1. Introduction
Cancer-related fatigue (CRF) is one of the most common and distressing symptoms associated with cancer and its treatment [ 1 , 2 , 3 , 4 , 5 ]. The frequency of CRF in cancer patients varies from 60% to 90% [ 1 , 2 , 3 , 4 , 5 , 6 ]. CRF negatively affects quality of life (QOL), interferes with daily activity, has potentially devastating social and economic consequences, affects the patient’s ability to receive cancer therapy, and may potentially negatively impact public health [ 2 , 6 , 7 , 8 , 9 ]. The National Comprehensive Cancer Network defines CRF as a “distressing, persistent, subjective sense of physical, emotional, and/or cognitive tiredness or exhaustion related to cancer or cancer treatment that is not proportional to activity and that interferes with usual functioning” [ 2 ].
CRF is pervasive in all cancer patients and survivors but more severe in advanced cancer patients and immediately after cancer treatments [ 10 ]. It also can be very debilitating in at risk patients for example in patients with psychological or cachexia related comorbidities [ 11 , 12 ]. While non-pharmacological treatments for CRF have the best evidence, most of these interventions are not feasible due to poor adherence due to progressive disease or associated symptoms such as drowsiness. Additionally, medication seems to be sometimes preferable among certain patients for the treatment of CRF due to ease of use, and this treatment may offer best expectation of improvement. Various pharmacological and nutraceuticals medications have been used for the treatment of CRF in both clinical trials and practice. Some of these nutraceuticals are often self-prescribed by the patients. The most investigated medications among them include psychostimulants, ginseng, corticosteroids, megestrol, mistletoe, guarana, antidepressants specifically selective serotonin receptor reuptake inhibitors (SSRI’s). Other agents investigated include Hemopoietin agents [ 13 ], Donepezil [ 14 ], Testosterone [ 15 , 16 ], Omega 3 polyunsaturated fatty acids [ 17 , 18 , 19 ], L-carnitine [ 20 , 21 , 22 ], Astragalus membranaceus [ 23 ], Ashwagandha [ 24 ], Thyrotropin releasing hormone [ 25 ], Microbiome [ 26 ], Coenzyme Q10 [ 27 ], Micro and macronutrients supplementation such as protein rich diet, vitamin or mineral supplementation [ 28 ], and others [ 29 , 30 , 31 , 32 ].
CRF is a complex subjective symptom due its multidimensional nature, and pathophysiological mechanisms may vary based on the stage of disease, treatment, and presence of comorbidities [ 1 , 2 , 3 , 4 , 5 ]. Majority of these factors impact the brain where CRF is perceived [ 33 ]. Different known factors result in the multidimensional manifestation of CRF including: Among them the most common factors include (a) central nervous system [CNS] factors including disturbance in cognition, sleep/wakefulness and mood (anxiety and depression), dysregulation of hypothalamic pituitary axis, autonomic nervous system, and circadian rhythm [ 33 ]; (b) dysregulation of inflammatory cytokines including IL-6, TNF-alpha, IL-1 and its receptor); and (c) reduced function (physical activity, deconditioning). Briefly, the molecular mechanisms of the commonly investigated agents used to treat CRF is unclear but target one these mechanisms.
Methylphenidate is a CNS stimulant. The mechanism by which it improves CRF may be due to its action by blockage of presynaptic dopamine and norepinephrine reuptake [ 34 ], reticular activating system (arousal), and its effects on mood, can impact the physical, cognitive, functional, and psychological contributors to CRF [ 35 ].
Like Methylphenidate, Modafinil and its Armodafinil may improve CRF by non-noradrenergic, dopamine-dependent adrenergic signaling in the wake-promoting mechanism [ 36 ]. Megestrol is a progestin is commonly used advanced cancer patients to improve anorexia. The mechanism of action it may improve CRF is by its effect on the hypothalamic-pituitary-gonadal axis [ 37 ]. One of the hypotheses about megestrol acetate’s mechanism of action is that it has inhibition of inflammatory cytokines such as tumor necrosis factor, IL-1, and IL-6 [ 37 , 38 ].
SSRI/Antidepressants: Antidepressants with selective serotonin reuptake inhibitors (SSRI) activity with and without norepinephrine (NE) and dopamine neurotransmitter systems have been used for the treatment of CRF. The exact molecular mechanisms are not clear but the likely related to the effects of SSRI’s on depression, as CRF and depression share some of the causative mechanisms such as dysregulations in inflammatory pathways, dysfunction of the hypothalamic pituitary axis, as well as psychostimulant like action [ 39 , 40 ].
Corticosteroids have direct central nervous system effects, including modulation of the hypothalamic pituitary axis and inflammatory cytokines such as IL-1, IL-6, TNF-alpha and their receptors [ 41 ], and effects on mood, which all cause the drug to impact the physical, cognitive, functional, and psychological contributors of CRF [ 42 ].
Ginseng is derived from processed plant root of ginseng species (Panax Ginseng, Panax quinquefolius L., Eleutherococcus senticosus (Rupr. & Maxim) [ 43 ]. Ginsenosides and saponins are the important pharmacological ingredients [ 44 ]. Possible mechanisms proposed by which ginseng may improves CRF is by its action on (a) CNS, including cognition/memory, sleep disturbance, anxiety/depression, (b) by Neuroprotection via potentiation of nerve growth factor activity [ 45 , 46 ], (c) inhibition of the activity of N -methyl-d-aspartate(NMDA) receptor activity [ 47 ], (d) inhibition of excitatotoxicity and calcium overflux into neurons, increase dopamine and norepinephrine in the cerebral cortex [ 48 ] and modulate the activity of presynaptic and postsynaptic receptors, and (e) modulation of inflammatory cytokines [ 49 , 50 ].
Guarana ( Paullinia cupana ) extract has been used to treat CRF, and it has high concentration of caffeine (2.5 and 5.8% of the fruit’s dry weight) [ 51 , 52 ], theobromine, theophylline (both below 0.3%) and flavonoids (proanthocyanidins, catechins, and epicatechines) [ 53 , 54 ]. The exact mechanism of improvement of CRF is unclear but may be due these compounds [ 55 ].
Mistletoe ( Viscum album L.) total plant extracts have been used for the treatment of CRF [ 56 ]. Possible role in improvement in CRF may be related to its immunomodulatory effect [ 57 ].
However, there are very limited recent meta-analysis conducted using rigorous methods including all pharmacological and nutraceuticals medications used for the treatment of CRF despite several full systematic reviews [ 2 , 33 , 58 , 59 , 60 , 61 , 62 , 63 ]. Due to which the current guidelines from various professional organizations such as American Society of Clinical Oncology (ASCO), NCCN, and European Society of Medical Oncology (ESMO) provide limited indications for the use of pharmacological or nutraceuticals [ 64 , 65 ]. Hence, to date there is no FDA approved pharmaceutical or nutraceutical treatment for cancer related fatigue. Therefore, in this study we aimed to conduct a metanalysis of pharmacological, nutraceutical, and phytopharmaceutical treatments on CRF to estimate their effectiveness to treat CRF.
2. Methods
This study was approved by the institutional review board (IRB) of the University of Texas, MD Anderson Cancer Center, Houston Texas. Our meta-analysis has been registered with PRISMA Transparent reporting of systematic reviews and meta-analyses ( http://www.prismastatement.org/Protocols/Registration accessed on 1 November 2022). The project ID in the PRISMA/PROSPERO website is CRD42021203093.
We reviewed pharmacological, nutraceutical, and phytopharmaceutical Intervention studies in adult cancer patients on CRF receiving treatment or during post-treatment. We had five reviewers (SY, ARM, ZL, NN, and MK), to assess the inclusion and exclusion of the studies via reviewing the titles, abstracts, and full texts. The reviewers were randomly assigned to two groups and each group received half of the papers identified by the librarian (GY). Individually, each member assessed the inclusion and exclusion criteria and then cross checked the results. In case of conflicts, the study principal investigator (SY) made the final decision whether include or not a given study.
3. Study Selection
To be eligible to be included in the study, the articles had to meet the following eligibility criteria: (a) English language publications, (b) available in Ovid MEDLINE, Ovid Embase, Ovid Psych info, CINHAHL and Cochrane Library databases, (c) pharmacological, nutraceutical, or phytopharmaceutical interventions in human subjects with cancer, (d) Use randomized clinical trial phase II or III design, (e) Timeline—from 1 January 1967 to 30 September 2021, (f) Use a valid and acceptable fatigue outcome measures [ 66 ] such as Functional Assessment of Chronic Illness Therapy–Fatigue (FACIT-F), Brief Fatigue Inventory (BFI), Piper Fatigue Scale (PFS), Fatigue Symptom Inventory (FSI), Multidimensional Fatigue Symptom Inventory (MFSI), Multidimensional Assessment of Fatigue (MAF), European Organization for the Research and Treatment of Cancer Quality of Life Questionnaire (EORTC-QLQ-30) fatigue, The MD Anderson Symptom Inventory(MDASI), that assess both presence of fatigue and severity of fatigue, (g) cancer related fatigue should be a primary or secondary outcome, (h) The duration of the selected intervention should be at least 1 week, (i) The selected intervention should be compared to a placebo or another intervention or standard care. The articles were excluded if there was: (a) assessment of CRF using treatment toxicity only using the Common Terminology Criteria for Adverse Events (CTCAE) or equivalent, (b) combinations treatments, (e.g., combination treatment of Coenzyme-Q, N acetyl Carnitine), (c) hemopoietin growth factors, (d) less than 30 patients enrolled in the study.
Based on the above strategies, we have included 32 eligible peer-reviewed manuscripts (4896 total patients) that assessed the effects of pharmacological, nutraceutical, and phytopharmaceutical interventions on cancer induced fatigue. These selected peer-reviewed manuscripts involved the interventions of psychostimulants (Methylphenidate, Dexamphetamine, Modafinil and armodafinil); corticosteroids (Dexamethasone and Methylprednisolone); selective serotonin reuptake inhibitors (SSRI’s); Ginseng; Guarana; Melatonin; Minocycline; Mistletoe; and Megestrol, and they were assessed using the meta-analysis approach.
4. Collection and Analysis
4.1. Search Terms Used
Fatigue And Cancer (Primary/Broad)
“cancer” “neoplasm”, “tumor”, “oncology”, “fatigue”, “tiredness”, “weary”, “weariness”, “exhaustion”, “exhausted”, “lackluster”, “asthenia”, “lassitude”, “lack of energy”, “drug therapy”, “diet therapy”, “central nervous system stimulants”, ”methylphenidate”, “dextroamphetamine”, “dexmethylphenidate”, “psychostimulants”, “psychotropic”, “modafinil”, “armodafinil”, “pemoline”, “donepezil”, “amantadine”, “etanercept”, “antidepressive agents”, “serotonin uptake inhibitors”, “sertraline”, “paroxetine”, “fluoxetine“, “acetylsalicylic acid”, “aspirin”, “adrenal cortex hormones”, “glucocorticoids”, “corticosteroids”, “steroids”, “dexamethasone”, “methylprednisolone”, “progestins”, “progestational steroids”, “testosterone”, “thyrotropin-releasing hormone”, “erythropoietin”, “darbepoetin”, “adenosine triphosphate”, “thyroliberin”, “fish oils”, “docosahexaenoic acids”, “vitamin D”, “carnitine”, “levocarnitine”, “anticytokine”, “antineoplastic agents”, “medicinal plant”, “herbal medicine”, “phytotherapy”, “mistletoe”, “ginseng”, “paullinia”, “astragalus” and “placebo” etc.
Subject heading searches were explored to include narrower terms in the Medical Subject Headings (MeSH) or EMTREE (subject headings unique to Embase) hierarchy as needed. The search terms were combined by “or” if they represented the similar concept, and by “and” if they represented different concepts. Searches were restricted to randomized clinical trials of phase II or Phase III design in adult cancer patients. Conference abstracts, systematic reviews and case reports were excluded.
We searched the abstracts of the identified articles from the databases for inclusion in the review. The primary author and the medical librarian independently carried out a study selection to determine that the articles meet the inclusion criteria. Any disagreement about a particular study was resolved by discussion.
4.2. Data Extraction
We extracted data from each included study into the following areas of focus: (I) General information: article identification (author, year), full citation, geographic location, setting (e.g., hospital-based, clinic-based, community-based, referral criteria/process, other), declared conflict of interest, and source(s) of funding. (II) Study characteristics including design (Randomized controlled trials (RCT’s), clinical trial, survey, other), duration of study, number of centers, sample size, follow-up assessments, primary outcome (definition, instrument used, scoring), and secondary outcomes (definition, instrument used, scoring) (III) Patient Population details included in the study such as inclusion criteria, exclusion criteria, baseline characteristics (targeted to the specific topic—e.g., mean age, gender (male/female %), description of the therapy (time, intensity, frequency, etc.), and description of the comparator (intensity, time, frequency). (IV) Quality appraisal: Physiotherapy Evidence Database (PEDro) scoring systems was used to evaluate the quality of the articles [ 67 ]. PEDro scoring is a checklist on 10 scored yes or no questions. It evaluates internal validity and statistical information. At least two blinded evaluators checked independently each include paper to score it and an average result was calculated. Any major discrepancies were evaluated and discussed with principal investigator. The Cochrane risk assessment tool [ 68 ] was used to assess the methodological quality of each study including the risk of selection, performance, detection, attrition, and reporting biases, Each reviewer scored the Cochrane assessment tool individually analyzing the study method of randomization, allocation concealment, blinding of patients, investigator and assessor, incomplete outcome data (participation rate, sampling procedure/sample size calculation, analysis, completion per study design, handling of missing outcome data), selective outcome reporting, and the possibility of other biases. (V) Outcomes: Continuous (fatigue scales such as FACIT-F, BFI, etc.), Group mean (final value or mean change from baseline), Group standard deviation (can be calculated from SE, T-test, p values), and total number of patients per group were assessed.
4.3. Statistical Analysis
Results were reviewed from 43 studies which assessed the effect of drug treatment on fatigue using a variety of scales. Drug treatments included ginseng, guarana, megestrol, mistletoe, psychostimulants, SSRI/antidepressants, and steroids. For studies which reported results on multiple scales, single scale results were selected, prioritized in order of FACIT-F, then BFI, followed by other scales. To allow meta-analysis of the pooled results from these different scales, reported score differences were converted to standardized mean difference (Z-scores), such that an increase in score corresponds to increase in fatigue, with 95% confidence intervals. Nine studies were excluded because they did not have sufficient information to estimate a standardized difference with confidence intervals, which left 32 studies for the meta-analysis ( Figure 1 ). We excluded the metanalysis of two pharmacological or nutraceutical agents (melatonin and minocycline) as there was only one study for each of these agents which were eligible as per the eligibility criteria of the study [ 69 , 70 ]. These studies were grouped by treatment sub-group analyses. Meta-analyses, both fixed-effect and mixed effect, were performed for each of the treatments as well as overall, using the inverse variance method, with the DerSimonian-Laird estimator for tau^2 and the Jackson method for confidence intervals of tau^2. Cochrane’s Q and I^2 were used to assess heterogeneity among studies. Given the heterogeneous nature of drug treatments and the design and conduct of these studies, the random effects model estimates of effect size, confidence interval, and p -values were prioritized over those of the fixed effects models. Therefore, in the results we reported only the random effects model estimates of treatment effects. Corresponding forest and funnel plots were produced. All statistical analyses were performed using R Core Team (2020) [ 71 ], with meta-analyses performed using the “meta” package [ 72 ].
5. Results
Among the 32 studies (4896 patients) reviewed ( Figure 1 , Table 1 ), 10 studies reported significantly lower fatigue due to treatment. Results of the meta-analyses on the effect of treatments on fatigue are illustrated in the forest plot ( Figure 2 ). Fatigue scales were transformed to standardized mean difference (Z-scores), such that an increase in score corresponds to increase in fatigue. The funnel plot ( Figure 3 ) shows that most treatment effects are clustered near zero and with some evidence of bias towards smaller effect sizes and some heterogeneity. Table 2 shows the Risk Bias for studies included in the Meta-Analysis. Figure 4 and Figure 5 show the Cochrane risk of bias assessment using traffic-light plot, and Cochrane risk of bias assessment summary plot, respectively.
For the overall meta-analysis, the random effect models yielded the estimates of treatment effect and 95% confidence interval of −0.29 (−0.48, −0.09), p < 0.001. Cochrane’s Q supports heterogeneity among studies (Q = 305.8 with 33 degrees of freedom, p < 0.0001), as did I^2, estimated as 89% (86%, 92%). Tau^2 was estimated as 0.27 (0.16, 0.50), suggesting a low variance of the estimated treatment effect size.
The ginseng meta-analysis of 6 studies [ 73 , 74 , 75 , 76 , 77 , 78 ], yielded random effects model treatment effects of −0.47 (−1.10, 0.17), p = 0.15. Cochrane’s Q supports heterogeneity among studies (Q = 81.3 with 5 degrees of freedom, p < 0.0001), as did I^2, estimated as 94% (89%, 97%). Tau^2 was estimated as 0.59 (0.20, 3.87), suggesting a low variance of the estimated treatment effect size.
The guarana meta-analysis of 3 studies [ 79 , 80 , 81 ] yielded random effects model treatment effects of −0.42 (−1.00, 0.17), p = 0.16. Cochrane’s does not support heterogeneity among studies (Q = 5.17 with 2 degrees of freedom, p = 0.08), though I^2 does suggest some heterogeneity, estimated as 61% (0%, 89%). Tau^2 was estimated as 0.15 (0.0, 7.9), suggesting a low variance of the estimated treatment effect size.
The megestrol meta-analysis of 2 studies [ 82 , 83 ] yielded random effects model treatment effects of −0.15 (−0.53, 0.22), p = 0.43. Cochrane’s Q does not support heterogeneity among studies (Q = 3.17 with 1 degrees of freedom, p = 0.08), nor does I^2, estimated as 68% (0%, 93%). Tau^2 was estimated as 0.05, suggesting a low variance of the estimated treatment effect size.
The mistletoe meta-analysis of 3 studies [ 84 , 85 , 86 ] yielded random effects model treatment effects of −0.76 (−2.00, 0.48), p = 0.23. Cochrane’s Q supports heterogeneity among studies (Q = 72.5 with 2 degrees of freedom, p < 0.0001), as did I^2, estimated as 97% (95%, 99%). Tau^2 was estimated as 0.16 (0.29, 47.3), suggesting a low variance of the estimated treatment effect size.
The psychostimulant meta-analysis of 14 studies [ 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 ] yielded random effects model treatment effects and 95% confidence interval of −0.05 (−0.11, 0.02), p = 0.14. Cochrane’s Q did not support heterogeneity among studies (Q = 11.99 with 13 degrees of freedom, p = 0.53), and nor did I^2, estimated as 0.0% (0.0%, 55%). Tau^2 was estimated as 0 (0.0, 0.03), suggesting a low variance of the estimated treatment effect size.
The SSRI/antidepressant meta-analysis of 2 studies [ 101 , 102 ], one of which had a significant negative-trending effect size, while the other was slightly positive-trending, yielded random effects model treatment effects of −0.25 (−0.88, 0.38), p = 0.44. Cochrane’s Q supports heterogeneity among studies (Q = 6.84 with 1 degrees of freedom, p = 0.009), as did I^2, estimated as 85% (41%, 96%). Tau^2 was estimated as 0.18, suggesting a low variance of the estimated treatment effect size.
The steroid meta-analysis of 2 studies [ 103 , 104 ], both of which had significant negative-trending effect sizes, yielded the random effects model treatment effects of −0.94 (−1.21, −0.67), p <0.0001. Cochrane’s Q does not support heterogeneity among studies (Q = 0.06 with 1 degrees of freedom, p = 0.80), nor did I^2, estimated as 0%. Tau^2 was estimated as 0.0, suggesting a low variance of the estimated treatment effect size.
6. Discussion
In our study, we found that the overall meta-analysis of all CRF treatment studies showed significant reduction of fatigue with treatment effect of −0.29. Our meta-analysis suggests significant reduction of CRF with Corticosteroids. Metanalysis of psychostimulants (Methylphenidate, Modafinil, Armodafinil), Ginseng, Guarana, Megestrol, Mistletoe, and antidepressants did not show significant reduction in CRF. Further studies are needed.
As compared to recent meta-analysis of pharmacological treatment for CRF studies (last 6 years) [ 62 , 63 , 105 , 106 , 107 , 108 , 109 , 110 ], the results of our study suggests significant reduction of CRF with Corticosteroids, whereas no significant reduction in CRF with psychostimulants such as Methylphenidate, Modafinil, Armodafinil, Ginseng, Guarana, Megestrol, Mistletoe, and SSRI/antidepressants. Our study was unique as it included only RCT’s with a control or placebo, publications in English, and included both nutraceuticals and pharmaceuticals used to treat CRF as most often patients prefer to use both the nutraceuticals and pharmaceuticals if they decide to address their CRF with medications [ 32 ]. In addition, in this study we employed more rigorous methods including strict eligibility criteria, use of Physiotherapy Evidence Database (PEDro) scale to assess the quality of the included RCT studies, and Cochrane risk assessment tool to assess risk bias of the RCT’s. Some of the published metanalysis articles focused on specific stage of disease or type of medication. Roji et al. [ 106 ], and Junior et al. [ 107 ], focused on placebo. Qu et al. [ 108 ], Minton et al. [ 109 ], and Gong et al. [ 110 ], focused on psychostimulants drugs. Mucke et al. [ 62 ] focused mainly on patients receiving palliative care. Our study is unique in contrast to other recent published metanalysis using pharmaceuticals and nutraceuticals in that we included all stages and types of cancer patient populations (early, advanced, cancer survivors as well as cancer types) as well as pharmaceuticals and nutraceuticals. Additionally, we excluded patients using erythropoietic agents as they no longer used to treat CRF due to concerns of increased risk for cardiovascular events and tumor growth [ 111 , 112 ]. Pharmacological or nutraceutical agents (Melatonin, Minocycline) which had only one eligible study were excluded from metanalysis of the agent as the results were more a representative of the single randomized control study rather than a metanalysis of various studies using the agent.
Despite the mixed findings of benefit of various interventions for CRF in our study, it is very early to state any of these interventions are not effective in treatment of CRF due to the limited clinical trials conducted using validated outcomes and in well-defined homogenous cancer patients. Further research is necessary to evaluate which subgroup of cancer patients these interventions will be most likely benefit.
The result from this study regards to the beneficial effects of corticosteroids on CRF is consistent with the results of prior studies using steroids [ 113 , 114 , 115 , 116 ] which were excluded as they did not meet all the eligibility criteria of our study but support the use of this agent. However, recent advent of immunotherapy as one of the important cancer treatment agents limits its use of corticosteroids, therefore other agents should be considered among patients on immunotherapy [ 117 ].
In contrast to other pharmaceutical and nutraceuticals, psychostimulants specifically Methylphenidate is the most investigated medication for the treatment of CRF. The results of the metanalysis showed non-significant trend towards benefit. These results are interesting as patients in clinical practice often report interest and benefit from the medication. Perhaps a possible reason for mixed results is that we have not targeted the intervention is a specific subgroup of patients (e.g., fatigued cancer patients with anxiety or depression or drowsiness) [ 90 ] or we have not used Methylphenidate in combination with other CRF treatments (e.g., exercise) as it may not target all the pathophysiologic mechanisms causing fatigue (especially physical fatigue) [ 118 , 119 ]. In clinical practice in view of the mixed results from metanalysis, as well as side effect profile which includes risk of addiction one should only consider using Methylphenidate in a short term and on a trial basis. In a study by our team [ 120 ], we found that patients who showed improvement in CRF (response) after 1 day of treatment of Methylphenidate will most likely have a benefit from methylphenidate treatment for CRF. Therefore, in clinical practice for patients appropriate to use Methylphenidate for CRF, one may consider a trial of Methylphenidate for 1 or 2 days and then extend treatment further on an empirical basis if beneficial with close monitoring.
Various methodological factors can make the interpretation of results of our study challenging: (a) CRF is a subjective symptom and various tools were used in the published clinical trials. (b) Most of the studies used statistical significance rather than clinically relevant improvement as a measure to conclude benefit [ 121 ]. Therefore, there were limited details in the published studies which show that improvement in subjective fatigue would result in actual improvement in physical function, activity or interference in daily activity. (c) Finally, during the analysis phase (see Figure 1 ) we had to exclude several studies due to availability of limited data to conduct the meta-analysis.
Finally, CRF is a complex multidimensional syndrome due to various physical, cognitive, psychosocial factors involving brain, muscle, cognition and effecting various pathophysiological changes including inflammation, neuro-immuno-pituitary adrenal axis, mitochondrial pathways [ 36 ]. Using a single agent may not target all the causes of this complex multifactorial syndrome. Hence, future studies targeting various predominant causative mechanisms in specific patient, i.e., multimodal personalized therapy similar to the current management of cancer specific therapy should be considered [ 122 ].
7. Conclusions
In this study the results of metanalysis of published studies for the treatment of CRF showed significant reduction of cancer related fatigue after treatment with Pharmacological, Nutraceutical and Phytopharmaceutical interventions with a pooled standardized treatment effect of −0.29. Metanalysis of Corticosteroids studies showed significant reduction in CRF. Metanalysis of agents such as the psychostimulants (Methylphenidate, Modafinil, Armodafinil), Ginseng, Guarana, Megestrol, Mistletoe, and antidepressants did not show significant reduction in CRF. Further studies are needed.
Acknowledgments
Terence Ng, Aashraya Ramu, Kyu-Hyoung Lim, and MASCC fatigue guidelines team including Karen M Mustian(chair), Fausto Riola, and Markus Horneber for help in article search, extraction, and support of our prior systematic review of Pharmacological, Nutraceutical and Phytopharmaceutical interventions for fatigue (2016).
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Author Contributions
S.Y. had full access to all data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: S.Y. and E.B. Acquisition, analysis, or interpretation of data: S.Y., Z.L. and A.R.D.M., Nguyen, M.J.K., Y.G. and N.N.T. Drafting of the manuscript: S.Y. and E.B. Critical revision of the manuscript for important intellectual S.Y., Z.L. and A.R.D.M., Nguyen, M.J.K., Y.G., N.N.T. and E.B. Statistical analysis: C.R.A., Z.L. and S.Y. Administrative, technical, or material support: S.Y. and E.B. Supervision: S.Y. and E.B. All authors have read and agreed to the published version of the manuscript.
Institutional Review Board Statement
This study was approved by the institutional review board (IRB) of the University of Texas, MD Anderson Cancer Center, Houston Texas.
Informed Consent Statement
Not applicable.
Data Availability Statement
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.
Conflicts of Interest
The authors declare no conflict of interest.
Abbreviations
BFI , Brief Fatigue Inventory. EORTC QLQ-C30 , European Organization for the Research and Treatment of Cancer Quality of Life Questionnaire. FACIT-F , Functional Assessment of Chronic Illness Therapy–Fatigue. FSI , Fatigue Symptom Inventory. MAF , Multidimensional Assessment of Fatigue. MDASI , The MD Anderson Symptom Inventory. MFSI-SF , Multidimensional Fatigue Symptom Inventory. PFS , Piper Fatigue Scale. Note: All studies in Table 1 had placebo or other interventions or standard care as comparison arms. PSS- Physiotherapy Evidence Database (PEDro) scoring systems
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