Frailty in Patients Undergoing Surgery for Brain Tumors: A Systematic Review of the Literature

Frailty in intracranial meningioma resection: the risk analysis index demonstrates strong discrimination for predicting non-home discharge and in-hospital mortality

PURPOSE

Frailty is an independent risk factor for adverse postoperative outcomes following intracranial meningioma resection (IMR). The role of the Risk Analysis Index (RAI) in predicting postoperative outcomes following IMR is nascent but may inform preoperative patient selection and surgical planning.

METHODS

IMR patients from the Nationwide Inpatient Sample were identified using diagnostic and procedural codes (2019-2020). The relationship between preoperative RAI-measured frailty and primary outcomes (non-home discharge (NHD), in-hospital mortality) and secondary outcomes (extended length of stay (eLOS), complication rates) was assessed via multivariate analyses. The discriminatory accuracy of the RAI for primary outcomes was measured in area under the receiver operating characteristic (AUROC) curve analysis.

RESULTS

A total of 23,230 IMR patients (mean age = 59) were identified, with frailty statuses stratified by RAI score: 0-20 "robust" (R)(N = 10,665, 45.9%), 21-30 "normal" (N)(N = 8,895, 38.3%), 31-40 "frail" (F)(N = 2,605, 11.2%), and 41+ "very frail" (VF)(N = 1,065, 4.6%). Rates of NHD (R 11.5%, N 29.7%, F 60.8%, VF 61.5%), in-hospital mortality (R 0.5%, N 1.8%, F 3.8%, VF 7.0%), eLOS (R 13.2%, N 21.5%, F 40.9%, VF 46.0%), and complications (R 7.5%, N 11.6%, F 15.7%, VF 16.0%) significantly increased with increasing frailty thresholds (p < 0.001). The RAI demonstrated strong discrimination for NHD (C-statistic: 0.755) and in-hospital mortality (C-statistic: 0.754) in AUROC curve analysis.

CONCLUSION

Increasing RAI-measured frailty is significantly associated with increased complication rates, eLOS, NHD, and in-hospital mortality following IMR. The RAI demonstrates strong discrimination for predicting NHD and in-hospital mortality following IMR, and may aid in preoperative risk stratification.

Frailty: Implications for Neuroanesthesia

Frailty is increasingly prevalent in the aging neurosurgical population and is an important component of perioperative risk stratification and optimization to reduce complications. Frailty is measured using the phenotypic or deficit accumulation models, with simplified tools most commonly used in studies of neurosurgical patients. There are a limited number of frailty measurement tools that have been validated for individuals with neurological disease, and those that exist are mainly focused on spine pathology. Increasing frailty consistently predicts worse outcomes for patients across a range of neurosurgical procedures, including early complications, disability, non-home discharge, and mortality. Evidence for interventions to improve outcomes for frail neurosurgical patients is limited, and the role of bundled care pathways, prehabilitation, and multidisciplinary involvement requires further investigation. Surgery itself may be an intervention to improve frailty in selected patients, and future research should focus on identifying effective interventions to improve both short-term complications and long-term outcomes.

The Hospital Frailty Risk Score Independently Predicts Postoperative Outcomes in Glioblastoma Patients

OBJECTIVE

The Hospital Frailty Risk Score (HFRS) is a tool for quantifying patient frailty using International Classification of Diseases, Tenth Revision codes. This study aimed to determine the utility of the HFRS in predicting surgical outcomes after resection of glioblastoma (GBM) and compare its prognostic ability with other validated indices such as American Society of Anesthesiologists score and Charlson Comorbidity Index.

METHODS

A retrospective analysis was conducted using a GBM patient database (2017-2019) at a single institution. HFRS was calculated using International Classification of Diseases, Tenth Revision codes. Bivariate logistic regression was used to model prognostic ability of each frailty index, and model discrimination was assessed using area under the receiver operating characteristic curve. Multivariate linear and logistic regression models were used to assess for significant associations between HFRS and continuous and binary postoperative outcomes, respectively.

RESULTS

The study included 263 patients with GBM. The HFRS had a significantly greater area under the receiver operating characteristic curve compared with American Society of Anesthesiologists score (P = 0.016) and Charlson Comorbidity Index (P = 0.037) for predicting 30-day readmission. On multivariate analysis, the HFRS was significantly and independently associated with hospital length of stay (P = 0.0038), nonroutine discharge (P = 0.018), and 30-day readmission (P = 0.0051).

CONCLUSIONS

The HFRS has utility in predicting postoperative outcomes for patients with GBM and more effectively predicts 30-day readmission than other frailty indices. The HFRS may be used as a tool for optimizing clinical decision making to reduce adverse postoperative outcomes in patients with GBM.

The Limited Utility of the Hospital Frailty Risk Score as a Frailty Assessment Tool in Neurosurgery: A Systematic Review

BACKGROUND AND OBJECTIVES

The Hospital Frailty Risk Score (HFRS) is an International Classification of Disease 10th Revision-based scale that was originally designed for, and validated in, the assessment of patients 75 years or older presenting in an acute care setting. This study highlights central tenets inherent to the concept of frailty; questions the logic behind, and utility of, HFRS' recent implementation in the neurosurgical literature; and discusses why there is no useful role for HFRS as a frailty-based neurosurgical risk assessment (FBNRA) tool.

METHODS

The authors performed a systematic review of the literature per Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, including all cranial and spinal studies that used HFRS as their primary frailty tool. Seventeen (N = 17) studies used HFRS to assess frailty's impact on neurosurgical outcomes. Thirteen total journals, 10 of which were neurosurgical journals, including the highest impact factor journals, published the 17 papers.

RESULTS

Increasing HFRS score was associated with adverse outcomes, including prolonged length of stay (11 of 17 studies), nonroutine discharge (10 of 17 studies), and increased hospital costs (9 of 17 studies). Four different HFRS studies, of the 17, predicted one of the following 4 adverse outcomes: worse quality of life, worse functional outcomes, reoperation, or in-hospital mortality.

CONCLUSION

Despite its rapid acceptance and widespread proliferation through the leading neurosurgical journals, HFRS lacks any conceptual relationship to the frailty syndrome or FBNRA for individual patients. HFRS measures acute conditions using International Classification of Disease 10th Revision codes and awards "frailty" points for symptoms and examination findings unrelated to the impaired baseline physiological reserve inherent to the very definition of frailty. HFRS lacks clinical utility as it cannot be deployed point-of-care at the bedside to risk stratify patients. HFRS has never been validated in any patient population younger than 75 years or in any nonacute care setting. We recommend HFRS be discontinued as an individual FBNRA tool.

Strategies to Assess and Manage Frailty among Patients Diagnosed with Primary Malignant Brain Tumors

OPINION STATEMENT

Frailty refers to a biologic process that results in reduced physiologic and functional reserve. Patients diagnosed with primary malignant brain tumors experience high symptom burden from tumor and tumor-directed treatments that, coupled with previous comorbidities, may contribute to frailty. Within the primary malignant brain tumor population, frailty is known to associate with mortality, higher healthcare utilization, and increased risk of postoperative complications. As such, methods to assess and manage frailty are paramount. However, there is currently no clear consensus on how to best assess and manage frailty throughout the entirety of the disease trajectory. Given the association between frailty and health outcomes, more research is needed to determine best practice protocols for the assessment and management of frailty among patients diagnosed with primary malignant brain tumors.

Frailty assessment by two screening instruments in non-elderly patients with head and neck cancer

PURPOSE

Frailty assessment is often overlooked in non-elderly patients with cancer, possibly due to the lack of an effective frailty screening tool. This study aimed to evaluate the performance of two modern frailty screening tools, the Flemish version of the Triage Risk Screening Tool (fTRST) and the modified 5-Item Frailty Index (mFI-5), compared to the gold standard comprehensive geriatric assessment (GA) among non-elderly patients with head and neck cancer (HNC).

METHODS

We prospectively included 354 consecutive patients aged < 65 years with newly diagnosed HNC scheduled for definitive concurrent chemoradiotherapy (CCRT) at three academic hospitals in Taiwan between January 2020 and December 2022. Frailty assessment using the GA, fTRST, and mFI-5 was performed in all patients to evaluate the relationship between frailty and treatment outcomes.

RESULTS

The prevalence of frailty was 27.1%, 37.0%, and 42.4% based on GA, mFI-5, and fTRST, respectively. mFI-5 and fTRST demonstrated good predictive value in identifying frail patients compared to the GA. Patients with frailty, as defined by GA, mFI-5, and fTRST, exhibited higher risks of treatment-related complications, incomplete treatment, and poorer baseline quality of life (QoL). However, only GA showed significant prognostic value for overall survival.

CONCLUSIONS

Frailty assessment using fTRST and mFI-5 is valuable for predicting treatment-related adverse events, treatment tolerance, and QoL in non-elderly patients with HNC. Incorporating frailty assessment into the management of non-elderly cancer patients can aid in the identification of high-risk individuals. However, the performance of these tools varies, highlighting the need for further validation and refinement.

Causes and Predictors of Unplanned Readmission in Patients Undergoing Intracranial Tumor Resection: A Multicenter Analysis of 31,776 Patients

BACKGROUND

Although unplanned readmission is a postoperative outcome metric associated with significant morbidity and financial burden, precise assessment tools for its prediction have not yet been developed. The Risk Analysis Index (RAI) could potentially be used to help improve the prediction of unplanned readmissions for patients undergoing intracranial tumor resection (ITR). In the present study, we evaluate the predictive accuracy of frailty on 30-day unplanned readmission after ITR using the RAI.

METHODS

Data were obtained from the American College of Surgeons National Surgical Quality Improvement Program database. The baseline characteristics, preoperative clinical status, and outcomes were compared between patients with and without unplanned readmission. Frailty was calculated using the RAI. Univariate and multivariate logistic regression analyses were performed to identify independent associations between unplanned readmissions and patient characteristics.

RESULTS

The unplanned readmission rate for this cohort (n = 31,776) was 10.8% (n = 3420). Of the 3420 readmitted patients, 958 required unplanned reoperation. Multiple characteristics were significantly different between the 2 groups, including age, body mass index, comorbidities, and RAI groups (P < 0.05). The common causes of unplanned readmission included infection (9.4%), seizures (6%), and pulmonary embolism (4%). The patient characteristics identified as reliable predictors of unplanned readmission included age, body mass index, functional status, diabetes, hypertension, hyponatremia, and the patient's RAI score (P < 0.05). Frail status, hyponatremia, leukocytosis, hypertension, and thrombocytosis were significant predictors of unplanned readmissions.

CONCLUSIONS

The RAI is a reliable preoperative frailty index for predicting unplanned readmissions after ITR. Using the RAI could decrease unplanned readmissions by identifying high-risk patients and enabling future implementation of appropriate management guidelines.

Frailty and postoperative outcomes in brain tumor patients: a systematic review subdivided by tumor etiology

PURPOSE

Frailty has gained prominence in neurosurgical oncology, with more studies exploring its relationship to postoperative outcomes in brain tumor patients. As this body of literature continues to grow, concisely reviewing recent developments in the field is necessary. Here we provide a systematic review of frailty in brain tumor patients subdivided by tumor type, incorporating both modern frailty indices and traditional Karnofsky Performance Status (KPS) metrics.

METHODS

Systematic literature review was performed using PRISMA guidelines. PubMed and Google Scholar were queried for articles related to frailty, KPS, and brain tumor outcomes. Only articles describing novel associations between frailty or KPS and primary intracranial tumors were included.

RESULTS

After exclusion criteria, systematic review yielded 52 publications. Amongst malignant lesions, 16 studies focused on glioblastoma. Amongst benign tumors, 13 focused on meningiomas, and 6 focused on vestibular schwannomas. Seventeen studies grouped all brain tumor patients together. Seven studies incorporated both frailty indices and KPS into their analyses. Studies correlated frailty with various postoperative outcomes, including complications and mortality.

CONCLUSION

Our review identified several patterns of overall postsurgical outcomes reporting for patients with brain tumors and frailty. To date, reviews of frailty in patients with brain tumors have been largely limited to certain frailty indices, analyzing all patients together regardless of lesion etiology. Although this technique is beneficial in providing a general overview of frailty's use for brain tumor patients, given each tumor pathology has its own unique etiology, this combined approach potentially neglects key nuances governing frailty's use and prognostic value.

Frailty Measured by the Risk Analysis Index Predicts Nonhome Discharge and Mortality After Resection in Refractory Epilepsy: Analysis of 1236 Patients From a Prospective Surgical Registry, 2012 to 2020

BACKGROUND

Risk stratification of epilepsy surgery patients remains difficult. The Risk Analysis Index (RAI) is a frailty measurement that augments preoperative risk stratification.

OBJECTIVE

To evaluate RAI's discriminative threshold for nonhome discharge disposition (NHD) and mortality (or discharge to hospice within 30 days of operation) in epilepsy surgery patients.

METHODS

Patients were queried from the American College of Surgeons-National Surgical Quality Improvement Program database (2012-2020) using diagnosis/procedure codes. Linear-by-linear trend tests assessed RAI's relationship with NHD and mortality. Discriminatory accuracy was assessed by C-statistics (95% CI) in receiver operating characteristic curve analysis.

RESULTS

Epilepsy resections (N = 1236) were grouped into temporal lobe (60.4%, N = 747) and nontemporal lobe (39.6%, N = 489) procedures. Patients were stratified by RAI tier: 76.5% robust (RAI 0-20), 16.2% normal (RAI 21-30), 6.6% frail (RAI 31-40), and 0.8% severely frail (RAI 41 and above). The NHD rate was 18.0% (N = 222) and positively associated with increasing RAI tier: 12.5% robust, 34.0% normal, 38.3% frail, and 50.0% severely frail ( P < .001). RAI had robust predictive discrimination for NHD in overall cohort (C-statistic 0.71), temporal lobe (C-statistic 0.70), and nontemporal lobe (C-statistic 0.71) cohorts. The mortality rate was 2.7% (N = 33) and significantly associated with RAI frailty: 1.1% robust, 8.0% normal, 6.2% frail, and 20.0% severely frail ( P < .001). RAI had excellent predictive discrimination for mortality in overall cohort (C-statistic 0.78), temporal lobe (C-statistic 0.80), and nontemporal lobe (C-statistic 0.74) cohorts.

CONCLUSION

The RAI frailty score predicts mortality and NHD after epilepsy surgery. This is accomplished with a user-friendly calculator: https://nsgyfrailtyoutcomeslab.shinyapps.io/epilepsy/ .

Assessment of the impact of frailty on adverse surgical outcomes in patients undergoing surgery for intracranial tumors using modified frailty index: A systematic review and meta-analysis

BACKGROUND

Modified frailty index (MFI) is an emerging quantitative measure of frailty; however, the quantified risk of adverse outcomes in surgeries for intracranial tumors associated with increasing MFI scores has not been thoroughly reviewed in a comprehensive manner.

METHODS

MEDLINE (PubMed), Scopus, Web of Science, and Embase were searched to identify observational studies on the association between 5 and 11 item-modified frailty index (MFI) and perioperative outcomes for neurosurgical procedures including complications, mortality, readmission, and reoperation rate. Primary analysis pooled all comparisons with MFI scores greater than or equal to 1 versus non-frail participants using mixed-effects multilevel model for each outcome.

RESULTS

In total, 24 studies were included in the review and 19 studies with 114,707 surgical operations were included in the meta-analysis. While increasing MFI scores were associated with worse prognosis for all included outcomes, reoperation rate was only significantly higher in patients with MFI ≥ 3. Among surgical pathologies, glioblastoma was influenced by a greater extent to the impact of frailty on complications and mortality that most other etiologies. In agreement with qualitative evaluation of the included studies, meta-regression did not reveal association between mean age of the comparisons and complications rate.

CONCLUSION

The results of this meta-analysis provides quantitative risk assessment of adverse outcomes in neuro-oncological surgeries with increased frailty. The majority of literature suggests that MFI is a superior and independent predictor of adverse outcomes compared to age.