty. Curr Opin Clin Nutr Metab Care. 2019;22(3):191195.

Supplementary Materials

Figure from Shahrestani et al. (E3). Created with Biorender.com.

  • View in gallery

    Distribution of propensity scores after matching. Frail patients are shown as the matched treatment units, and propensity-matched nonfrail patients are shown as the matched control units. The unmatched control units represent nonfrail patients who were not chosen by the propensity-matching algorithm. The comparable distribution of patients in both matched treatment and control units implies the achievement of excellent propensity score matching.

  • View in gallery

    Breakdown of comorbidities seen in patients with NF1 that contribute toward increased ECI and frailty scores. Endo/Rheum = endocrine/rheumatological; Psych/Drug = psychiatric/drug use. Created with Biorender.com.

  • View in gallery

    ROC plot for the prediction of mortality. The black ROC (cannot be seen because the red ROC is the same) represents the model using ECI alone as the primary predictor, the blue ROC represents the model using frailty alone as the primary predictor, and the red ROC represents the model using frailty status and ECI as the primary predictors. There was no statistically significant difference between any of the model ROCs for mortality.

  • View in gallery

    ROC plot for the prediction of nonroutine discharge. The black ROC represents the model using ECI alone as the primary predictor, the blue ROC represents the model using frailty alone as the primary predictor, and the red ROC represents the model using frailty status and ECI as the primary predictors. There was no statistically significant difference between any of the model ROCs for nonroutine discharge.

  • View in gallery

    ROC plot for the prediction of cost in the top quartile. The black ROC represents the model using ECI alone as the primary predictor, the blue ROC represents the model using frailty alone as the primary predictor, and the red ROC represents the model using frailty status and ECI as the primary predictors. There was no statistically significant difference between any of the model ROCs for cost.

  • View in gallery

    ROC plot for the prediction of LOS in the top quartile. The black ROC represents the model using ECI alone as the primary predictor, the blue ROC represents the model using frailty alone as the primary predictor, and the red ROC represents the model using frailty status and ECI as the primary predictors. The model utilizing frailty+ECI outperformed the model using frailty alone for the prediction of LOS (p = 0.013).

  • View in gallery

    ROC plot for the prediction of readmission within 1 year. The black ROC represents the model using ECI alone as the primary predictor, the blue ROC represents the model using frailty alone as the primary predictor, and the red ROC represents the model using frailty status and ECI as the primary predictors. There was no statistically significant difference between any of the model ROCs for 1-year readmission. The model using frailty was outperformed by the models using ECI (p = 0.039) and frailty+ECI (p = 0.038).

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    Lieberman R, Abrams C, Weiner J. Development and evaluation of the Johns Hopkins University risk adjustment models for Medicare+ Choice plan payment. Johns Hopkins ACG System. Published June 6 2003.Accessed March 9, 2022. https://www.hopkinsacg.org/document/development-and-evaluation-of-the-johns-hopkins-university-risk-adjustment-models-for-medicarechoice-plan-payment/

    • Search Google Scholar
    • Export Citation
  • 23

    McIsaac DI, Bryson GL, van Walraven C. Association of frailty and 1-year postoperative mortality following major elective noncardiac surgery: a population-based cohort study. JAMA Surg. 2016;151(6):538545.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 24

    Chang HJ, Chen PC, Yang CC, Su YC, Lee CC. Comparison of Elixhauser and Charlson methods for predicting oral cancer survival. Medicine (Baltimore). 2016;95(7):e2861.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 25

    Quail JM, Lix LM, Osman BA, Teare GF. Comparing comorbidity measures for predicting mortality and hospitalization in three population-based cohorts. BMC Health Serv Res. 2011;11:146.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 26

    Menendez ME, Neuhaus V, van Dijk CN, Ring D. The Elixhauser comorbidity method outperforms the Charlson index in predicting inpatient death after orthopaedic surgery. Clin Orthop Relat Res. 2014;472(9):28782886.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 27

    Lieffers JR, Baracos VE, Winget M, Fassbender K. A comparison of Charlson and Elixhauser comorbidity measures to predict colorectal cancer survival using administrative health data. Cancer. 2011;117(9):19571965.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 28

    Quan H, Sundararajan V, Halfon P, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;43(11):11301139.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 29

    Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care. 1998;36(1):827.

  • 30

    Hosmer DW Jr, Lemeshow S, Sturdivant RX. Applied Logistic Regression. John Wiley & Sons;2013.

  • 31

    Feucht M, Griffiths B, Niemüller I, Haase W, Richard G, Mautner VF. Neurofibromatosis 2 leads to higher incidence of strabismological and neuro-ophthalmological disorders. Acta Ophthalmol. 2008;86(8):882886.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 32

    Bakhsheshian J, Shahrestani S, Buser Z, et al. The performance of frailty in predictive modeling of short-term outcomes in the surgical management of metastatic tumors to the spine. Spine J. 2022;22(4):605615.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 33

    Shahrestani S, Bakhsheshian J, Solaru S, et al. Inclusion of frailty improves predictive modeling for postoperative outcomes in surgical management of primary and secondary lumbar spine tumors. World Neurosurg. 2021;153:e454e463.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 34

    Shahrestani S, Ballatori AM, Chen XT, et al. Analysis of modifiable and nonmodifiable risk factors in patients undergoing pituitary surgery. J Neurosurg. 2020;134(6):18161823.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 35

    Theriault BC, Pazniokas J, Adkoli AS, et al. Frailty predicts worse outcomes after intracranial meningioma surgery irrespective of existing prognostic factors. Neurosurg Focus. 2020;49(4):E16.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 36

    Jimenez AE, Khalafallah AM, Huq S, et al. Predictors of nonroutine discharge disposition among patients with parasagittal/parafalcine meningioma. World Neurosurg. 2020;142:e344e349.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 37

    Dicpinigaitis AJ, Hanft S, Cooper JB, et al. Comparative associations of baseline frailty status and age with postoperative mortality and duration of hospital stay following metastatic brain tumor resection. Clin Exp Metastasis. Published online January 13, 2022. doi: 10.1007/s10585-021-10138-3

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 38

    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
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 39

    Sastry RA, Pertsch NJ, Tang O, Shao B, Toms SA, Weil RJ. Frailty and outcomes after craniotomy for brain tumor. J Clin Neurosci. 2020;81:95100.

  • 40

    Bonney PA, Chartrain AG, Briggs RG, et al. Frailty is associated with in-hospital morbidity and nonroutine disposition in brain tumor patients undergoing craniotomy. World Neurosurg. 2021;146:e1045e1053.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 41

    Torres-Perez P, Álvarez-Satta M, Arrazola M, et al. Frailty is associated with mortality in brain tumor patients. Am J Cancer Res. 2021;11(6):32943303.

  • 42

    Yang X, Desai K, Agrawal N, et al. Treatment, resource use and costs among pediatric patients with neurofibromatosis type 1 and plexiform neurofibromas. Pediatric Health Med Ther. 2020;11:421428.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 43

    Campian J, Gutmann DH. CNS tumors in neurofibromatosis. J Clin Oncol. 2017;35(21):23782385.

  • 44

    Costa AA, Gutmann DH. Brain tumors in neurofibromatosis type 1. Neurooncol Adv. 2019;1(1):vdz040.

  • 45

    Tsuchihashi J, Koya S, Hirota K, et al. Effects of in-hospital exercise on frailty in patients with hepatocellular carcinoma. Cancers (Basel). 2021;13(2):E194.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 46

    Lorenzo-López L, Maseda A, de Labra C, Regueiro-Folgueira L, Rodríguez-Villamil JL, Millán-Calenti JC. Nutritional determinants of frailty in older adults: a systematic review. BMC Geriatr. 2017;17(1):108.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 47

    Cruz-Jentoft AJ, Woo J. Nutritional interventions to prevent and treat frailty. Curr Opin Clin Nutr Metab Care. 2019;22(3):191195.

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