Risk factors for fatigue in patients with epilepsy

Journal of Clinical Neuroscience xxx (2016) xxx–xxx

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Clinical commentary

Risk factors for fatigue in patients with epilepsy Song Yan a, Yuanbin Wu a, Yanchun Deng b, Yonghong Liu b, Jingjing Zhao b, Lei Ma b,⇑ a b

No. 13 Cadets Company, Fourth Military Medical University, Xi’an, China Department of Neurology, Xijing Hospital, Fourth Military Medical University, Xi’an 710032, China

a r t i c l e

i n f o

Article history: Received 30 June 2015 Accepted 6 March 2016 Available online xxxx Keywords: Anxiety Depression Epilepsy Fatigue Sleep

a b s t r a c t Fatigue is highly prevalent in patients with epilepsy and has a major impact on quality of life, but little data is available on its effects and management in epilepsy. To identify the incidence and risk factors of fatigue in patients with epilepsy, 105 epilepsy patients (45 women and 60 men) were enrolled in our study. Demographic and clinical data were collected and psychological variables including fatigue, sleep quality, excess daytime sleepiness, anxiety, and depression were measured by Fatigue Severity Scale, Pittsburgh Sleep Quality Index, Epworth Sleepiness Scale, and Hospital Anxiety and Depression Scale, respectively. Of 105 patients, 29.5% exhibited fatigue (FSS score P4). We found no correlation between the occurrence of fatigue and any of our demographic or clinical variables. Fatigue is correlated with low sleep quality, anxiety, and depression, but not with excess daytime sleepiness. Thus, we concluded that fatigue is highly prevalent in patients with epilepsy, and that low sleep quality, anxiety, and depression are significantly correlated with fatigue in epileptics, while excess daytime sleepiness not. Ó 2016 Published by Elsevier Ltd.

1. Introduction Fatigue is common in a number of systemic and neurological diseases, and is a powerful predictor for quality of life in epilepsy patients [1]. Fatigue can be defined as a subjective experience or feeling; the most cited definition of fatigue, from MeSH, is ‘‘a sense of physical tiredness and lack of energy, distinct from sadness or weakness.” Fatigue can be seriously debilitating, but its effects in epilepsy patients have not been well researched, primarily because doctors’ attention is mainly focused on seizure control [2]. Previous studies have reported that the prevalence of fatigue in epileptics was 42.4%, which is four times more frequent than in the general Canadian population [1,3]. Ettinger et al. concluded that fatigue is related mostly to age and depression [4], while Soyuer and colleagues reported a correlation with depression, P300 latency (a sensitive electrophysiological indicator of cognitive performance), and polytherapy [5]. While Hamelin et al. did not find a correlation between fatigue and polytherapy, seizure types, and seizure frequency, they did find a significant difference in gender, depression, and excess daytime sleepiness [6]. Moreover, the correlations between fatigue and other epileptic comorbidities (sleep disorders, excessive daytime sleepiness, anxiety, and depression) have not been extensively studied.

⇑ Corresponding author. Fax: +86 29 84775368. E-mail address: [email protected] (L. Ma).

In this study, we aimed to establish the incidence and the risk factors of fatigue in patients with epilepsy, which could be used to develop strategies for planning of both care and therapy. 2. Material and methods 2.1. Subjects 105 patients were recruited, all of whom had been diagnosed with epilepsy for at least one year and had visited the Epilepsy Center, Neurology Department, Xijing Hospital, a tertiary referral hospital in northwest China, between September 2014 and February 2015. All patients were older than 18 at the time of recruitment. Patients who had diseases that would impair judgment or impact the assessment of sleep quality, daytime sleepiness, depression, anxiety, and fatigue were excluded from the study. Patients with other active neurological diseases were also excluded. 2.2. Questionnaires Fatigue of interictal phase was measured using Fatigue Severity Scale (FSS), a nine item questionnaire. Subjects answered on a Likert scale ranging from 1 (completely disagree) to 7 (completely agree). Cases of fatigue were defined by a mean FSS score P4, whereas cases without fatigue were defined by a mean FSS score <4. The FSS has high internal consistency, good test–retest reliability, and is sensitive to change [7].

http://dx.doi.org/10.1016/j.jocn.2016.03.043 0967-5868/Ó 2016 Published by Elsevier Ltd.

Please cite this article in press as: Yan S et al. Risk factors for fatigue in patients with epilepsy. J Clin Neurosci (2016), http://dx.doi.org/10.1016/j. jocn.2016.03.043

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S. Yan et al. / Journal of Clinical Neuroscience xxx (2016) xxx–xxx

The Pittsburgh Sleep Quality Index (PSQI) comprises 19 questions (each scored ranging from 0 to 3) that are combined to form seven components, and these seven components are collected to form an overall score that ranges from 0 to 21, such that 0 indicates no difficulty and 21 indicates severe difficulties with sleep quality [8]. We used the Chinese version of PSQI in this study, which has been approved by the original PSQI authors. A global PSQI score >7 had a diagnostic sensitivity of 98.3% and specificity of 90.2% in distinguishing normal or problem subjects [9]. Therefore, we defined a PSQI score 67 and P8 as ‘‘good sleep quality” and ‘‘poor sleep quality,” respectively. The Epworth Sleepiness Scale (ESS) is the scale most frequently used worldwide to determine the level of daytime sleepiness due to its reliability, consistency, and ease of use [10–12]. It asks subjects to rate their likelihood of falling asleep on a scale of increasing probability from 0 to 3, in eight different situations that most people engage in during their daily lives. A score of >10 is generally considered clinically abnormal [10,13,14]. The Hospital Anxiety and Depression Scale (HADS) is a reliable screening scale to be used in general hospital settings to determine levels of anxiety and depression, and a cut-off point of P9 is advised [15]. In our study, a Chinese version of HADS was used to assess anxiety and depression in epileptics. 2.3. Statistical analysis Qualitative data are described as frequencies and quantitative data are presented as the mean ± standard deviation (SD). In cases where data were not normally distributed we used non-parametric

statistical tests. Categorical variables were compared using the X2 test. Spearman’s correlation coefficient was used to measure the association between numerical variables (FSS, PSQI, ESS, Anxiety, and Depression scores). All statistical tests were two-sided, and p values <0.05 were considered to be statistically significant. 3. Results Of 105 patients with epilepsy recruited for our study, 60 (57.1%) were men and 45 (42.9%) were women. More detailed demographic and clinical characteristics of the subjects are summarized in Table 1. Seizure types in our participants were classified as generalized (67.6%), partial (19.0%), or unclassified (13.3%); 31(29.5%) patients had not experienced a seizure in the last year, and 36 (34.3%) individuals had suffered epilepsy for over 10 years. In addition, 54(51.4%) of participants received antiepileptic drug (AED) monotherapy while 22(21.0%) took at least 2 AEDs during the period of the study. Patients’ psychosocial characteristics (assayed by:

Table 2 Correlations between psychological factors and fatigue Variables

Mean ± standard deviation

Spearman’s q

P value

PSQI ESS Anxiety Depression

4.60 ± 2.611 5.92 ± 5.040 7.06 ± 4.111 6.30 ± 3.726

0.386 0.046 0.416 0.319

<0.001** 0.644 <0.001** 0.001**

** Correlation is significant at the 0.01 level (two-tailed test). PSQI = Pittsburgh Sleep Quality Index; ESS = Epworth Sleepiness Scale.

Table 1 Correlations between demographic and clinical factors and fatigue Variables

All (N = 105)

Fatigue

Without fatigue

P value

Male Female

60 (57.1%) 45 (42.9%)

17 (16.2%) 14 (13.3%)

43 (41.0%) 31 (29.5%)

0.757

630 years ＞30 years

77 (73.3%) 28 (26.7%)

22 (21.0%) 9 (8.6%)

55 (52.4%) 19 (18.1%)

0.723

Less than high school High school College and above

29 (27.6%) 58 (55.2%) 18 (17.1%)

11 (10.5%) 16 (15.2%) 4 (3.8%)

18 (17.1%) 42 (40.0%) 14 (13.3%)

0.46

City Country

37 (35.2%) 68 (64.8%)

10 (9.5%) 21 (20.0%)

27 (25.7%) 47 (44.8%)

0.679

Less than Ұ1,000 Ұ1,000–3,000 More than Ұ3,000

10 (9.5%) 62 (59.0%) 33 (31.4%)

1 (1.0%) 21 (20.0%) 9 (8.6%)

9 (8.6%) 41 (39.0%) 24 (22.9%)

0.29

71 (67.6%) 20 (19.0%) 14 (13.3%)

19 (18.1%) 8 (7.6%) 4 (3.8%)

52 (49.5%) 12 (11.4%) 10 (9.5%)

0.516

31 (29.5%) 61 (58.1%) 13 (12.4%)

8 (7.6%) 18 (17.1%) 5 (4.8%)

23 (21.9%) 43 (41.0%) 8 (7.6%)

0.703

34 (32.4%) 71 (67.6%)

13 (12.4%) 18 (17.1%)

21 (20.0%) 53 (50.5%)

0.176

69 (65.7%) 36 (34.3%)

19 (18.1%) 12 (11.4%)

50 (47.6%) 24 (22.9%)

0.536

88 (83.8%) 17 (16.2%)

26 (24.8%) 5 (4.8%)

62 (59.0%) 12 (11.4%)

0.991

29 (27.6%) 54 (51.4%) 22 (21.0%)

11 (10.5%) 13 (12.4%) 7 (6.7%)

18 (17.1%) 41 (39.0%) 52 (49.5%)

0.404

Gender

Age

Education level

Residential area

Monthly income

Seizure type Generalized epilepsy Partial epilepsy Unclassified Seizure frequency (in the last year) 0 610 ＞10 Age at epilepsy onset (year) 610 ＞10 Duration (year) 610 ＞10 Period since last seizure (year) 61 ＞1 No. of antiepileptic drugs 0 1 P2

Please cite this article in press as: Yan S et al. Risk factors for fatigue in patients with epilepsy. J Clin Neurosci (2016), http://dx.doi.org/10.1016/j. jocn.2016.03.043

S. Yan et al. / Journal of Clinical Neuroscience xxx (2016) xxx–xxx Table 3 Multiple linear regression Model 1 2

PSQI PSQI Anxiety

Unstandardized coefficients B

Standardized coefficients b

Standard error

P value

R2

0.274 0.199 0.116

0.432 0.306 0.281

1.543 1.387

<0.001 <0.001

0.179 0.244

Dependent variable: fatigue; R2 = squared multiple correlation coefficient. PSQI = Pittsburgh Sleep Quality Index.

Pittsburgh Sleep Quality Index = 4.60 ± 2.611; Epworth Sleepiness Scale = 5.92 ± 5.040; Hospital Anxiety and Depression Scale, Depression = 6.30 ± 3.726, Anxiety = 7.06 ± 4.111, respectively) are presented in Table 2. The Fatigue Severity Scale (FSS) was used to measure fatigue in our experimental cohort. The average FSS score for all participants was 3.08 ± 1.695, 31 of 105 patients (29.5%) in our study were classified as exhibiting fatigue (FSS = 5.05 ± 0.707), and the rest 74 patients (70.5%) were not (FSS = 2.25 ± 1.243). Univariate analysis using the X2 test did not reveal a significant correlation between fatigue and any one of our demographic or clinical factors (Table 1). As for psychosocial variables, it was found that high PSQI scores (Spearman’s q = 0.386, P < 0.001) anxiety (Spearman’s q = 0.416, P < 0.001), and depression (Spearman’s q = 0.319, P = 0.001) were significantly correlated with fatigue, whereas ESS scores were not (Spearman’s q = 0.046, P = 0.644) (Table 2). In addition, multiple linear regression analyses were performed to explore the power of these psychosocial variables as predictors of fatigue in epilepsy patients. PSQI and anxiety (P < 0.001) remained independently associated with the occurrence of fatigue, while depression did not (Table 3).

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difference in fatigue levels between patients taking one of three commonly described AED monotherapies (valproate, lamotrigine, and levetiracetam) [5,6], while Carpay et al. surveyed patients’ complaints about AEDs in a community-based population and found that memory problems (21.4%) and fatigue (20.3%) were commonly reported [18]. In this study, we found that epilepsy-related fatigue is correlated with low sleep quality, anxiety, and depression, but not with excess daytime sleepiness. These results suggested that therapeutic approaches to improve sleep quality, anxiety, and depression may alleviate fatigue in patients with epilepsy as well. Furthermore, it is not necessary to adjust the kind or doses of AEDs as one of the treatments for epilepsy-related fatigue. Studies on the treatment and prevention of fatigue in epilepsy are rare. Hamelin et al. suggested that central fatigue (characterized by an enhanced perception of effort and limited endurance of sustained physical and mental activities) plays a key role in epileptic fatigue [19]. It has also been suggested that dysfunction of the dopaminergic system is involved in both fatigue and depression, suggesting that drugs modulating the dopaminergic system might be effective in treating fatigue in patients with epilepsy [6]. The effectiveness of vitamins and herbs could also be evaluated in future studies, as they were previously shown to reduce epilepsy-related fatigue [20,21]. Non-pharmacologic approaches to the fatigue management including aerobic exercise programs, energy conservation strategies, and cognitive behavior therapy should also be considered. In conclusion, we found that while fatigue is prevalent in patients with epilepsy, there was no correlation between the occurrence of fatigue and any demographic or clinical variables. Instead, fatigue was correlated with anxiety, depression, and low sleep quality. Both pharmacologic and non-pharmacologic approaches for the treatment of fatigue in patients with epilepsy should be adopted.

4. Discussion In the present study, we found that 29.5% of epilepsy patients exhibited fatigue (FSS score P4), a proportion which was lower than that reported by Soyuer et al. (42.2%) and Ettinger et al. (44.0%) using the same fatigue scale [4,5]. This difference might arise from disease variance, cultural background, and different social support conditions. We found significant correlations between fatigue severity and some psychological variables. Patients with low sleep quality tended to be more fatigue, a finding in agreement with that of Neves and colleagues using the same method in a group of 98 epilepsy patients [16]. The incidence of depression was significantly associated with fatigue in univariate analysis (P = 0.001), though the significance vanished in multiple linear regression, which was also in accordance with previous studies [4–6]. Similarly, we found that anxiety could be a powerful predictor of fatigue in epilepsy (P < 0.001), but this result could be an epiphenomenon due to the high incidence of anxiety in epilepsy (affecting more than 40% of patients in some reports) [17]. Contrary to Hamelin and colleagues’ conclusion that fatigue was associated with high ESS scores, we did not find a significant correlation between fatigue severity and levels of excessive daytime sleepiness. The occurrence of ESS is to a large extent due to primary illness rather than epilepsy-related factors, and that may in part account for this discrepancy. Seizure frequency and the number of different AEDs taken by patients could be indicators for the severity of epilepsy, but we did not find any correlation between these variables and fatigue severity. It is likely that the neurobiological mechanism between epileptic fatigue and seizures is a more complex one, and that disease severity has a limited influence on fatigue. Hamelin et al. pointed out that there was no

Conflicts of Interest/Disclosures The authors declare that they have no financial or other conflicts of interest in relation to this research and its publication.

Acknowledgements This work was supported by grants from the Natural Science Foundation of China (31570848), Chinese national key laboratory for prevention and control of infectious disease (2014SKLID304), and Fourth Military Medical University undergraduate students scientific research (2013100).

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