- Email: [email protected]
Frailty as a Predictor of Neurosurgical Outcomes in Brain Tumor Patients
Original Article
Frailty as a Predictor of Neurosurgical Outcomes in Brain Tumor Patients Tessa A. Harland, Mary Wang, Dicle Gunaydin, Anthony Fringuello, Jacob Freeman, Patrick W. Hosokawa, D. Ryan Ormond
BACKGROUND: Preoperative risk assessment is important, but inexact because physiologic reserves are difficult to measure. When assessing quality of life for patients with brain tumors, having a better predictor of postsurgical outcome would be beneficial in counseling these patients. Frailty is thought to estimate physiologic reserves, and it has been found to predict postoperative complications, length of stay, and discharge to a skilled nursing facility or assisted living facility in patients undergoing various types of surgery. Frailty as an adjunct to preoperative assessment of neurosurgical patients has never been evaluated. This study aimed to determine whether frailty predicts neurosurgical complications in patients with brain tumors and enhances current perioperative risk models.
-
METHODS: Frailty was preoperatively assessed in 260 patients undergoing surgery for brain tumor resection using a validated scale that assessed weakness, weight loss, exhaustion, low physical activity, and slowed walking speed. Patients were classified as nonfrail (score of 0e1), moderately frail (score of 2e3), or frail (score of 4e5). Moderately frail and frail patients were combined for analysis.
-
RESULTS: Preoperative frailty was associated with an increased risk for discharge to a location other than home (10.36; 95% confidence interval, 3.6e30.1), postoperative complications (2.09; 95% confidence interval, 1.09e3.98), and a longer length of stay (1.66; 95% confidence interval, 1.24e2.21).
-
CONCLUSIONS: Frailty independently predicts discharge disposition, postoperative complications, and length of stay in patients undergoing surgery for brain
-
Key words Brain tumor outcomes - Frailty - Hopkins Frailty score - Neurosurgery -
Abbreviations and Acronyms ASA: American Society of Anesthesiologists CI: Confidence interval HFS: Hopkins Frailty score LOS: Length of stay
WORLD NEUROSURGERY -: e1-e6, - 2019
tumor resection. Preoperative assessment of frailty can help neurosurgeons and patients make more informed decisions about pursing surgical treatment.
INTRODUCTION
T
he decision of whether a patient will tolerate a surgical procedure is typically subjective, often based on the anecdotal experience of the surgeon and the desires of the patient. This is especially true in higher risk patients, such as elderly patients or patients with significant comorbidities. There is a paucity of standardized, easily reproducible tools to predict postoperative outcomes, especially in patients with brain tumors.1-3 Relying on anecdotal evidence alone is insufficient because there is significant discordance between perception of life expectancy among physicians.2,4 Whereas cognitive impairments have been linked to poor outcomes in elderly patients and in patients with brain tumors, the most commonly used instruments to assess preoperative risk do not consider a patient’s physiologic reserve, instead accounting only for existing deficits of discrete organ systems.2,5-7 Surgeons need a standardized, validated, preoperative risk assessment tool to aid in preoperative decision making. Frailty is defined as a decrement in reserves that can determine the resilience of a patient to recover from illness or disease (i.e., a measure of their vulnerability to death or poor outcome with a given diagnosis independent of age). Research by geriatricians over the past 2 decades has led to an understanding of frailty as an important clinical entity.2,8 The presence of frailty has been linked to increased risk of poor outcomes in medical and surgical patients, such as disability, dementia, falls, hospitalization or institutionalization, increased hospital length of stay (LOS), or increased mortality.2,9-14 To identify a standardized, verified,
Department of Neurosurgery, University of Colorado School of Medicine, Aurora, Colorado, USA To whom correspondence should be addressed: D. Ryan Ormond, M.D., Ph.D. [E-mail: [email protected]] Citation: World Neurosurg. (2019). https://doi.org/10.1016/j.wneu.2019.10.010 Journal homepage: www.journals.elsevier.com/world-neurosurgery Available online: www.sciencedirect.com 1878-8750/$ - see front matter ª 2019 Elsevier Inc. All rights reserved.
www.journals.elsevier.com/world-neurosurgery
e1
ORIGINAL ARTICLE TESSA A. HARLAND ET AL.
FRAILTY IN BRAIN TUMOR PATIENTS
preoperative risk assessment tool for surgical patients, the Hopkins Frailty score (HFS) was developed by Makary et al.8 The HFS was validated in a heterogeneous cohort of patients undergoing major and minor general surgical and urologic procedures.2 This score was later additionally validated in an elderly population of similar general surgery and urology patients.2 Frailty has also been found to be an important predictor of surgical outcomes in patients undergoing cardiac surgery.14 Frailty as an adjunct to preoperative assessment of neurosurgical patients has never been evaluated. This study aimed to determine whether frailty predicts neurosurgical complications in patients with brain tumors and enhances current perioperative risk models.
Table 1. Frailty Classification Shrinking
Shrinking was defined based on self-reported unintentional weight loss of 10 pounds in the last year.
Exhaustion
Exhaustion was measured based on responses to “I felt that everything I did was an effort” and “I could not get going.” Possible responses included 0 ¼ rarely or none of the time (<1 day); 1 ¼ some or little of the time (1e2 days); 2 ¼ a moderate amount of time (3e4 days); 3 ¼ most of the time. Subjects answering either question with response 2 or 3 met this criterion.
Low activity
Low activity was measured by self-reported physical activity for the 2 weeks before assessment and was converted to equivalent kilocalories of expenditure. Men reporting <383 kcal/week and women reporting <270 kcal/week met this criterion.
Weakness
Weakness was measured using a handheld dynamometer. Three serial trials of maximum grip strength with the dominant hand were performed and averaged. Men met this criterion if BMI (kg/m2) and grip strength (kg of force) were 24 and 29, 24.1e26 and 30, 26.1e28 and 31, or >28 and 32. Women met this criterion if BMI (kg/m2) and grip strength (kg of force) were 23 and 17, 23.1e26 and 17.3, 26.1e29 and 18, or >29 and 21.
MATERIALS AND METHODS Study Design and Participants This prospective study enrolled patients 18 years old with a history of brain tumor scheduled for elective resection of tumor at the University of Colorado over a 3-year period (October 2014 to August 2017). Following institutional review board approval, all participants provided written consent before any data collection. Participants underwent a standardized preoperative interview and frailty assessment by a research assistant. Demographic information and comprehensive medical history, including current prescription medications, were collected during the interview and verified with the patient’s electronic medical records. Patients were excluded if they were unable to grip the dynamometer to measure weakness; were unable to ambulate; had Parkinson disease; had a history of stroke; were unable to understand and sign the informed consent (used as a proxy for a Mini-Mental State Examination in patients <18 years old); and were taking carbidopa/levodopa, donepezil hydrochloride, or antidepressants. Previous studies have found that these medications cause symptoms that are potentially collinear with domains of frailty.13 Outcome Measures Preoperative assessment of frailty included the 5 components of the HFS: shrinking, weakness, exhaustion, low activity, and slowed walking speed (Table 1). Each domain yielded a dichotomous score of 0 or 1 based on the criteria presented in Table 1. Patients were divided into frail (HFS 4e5), moderately frail (HFS 2e3), and nonfrail (HFS 0e1) subgroups. The main outcome variables were obtained from the patient’s medical record and included postoperative complications within 30 days of surgery, including mortality; new neurologic deficit; LOS; and discharge to a skilled nursing facility, acute rehabilitation facility, or hospice at 30 days when previously independent. The following clinical and demographic variables were collected to assess for possible confounders and to assess postsurgical outcomes: age, race, sex, height, weight, body mass index, medical comorbidities (e.g., history of myocardial infarction, angina, congestive heart failure, claudication, arthritis, cancer, hypertension, diabetes, chronic obstructive lung disease, smoking), surgical procedure, site and side of brain tumor, brain tumor diagnosis, perioperative seizure, and estimated blood loss from surgery. Similar to Makary et al.,8 4 risk models were evaluated, including HFS, American Society of Anesthesiologists (ASA)
e2
www.SCIENCEDIRECT.com
Slowed walking Slowed walking speed was measured by averaging 3 trials of speed walking 15 feet at a normal pace. Men met this criterion if height and walking time were 173 cm and 7 seconds or >173 cm and 6 seconds. Women met this criterion if height and walking time were 159 cm and 7 seconds or >173 cm and 6 seconds. BMI, body mass index.
score, Lee’s revised cardiac risk index, and Eagle score. Lee score (0e4) was determined by the presence of specific preoperative cardiac risk factors.15 Eagle score (0e6) was also based on standardized criteria.16 ASA score (1e6) was independently assigned by an anesthesiologist blinded to the patient’s HFS.17
Data Analysis Analyses were performed using IBM SPSS Statistics Version 20 software (IBM Corporation, Armonk, New York, USA) and SAS 9.4 (SAS Institute Inc., Cary, North Carolina, USA). Results with P < 0.05 were considered significant. For univariate analysis, c2 test or Fisher exact test was used for categorical variables, and t test was used for continuous variables. Multivariate regression models were constructed to account for potential confounders by adjusting frailty status and other variables significant on univariate analysis for ASA, Lee, and Eagle scores and age. Because these variables represent traditional risk assessment tools and known prognostic factors, this allowed accounting for their effect on the relationship of frailty and outcome. Analysis of subcomponents of HFS (e.g., weakness, slowed walking speed) was done via c2 test to assess whether any component of the HFS was more predictive of poor surgical outcomes.
WORLD NEUROSURGERY, https://doi.org/10.1016/j.wneu.2019.10.010
ORIGINAL ARTICLE TESSA A. HARLAND ET AL.
FRAILTY IN BRAIN TUMOR PATIENTS
Table 2. Continued
Table 2. Patient Characteristics Characteristic
Nonfrail (n [ 194)
Age, years, mean (range) 50.1 (19e78) Female, %
41.7
Moderately Frail (n [ 56)
Frail (n [ 10)
Characteristic
Nonfrail (n [ 194)
Moderately Frail (n [ 56)
Frail (n [ 10)
56.1 (21e82)
61.36 (40e73)
Diabetes
3.6
30
10
52.6
50
ASA score, % 1
2.6
0
0
2
53.1
21
20
3
41.5
73.6
70
4
1.1
3.5
10
0
2.5
0
Hypertension
13.6
32.5
40
Chronic kidney disease
0.7
7.5
0
ASA, American Society of Anesthesiologists; KPS, Karnofsky performance scale; mRS, modified Rankin Scale; MI, myocardial infarction; CHF, congestive heart failure.
RESULTS
Lee score, % 0
85.5
58.9
70
1
10.3
30.3
20
2
4.1
8.9
10
3
0
0
0
4
0
1.8
0
5
0
0
0
0
87.1
58.9
50
1
9.3
28.6
20
2
3.6
8.9
10
3
0
1.8
20
4
0
0
0
5
0
1.8
0
0
0
10
50
0
3.6
10
60
0.5
8.9
10
70
0.5
10.7
40
80
3.6
35.7
10
90
22.1
33.9
20
100
73.3
7.1
0
0
73.3
5.3
0
1
20.3
37.5
20
2
5.9
25
20
3
0.5
21.4
30
4
0
10.7
30
Eagle score, %
KPS score, % 40
Stroke
mRS score, %
Comorbidities, % Angina
4.3
10
20
MI
2.1
2.5
20
CHF
0.7
2.5
0 Continues
WORLD NEUROSURGERY -: e1-e6, - 2019
Of 260 enrolled participants, 194 (74.6%) were nonfrail, 56 (21.5%) were moderately frail, and 10 (3.8%) were frail. The moderately frail and frail subgroups were combined owing to previous studies that have combined moderately frail and frail patients after demonstrating no significant difference between the groups.2 With this adjustment, 66 (25.4%) patients were defined as frail. The average age of the nonfrail group was 50.6 years compared with 56.1 years in the frail group. The nonfrail group was 41% women, whereas the frail group was 53% women. There was no difference in tumor location (cerebrum, brainstem, suprasellar/ pituitary, or cerebellum) between the nonfrail and frail groups (P ¼ 0.834). Risk index scores and comorbidities are listed in Table 2. Frailty and Discharge Disposition The incidence of patients in the nonfrail group who were discharged home was 97.4% compared with 78.8% in the frail group (P ¼ 0.000001). At 30 days, 6.1% of the previously independent frail group were at an acute rehabilitation facility, 7.6% were still inpatients, 3.0% were in a skilled nursing facility, and 3.0% had died. In an adjusted model, frailty independently predicted the odds of being discharged somewhere other than home. Frail patients had 10.36-fold higher odds (95% confidence interval [CI], 3.6e30.1) of being discharged to an acute rehabilitation center or skilled nursing facility. Eagle score (95% CI, 0.96e2.63), Lee score (95% CI, 0.544e2.34), and ASA score (95% CI, 0.62e3.18) did not independently predict discharge disposition. After adjusting for known risk indices, frailty remained an independent predictor of discharge disposition (Table 3). Frailty and LOS The mean LOS for the nonfrail group was 4.0 days compared with 6.18 days for the frail group with a t test showing a significant difference (P ¼ 0.009). In a Cox survival model, frailty independently predicted longer LOS. Frail patients had 1.66-fold higher odds (95% CI, 1.24e2.21) of going home sooner than nonfrail patients After adjusting for known risk indices, frailty remained an independent predictor of LOS (Table 3). Frailty and Postoperative Complications The incidence of all postoperative complications (including pneumonia, urinary tract infection, deep vein thrombosis, pulmonary embolism, new neurologic deficit, cerebrospinal fluid
www.journals.elsevier.com/world-neurosurgery
e3
ORIGINAL ARTICLE TESSA A. HARLAND ET AL.
FRAILTY IN BRAIN TUMOR PATIENTS
Table 3. Hopkins Frailty Score Adjusted Models Showing Odds Ratios for Frail Versus Nonfrail Postoperative Outcomes Postoperative Complication HFS only
Discharge to Other LOS (Cox than Home Survival Model)
2.09 (1.09e3.98)
10.36 (3.56e30.14)
1.66 (1.24e2.21)
HFS and ASA 2.05 (1.03e4.08) score
12.29 (3.81e39.56)
1.59 (1.17e2.14)
HFS and Eagle score
1.99 (1.00e3.97)
11.15 (3.64e34.17)
1.67 (1.23e2.26)
HFS and Lee score
2.47 (1.25e4.89)
13.91 (4.60e42.13)
1.70 (1.27e2.29)
HFS and age
1.70 (0.87e3.33)
6.98 (2.31e21.11)
1.58 (1.17e2.13)
Odds ratios are reported with 95% confidence intervals. LOS, length of stay; HFS, Hopkins Frailty score; ASA, American Society of Anesthesiologists.
leak, and wound dehiscence or infection) was 18.0% in the nonfrail group compared with 30.3% in the frail group (P ¼ 0.035). New neurologic symptoms occurred in 6.7% of the nonfrail group compared with 7.5% in the frail group (P ¼ 0.808). Infection occurred in 1.0% of the nonfrail group compared with 1.5% of the frail group (P ¼ 0.750). In an adjusted model, frailty independently predicted the odds of a postoperative complication. Frail patients had 2.09-fold higher odds (95% CI, 1.09e3.98) of having a complication. Eagle score (95% CI, 0.60e1.64), Lee score (95% CI, 0.78e2.22), and ASA score (95% CI, 0.91e2.01) did not independently predict postoperative complications. After adjusting for known risk indices, frailty remained an independent predictor of discharge disposition (Table 3). Components of Frailty and Postoperative Complications Slowed walking speed was predictive of postoperative complications (P ¼ 0.006). Shrinking, weakness, exhaustion, and low activity were not individually significantly associated with postoperative complications. DISCUSSION Although this is the first study to show frailty as a predictor of poor outcomes in patients with brain tumors, this phenomenon has been established across other surgery types, including general surgery, urologic, oncologic, and cardiac surgery.2,14,18 Across these surgery subtypes, frailty has consistently been associated with increased LOS, postoperative complications, and discharge disposition to somewhere other than home, as also demonstrated in the present study.2,13,14,18 It also has a wellestablished association in medical patients with poor outcomes, such as morbidity, mortality, falls, and hospitalization.9,13,19,20 Notably, the postoperative effects of frailty are most evident in patients who have undergone a major surgical procedure as opposed to a minor procedure.13 This is consistent with the idea that frailty is the ability to adapt to stressors.21 This concept is particularly applicable to patients undergoing an invasive craniotomy for brain tumor resection in the present study.
e4
www.SCIENCEDIRECT.com
Whereas surgical resection of a brain tumor is often considered standard of care, discussion surrounding the risks and benefits of the procedure for the patient and how it aligns with their goals of care is necessary to proceed with the surgery. Despite the invasive nature of this elective procedure and the importance of discussing risk to the patient, there is a paucity of literature evaluating the role of established risk measures to assess surgical outcomes. Most studies that have evaluated outcomes following brain tumor surgery have predominately focused on extent of resection and survival.22-24 Consequently, the decision is often subjectively made based on the anecdotal experience of the surgeon and the desire of the patient, particularly for benign tumor subtypes. Thus, the association of poor outcomes with the established measure of frailty shown in the present study suggests a promising tool that can be used to guide patients’ decisions about whether to proceed with brain tumor resection or alternatives, such as watchful waiting or radiotherapy. In the present study, the HFS was able to assess a patient’s frailty in <10 minutes during the preoperative clinic visit. Once a patient is identified as frail, surgical counseling can be tailored to the patient with the expectation that the LOS, rate of postoperative complications, and chances of discharge destination other than home may be increased. Furthermore, the HFS can potentially be used to tailor interventions to a patient before undergoing surgery to minimize poor outcomes. It has been shown that a multifaceted interdisciplinary treatment program targeting specific elements of frailty can improve frailty and increase mobility.25 As previously discussed, the HFS is made up of 5 components: weight loss, weakness, exhaustion, low activity, and slowed walking speed. In the study by Cameron et al.,25 if patients were underweight, a dietitian evaluated nutritional intake, and nutritional supplements were recommended if appropriate clinical criteria applied. If the exhaustion criterion was met and the Geriatric Depression Scale score was high, the study team considered referral to a psychiatrist or psychologist and encouraged greater social engagement.25 Participants who met the weakness, slowness, or low energy expenditure criteria received physiotherapy sessions and performed a home exercise program.25 This study showed a lower incidence of frailty in the intervention group with a between group-difference of 14.7% (P ¼ 0.02).25 Thus, similar intervention in particular subsets of frail patients with brain tumors may improve their HFS and subsequently their surgical outcomes. Importantly, the present study analyzed each HFS component as a predictor of poor outcomes and found that slowed walking speed is most associated with postoperative outcomes. This association could help further tailor preoperative counseling and rehabilitation. At a minimum, the HFS can be used to alert surgeons to the risks of frail patients and lead to increased postoperative monitoring with attention to hydration, nutrition, and mobilization.26,27 Further randomized controlled studies will be needed to develop targeted risk reduction strategies for frail patients. Notably, analysis of postoperative complications in nonfrail versus frail patients was statistically significant when analyzing all postoperative complications (e.g., urinary tract infection, pneumonia, deep vein thrombosis, pulmonary embolism) and was nonsignificant when looking only at complications related to surgical technique (e.g., infection and new neurologic deficit).
WORLD NEUROSURGERY, https://doi.org/10.1016/j.wneu.2019.10.010
ORIGINAL ARTICLE TESSA A. HARLAND ET AL.
FRAILTY IN BRAIN TUMOR PATIENTS
This is consistent with other studies looking at postoperative complications in frail patients.28 However, compared with previous studies focusing on older frail populations, our cohort had a lower incidence of cardiac complications and pulmonary complications.28 This could be due to the lower mean age of our population, with an average age of 50.6 years and 56.1 years in the 2 subgroups, compared with studies that looked exclusively at patients >65 years old. In addition to the absence of an age exclusion criterion, the lower age of our patients may be largely due to the fact that this is the only assessment of frailty in patients whose pathology can influence HFS based on location. However, it should be noted that patients were excluded if the tumor location was directly associated with a symptom that would score on HFS (e.g., weakness), and there was no significant difference when looking at incidence of frailty based on brain tumor location. Additionally, patients were required to score in at least 2 categories to be considered frail, reducing the likelihood that a patient’s neurologic deficits would exclusively lead to classification as frail. This study had several potential limitations. Importantly, this study did not discriminate on the basis of tumor type and included patients with glioblastoma, in which the standard of care is maximum tumor resection at diagnosis. It is commonly known that extent of resection of glioblastoma is linked to prognosis and
REFERENCES 1. Partridge JS, Harari D, Dhesi JK. Frailty in the older surgical patient: a review. Age Ageing. 2012; 41:142-147.
survival.22,29,30 Consequently, the idea of using frailty as a data point to determine the benefit of surgery is less applicable to patients with glioblastoma than it is to patients with less aggressive or benign tumors. Additionally, results from an academic center may not be generalizable to other, nonacademic centers with lower brain tumor resection volumes. Also, the providers were blind to the frailty results, thus limiting the ability to assess the influence that knowledge of HFS could have on patient care in terms of preventing complications. Finally, this study evaluated only short-term outcomes up to 30 days, limiting its ability to assess long-term outcomes as well as qualityof-life measures.
Conclusions Frailty is an independent predictor of discharge disposition, postoperative complications, and LOS in patients undergoing surgery for brain tumor resection. Preoperative assessment of frailty may be used to help neurosurgeons and patients make more informed decisions about pursuing surgical treatment. Randomized controlled clinical studies will be needed to further evaluate the use of HFS to guide clinical decisions about tumor resection and to assess the effectiveness of risk reduction strategies to improve outcomes for frail patients.
9. Boyd CM, Darer J, Boult C, et al. Clinical practice guidelines and quality of care for older patients with multiple comorbid diseases: implications for pay for performance. JAMA. 2005;294:716-724. 10. Buchner DM, Wagner EH. Preventing frail health. Clin Geriatr Med. 1992;8:1-17.
2. Revenig LM, Canter DJ, Taylor MD, et al. Too frail for surgery? Initial results of a large multidisciplinary prospective study examining preoperative variables predictive of poor surgical outcomes. J Am Coll Surg. 2013;217:665-670.
11. Campbell AJ, Buchner DM. Unstable disability and the fluctuations of frailty. Age Ageing. 1997;26: 315-318.
3. Sullivan LM, Massaro JM, D’Agostino RB Sr. Presentation of multivariate data for clinical use: the Framingham Study risk score functions. Stat Med. 2004;23:1631-1660.
12. Fried LP, Kronmal RA, Newman AB, et al. Risk factors for 5-year mortality in older adults: the cardiovascular health study. JAMA. 1998;279: 585-592.
4. Wilson JR, Clarke MG, Ewings P, et al. The assessment of patient life-expectancy: how accurate are urologists and oncologists? BJU Int. 2005; 95:794-798.
13. Fried LP, Tangen CM, Walston J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56:146-156.
5. Davenport DL, Bowe EA, Henderson WG, et al. National Surgical Quality Improvement Program (NSQIP) risk factors can be used to validate American Society of Anesthesiologists Physical Status Classification (ASA PS) levels. Ann Surg. 2006;243:636-641. 6. Inouye SK, Studenski S, Tinetti ME, Kuchel GA. Geriatric syndromes: clinical, research, and policy implications of a core geriatric concept. J Am Geriatr Soc. 2007;55:780-791. 7. Robinson TN, Eiseman B, Wallace JI, et al. Redefining geriatric preoperative assessment using frailty, disability and comorbidity. Ann Surg. 2009;250:449-455. 8. Makary MA, Segev DL, Pronovost PJ, et al. Frailty as a predictor of surgical outcomes in older patients. J Am Coll Surg. 2010;210:901-908.
14. Lee DH, Buth KJ, Martin BJ, et al. Frail patients are at increased risk for mortality and prolonged institutional care after cardiac surgery. Circulation. 2010;121:973-978. 15. Lee TH, Marcantonio ER, Mangione CM, et al. Derivation and prospective validation of a simple index for prediction of cardiac risk of major noncardiac surgery. Circulation. 1999;100: 1043-1049. 16. Eagle KA, Berger PB, Calkins H, et al. ACC/AHA guideline update for perioperative cardiovascular evaluation for noncardiac surgery—executive summary: a report of the American College of Cardiology/American Heart Association task force on practice guidelines (committee to update the 1996 guidelines on perioperative cardiovascular evaluation for noncardiac surgery). J Am Coll Cardiol. 2002;39:542-553.
WORLD NEUROSURGERY -: e1-e6, - 2019
17. Saklad M. Grading of patients for surgical procedures. Anesthesiology. 1941;2:281-284. 18. Lin HS, Watts JN, Peel NM, Hubbard RE. Frailty and post-operative outcomes in older surgical patients: a systematic review. BMC Geriatr. 2016;16: 157. 19. Bandeen-Roche K, Xue QL, Ferrucci L, et al. Phenotype of frailty: characterization in the women’s health and aging studies. J Gerontol A Biol Sci Med Sci. 2006;61:262-266. 20. Woods N, LaCroix AZ, Gray SL, et al. Frailty: emergence and consequences in women aged 65 and older in the Women’s Health Initiative Observational Study. J Am Geriatr Soc. 2005;53: 1321-1330. 21. Purser JL, Kuchibhatla MN, Fillenbaum GG, et al. Identifying frailty in hospitalized older adults with significant coronary artery disease. J Am Geriatr Soc. 2006;54:1674-1681. 22. Lacroix M, Abi-Said D, Fourney DR, et al. A multivariate analysis of 416 patients with glioblastoma multiforme: prognosis, extent of resection, and survival. J Neurosurg. 2001;95:190-198. 23. Smith JS, Chang EF, Lamborn KR, et al. Role of extent of resection in the long-term outcome of low-grade hemispheric gliomas. J Clin Oncol. 2008; 26:1338-1345. 24. Sanai N, Berger MS. Glioma extent of resection and its impact on patient outcome. Neurosurgery. 2008;62:753-766. 25. Cameron ID, Fairhall N, Langron C, et al. A multifactorial interdisciplinary intervention reduces frailty in older people: randomized trial. BMC Med. 2013;11:65.
www.journals.elsevier.com/world-neurosurgery
e5
ORIGINAL ARTICLE TESSA A. HARLAND ET AL.
26. Gill TM, Baker DI, Gottschalk M, et al. A prehabilitation program for physically frail community-living older persons. Arch Phys Med Rehabil. 2003;84:394-404. 27. Gill TM, Allore HG, Holford TR, Guo Z. Hospitalization, restricted activity, and the development of disability among older persons. JAMA. 2004; 292:2115-2124. 28. Dasgupta M, Rolfson DB, Stolee P, Borrie MJ, Speechley M. Frailty is associated with postoperative complications in older adults with medical problems. Arch Gerontol Geriatr. 2009;48: 78-83.
e6
www.SCIENCEDIRECT.com
FRAILTY IN BRAIN TUMOR PATIENTS
29. Keles GE, Anderson B, Berger MS. The effect of extent of resection on time to tumor progression and survival in patients with glioblastoma multiforme of the cerebral hemisphere. Surg Neurol. 1999;52:371-379. 30. Simpson JR, Horton J, Scott C, et al. Influence of location and extent of surgical resection on survival of patients with glioblastoma multiforme: results of three consecutive Radiation Therapy Oncology Group (RTOG) clinical trials. Int J Radiat Oncol. 1993;26:239-244.
commercial or financial relationships that could be construed as a potential conflict of interest. Received 28 July 2019; accepted 2 October 2019 Citation: World Neurosurg. (2019). https://doi.org/10.1016/j.wneu.2019.10.010 Journal homepage: www.journals.elsevier.com/worldneurosurgery Available online: www.sciencedirect.com 1878-8750/$ - see front matter ª 2019 Elsevier Inc. All rights reserved.
Conflict of interest statement: The authors declare that the article content was composed in the absence of any
WORLD NEUROSURGERY, https://doi.org/10.1016/j.wneu.2019.10.010
Frailty as a Predictor of Neurosurgical Outcomes in Brain Tumor Patients Tessa A. Harland, Mary Wang, Dicle Gunaydin, Anthony Fringuello, Jacob Freeman, Patrick W. Hosokawa, D. Ryan Ormond
BACKGROUND: Preoperative risk assessment is important, but inexact because physiologic reserves are difficult to measure. When assessing quality of life for patients with brain tumors, having a better predictor of postsurgical outcome would be beneficial in counseling these patients. Frailty is thought to estimate physiologic reserves, and it has been found to predict postoperative complications, length of stay, and discharge to a skilled nursing facility or assisted living facility in patients undergoing various types of surgery. Frailty as an adjunct to preoperative assessment of neurosurgical patients has never been evaluated. This study aimed to determine whether frailty predicts neurosurgical complications in patients with brain tumors and enhances current perioperative risk models.
-
METHODS: Frailty was preoperatively assessed in 260 patients undergoing surgery for brain tumor resection using a validated scale that assessed weakness, weight loss, exhaustion, low physical activity, and slowed walking speed. Patients were classified as nonfrail (score of 0e1), moderately frail (score of 2e3), or frail (score of 4e5). Moderately frail and frail patients were combined for analysis.
-
RESULTS: Preoperative frailty was associated with an increased risk for discharge to a location other than home (10.36; 95% confidence interval, 3.6e30.1), postoperative complications (2.09; 95% confidence interval, 1.09e3.98), and a longer length of stay (1.66; 95% confidence interval, 1.24e2.21).
-
CONCLUSIONS: Frailty independently predicts discharge disposition, postoperative complications, and length of stay in patients undergoing surgery for brain
-
Key words Brain tumor outcomes - Frailty - Hopkins Frailty score - Neurosurgery -
Abbreviations and Acronyms ASA: American Society of Anesthesiologists CI: Confidence interval HFS: Hopkins Frailty score LOS: Length of stay
WORLD NEUROSURGERY -: e1-e6, - 2019
tumor resection. Preoperative assessment of frailty can help neurosurgeons and patients make more informed decisions about pursing surgical treatment.
INTRODUCTION
T
he decision of whether a patient will tolerate a surgical procedure is typically subjective, often based on the anecdotal experience of the surgeon and the desires of the patient. This is especially true in higher risk patients, such as elderly patients or patients with significant comorbidities. There is a paucity of standardized, easily reproducible tools to predict postoperative outcomes, especially in patients with brain tumors.1-3 Relying on anecdotal evidence alone is insufficient because there is significant discordance between perception of life expectancy among physicians.2,4 Whereas cognitive impairments have been linked to poor outcomes in elderly patients and in patients with brain tumors, the most commonly used instruments to assess preoperative risk do not consider a patient’s physiologic reserve, instead accounting only for existing deficits of discrete organ systems.2,5-7 Surgeons need a standardized, validated, preoperative risk assessment tool to aid in preoperative decision making. Frailty is defined as a decrement in reserves that can determine the resilience of a patient to recover from illness or disease (i.e., a measure of their vulnerability to death or poor outcome with a given diagnosis independent of age). Research by geriatricians over the past 2 decades has led to an understanding of frailty as an important clinical entity.2,8 The presence of frailty has been linked to increased risk of poor outcomes in medical and surgical patients, such as disability, dementia, falls, hospitalization or institutionalization, increased hospital length of stay (LOS), or increased mortality.2,9-14 To identify a standardized, verified,
Department of Neurosurgery, University of Colorado School of Medicine, Aurora, Colorado, USA To whom correspondence should be addressed: D. Ryan Ormond, M.D., Ph.D. [E-mail: [email protected]] Citation: World Neurosurg. (2019). https://doi.org/10.1016/j.wneu.2019.10.010 Journal homepage: www.journals.elsevier.com/world-neurosurgery Available online: www.sciencedirect.com 1878-8750/$ - see front matter ª 2019 Elsevier Inc. All rights reserved.
www.journals.elsevier.com/world-neurosurgery
e1
ORIGINAL ARTICLE TESSA A. HARLAND ET AL.
FRAILTY IN BRAIN TUMOR PATIENTS
preoperative risk assessment tool for surgical patients, the Hopkins Frailty score (HFS) was developed by Makary et al.8 The HFS was validated in a heterogeneous cohort of patients undergoing major and minor general surgical and urologic procedures.2 This score was later additionally validated in an elderly population of similar general surgery and urology patients.2 Frailty has also been found to be an important predictor of surgical outcomes in patients undergoing cardiac surgery.14 Frailty as an adjunct to preoperative assessment of neurosurgical patients has never been evaluated. This study aimed to determine whether frailty predicts neurosurgical complications in patients with brain tumors and enhances current perioperative risk models.
Table 1. Frailty Classification Shrinking
Shrinking was defined based on self-reported unintentional weight loss of 10 pounds in the last year.
Exhaustion
Exhaustion was measured based on responses to “I felt that everything I did was an effort” and “I could not get going.” Possible responses included 0 ¼ rarely or none of the time (<1 day); 1 ¼ some or little of the time (1e2 days); 2 ¼ a moderate amount of time (3e4 days); 3 ¼ most of the time. Subjects answering either question with response 2 or 3 met this criterion.
Low activity
Low activity was measured by self-reported physical activity for the 2 weeks before assessment and was converted to equivalent kilocalories of expenditure. Men reporting <383 kcal/week and women reporting <270 kcal/week met this criterion.
Weakness
Weakness was measured using a handheld dynamometer. Three serial trials of maximum grip strength with the dominant hand were performed and averaged. Men met this criterion if BMI (kg/m2) and grip strength (kg of force) were 24 and 29, 24.1e26 and 30, 26.1e28 and 31, or >28 and 32. Women met this criterion if BMI (kg/m2) and grip strength (kg of force) were 23 and 17, 23.1e26 and 17.3, 26.1e29 and 18, or >29 and 21.
MATERIALS AND METHODS Study Design and Participants This prospective study enrolled patients 18 years old with a history of brain tumor scheduled for elective resection of tumor at the University of Colorado over a 3-year period (October 2014 to August 2017). Following institutional review board approval, all participants provided written consent before any data collection. Participants underwent a standardized preoperative interview and frailty assessment by a research assistant. Demographic information and comprehensive medical history, including current prescription medications, were collected during the interview and verified with the patient’s electronic medical records. Patients were excluded if they were unable to grip the dynamometer to measure weakness; were unable to ambulate; had Parkinson disease; had a history of stroke; were unable to understand and sign the informed consent (used as a proxy for a Mini-Mental State Examination in patients <18 years old); and were taking carbidopa/levodopa, donepezil hydrochloride, or antidepressants. Previous studies have found that these medications cause symptoms that are potentially collinear with domains of frailty.13 Outcome Measures Preoperative assessment of frailty included the 5 components of the HFS: shrinking, weakness, exhaustion, low activity, and slowed walking speed (Table 1). Each domain yielded a dichotomous score of 0 or 1 based on the criteria presented in Table 1. Patients were divided into frail (HFS 4e5), moderately frail (HFS 2e3), and nonfrail (HFS 0e1) subgroups. The main outcome variables were obtained from the patient’s medical record and included postoperative complications within 30 days of surgery, including mortality; new neurologic deficit; LOS; and discharge to a skilled nursing facility, acute rehabilitation facility, or hospice at 30 days when previously independent. The following clinical and demographic variables were collected to assess for possible confounders and to assess postsurgical outcomes: age, race, sex, height, weight, body mass index, medical comorbidities (e.g., history of myocardial infarction, angina, congestive heart failure, claudication, arthritis, cancer, hypertension, diabetes, chronic obstructive lung disease, smoking), surgical procedure, site and side of brain tumor, brain tumor diagnosis, perioperative seizure, and estimated blood loss from surgery. Similar to Makary et al.,8 4 risk models were evaluated, including HFS, American Society of Anesthesiologists (ASA)
e2
www.SCIENCEDIRECT.com
Slowed walking Slowed walking speed was measured by averaging 3 trials of speed walking 15 feet at a normal pace. Men met this criterion if height and walking time were 173 cm and 7 seconds or >173 cm and 6 seconds. Women met this criterion if height and walking time were 159 cm and 7 seconds or >173 cm and 6 seconds. BMI, body mass index.
score, Lee’s revised cardiac risk index, and Eagle score. Lee score (0e4) was determined by the presence of specific preoperative cardiac risk factors.15 Eagle score (0e6) was also based on standardized criteria.16 ASA score (1e6) was independently assigned by an anesthesiologist blinded to the patient’s HFS.17
Data Analysis Analyses were performed using IBM SPSS Statistics Version 20 software (IBM Corporation, Armonk, New York, USA) and SAS 9.4 (SAS Institute Inc., Cary, North Carolina, USA). Results with P < 0.05 were considered significant. For univariate analysis, c2 test or Fisher exact test was used for categorical variables, and t test was used for continuous variables. Multivariate regression models were constructed to account for potential confounders by adjusting frailty status and other variables significant on univariate analysis for ASA, Lee, and Eagle scores and age. Because these variables represent traditional risk assessment tools and known prognostic factors, this allowed accounting for their effect on the relationship of frailty and outcome. Analysis of subcomponents of HFS (e.g., weakness, slowed walking speed) was done via c2 test to assess whether any component of the HFS was more predictive of poor surgical outcomes.
WORLD NEUROSURGERY, https://doi.org/10.1016/j.wneu.2019.10.010
ORIGINAL ARTICLE TESSA A. HARLAND ET AL.
FRAILTY IN BRAIN TUMOR PATIENTS
Table 2. Continued
Table 2. Patient Characteristics Characteristic
Nonfrail (n [ 194)
Age, years, mean (range) 50.1 (19e78) Female, %
41.7
Moderately Frail (n [ 56)
Frail (n [ 10)
Characteristic
Nonfrail (n [ 194)
Moderately Frail (n [ 56)
Frail (n [ 10)
56.1 (21e82)
61.36 (40e73)
Diabetes
3.6
30
10
52.6
50
ASA score, % 1
2.6
0
0
2
53.1
21
20
3
41.5
73.6
70
4
1.1
3.5
10
0
2.5
0
Hypertension
13.6
32.5
40
Chronic kidney disease
0.7
7.5
0
ASA, American Society of Anesthesiologists; KPS, Karnofsky performance scale; mRS, modified Rankin Scale; MI, myocardial infarction; CHF, congestive heart failure.
RESULTS
Lee score, % 0
85.5
58.9
70
1
10.3
30.3
20
2
4.1
8.9
10
3
0
0
0
4
0
1.8
0
5
0
0
0
0
87.1
58.9
50
1
9.3
28.6
20
2
3.6
8.9
10
3
0
1.8
20
4
0
0
0
5
0
1.8
0
0
0
10
50
0
3.6
10
60
0.5
8.9
10
70
0.5
10.7
40
80
3.6
35.7
10
90
22.1
33.9
20
100
73.3
7.1
0
0
73.3
5.3
0
1
20.3
37.5
20
2
5.9
25
20
3
0.5
21.4
30
4
0
10.7
30
Eagle score, %
KPS score, % 40
Stroke
mRS score, %
Comorbidities, % Angina
4.3
10
20
MI
2.1
2.5
20
CHF
0.7
2.5
0 Continues
WORLD NEUROSURGERY -: e1-e6, - 2019
Of 260 enrolled participants, 194 (74.6%) were nonfrail, 56 (21.5%) were moderately frail, and 10 (3.8%) were frail. The moderately frail and frail subgroups were combined owing to previous studies that have combined moderately frail and frail patients after demonstrating no significant difference between the groups.2 With this adjustment, 66 (25.4%) patients were defined as frail. The average age of the nonfrail group was 50.6 years compared with 56.1 years in the frail group. The nonfrail group was 41% women, whereas the frail group was 53% women. There was no difference in tumor location (cerebrum, brainstem, suprasellar/ pituitary, or cerebellum) between the nonfrail and frail groups (P ¼ 0.834). Risk index scores and comorbidities are listed in Table 2. Frailty and Discharge Disposition The incidence of patients in the nonfrail group who were discharged home was 97.4% compared with 78.8% in the frail group (P ¼ 0.000001). At 30 days, 6.1% of the previously independent frail group were at an acute rehabilitation facility, 7.6% were still inpatients, 3.0% were in a skilled nursing facility, and 3.0% had died. In an adjusted model, frailty independently predicted the odds of being discharged somewhere other than home. Frail patients had 10.36-fold higher odds (95% confidence interval [CI], 3.6e30.1) of being discharged to an acute rehabilitation center or skilled nursing facility. Eagle score (95% CI, 0.96e2.63), Lee score (95% CI, 0.544e2.34), and ASA score (95% CI, 0.62e3.18) did not independently predict discharge disposition. After adjusting for known risk indices, frailty remained an independent predictor of discharge disposition (Table 3). Frailty and LOS The mean LOS for the nonfrail group was 4.0 days compared with 6.18 days for the frail group with a t test showing a significant difference (P ¼ 0.009). In a Cox survival model, frailty independently predicted longer LOS. Frail patients had 1.66-fold higher odds (95% CI, 1.24e2.21) of going home sooner than nonfrail patients After adjusting for known risk indices, frailty remained an independent predictor of LOS (Table 3). Frailty and Postoperative Complications The incidence of all postoperative complications (including pneumonia, urinary tract infection, deep vein thrombosis, pulmonary embolism, new neurologic deficit, cerebrospinal fluid
www.journals.elsevier.com/world-neurosurgery
e3
ORIGINAL ARTICLE TESSA A. HARLAND ET AL.
FRAILTY IN BRAIN TUMOR PATIENTS
Table 3. Hopkins Frailty Score Adjusted Models Showing Odds Ratios for Frail Versus Nonfrail Postoperative Outcomes Postoperative Complication HFS only
Discharge to Other LOS (Cox than Home Survival Model)
2.09 (1.09e3.98)
10.36 (3.56e30.14)
1.66 (1.24e2.21)
HFS and ASA 2.05 (1.03e4.08) score
12.29 (3.81e39.56)
1.59 (1.17e2.14)
HFS and Eagle score
1.99 (1.00e3.97)
11.15 (3.64e34.17)
1.67 (1.23e2.26)
HFS and Lee score
2.47 (1.25e4.89)
13.91 (4.60e42.13)
1.70 (1.27e2.29)
HFS and age
1.70 (0.87e3.33)
6.98 (2.31e21.11)
1.58 (1.17e2.13)
Odds ratios are reported with 95% confidence intervals. LOS, length of stay; HFS, Hopkins Frailty score; ASA, American Society of Anesthesiologists.
leak, and wound dehiscence or infection) was 18.0% in the nonfrail group compared with 30.3% in the frail group (P ¼ 0.035). New neurologic symptoms occurred in 6.7% of the nonfrail group compared with 7.5% in the frail group (P ¼ 0.808). Infection occurred in 1.0% of the nonfrail group compared with 1.5% of the frail group (P ¼ 0.750). In an adjusted model, frailty independently predicted the odds of a postoperative complication. Frail patients had 2.09-fold higher odds (95% CI, 1.09e3.98) of having a complication. Eagle score (95% CI, 0.60e1.64), Lee score (95% CI, 0.78e2.22), and ASA score (95% CI, 0.91e2.01) did not independently predict postoperative complications. After adjusting for known risk indices, frailty remained an independent predictor of discharge disposition (Table 3). Components of Frailty and Postoperative Complications Slowed walking speed was predictive of postoperative complications (P ¼ 0.006). Shrinking, weakness, exhaustion, and low activity were not individually significantly associated with postoperative complications. DISCUSSION Although this is the first study to show frailty as a predictor of poor outcomes in patients with brain tumors, this phenomenon has been established across other surgery types, including general surgery, urologic, oncologic, and cardiac surgery.2,14,18 Across these surgery subtypes, frailty has consistently been associated with increased LOS, postoperative complications, and discharge disposition to somewhere other than home, as also demonstrated in the present study.2,13,14,18 It also has a wellestablished association in medical patients with poor outcomes, such as morbidity, mortality, falls, and hospitalization.9,13,19,20 Notably, the postoperative effects of frailty are most evident in patients who have undergone a major surgical procedure as opposed to a minor procedure.13 This is consistent with the idea that frailty is the ability to adapt to stressors.21 This concept is particularly applicable to patients undergoing an invasive craniotomy for brain tumor resection in the present study.
e4
www.SCIENCEDIRECT.com
Whereas surgical resection of a brain tumor is often considered standard of care, discussion surrounding the risks and benefits of the procedure for the patient and how it aligns with their goals of care is necessary to proceed with the surgery. Despite the invasive nature of this elective procedure and the importance of discussing risk to the patient, there is a paucity of literature evaluating the role of established risk measures to assess surgical outcomes. Most studies that have evaluated outcomes following brain tumor surgery have predominately focused on extent of resection and survival.22-24 Consequently, the decision is often subjectively made based on the anecdotal experience of the surgeon and the desire of the patient, particularly for benign tumor subtypes. Thus, the association of poor outcomes with the established measure of frailty shown in the present study suggests a promising tool that can be used to guide patients’ decisions about whether to proceed with brain tumor resection or alternatives, such as watchful waiting or radiotherapy. In the present study, the HFS was able to assess a patient’s frailty in <10 minutes during the preoperative clinic visit. Once a patient is identified as frail, surgical counseling can be tailored to the patient with the expectation that the LOS, rate of postoperative complications, and chances of discharge destination other than home may be increased. Furthermore, the HFS can potentially be used to tailor interventions to a patient before undergoing surgery to minimize poor outcomes. It has been shown that a multifaceted interdisciplinary treatment program targeting specific elements of frailty can improve frailty and increase mobility.25 As previously discussed, the HFS is made up of 5 components: weight loss, weakness, exhaustion, low activity, and slowed walking speed. In the study by Cameron et al.,25 if patients were underweight, a dietitian evaluated nutritional intake, and nutritional supplements were recommended if appropriate clinical criteria applied. If the exhaustion criterion was met and the Geriatric Depression Scale score was high, the study team considered referral to a psychiatrist or psychologist and encouraged greater social engagement.25 Participants who met the weakness, slowness, or low energy expenditure criteria received physiotherapy sessions and performed a home exercise program.25 This study showed a lower incidence of frailty in the intervention group with a between group-difference of 14.7% (P ¼ 0.02).25 Thus, similar intervention in particular subsets of frail patients with brain tumors may improve their HFS and subsequently their surgical outcomes. Importantly, the present study analyzed each HFS component as a predictor of poor outcomes and found that slowed walking speed is most associated with postoperative outcomes. This association could help further tailor preoperative counseling and rehabilitation. At a minimum, the HFS can be used to alert surgeons to the risks of frail patients and lead to increased postoperative monitoring with attention to hydration, nutrition, and mobilization.26,27 Further randomized controlled studies will be needed to develop targeted risk reduction strategies for frail patients. Notably, analysis of postoperative complications in nonfrail versus frail patients was statistically significant when analyzing all postoperative complications (e.g., urinary tract infection, pneumonia, deep vein thrombosis, pulmonary embolism) and was nonsignificant when looking only at complications related to surgical technique (e.g., infection and new neurologic deficit).
WORLD NEUROSURGERY, https://doi.org/10.1016/j.wneu.2019.10.010
ORIGINAL ARTICLE TESSA A. HARLAND ET AL.
FRAILTY IN BRAIN TUMOR PATIENTS
This is consistent with other studies looking at postoperative complications in frail patients.28 However, compared with previous studies focusing on older frail populations, our cohort had a lower incidence of cardiac complications and pulmonary complications.28 This could be due to the lower mean age of our population, with an average age of 50.6 years and 56.1 years in the 2 subgroups, compared with studies that looked exclusively at patients >65 years old. In addition to the absence of an age exclusion criterion, the lower age of our patients may be largely due to the fact that this is the only assessment of frailty in patients whose pathology can influence HFS based on location. However, it should be noted that patients were excluded if the tumor location was directly associated with a symptom that would score on HFS (e.g., weakness), and there was no significant difference when looking at incidence of frailty based on brain tumor location. Additionally, patients were required to score in at least 2 categories to be considered frail, reducing the likelihood that a patient’s neurologic deficits would exclusively lead to classification as frail. This study had several potential limitations. Importantly, this study did not discriminate on the basis of tumor type and included patients with glioblastoma, in which the standard of care is maximum tumor resection at diagnosis. It is commonly known that extent of resection of glioblastoma is linked to prognosis and
REFERENCES 1. Partridge JS, Harari D, Dhesi JK. Frailty in the older surgical patient: a review. Age Ageing. 2012; 41:142-147.
survival.22,29,30 Consequently, the idea of using frailty as a data point to determine the benefit of surgery is less applicable to patients with glioblastoma than it is to patients with less aggressive or benign tumors. Additionally, results from an academic center may not be generalizable to other, nonacademic centers with lower brain tumor resection volumes. Also, the providers were blind to the frailty results, thus limiting the ability to assess the influence that knowledge of HFS could have on patient care in terms of preventing complications. Finally, this study evaluated only short-term outcomes up to 30 days, limiting its ability to assess long-term outcomes as well as qualityof-life measures.
Conclusions Frailty is an independent predictor of discharge disposition, postoperative complications, and LOS in patients undergoing surgery for brain tumor resection. Preoperative assessment of frailty may be used to help neurosurgeons and patients make more informed decisions about pursuing surgical treatment. Randomized controlled clinical studies will be needed to further evaluate the use of HFS to guide clinical decisions about tumor resection and to assess the effectiveness of risk reduction strategies to improve outcomes for frail patients.
9. Boyd CM, Darer J, Boult C, et al. Clinical practice guidelines and quality of care for older patients with multiple comorbid diseases: implications for pay for performance. JAMA. 2005;294:716-724. 10. Buchner DM, Wagner EH. Preventing frail health. Clin Geriatr Med. 1992;8:1-17.
2. Revenig LM, Canter DJ, Taylor MD, et al. Too frail for surgery? Initial results of a large multidisciplinary prospective study examining preoperative variables predictive of poor surgical outcomes. J Am Coll Surg. 2013;217:665-670.
11. Campbell AJ, Buchner DM. Unstable disability and the fluctuations of frailty. Age Ageing. 1997;26: 315-318.
3. Sullivan LM, Massaro JM, D’Agostino RB Sr. Presentation of multivariate data for clinical use: the Framingham Study risk score functions. Stat Med. 2004;23:1631-1660.
12. Fried LP, Kronmal RA, Newman AB, et al. Risk factors for 5-year mortality in older adults: the cardiovascular health study. JAMA. 1998;279: 585-592.
4. Wilson JR, Clarke MG, Ewings P, et al. The assessment of patient life-expectancy: how accurate are urologists and oncologists? BJU Int. 2005; 95:794-798.
13. Fried LP, Tangen CM, Walston J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56:146-156.
5. Davenport DL, Bowe EA, Henderson WG, et al. National Surgical Quality Improvement Program (NSQIP) risk factors can be used to validate American Society of Anesthesiologists Physical Status Classification (ASA PS) levels. Ann Surg. 2006;243:636-641. 6. Inouye SK, Studenski S, Tinetti ME, Kuchel GA. Geriatric syndromes: clinical, research, and policy implications of a core geriatric concept. J Am Geriatr Soc. 2007;55:780-791. 7. Robinson TN, Eiseman B, Wallace JI, et al. Redefining geriatric preoperative assessment using frailty, disability and comorbidity. Ann Surg. 2009;250:449-455. 8. Makary MA, Segev DL, Pronovost PJ, et al. Frailty as a predictor of surgical outcomes in older patients. J Am Coll Surg. 2010;210:901-908.
14. Lee DH, Buth KJ, Martin BJ, et al. Frail patients are at increased risk for mortality and prolonged institutional care after cardiac surgery. Circulation. 2010;121:973-978. 15. Lee TH, Marcantonio ER, Mangione CM, et al. Derivation and prospective validation of a simple index for prediction of cardiac risk of major noncardiac surgery. Circulation. 1999;100: 1043-1049. 16. Eagle KA, Berger PB, Calkins H, et al. ACC/AHA guideline update for perioperative cardiovascular evaluation for noncardiac surgery—executive summary: a report of the American College of Cardiology/American Heart Association task force on practice guidelines (committee to update the 1996 guidelines on perioperative cardiovascular evaluation for noncardiac surgery). J Am Coll Cardiol. 2002;39:542-553.
WORLD NEUROSURGERY -: e1-e6, - 2019
17. Saklad M. Grading of patients for surgical procedures. Anesthesiology. 1941;2:281-284. 18. Lin HS, Watts JN, Peel NM, Hubbard RE. Frailty and post-operative outcomes in older surgical patients: a systematic review. BMC Geriatr. 2016;16: 157. 19. Bandeen-Roche K, Xue QL, Ferrucci L, et al. Phenotype of frailty: characterization in the women’s health and aging studies. J Gerontol A Biol Sci Med Sci. 2006;61:262-266. 20. Woods N, LaCroix AZ, Gray SL, et al. Frailty: emergence and consequences in women aged 65 and older in the Women’s Health Initiative Observational Study. J Am Geriatr Soc. 2005;53: 1321-1330. 21. Purser JL, Kuchibhatla MN, Fillenbaum GG, et al. Identifying frailty in hospitalized older adults with significant coronary artery disease. J Am Geriatr Soc. 2006;54:1674-1681. 22. Lacroix M, Abi-Said D, Fourney DR, et al. A multivariate analysis of 416 patients with glioblastoma multiforme: prognosis, extent of resection, and survival. J Neurosurg. 2001;95:190-198. 23. Smith JS, Chang EF, Lamborn KR, et al. Role of extent of resection in the long-term outcome of low-grade hemispheric gliomas. J Clin Oncol. 2008; 26:1338-1345. 24. Sanai N, Berger MS. Glioma extent of resection and its impact on patient outcome. Neurosurgery. 2008;62:753-766. 25. Cameron ID, Fairhall N, Langron C, et al. A multifactorial interdisciplinary intervention reduces frailty in older people: randomized trial. BMC Med. 2013;11:65.
www.journals.elsevier.com/world-neurosurgery
e5
ORIGINAL ARTICLE TESSA A. HARLAND ET AL.
26. Gill TM, Baker DI, Gottschalk M, et al. A prehabilitation program for physically frail community-living older persons. Arch Phys Med Rehabil. 2003;84:394-404. 27. Gill TM, Allore HG, Holford TR, Guo Z. Hospitalization, restricted activity, and the development of disability among older persons. JAMA. 2004; 292:2115-2124. 28. Dasgupta M, Rolfson DB, Stolee P, Borrie MJ, Speechley M. Frailty is associated with postoperative complications in older adults with medical problems. Arch Gerontol Geriatr. 2009;48: 78-83.
e6
www.SCIENCEDIRECT.com
FRAILTY IN BRAIN TUMOR PATIENTS
29. Keles GE, Anderson B, Berger MS. The effect of extent of resection on time to tumor progression and survival in patients with glioblastoma multiforme of the cerebral hemisphere. Surg Neurol. 1999;52:371-379. 30. Simpson JR, Horton J, Scott C, et al. Influence of location and extent of surgical resection on survival of patients with glioblastoma multiforme: results of three consecutive Radiation Therapy Oncology Group (RTOG) clinical trials. Int J Radiat Oncol. 1993;26:239-244.
commercial or financial relationships that could be construed as a potential conflict of interest. Received 28 July 2019; accepted 2 October 2019 Citation: World Neurosurg. (2019). https://doi.org/10.1016/j.wneu.2019.10.010 Journal homepage: www.journals.elsevier.com/worldneurosurgery Available online: www.sciencedirect.com 1878-8750/$ - see front matter ª 2019 Elsevier Inc. All rights reserved.
Conflict of interest statement: The authors declare that the article content was composed in the absence of any
WORLD NEUROSURGERY, https://doi.org/10.1016/j.wneu.2019.10.010