|Year : 2021 | Volume
| Issue : 1 | Page : 44-49
Cancer-related fatigue and its impact on quality of life in patients with central nervous system tumors: A cross-sectional analysis
Gunjesh Kumar Singh, Litty Varghese, Nandini Menon, Ochin Dale, Vijay M Patil
Department of Medical Oncology, Tata Memorial Hospital, Mumbai, 400012, India
|Date of Submission||18-Dec-2020|
|Date of Decision||24-Jan-2021|
|Date of Acceptance||06-Mar-2021|
|Date of Web Publication||26-Mar-2021|
Vijay M Patil
Department of Medical Oncology, Tata Memorial Hospital, Mumbai, 400012
Source of Support: None, Conflict of Interest: None
Background: Cancer-related fatigue (CRF) has a high prevalence in individuals with cancer, especially in those with central nervous system (CNS) tumors, and impacts the quality of life (QOL). However, there are limited data on CRF in Indian patients with CNS tumors.
Objective: We aimed to estimate the CRF scores in patients with CNS tumors.
Materials and Methods: This cross-sectional study was conducted in the Department of Medical Oncology of the Tata Memorial Hospital in Mumbai, India, between May 2019 and August 2019. Patients with CNS tumors aged ≥18 years, who presented to the Neuro-Oncology Disease Management Group, were enrolled in the study. The Functional Assessment of Chronic Illness Therapy Fatigue Scale was used to collect data related to well-being and fatigue. Data were captured in a single visit. Descriptive statistics and multiple regression analyses were performed to identify the factors associated with a high fatigue score.
Results: There were 100 patients in our cohort with a median age of 40 (range, 18–64) years. The median fatigue score was 36. The median physical well-being, social well-being, emotional well-being, functional well-being, and Functional Assessment of Cancer Therapy-General (FACT-G) scores were 19, 19.9, 18, 17, and 72, respectively. There was a significant correlation between the fatigue score and the various subscales of FACT-G (P < 0.0001). The associated risk factors for CRF were age (P = 0.021), poor Eastern Cooperative Oncology Group-Performance Status (ECOG PS) (2–3) (P < 0.0001), general category based on the payment ability of the patient (P = 0.004), ongoing treatment status (P = 0.0003), and the presence of recurrent disease (P = 0.001).
Conclusion: CRF is common in patients with CNS tumors and impacts all aspect of the QOL. It is affected by age, ECOG PS, payment ability, treatment status, and disease recurrence status.
Keywords: Cancer-related fatigue, central nervous system tumor, neuro-oncology, quality of life, risk factors
|How to cite this article:|
Singh GK, Varghese L, Menon N, Dale O, Patil VM. Cancer-related fatigue and its impact on quality of life in patients with central nervous system tumors: A cross-sectional analysis. Cancer Res Stat Treat 2021;4:44-9
|How to cite this URL:|
Singh GK, Varghese L, Menon N, Dale O, Patil VM. Cancer-related fatigue and its impact on quality of life in patients with central nervous system tumors: A cross-sectional analysis. Cancer Res Stat Treat [serial online] 2021 [cited 2021 May 6];4:44-9. Available from: https://www.crstonline.com/text.asp?2021/4/1/44/312092
| Introduction|| |
Cancer-related fatigue (CRF) is a common, distressing, and worrisome cancer-related symptom that has a significant effect on a patient's quality of life (QOL) and functional ability. The problem remains unaddressed, and hence, goes untreated in these patients, only adding to the morbidity. The condition needs attention as it has been recently recognized as an important risk factor for decreased survival in patients with cancer. CRF is defined as a constant feeling of distress and tiredness associated with cancer or its treatment, that is not related to the recent activity, and crucially hampers normal functioning. Approximately 80% of the patients with central nervous system (CNS) tumors, who receive treatment in the form of chemotherapy and/or radiotherapy, have reported fatigue, and this number increases to 89–94% in case of recurrent disease. Pain, emotional distress, anemia, nutritional deficiencies, comorbidities, and physical disability are reported as the important contributing factors to CRF., The pathophysiology of CRF is not well understood. O'Higgins et al. proposed that the mechanism of CRF is complex and involves the interaction of multiple central and peripheral factors. The key mechanisms include cytokine imbalance, defective hypothalamic–pituitary–adrenal axis, dysregulated circadian rhythm and serotonin levels, and vagal nerve involvement; the proposed peripheral mechanisms include adenosine triphosphate and altered muscle contractility. CRF profoundly affects every aspect of life, including physical, psychosocial, economic, and occupational, and has a significant negative impact on the QOL of both the patients and their caregivers.
Unlike other solid malignancies, the tools to measure QOL of patients with brain tumors do not offer any predictive information with regard to survival. Traditionally, the factors that are prognostically important are the age, tumor grade, histology, number of prior disease progressions, and performance status. Recently, fatigue is being increasingly recognized as an important independent predictive factor for survival in patients with high-grade gliomas, thereby necessitating a deeper understanding of this phenomenon in order to not only improve the QOL of these patients who already have a compromised life expectancy, but also to predict the survival.,
CRF is a common symptom in patients with CNS tumors which is often reported throughout the disease course and can even be seen years after treatment., As the data on fatigue in patients with CNS tumors are sparse worldwide, and more so in India, we conducted this study to assess the CRF in Indian patients with CNS tumors.
| Materials and Methods|| |
General study details
This cross-sectional study was conducted in the Neuro-Oncology Disease Management Group of the Department of Medical Oncology at the Tata Memorial Hospital, a tertiary care and oncology-only teaching center in Mumbai, India, between May 2019 and August 2019. Given the non interventional nature of the study, approval from the Institutional Ethics Committee was not required. Verbal informed consent was obtained from the participants in real time during a private interview session. The study was conducted according to various ethical guidelines including the Declaration of Helsinki, International Committee on Harmonization Good Clinical Practice guidelines, and the Indian Council of Medical Research.
All consecutive patients aged ≥18 years with biopsy-proven primary CNS tumors with or without recurrence, who visited the Neuro Medical Oncology Department during the study period, were invited to participate in this study. Patients with an Eastern Cooperative Oncology Group Performance Status (ECOG PS) of 0–3 were enrolled. Patients from both general and private categories participated in the study. The general category patients are either not charged or charged minimally for consultation and investigations, whereas private category patients are charged in full for the consultation and investigations. Patients who did not understand English, Hindi, or the local language were excluded.
The primary endpoint of this study was to estimate the CRF scores in patients with CNS tumors. The secondary endpoint was to evaluate the factors affecting the CRF scores.
The fatigue data were captured by administering the Functional Assessment of Chronic Illness Therapy (FACIT) Fatigue Scale (version 4) to the participants. The scale was earlier validated by Chandran et al. The scale comprises 41 questions. Out of these, seven questions are on physical well-being (PWB), eight on social/family well-being (SWB), six on emotional well-being (EWB), and seven on functional well-being (FWB). The Functional Assessment of Cancer Therapy-General (FACT-G) score was obtained by adding the PWB, SWB, EWB, and FWB scores. FACT-G was used to assess the QOL. The total score for FACT-G can range from 0 to 108 points, with a higher score indicative of better QOL. A total of 13 questions were related to the actual fatigue data (fatigue score). The level of fatigue was measured on a four-point Likert scale (4 = not at all fatigued and 0 = very fatigued). According to the FACIT fatigue scoring guidelines, reversal of the item scores is performed for some subscales where the scores are inversely proportional to fatigue, that is, higher scores indicate less fatigue. The fatigue score can range from 0–52. A score <30 is considered as severe fatigue. The questionnaire was filled by the patients, and if they were unable to do it themselves, they were helped by the doctor, nurse, or social worker. The fatigue data were captured in a single visit. No longitudinal data were collected, and none of the patients were contacted after the single visit for the study.
No formal sample size calculation was done, and all consecutive patients fulfilling the eligibility criteria were enrolled in the study. The Statistical Package for the Social Sciences (IBM Corp. Released 2011. IBM SPSS Statistics for Windows, version 20.0. Armonk, NY, USA: IBM Corp.) was used for data analysis. Descriptive analysis was performed. Median with interquartile range (IQR) and the distribution was plotted using a violin plot. Pearson correlation analysis was performed, and the Pearson correlation coefficient between fatigue and the various domains of the QOL was estimated. Multiple regression analysis was performed to identify the factors affecting CRF, and P < 0.05 was considered statistically significant. The factors tested were age, sex (male vs. female), ECOG PS, hospital payment category (general vs. private), treatment status (ongoing vs. complete), place of residence (regional vs. from outside Maharashtra), and disease recurrence (yes vs. no).
| Results|| |
Of 143 patients with CNS tumors, 100 patients fulfilling the eligibility criteria were included in the final analysis [Figure 1]. There were 72 men and 28 women in the cohort with a median age of 40 (range, 18–64) years. The majority had ECOG PS 2, followed by ECOG PS 3. High-risk low-grade glioma (grade 2) and glioblastoma multiforme were the most common histologies seen. About 34% of the patients were receiving treatment for recurrent disease. Temozolomide was the most common regimen (concurrent, adjuvant, and salvage) used. Further details of baseline characteristics are mentioned in [Table 1].
|Table 1: Baseline characteristics of patients with central nervous system tumors enrolled in the cross-sectional study evaluating cancer-related fatigue and its impact on quality of life (n=100)|
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Fatigue, quality of life, and their relationship
In 66% of the patients, the calculated fatigue score was >30, while in 34%, the score was <30; the median fatigue score for the cohort was 36. The median PWB was 19 (IQR, 11.25–25), the median EWB was 18 (IQR, 14–21), the median FWB was 17 (IQR, 9–21), the median SWB was 19.9 (IQR, 15.2–24.5), and the FACT-G score was 72 (IQR, 54–87.5) [Figure 2] and [Figure 3]. There was a significant correlation between fatigue score and the various subscales of FACT-G (P < 0.0001) [Table 2]. We also stratified the patients as per their ECOG-PS into two groups, one with ECOG-PS 1 and the other with ECOG-PS 2–3. The median fatigue scores were 39 (IQR, 30.25–47) and 18 (IQR, 10–35.7) in patients with ECOG-PS 0–1 and ECOG-PS 2–3, respectively.
|Figure 2: Median and interquartile score of different domains of the quality of life (physical well-being, emotional well-being, functional well-being, social/family well-being)|
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|Figure 3: Median and interquartile score for Functional Assessment of Cancer Therapy-General score and Functional Assessment of Chronic Illness Therapy-Fatigue score|
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|Table 2: Correlation of fatigue with Functional Assessment of Cancer Therapy-General subscales|
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Factors affecting fatigue
Multiple regression analysis was performed to identify factors contributing to fatigue. On analysis, no intervariable correlation was observed. However, the combination of variables was significantly associated with the risk of development of fatigue, F (6, 93) = 10.22 (P < 0.0001), with five factors having statistically significant contributions. The factors were age (P = 0.021), poor ECOG PS (2–3) (P < 0.0001), general hospital category (P = 0.004), ongoing treatment status (P = 0.0003), and the presence of recurrent disease (P = 0.001) [Table 3]. The adjusted R2 value for factors leading to fatigue was 0.36. This indicates that 36% of variance in fatigue was explained by the model. According to Cohen, this is a moderate effect. This suggests that patients with advanced age, poor PS, those in general category (low payment ability), those on active treatment, and those with recurrent disease were more likely to have higher fatigue scores.
|Table 3: Result of multiple regression analysis to identify factors contributing to fatigue|
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| Discussion|| |
The median fatigue score in our cohort was 36, while the median FACT-G score was 72. This indicated a high level of CRF in patients with CNS tumors, thus reinforcing the already described literature with regard to increased fatigue observed in these patients. Our analysis showed that 34% of the patients had a fatigue score of <30, which falls in the severe category. In addition, the CRF was significantly correlated with their physical, emotional, functional and social/family well-being, described in the various subscales of the FACT-G, and this is in line with the current knowledge that fatigue leads to a decrement in the QOL.
Further, we tried to determine the factors impacting the fatigue score and found that age of the patient, poor ECOG PS, low payment ability, ongoing treatment status, and the presence of disease recurrence were the significantly associated entities. In addition, we considered the category of the patient (general or private) as a possible factor impacting CRF and obtained positive results. As the majority of the general category patients had a weak socioeconomic background and poor nutritional profile, poor living conditions could have further added to the severity of the fatigue.
There are only a few studies that have assessed all the aspects of CRF in patients with CNS tumors.,,, These studies have evaluated the roles of various parameters in deciding the severity of CRF such as age, gender, ECOG PS, treatment type, and recurrent disease. These factors have been studied by independent authors with varying outcomes., For instance, Karthikeyan et al. in a cross-sectional study found that the severity of fatigue was significantly associated with anticancer treatment and had an impact on QOL by stratifying the patients on the basis of the treatment they received and not the tumor sites. On the other hand, Armstrong et al. found that poor PS, active disease, and female sex were risk factors for moderate-to-severe fatigue in patients with CNS tumors.
A variety of assessment tools have been described and validated in the literature [Table 4].,,, In our study, we used the FACT-G and FACIT Fatigue Scale to collect fatigue data that are based on a set of validated questionnaires, and according to the FACIT Fatigue Scale (version 4), a score of <30 is considered as severe fatigue.
CRF data of Indian patients with CNS tumors have not been detailed so far in the literature. To the best of our knowledge, this is the first study with an adequate sample size to assess the CRF scores in the Indian population with an added emphasis on the associated risk factors and assessment of its relative impact on the QOL. However, the study has some limitations, including the lack of longitudinal data (cross-sectional study) and the heterogeneous study population. In addition, because the severity of CRF cannot be assessed using the FACT scoring system, it could not be commented upon.
We hope for more studies like ours in this category of patients from the Indian subcontinent, with an intense probe into the pathophysiology, screening methods, and treatment. This may provide a deeper insight into CRF, thereby possibly improving the patient outcomes, as CRF has a prognostic value and affects survival as described by Peters et al.
Our study can serve as a strong base for future endeavors and research on CRF and may offer opportunities to explore new interventions and treatment with the intent to improve QOL in patients with CNS tumors.
| Conclusion|| |
CRF is common and has a significant impact on QOL. The patient's age, ECOG PS, payment ability, treatment status, and recurrent disease status are the factors that are significantly associated with CRF.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Bower JE. Cancer-related fatigue – Mechanisms, risk factors, and treatments. Nat Rev Clin Oncol 2014;11:597-609.
Asher A, Fu JB, Bailey C, Hughes JK. Fatigue among patients with brain tumors. CNS Oncol 2016;5:91-100.
Campos MP, Hassan BJ, Riechelmann R, Del Giglio A. Cancer-related fatigue: A practical review. Ann Oncol 2011;22:1273-9.
O'Higgins CM, Brady B, O'Connor B, Walsh D, Reilly RB. The pathophysiology of cancer-related fatigue: Current controversies. Support Care Cancer 2018;26:3353-64.
Osoba D, Brada M, Prados MD, Yung WK. Effect of disease burden on health-related quality of life in patients with malignant gliomas. Neuro Oncol 2000;2:221-8.
Brown PD, Ballman KV, Rummans TA, Maurer MJ, Sloan JA, Boeve BF, et al
. Prospective study of quality of life in adults with newly diagnosed high-grade gliomas. J Neurooncol 2006;76:283-91.
Chandran V, Bhella S, Schentag C, Gladman DD. Functional assessment of chronic illness therapy-fatigue scale is valid in patients with psoriatic arthritis. Ann Rheum Dis 2007;66:936-9.
Tennant KF. Assessment of fatigue in older adults: The FACIT Fatigue Scale (version 4). Psychosom Med 2015;65:771-7.
Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd
ed. New York, NY: Routledge Academic; 1988.
Armstrong TS, Cron SG, Bolanos EV, Gilbert MR, Kang DH. Risk factors for fatigue severity in primary brain tumor patients. Cancer 2010;116:2707-15.
Peters KB, West MJ, Hornsby WE, Waner E, Coan AD, McSherry F, et al
. Impact of health-related quality of life and fatigue on survival of recurrent high-grade glioma patients. J Neurooncol 2014;120:499-506.
Karthikeyan G, Jumnani D, Prabhu R, Manoor UK, Supe SS. Prevalence of fatigue among cancer patients receiving various anticancer therapies and its impact on quality of life: A cross-sectional study. Indian J Palliat Care 2012;18:165-75.
] [Full text]
Berger AM, Mooney K, Alvarez-Perez A, Breitbart WS, Carpenter KM, Cella D, et al
. Cancer-Related Fatigue, Version 2.2015. J Natl Compr Canc Netw 2015;13:1012-39.
Mystakidou K, Tsilika E, Parpa E, Mendoza TR, Pistevou-Gombaki K, Vlahos L, et al
. Psychometric properties of the brief fatigue inventory in Greek patients with advanced cancer. J Pain Symptom Manage 2008;36:367-73.
Yellen SB, Cella DF, Webster K, Blendowski C, Kaplan E. Measuring fatigue and other anemia-related symptoms with the Functional Assessment of Cancer Therapy (FACT) measurement system. J Pain Symptom Manage 1997;13:63-74.
Lin JM, Brimmer DJ, Maloney EM, Nyarko E, Belue R, Reeves WC. Further validation of the Multidimensional Fatigue Inventory in a US adult population sample. Popul Health Metr 2009;7:18.
[Figure 1], [Figure 2], [Figure 3]
[Table 1], [Table 2], [Table 3], [Table 4]