|Year : 2016 | Volume
| Issue : 2 | Page : 78-85
Etiology and prevalence of fatigue in chronic liver disease: clinical view
Zakaria A Salama1, Samar K Darweesh1, Hany M Shehab1, Manal A Abd-Elhameed2
1 Hepatogastroenterology and Endemic Medicine Department, Faculty of Medicine, Cairo University, Cairo, Egypt
2 Hepatogastroenterology Department, Police Hospital, Cairo, Egypt
|Date of Submission||19-Jun-2016|
|Date of Acceptance||30-Jun-2016|
|Date of Web Publication||11-Nov-2016|
Samar K Darweesh
Hepatogastroenterology and Endemic Medicine Department, Faculty of Medicine, Cairo University, 63, Abo Dawood El-Thahery St, Cairo, 11765
Source of Support: None, Conflict of Interest: None
Introduction and aim Fatigue is one of the most common and prominent symptoms in liver cirrhosis and was reported in 60–80% of these patients. The study outcome was to prospectively evaluate the etiology and the degree of fatigue and how to improve it in chronic liver disease patients.
Patients and methods A prospective cross-sectional study on fatigue in chronic liver diseases was conducted on 500 patients: 475 patients had hepatitis C virus (HCV) and 25 had combined HCV and hepatitis B virus. They were divided into five groups: group 1 included 100 patients with chronic hepatitis, group 2 included 100 patients with Child class A cirrhosis, group 3 included 100 patients with Child class B cirrhosis, group 4 included 100 patients with Child class C cirrhosis, and group 5 included 100 patients with hepatocellular carcinoma (HCC). They were administered the Fatigue Impact Scale and the Fatigue Severity Scale questionnaires (translated into Arabic) as well as subjected to laboratory investigations, abdominal ultrasonography, and upper endoscopy.
Results All (100%) patients complained of longstanding fatigue. HCC had the highest prevalence of high fatigue (65%) and Child class C cirrhosis had the longest fatigue duration. Female sex and anemia were significantly related to both the Fatigue Impact Scale and the Fatigue Severity Scale in each group separately and all patients collectively. Age had a significant relation with all patients collectively but not separately. Fatigue scores were related to Child score but not related to liver profile, α-fetoprotein, varices, ascites, and HCV load.
Conclusion Correction of anemia, not liver profile, helps in alleviating fatigue in cirrhotic patients. Female patients suffered from fatigue more frequently compared with male patients. HCC patients had highest fatigue and patients with Child class C cirrhosis had longest fatigue indices.
Keywords: fatigue, Fatigue Impact Scale, Fatigue Severity Scale, hepatocellular carcinoma, hepatitis C virus liver disease, quality of life
|How to cite this article:|
Salama ZA, Darweesh SK, Shehab HM, Abd-Elhameed MA. Etiology and prevalence of fatigue in chronic liver disease: clinical view. Egypt J Intern Med 2016;28:78-85
|How to cite this URL:|
Salama ZA, Darweesh SK, Shehab HM, Abd-Elhameed MA. Etiology and prevalence of fatigue in chronic liver disease: clinical view. Egypt J Intern Med [serial online] 2016 [cited 2020 Feb 22];28:78-85. Available from: http://www.esim.eg.net/text.asp?2016/28/2/78/193892
| Introduction|| |
Fatigue is one of the most common and prominent symptoms in patients with liver cirrhosis and can result in reduced physical activity, constraints on daily life, and even a decrease in working hours and social activities. In early reports on the clinical profile of cirrhosis, fatigue was reported to be present in 60–80% of these patients ,,.
However, because of difficulties in defining and treating fatigue, this symptom is often overlooked or minimized by physicians caring for patients with liver disease.
Fatigue experienced by patients with cirrhosis has many possible contributing factors, including the severity of liver disease, anemia, and psychological distress. Several previous studies found no significant correlation between fatigue level and disease duration or severity in patients with primary billiary cirrhosis (PBC) .
The pathogenesis of fatigue in general is poorly understood and this holds true for fatigue in the setting of liver disease .
The primary outcome of this study was to prospectively evaluate the etiology and degree of fatigue and how to improve it in patients with chronic liver disease.
| Patients and methods|| |
This was a prospective cross-sectional study evaluating the causes and prevalence of fatigue in chronic liver diseases conducted on 500 patients, 475 patients with hepatitis C virus (HCV) and 25 patients with combined HCV and hepatitis B virus (HBV), who attended the Hepatogastroenterology and Tropical Medicine Department, Faculty of Medicine, Cairo University, and the Police Hospital during the period between 2011 and 2013. These patients were divided into five groups:
- Group 1 included 100 patients with chronic hepatitis.
- Group 2 included 100 patients with Child class A cirrhosis.
- Group 3 included 100 patients with Child class B cirrhosis.
- Group 4 included 100 patients with Child class C cirrhosis.
- Group 5 included 100 patients with hepatocellular carcinoma (HCC).
We included (a) patients with chronic liver disease due to HCV or HBV (b) between 18 and 65 years of age and (c) not previously treated with any specific treatment for HCV or HBV.
Exclusion criteria were as follows: (a) presence of any other primary comorbidity (cardiac, renal, pulmonary, hematologic, etc.); (b) currently on or had a history of Peg/Riba therapy for HCV, and (c) severe psychiatric disease.
This study was approved by the institutional review board and ethics committee based on the 1975 Declaration of Helsinki (revised in 2000). A signed informed consent was obtained from all patients. They were subjected to the following: (a) history taking and full clinical assessment; (b) laboratory investigations, including complete blood count, liver biochemical profile [bilirubin, aspartate aminotransferase, alanine aminotransferase (ALT), alkaline phosphatase (ALP), albumin, and international normalized ratio], fasting blood glucose, hepatitis markers (HBsAg, HBsAb, HBcAb, HCVAb), quantitative HCV RNA using PCR, serum urea and creatinine, antinuclear antibody, and serum α-fetoprotein (AFP); (c) abdominal ultrasonography; (d) upper endoscopy (for the cirrhosis and HCC groups); and (e) Fatigue Impact Scale (FIS) and Fatigue Severity Scale (FSS) questionnaires.
Fatigue Impact Scale and Fatigue Severity Scale questionnaires
These two questionnaires score the effect of fatigue in chronic disease; both questionnaires were translated professionally and objectively into Arabic and each patient completed both questionnaires.
The FIS is based on the patient’s perceived functional limitations and impact of fatigue on patients’ quality of life (QoL), considering cognitive, physical, and psychosocial aspects. It consists of 10 cognitive, 10 physical, and 20 social questions. The total score has a maximum of 160 points and a minimum of 0 point. Higher scores are considerable merit as a measure of patient’s attribution of functional limitations to symptoms of fatigue .
The FSS can be used to assess the level of fatigue in patients and to monitor its change over time or in response to therapeutic interventions. It consists of nine statements that need a graded response from strongly agree to strongly disagree (seven grades). The total score of the FSS has a maximum of 63 points and a minimum of 9 points .
The short form 36 (SF-36) scale was not used as it focuses mainly on vitality and physical functioning and includes questions on bodily pain, which is not a common symptom in chronic liver disease. However, the FSS and the FIS are better used for any disorder for which fatigue is a major clinical component.
Data were coded and entered using the statistical package for the social sciences (SPSS; SPSS Inc., Chicago, Illinois, USA). Data were summarized using mean, SD, and range (minimum and maximum) for quantitative variables and number and percent for qualitative variables.
Comparisons between groups were made using the χ2-test and Fisher’s exact test for qualitative variables. The independent sample t-test was used for normally distributed quantitative variables and for nonparametric the Mann–Whitney test was used for quantitative variables, which are not normally distributed; the same applied on all patients collectively. P values less than 0.05 were considered statistically significant.
| Results|| |
This study included 500 patients with chronic liver disease. There were 270 (54%) male and 230 (46%) female patients. In cirrhotic patients (groups 2–5), 100 (20%) patients were of Child class A, 120 (24%) were of Child class B, and 180 (36%) were of Child class C. Demographic data and baseline laboratory parameters are shown in [Table 1]. The HBV coinfected patients were not removed from the study to make it applicable for different etiologies of chronic liver disease. Twelve (2.4%) patients in the study had mild controlled diabetes mellitus (DM) (as severe uncontrolled DM patients were excluded); the relation to fatigue was not determined due to the small number and mildness of DM.
|Table 1: Demographic data and baseline laboratory parameters of the studied groups|
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All (100%) patients complained of longstanding fatigue. Patients were classified as high fatigue (FIS>40) or low fatigue (FIS<40). The HCC group had the highest prevalence of high fatigue, 65 (65%) patients. Moreover, the mean of the FIS and the FSS of all studied groups showed that the HCC group had the highest score. These results were statistically significant [Table 2].
|Table 2: Mean and grade of the Fatigue Impact Scale and the Fatigue Severity Scale in each group|
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However, the Child class C cirrhosis group had the longest fatigue duration (46±99 months), followed by the HCC group (41±34 months). This finding did not record statistical significance (P>0.05).
Relation of fatigue with different parameters
There was a significant relation between age and FIS and FSS scores as predictors of fatigue level when applied on all patients collectively (P=0.03 for FIS and 0.04 for FSS) [Table 3], but the relation did not reach significance when applied on each group separately. We believe the significant relation appeared when patients were analyzed collectively due to the much larger number of patients included.
|Table 3: The relation between age, sex, smoking, and fatigue scales in all patients collectively|
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Female patients had higher fatigue scores compared with male patients in both the FIS and the FSS with statistical significance (P=0.002 for FIS and 0.04 for FSS). These results were also reflected in each group separately. Female patients suffered fatigue more frequently either subjectively or objectively compared with male patients [Table 3] and [Table 4].
Patients with anemia had a significant relation, with higher scores in both fatigue scales among each group separately and all patients collectively [Table 5] and [Table 6]. However, no significant relation was found between fatigue scales and white blood cell or platelet count in all groups.
|Table 5: The relation between fatigue scales and hemoglobin in the five groups|
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|Table 6: The relation between complete blood count and fatigue scales in all patients|
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The relation between smoking and fatigue level was significant in the HCC group, as there was a significant difference between those who had and those who did not have a previous history of smoking as regards the FIS; strangely, the patients who had a history of smoking reported lower scores of fatigue (P=0.01), but significant difference was not found in FSS.
The current study could not demonstrate a significant relation between the fatigue scales and any item of liver profile (bilirubin, aspartate aminotransferase, ALT, ALP, albumin, and international normalized ratio) (P>0.05), but strangely higher FIS and FSS scores were reported with lower ALT and ALP [Table 7]. This applied when the groups were studied separately or collectively.
|Table 7: The relation between liver profile and fatigue scales in all patients|
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In 220 (44%) patients with AFP more than 10, the FIS and FSS mean scores were 46±24.6 and 45.8±12.3, respectively, whereas in 280 (56%) patients with AFP less than 10 the FIS and FSS mean scores were 48.9±26 and 41.7±22, respectively. The relation was nonsignificant (P>0.05). Moreover, HCV viral load had a nonsignificant relation with the FIS and the FSS (P>0.05). These relations applied when the groups were studied separately or collectively.
The presence of esophageal varices in relation to FIS and severity scales showed a nonsignificant relation, as 345 (69%) patients with esophageal varices (OV) (45, 100, 100, and 100 patients in groups 2, 3, 4, and 5, respectively) had FIS and FSS mean scores of 42.2±24.7 and 46±12.8, respectively. Moreover, 155 (31%) patients without OV had FIS and FSS scores of 40.3±34.5 and 39.7±12.7, respectively (P>0.05). This applied regardless of whether the groups were studied separately or collectively.
The presence of ascites in relation to the FIS and the FSS showed a nonsignificant relation, as 300 (60%) patients with ascites (all patients in 3, 4, and 5 groups) had FIS and FSS mean scores of 46±24 and 46.6±11.7, respectively. Moreover, 200 (40%) patients without ascites had FIS and FSS mean scores of 41±31.5 and 42.7±15.5, respectively (P>0.05). This applied regardless of whether the groups were studied separately or collectively.
However, despite the absence of correlation of fatigue with parameters of liver function or ascites, fatigue scores increased with the increase in degree of liver dysfunction indicated by Child–Pugh scoring and this was statistically significant [Table 2].
| Discussion|| |
Fatigue in chronic liver disease significantly impacts the QoL, interfering with physical activity, family life, and job performance. The exact prevalence of fatigue in patients with chronic liver disease is somewhat variable in different studies and with different specific liver diseases . However, this high prevalence of fatigue does not appear to hold for patients infected with hepatitis C who are unaware of their diagnosis.
The importance of determining the health-related QoL has gained quite a momentum in the past few decades . Since 1947, the WHO has redefined health as not only the absence of disease but also a complete state of physical, mental, and social well-being .
Among the various generic instruments developed to measure health-related quality of life (HRQoL) are the FIS and the FSS. The FIS measures both qualitative and quantitative aspects of fatigue to assess fatigue and its effect on the QoL. This tool consists of a questionnaire that scores the effect of fatigue on 40 aspects of day-to-day life. These aspects broadly pertain to psychosocial, cognitive, and physical activity .
The FSS is one of the best known and most frequently used fatigue scales. The name is, however, slightly misleading. The FSS principally measures the impact of fatigue on specific types of functioning rather than the intensity of fatigue-related symptoms .
One of the main strengths of this study is the inclusion of all stages of chronic liver disease (even HCC). Moreover, our use of the FSS and the FIS with their easy-to-respond questionnaires and reliable assessment of fatigue adds to the study.
The relation between age and fatigue level was significant, in our study, when all patients were studied collectively but not separately. In the study by Sumskiene et al.  on 131 patients with liver cirrhosis, they reported that there was only small and nonsignificant impairment with age (P>0.05). Moreover, Mahmood et al.  studied 120 chronic hepatitis C patients and reported that chronic liver disease score was unaffected by age. Similar results were found by Cauch-Dudek et al. , Huet et al. , and Prince et al. .
However, the results of Gao et al.  and Afendy et al.  on 392 patients and 1103 patients of chronic liver diseases, respectively, concluded that age correlated weakly but significantly (P<0.05) with every scale of the SF-36 (HRQoL questionnaire). We believe the relation between fatigue and age was reported when all patients together were analyzed due to the much larger number included.
In our study, female patients had higher scores in the FIS and the FSS compared with male patients, with a significant correlation between female sex and the two scales. These results were also reflected in each group separately. The studies conducted by Miaskowski , Baldwin et al. , Gao et al. , and Afendy et al.  also showed significantly greater initial levels of fatigue in female patients compared with male patients.
In contrast, in a study by Sumskiene et al.  on 131 liver cirrhosis patients, they reported that sex did not show any effect on the QoL of liver cirrhosis patients.
Mechanisms behind the sex difference in the fatigue experience are not clear. A component of the explanation for higher rates of symptom reporting in women is that there are differences in the way symptoms are perceived, evaluated, and acted upon . Women may have a lower threshold for symptom reporting  or a greater readiness to perceive physical sensations as symptoms of illness . Other theories pertain to the way symptoms are measured. Differences in symptom reporting may be the result of an artifact of measurement.
Similarly, a research by Stommel et al.  found that certain items on the Centre for Epidemiologic Studies Depression Scale produced biased responses when comparisons were made on men’s and women’s responses to these items. It is possible that other instruments may have sex bias in items that may artificially raise women’s levels of these symptoms. Research has suggested that women have a more expansive vocabulary for fatigue, including terms such as ‘tiredness’, ‘reduced energy’, and ‘lack of vigor’, which may affect the likelihood of fatigue being detected and could argue for use of a sex-specific fatigue taxonomy ,.
The cirrhosis Child C group, in our study, had the longest fatigue duration, followed by the HCC group. This finding could not be explained, as the highest scores of fatigue of both scales were found in the HCC group, and, by logic, usually the clinicians find more fatigue in HCC patients. However, probably these patients were not asked about the duration of their fatigue, and probably patients with Child class C cirrhosis had more fluid overload (edema and ascites) that was gained over a period of time owing to the longer duration of perception of fatigue.
In our study, there was no significant relation between any item of liver profile and the two fatigue scales. Atiq et al.  in their study that included 56 patients, with hepatitis C in 52 of them, found that there was no association between prothrombin time or albumin and the scores in the Chronic Liver Disease Questionnaire. Similarly, Younossi et al.  found that there was no correlation between ALT levels and poor Chronic Liver Disease Questionnaire scores. Rosa et al.  also found no correlation between fatigue and liver function tests.
Another study involving 116 patients reported that the severity of fatigue does not correlate with markers of liver disease severity, such as age, serum albumin, bilirubin, and prothrombin time . They also found a lack of correlation between stage of liver histology in PBC and fatigue, as fatigue severity did not correlate with stage of liver disease when patients with grade I–II PBC were compared with patients with grade III–IV. However, Gao et al.  reported that hyperbilirubinemia and prolonging prothrombin time were important factors reducing HRQoL.
This lack of correlation between fatigue and liver biochemical profile may have a clinical implication. Physicians may focus on reducing liver enzymes to alleviate the fatigue of patients using drugs such as silymarin, methionine, etc. It is now obvious that the main focus to alleviate fatigue should be on modifying factors such as anemia or depression. The focus on the liver profile should be complimentary.
However, despite the absence of correlation of fatigue with different laboratory parameters of liver profile, fatigue scores increased progressively with the degree of liver dysfunction as indicated by Child–Pugh scoring and reported statistical significance. Thus, if liver profile with ascites were studied collectively (in a parameter model) in relation to fatigue scales, this could give a significant result.
Relation between viral load and fatigue level, in our study, was not found. Sinakos et al.  also found that correlations between HRQoL and viral load lost showed statistical significance. This finding coincides with other facts that negate the role of viremia level per se in the progress or severity of liver disease, such as the fact that viremia level does not correlate with transaminases or fibrosis levels in patients with HCV.
There was a nonsignificant correlation, in our study, between the presence of OV or ascites and fatigue level. However, Gao et al.  reported that the presence of varices and ascites had an effect on physical and mental health area of the SF-36 questionnaire, and hence it had an effect on HRQoL.
There was a significant relation between the two fatigue scales and anemia in our study. Dan et al.  also reported that anemia was associated with poorer HRQoL in HCV patients. However, Rosa et al.  reported different results as they found that hemoglobin abnormalities were only weakly related to FSS score.
The correlation of fatigue with anemia is logical, and fortunately some causes of anemia are modifiable. In chronic liver disease, a body cannot use its stored iron as erythropoietin is suppressed and the bone marrow does not respond normally. The shortage of iron and erythropoietin can result in a shortage of red blood cells . Gastrointestinal bleeding may also contribute to blood loss. Thus, patients with chronic HCV, fatigue, and anemia may get benefit from workup of hematological indices, including folate, vitamin B12, and iron studies. Correction of anemia will then be feasible and will positively reflect on the QoL.
A significant inverse relation was found, in our study, between smoking and fatigue level in the HCC group but not other groups; strangely, the patients who had a history of smoking reported less scores of fatigue. This finding was also reported in the FSS but did not reach statistical significance. The explanation of this finding is not clear and other studies arguing or covering it were not found, and more studies are needed to discuss this point.
| Conclusion|| |
Fatigue is a prominent symptom in patients with liver cirrhosis. Thus, determining health-related QoL becomes necessary in managing cirrhotic patients. Correction of anemia but not liver profile may improve fatigue in cirrhotic patients. Female patients and HCC patients had significant fatigue indices. Studies should be carried out on the role of cognitive behavior therapy and graded exercise therapy in the management of fatigue.
In the era of new direct acting antiviral (DAAs) for treatment of HCV, assessment of fatigue should be carried out after eradication of HCV in different stages of chronic liver disease to confirm whether or not fatigue is related to the presence of HCV.
Summary of strengths of the study:
- This study shows the magnitude of the forgotten symptom ‘fatigue’ in chronic liver disease.
- Fatigue was recorded in all patients with chronic liver disease.
- Fatigue was related significantly to female sex and anemia.
- Thus, correction of anemia but not liver profile helps in alleviating fatigue in cirrhotic patients.
- Fatigue was not related to liver profile, AFP, varices, ascites, and HCV load.
- Fatigue questionnaires should be presented to all liver disease patients and should be managed and not ignored.
Zakaria A. Salama, Samar K. Darweesh, Hany M. Shehab, and Manal A. Abd-Elhameed contributed to the conception and design of the study. Samar K. Darweesh, Hany M. Shehab, Manal A. Abd-Elhameed contribute to the provision of study materials or patients. Samar K. Darweesh and Manal A. Abd-Elhameed carried out collection and assembly of data. Zakaria A. Salama, Samar K. Darweesh, Hany M. Shehab carried out data analysis and interpretation. All authors contribute to manuscript writing and final approval of the manuscript.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7]