Epilepsy & Behavior 43 (2015) 81–88

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Quality of life in psychogenic nonepileptic seizures and epilepsy: The role of somatization and alexithymia Laurie Dempsey Wolf a, Joseph G. Hentz b, Kristine S. Ziemba c, Kristin A. Kirlin d, Katherine H. Noe c, Matthew T. Hoerth c, Amy Z. Crepeau c, Joseph I. Sirven c, Joseph F. Drazkowski c, Dona E.C. Locke d,⁎ a

Arizona State University, Department of Psychology, 651 E. University Drive, Tempe, AZ 86287, USA Mayo Clinic Arizona, Department of Biostatistics, 13400 E. Shea Blvd., Scottsdale, AZ 85259, USA Mayo Clinic Arizona, Department of Neurology, 5777 E. Mayo Blvd., Phoenix, AZ 85054, USA d Mayo Clinic Arizona, Division of Psychology, 13400 E. Shea Blvd., Scottsdale, AZ 85259, USA b c

a r t i c l e

i n f o

Article history: Received 2 October 2014 Revised 29 November 2014 Accepted 7 December 2014 Available online xxxx Keywords: Psychogenic seizures Epilepsy Quality of life Somatization Alexithymia

a b s t r a c t It is clear that many individuals with psychogenic nonepileptic seizures (PNESs) often present with poorer quality of life compared with those with epileptic seizures (ESs). However, the mechanisms linking seizure diagnosis to quality-of-life outcomes are much less clear. Alexithymia and somatization are emotional markers of psychological functioning that may explain these differences in quality of life. In the current study, patients from an epilepsy monitoring unit with vEEG-confirmed diagnosis of PNESs or ESs were compared on measures of alexithymia, somatization, quality of life, and a variety of demographic and medical variables. Two models using alexithymia and somatization individually as mediators of the relations between diagnosis and quality of life were tested. Results indicated that patients with PNESs had significantly poorer quality of life compared with those with ESs. Alexithymia was associated with poor quality of life in both groups but did not differentiate between diagnostic groups. Further, alexithymia did not mediate the relationship between diagnosis and quality of life. Somatization was associated with poor quality of life, and patients with PNESs reported greater somatization compared with patients with ESs. Somatization also significantly mediated the relationship between diagnosis and quality of life. In conclusion, somatization may be one mechanism affecting poor quality of life among patients with PNESs compared with ESs and should be a target of comprehensive treatments for PNESs. Alexithymia proved to be an important factor impacting quality of life in both groups and should also be targeted in treatment for patients with PNESs and patients with ESs. © 2014 Elsevier Inc. All rights reserved.

1. Introduction Beyond EEG differences, patients with epileptic seizures (ESs) and psychogenic nonepileptic seizures (PNESs) often present with distinct psychological histories and levels of physical functioning, including the ability to complete daily activities without interference from seizure symptoms [1]. Of particular importance, PNES is associated with significantly poorer health-related quality of life compared with ES [2,3]. Research has suggested that a history of trauma may be one reason for this difference in quality of life because, although both diagnoses may be associated with a history of psychological trauma and abuse, rates of trauma are often significantly higher in PNESs than in ESs [4,5]. Further, in a sample of individuals with PNESs, nearly 74% had experienced at least one traumatic event, and 40% reported physical or sexual abuse [6]. The elevated frequency of trauma among populations with PNESs suggests that seizure-like symptoms in PNESs may be ⁎ Corresponding author at: Division of Psychology, 13400 E. Shea Blvd., Scottsdale, AZ 85259, USA. Tel.: +1 480 301 8297; fax: +1 480 301 6258. E-mail address: [email protected] (D.E.C. Locke).

http://dx.doi.org/10.1016/j.yebeh.2014.12.010 1525-5050/© 2014 Elsevier Inc. All rights reserved.

physical expressions of psychologically traumatic experiences or psychological distress [7]. In addition to trauma history, poorer quality of life in patients with PNESs is also associated with higher levels of comorbid psychopathology, including depressive, anxiety, and somatization disorders, compared with people with ESs [3,5,8–11]. In fact, emotional regulation difficulties in people with PNESs are associated with poorer psychiatric health, emotional awareness, and quality of life compared with patients with ESs [10–12]. Trouble regulating emotions may contribute significantly to emotional distress and poor quality of life if individuals are unable to cope adaptively with the emotional and physical challenges of having a seizure or seizure-like disorder. Instead of using adaptive coping strategies to handle psychological distress, individuals may exhibit avoidant coping or somatic symptoms. Taken together, trouble understanding and coping with emotions and related distress among individuals with PNESs may play a role in decreasing their quality of life more so than in patients with epilepsy. Despite research demonstrating these differences in quality of life, however, the mechanisms explaining how poor emotional regulation among individuals with PNESs translates into poorer quality of life remain unclear.

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Alexithymia, a construct referring to difficulty identifying and expressing emotions and differentiating them from bodily sensations [13,14], may be one mechanism that can explain poorer quality of life in patients with PNESs. Individuals with alexithymia have trouble recognizing and expressing emotions, which may lead to a tendency to express difficult emotions in a somatic form. For example, high alexithymia scores have been associated with PNESs and psychological symptoms of trauma, suggesting that PNES may be a type of somatic conversion disorder associated with an inability to identify and properly express emotions [13,15,16]. However, little research has examined whether people with PNESs and those with ESs differ in measures of alexithymia. The scant research available suggests that although alexithymia is higher in PNESs than in ESs, the differences are often negligible. For example, in one study, alexithymia was reported in approximately one-third of both patients with PNESs and patients with ESs [16]. In a second study, 90.5% of individuals with PNESs had alexithymia compared with 76.2% of individuals with ESs, with no significant differences between the two diagnoses, suggesting that alexithymia may be equally important in both conditions [17]. Despite the mixed research on alexithymia differences between PNESs and ESs, an inability to properly identify and express emotions may have important implications for predicting quality of life in the two conditions. Specifically, research on PNESs and ESs has not yet examined the relationship between alexithymia and quality of life such that quality of life may be affected differently by alexithymia in PNESs than in ESs even if the prevalence of alexithymia is similar. Similar to the lack of emotional awareness found in alexithymia, somatization may be an especially important mechanism in explaining differences in quality of life between ESs and PNESs through its association with emotional dysregulation. In fact, patients with PNESs also are known to have higher somatization tendencies compared with patients with ESs [18–20]. For example, PNES is associated with higher scores on scales related to somatization on the Minnesota Multiphasic Personality Inventory — 2 (MMPI-2 and MMPI-2-RF) [2,21,22]. Compared with patients with ESs, patients with PNESs display an increased tendency to experience negative emotion as measured by the NEO Personality Inventory — Revised (NEO-PI-R), as well as higher somatic complaints, higher somatization tendency, and higher physiological features of depression and anxiety on the Personality Assessment Inventory (PAI) [7,23–27]. Further, patients with PNESs are also more likely than patients with ESs to meet the criteria for cluster A or B personality disorders (e.g., paranoid, borderline, and histrionic), whereas patients with ESs more commonly meet the criteria for cluster C personality disorders (e.g., avoidant and dependent) [25,28], providing more support for emotional dysregulation as a key feature of PNESs. Taken together, difficulties in emotional recognition, expression, and regulation within PNESs may result in somatization symptoms that significantly decrease quality of life in patients with PNESs compared with patients with ESs. The current study hypothesized that [1] patients with PNESs would have significantly lower quality of life compared with patients with ESs; [2] patients with PNESs would have significantly higher scores on an alexithymia measure, both on overall scores and subscales, compared with patients with ESs; [3] patients with PNESs would have higher levels of somatization compared with patients with ESs; [4] alexithymia would mediate the relations between diagnosis (i.e., PNESs vs. ESs) and quality of life; and [5] somatization would mediate the relations between diagnosis (i.e., PNESs versus ESs) and quality of life. 2. Methods 2.1. Participants In July of 2011, a self-report measure of alexithymia and a self-report measure of quality of life were added to the standard neuropsychological and personality evaluation of patients in our epilepsy monitoring unit (EMU). Since that time until the end of 2013, a total of 285

individuals completed these measures as part of the neuropsychological evaluation during diagnostic admission to the EMU. The final consensus diagnosis was determined from the video-EEG discharge summary. Our diagnostic classifications were based on the following definitions: [1] epilepsy only = typical events occurred with epileptiform discharges, or, if no typical events occurred, the described semiology was concerning for epilepsy, interictal epileptiform discharges were seen on EEG, and medication was started at discharge; [2] PNES only = typical events occurred with no epileptiform discharges, no interictal epileptiform abnormalities, and no other physiological reasons for seizure-like events; [3] both = multiple typical events occurred, with some events showing no EEG correlate suggesting PNESs, and others showing EEG correlate consistent with epilepsy, or typical events were recorded, with no EEG correlate consistent with NES, but interictal epileptiform activity was also recorded independent of clinical symptoms, and seizure medications were continued or started; and [4] nondiagnostic = no typical events recorded and no interictal EEG abnormalities to suggest epilepsy. These criteria revealed 91 (32%) patients diagnosed with epilepsy and 85 (30%) diagnosed with PNESs. These patients were the focus of the current study. Of the remaining sample of patients (who were excluded), 71 (25%) had a nondiagnostic admission, 8 (3%) had both epilepsy and PNESs, and 30 (10%) had other physiological diagnoses (e.g., migraine, sleep disorder, and autonomic dysregulation). It is noted that there is some overlap between this sample and the sample included in Purdom et al. [29]. However, it is not the same sample as a portion of the Purdom et al. sample predates administration of the alexithymia and quality-of-life measure, and a portion of the current sample was collected since the Purdom et al. study was reported. 2.2. Measures 2.2.1. 20-Item Toronto Alexithymia Scale Alexithymia was measured using the Toronto Alexithymia Scale (TAS-20; 30). Twenty items measure three different factors: difficulty identifying feelings, difficulty describing feelings, and externally orientated thinking. Items are scored on a Likert scale ranging from “strongly disagree” to “strongly agree”. Higher scores represent higher levels of alexithymia. The TAS-20 has been shown to be a reliable and valid measure of alexithymia [30]. Gender normed T-scores were used to generate TAS-20 scores used in analyses. 2.2.2. Quality of life in epilepsy Quality of life was measured using the 31-item version of the Quality of Life in Epilepsy Inventory (QOLIE-31; [31]). Responses are rated on several Likert scales including scales ranging from “worst possible quality of life” to “best possible quality of life” and items asking about how the patient feels ranging from “all of the time” to “none of the time”. The QOLIE-31 generates a total quality-of-life score as well as qualityof-life subscales within the areas of seizure worry, overall quality of life, emotional well-being, fatigue, cognitive, medication effects, and social functioning. The QOLIE-31 has high reliability and validity among adults with epilepsy diagnoses [31]. Higher scores represent greater quality of life. T-scores normed from epilepsy samples were used to generate QOLIE-31 scores used in analyses. 2.2.3. Personality Assessment Inventory Somatization personality features were assessed using the Personality Assessment Inventory (PAI; 32). The PAI is a 344-item questionnaire that uses a Likert scale ranging from “false” to “very true”. The PAI includes 22 scales including 4 validity scales, 11 clinical scales, 5 treatment indicator scales, and 2 interpersonal scales. The PAI Somatic Complaints Scale (SOM) was designed to evaluate various somatoform presentations by assessing preoccupation with health and physical functioning that can cause significant impairment and psychological distress and indicate possible somatoform disorders [32]. It has been shown to

L.D. Wolf et al. / Epilepsy & Behavior 43 (2015) 81–88

accurately classify approximately 80% of EMU-based samples with PNESs or ESs [26,29,33]. SOM also outperforms similar measures on the MMPI-2 and MMPI-2-RF in distinguishing between PNESs and ESs [26]. To assess the potential influence of anxiety and depression, we also examined the PAI Anxiety (ANX), Anxiety-Related Disorders (ARD), and Depression (DEP) Scales. High scores on these scales reflect significant anxiety, traumatic stress, and depressive symptoms [32]. Tscore norms in comparison with the general population were used in analyses. 2.3. Procedure As part of the neuropsychologist's semistructured interview in the EMU, we prospectively collected the following demographic, medical history, and psychosocial information: age, gender, handedness, years of education, employment status, disability status, driving status, age at onset, frequency of seizures at the time of admission, number of antiepileptic medications (AEDs) at the time of admission, presence of current psychotropic medications, past psychological treatment history, substance abuse history, abuse history, and nonabuse trauma/stressor history. Psychological treatment history was broadly defined to include current or past history of psychotropic medications from a primary care or other physicians; patient self-report of receiving treatment for mood, anxiety, or other psychological problems; history of marital or family counseling; history of individual counseling/therapy; history of seeing a psychiatrist for evaluation; or inpatient psychiatric hospitalization. Substance abuse history was coded separately and was not included under psychological treatment history. We did not administer structured diagnostic interviews for any disorder [e.g., major depression and posttraumatic stress disorder (PTSD)]. Therefore, specific diagnostic categories were not created, but rather patients were coded as having a psychiatric treatment history if they self-reported treatment for any disorder. For AEDs that are also used for chronic pain or psychological symptoms, the reason for prescription was clarified with the patient, and it was coded under that category. Demographic information, medical history variables, psychological history variables, TAS-20, PAI scales of interest, and QOLIE-31 scores were evaluated using independent sample t-tests with Cohen's d effect sizes (for continuous variables) or using X2 (for categorical variables). The critical α was 0.05. Cohen's d effect sizes were considered small when N.20, medium when N.50, and large when N.80 [34]. Individuals who demonstrated cognitive difficulties during the neuropsychologist interview were excluded from completing the PAI by the treating neuropsychologist. Completed PAI profiles were reviewed, and invalid profiles (n = 3) suggestive of inability to understand the measure or random responding (inconsistency scores ≥ 73 and infrequency scores ≥ 75) as recommended by the test manual were excluded from PAI-related analyses.

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To identify potential covariates, demographic, medical, and psychological variables were tested individually as predictors of quality of life in a regression including diagnosis to determine if any of variables significantly impacted the relationship between diagnosis and quality of life. Additionally, we tested whether any of the demographic, medical, and psychological variables interacted with each other to affect quality of life. Only variables that contributed significant variation to the relationship between diagnosis and quality of life were retained in the models. To identify the mediating effect of alexithymia in model 1 (see Fig. 1) and the mediating effect of somatization in model 2 (see Fig. 2) on the relationship between diagnosis and quality of life while controlling for relevant covariates, we utilized the PROCESS macro [35] for SPSS 21. The PROCESS macro uses a regression-based framework including bootstrapping techniques to create confidence intervals and estimates of mediating and moderating effects while controlling for specified covariates [35]. The results provide an estimate of each path within the model as well as an overall indirect mediation effect. Mediation occurs when a particular variable (the mediator) is responsible for part or all of the relationship between the predictor variable (e.g., diagnosis) and the dependent variable (e.g., quality of life). An indirect effect (i.e., mediating effect) is considered significant if the confidence interval does not include zero. The bootstrapping mediation procedure is recommended over previous frameworks (e.g., Baron and Kenny and Sobel) as it provides increased power to detect a mediating effect while controlling the Type 1 error rate [35,36]. These analyses were conducted using data from an existing clinical database that had been de-identified and without requiring any additional contact with patients. Given that design, the Mayo Clinic IRB determined this study was exempt from IRB review or continuing review (3/24/14). 3. Results Table 1 summarizes comparisons of demographic, medical history, psychological history, TAS-20, PAI scales of interest, and QOLIE-31 scores. Independent sample t-tests indicated that patients with ESs (M = 1.62, SD = 1.11) were on more antiepileptic medications upon admission to the EMU compared with patients with PNESs (M = .89, SD = .91), t(171) = 4.71, p b .001, d = 72. Patients with PNESs (M = 35.97, SD = 16.54) had a significantly later age at seizure onset compared with patients with ESs (M = 25.31, SD = 17.71), t(174) = − 4.12, p b .001, d = 62. Chi-square results indicated that 39% of patients with PNESs were on or applying for disability versus 23% of patients with ESs (p b .05); 88% of patients with PNESs had a psychological treatment history versus 71% of patients with ESs (p b .01); 51% of patients with PNESs were currently taking prescribed psychiatric medications versus 31% of patients with ESs (p b 01); 52% of patients with PNESs had a history of abuse versus 18% of patients with ESs

Fig. 1. Model testing alexithymia as a mediator of the relations between diagnosis and quality of life with disability status, psychiatric treatment history, and antiepileptic drugs as covariates. Note: *significant at 0.5 level; **significant at .01 level; ***significant at .001 level. The indirect mediation effect was not significant, point estimate = -.51, (CI -1.94 to .64).

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Fig. 2. Model testing somatization as a mediator of the relations between diagnosis and quality of life with disability status, psychiatric treatment history, and antiepileptic drugs as covariates. Note: *significant at 0.5 level; **significant at .01 level; ***significant at .001 level. The indirect mediation effect was not significant, point estimate = −4.08, CI (−6.35, −2.23).

(p b .001); 60% of patients with PNESs had a history of other traumas versus 37% of patients with ESs (p b .01); and 39% of patients with PNESs had daily seizures versus 14% of patients with ESs (p b .001). Patient groups did not differ on age; handedness; education; employment status; driving status; febrile seizures; multiple seizure types; substance abuse history; or PAI ANX, ARD, or DEP scores. All of the above were considered potential covariates in the planned regression analyses of the primary study variables. Given the high number of group differences, there is not enough statistical power to run a model with all possible covariates in the model. Including all variables in the model at the same time would create unstable estimates of coefficients because of multicollinearity and insufficient degrees of freedom. Therefore, we first modeled the relationship between diagnosis and QOL. Each covariate was then added individually to this model to

determine if the covariate explained any of the relationship between diagnosis and quality of life. We retained covariates that most strongly impacted the relationship between diagnosis and QOL as defined by a 25% or more change in the coefficient for diagnosis when the covariate was in the model as compared to when it was out of the model. We then ran another model serially adding those with the largest change until the change with additional coefficients was minimal. Through that process, it was determined that disability status, psychiatric treatment history, and AEDs were the most impactful covariates and were retained in the final model demonstrated in Figs. 1–3. Despite evidence of significantly different rates of trauma and abuse in the group with PNESs compared with the group with ESs, trauma and abuse history did not explain any of the relationship between diagnosis and quality of life. There were no significant interactions between these covariates.

Table 1 Characteristics and comparisons of patients with PNESs and ESs. Variable

Age at evaluation Gender Handedness Education Employment status Disability status Driving status Age at onset Seizure frequency Febrile seizures Multiple seizure types No. of AEDs upon admission Current psychiatric medications Psychological history Substance abuse history Any abuse history Other trauma histories TAS-20 total TAS-20 factor 1 TAS-20 factor 2 TAS-20 factor 3 Total quality of life Seizure worry quality of life Overall quality of life Emotional quality of life Fatigue quality of life Cognitive quality of life Medication quality of life Social quality of life PAI somatic complaints PAI anxiety PAI anxiety-related disorders PAI depression

PNESs (n = 85)

ESs (n = 91)

M

SD

M

SD

41.79 66% female 93% right-handed 14 years 46% yes 39% yes 36% yes 35.97 39% daily 5% yes 52% yes .89 51% yes 88% yes 15% yes 52% yes 60% yes 54.73 59.11 51.69 49.85 38.63 44.67 44.61 48.27 39.90 41.69 46.60 38.20 74.83 56.81 54.19 61.54

14.13

39.45 53% female 93% right-handed 14 years 57% yes 23% yes 49% yes 25.31 14% daily 7% yes 54% yes 1.62 31% yes 71% yes 22% yes 18% yes 37% yes 53.07 55.42 50.17 51.22 43.34 45.60 45.21 47.82 45.27 46.71 46.31 42.78 64.98 55.34 52.77 58.84

16.28

2.55

16.54

.91

11.99 12.09 11.63 11.10 10.48 11.47 32.47 10.55 10.19 10.57 10.70 10.02 14.43 13.53 12.99 12.39

Note. d = Cohen's d effect size, PNESs = psychogenic nonepileptic seizures, ESs = epileptic seizures.

2.45

17.71

1.11

10.81 13.19 11.19 9.47 11.84 10.40 10.63 10.36 10.91 12.37 10.77 10.83 11.61 12.42 11.55 12.59

p-Value

d-Value

.31 .08 .87 1.00 .14 .03 .08 b.001 b.001 .86 .37 b.001 b.01 b.01 .26 b.001 b.01 .33 .06 .38 .38 b.01 .58 .87 .78 b.01 b.01 .86 b.01 b.001 .46 .45 .16

.15 N/A N/A 0 N/A N/A N/A .62 N/A N/A N/A .72 N/A N/A N/A N/A N/A .15 .29 .13 .13 .42 .08 .02 .04 .51 .44 .03 .44 .75 .11 .12 .22

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Fig. 3. Moderated mediation model in which somatization mediates the relations between diagnosis and quality of life and alexithymia moderates the relations between somatization and quality of life with disability status, psychiatric treatment history, and antiepileptic drugs as covariates. Note: *significant at .05 level; **significant at .01 level; ***significant at .001 level.

In regard to primary study variables, independent sample t-tests indicated that the group with PNESs (M = 38.63, SD = 10.48) had significantly poorer quality of life on the QOLIE-31 total compared with the group with ESs (M = 43.34, SD = 11.84), t(168) = 2.74, p b .01, d = .42. On subscales of the QOLIE-31, patients with PNESs had significantly poorer quality of life related to fatigue t(171) = 3.33, p b .001, d = .51; cognitive concerns t(171) = 2.86, p b .01, d = .44; and social functioning t(170) = 2.87, p b .01, d = .44 compared with patients with ESs. Patients with PNESs (M = 54.73, SD = 11.99) and patients with ESs (M = 53.07, SD = 10.81) did not differ on total TAS-20 alexithymia scores, t(174) = −.97, p = .33, CI (−5.06, 1.73), d = .15. The groups also did not differ on the first factor, t(174) = − 1.93, p = .06, CI (− 7.46, .08), d = .29, second factor, t(174) = − .89, p = .38, CI (− 4.92, 1.87), d = .13, or third factor, t(174) = .88, p = .38, CI (−1.69, 4.43), d = .13 of the TAS-20 subscales. Twenty-eight percent of individuals in the group with ESs fell into the alexithymic range (raw score N 61) compared with 31% of individuals in the group with PNESs. Finally, the group with PNESs (M = 74.83, SD = 14.43) had significantly higher SOM scores on the PAI compared with the group with ESs (M = 64.98, SD = 11.61), t(157) = −4.92, p b .001, d = .75. Thirty-four percent of individuals in the group with ESs demonstrated high somatization scores (T-score ≥ 70) compared with 60% of individuals in the group with PNESs. Table 2 presents the correlation coefficients between TAS-20, SOM, QOLIE, abuse, and trauma variables. PNES diagnosis was significantly associated with higher scores on the PAI SOM, the presence of abuse and trauma, and lower total QOLIE-31 scores. Higher scores on the TAS-20 were significantly correlated with higher PAI SOM, lower QOLIE-31, and the presence of abuse. Higher SOM scores were significantly correlated with lower QOLIE-31 and the presence of abuse. Lastly, the

presence of trauma history was significantly correlated with poorer quality of life. It is important to note, however, that the correlations between abuse and trauma and the TAS-20, SOM, and QOLIE-31 were smaller (r b .20) than the associations between abuse and trauma and diagnosis (r = .30 and r = .23). Model 1, in which we tested whether alexithymia mediated the relationship between diagnosis and quality of life controlling for medical history covariates (disability, AEDs, and psychological treatment history), was not significant (see Fig. 1). Specifically, diagnosis did not predict alexithymia, B = 1.69, SE = 1.89, p = .37, CI (−2.05, 5.42). Alexithymia significantly predicted lower quality of life such that a one-point increase in alexithymia equated to a .30-point decrease in quality of life, B = −.30, SE = .07, p b .001, CI (−.43, −.17). Contrary to our hypothesis, the total indirect effect of alexithymia as a mediator between diagnosis and quality of life was not significant, point estimate = − .51, CI (−1.94, .64). Diagnosis also did not predict the direct effect on quality of life, B = −2.63, SE = 1.63, p = .11, CI (−5.85, .58). Lastly, being on or seeking disability decreased quality-of-life scores by 6.97 points, B = −6.97, SE = 1.64, p b .001, CI (−10.20, −3.74); positive psychiatric treatment history decreased quality-of-life scores by 6.27 points, B = − 6.27, SE = 1.91, p b .01, CI (− 10.05, − 2.49); but the addition of each AED did not significantly predict quality of life, B = − .94, SE = .74, p = .21, CI (-2.41, .53). Model 2, in which we tested whether somatization mediated the relationship between diagnosis and quality of life controlling for medical history covariates (disability, AEDs, psychiatric treatment history), was significant (see Fig. 2). Specifically, diagnosis predicted greater somatization such that patients with PNESs scored 8.82 points higher on the SOM scale, B = 8.82, SE = 2.07, p b .001, CI (4.73, 12.90). A one-point increase in SOM significantly decreased quality of life by .46

Table 2 Intercorrelations of study variables. Variable

1

2

3

1. Diagnosis 2. TAS total 3. TAS F1 4. TAS F2 5. TAS-F3 6. SOM 7. QOLIE 8. Abuse 9. Trauma

– .07 .15 .07 −.07 . 36⁎⁎⁎ −.21⁎⁎ .30⁎⁎⁎ .23⁎⁎

– .83⁎⁎⁎ .87⁎⁎⁎ .63⁎⁎⁎ .36⁎⁎⁎ −.39⁎⁎⁎ .15⁎

– .64⁎⁎⁎ .18⁎ .47⁎⁎⁎ −.47⁎⁎⁎

.04

Note. Diagnosis is coded ESs = 1 and PNESs = 2. ⁎ Significant at .05 level. ⁎⁎ Significant at .01 level. ⁎⁎⁎ Significant at .001 level.

.12 .08

4

– .46⁎⁎⁎ .34⁎⁎⁎ −.44⁎⁎⁎ .19⁎ .10

5

6

7

8

9

– −.05 .07 .06 −.11

– −.64⁎⁎⁎ .17⁎ .07

– −.13 −.18⁎

– .28⁎⁎⁎

–

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points, B = −.46, SE = .06, p b .001, CI (−.57, −.35). Consistent with our hypothesis, the total indirect effect of SOM as a mediator between diagnosis and quality of life was significant, point estimate = − 4.08, CI (–6.35, −2.23) indicating that SOM significantly mediated the relationship between diagnosis and quality of life. Further, diagnosis did not predict quality of life directly, B = .80, SE = 1.55, p = .61, CI (−2.27, 3.86). Lastly, being on or seeking disability lowered quality of life by 4.10 points, B = − 4.10, SE = 1.54, p b .01, CI (− 7.14, − 1.06), and psychological treatment history lowered quality of life by 4.34 points, B = − 4.34, SE = 1.74, p b .05, CI (− 7.78, − .90). Number of AEDs, B = − .48, SE = .67, p = .48, CI (− 1.81, .85), did not predict quality of life. Alexithymia was not a significant mediator in Model 1, but it was correlated with greater somatization, as measured by the PAI SOM scale, and predicted lower quality of life. Thus, alexithymia may play an important role in determining differences in quality of life between patients with ESs and patients with PNESs indirectly through its association with somatization. To test this hypothesis, we completed post hoc analyses to explore how alexithymia might impact the relationship between somatization and quality of life in patients with ESs and in those with PNESs. Specifically, we tested a moderated mediation model that extended Model 2 (somatization mediating the relationship between diagnosis and quality of life) by adding alexithymia as a moderator of the relationship between somatization and quality of life (see Fig. 3). This moderated mediation model was not significant. Diagnosis predicted somatization such that patients with PNESs scored 8.82 points higher on the somatization scale, B = 8.82, SE = 2.07, p b .001, CI (4.73, 12.90). A one-point increase in somatization significantly decreased quality of life by .53 points, B = −.53, SE = .23, p b .05, CI (−.99, −.07). Contrary to our hypothesis, alexithymia did not moderate the relationship between somatization and quality of life within the mediation model, B = .002, SE = .004, p = .62, CI (− .006, .01). Further, diagnosis did not predict the direct effect on quality of life, B = .73, SE = 1.52, p = .63, CI (−2.28, 3.74). Lastly, being on or seeking disability lowered quality of life by 4.24 points, B = −4.24, SE = 1.54, p b .01, CI (−7.29, − 1.19), and psychological treatment history lowered quality of life by 3.85 points, B = − 3.85, SE = 1.72, p b .05, CI (− 7.26, − .45). Number of AEDs, B = − .24, SE = .67, p = .72, CI (− 1.56, 1.08), did not predict quality of life. 4. Discussion The primary aims of this study were to compare quality of life among people with PNESs and those with ESs and to determine how alexithymia and somatization may play a role in explaining potential differences. Lower quality of life among patients with PNESs compared with patients with ESs in the current study corroborates previous research [2,3]. Further, patients with PNESs in the current study reported poorer quality of life in specific domains including fatigue, cognitive abilities, and social functioning compared with patients with ESs. In addition, quality of life among all patients in the current study was negatively associated with taking a higher number of epilepsy medications, having a significant psychological treatment history, and being on or applying for disability. These associations are useful as they signify the importance of demographic-related medical factors in understanding quality of life in both patients with ESs and patients with PNESs. Therefore, not only do patients with PNESs and patients with ESs struggle with both domain-specific quality of life concerns as well as qualityof-life issues in general, but their quality of life is also impacted by psychosocial and medical factors. Why might patients with PNESs have poorer quality of life compared with patients with ESs? The ways in which patients with PNESs express their psychological experiences may be key. Alexithymia is one factor that has been explored as a way to differentiate between patients with PNESs and those with ESs in relation to quality of life because of the tendency to be unable to properly understand or express emotional

experiences found among patients with PNESs [15,16]. In the current study, however, patients with PNESs did not demonstrate higher levels of alexithymia in overall scores or subscale scores of an alexithymia measure compared with patients with ESs. In fact, similar to Myers et al. [16], approximately one-third of individuals in either diagnostic group were alexithymic. Both our findings and Myers et al. [16] show a much smaller percentage of individuals with alexithymia compared with Bewley et al. [17], suggesting that there may be room for further research. For example, might patient referral patterns or other situational factors explain such wide variability in alexithymic categorization across studies? Further, alexithymia did not significantly mediate the relationship between diagnosis and quality of life, meaning that alexithymia did not contribute to quality-of-life differences between the two groups. Rather, high alexithymia scores were associated with poorer quality of life in both patient groups. This is consistent with past studies reporting negligible differences in alexithymia between PNESs and ESs [16,17]. Therefore, alexithymia does not appear to be an important factor associated with PNESs or ESs in particular but instead is equally important in impacting quality of life in patients with either diagnostic condition. It may be that more specific aspects of emotional detachment similar to alexithymia, such as dissociative experiences often found in PNESs [37], play a role in linking alexithymia to quality-of-life differences between the two patient groups. For example, dissociative experiences in PNESs are associated with significantly lower health-related quality of life [38]. Future research may benefit from examining whether dissociative experiences are linked to emotional detachment components of alexithymia in explaining the complex relations between emotional regulation and quality of life in patients with PNESs. Similar to alexithymia, somatization has also been explored as a differentiating factor between patients with PNESs and those with ESs and may explain quality-of-life differences. In fact, patients with PNESs in the current study reported greater somatization compared with patients with ESs, which is consistent with previous research [2, 18,21,22]. Further, somatization scores mediated the link between diagnosis and poorer quality of life, meaning that somatization is responsible for the relationship between diagnosis and quality of life. That is, diagnosis did not predict quality of life directly, but diagnosis predicted somatization, which then predicted quality of life. This link existed even when psychological treatment history and diagnosis-related severity factors (e.g., disability and number of AEDs) were taken into account, suggesting a strong effect of somatization on quality of life of patients with PNESs compared with patients with ESs. Overall, results suggest that patients with PNESs in the current study experienced more symptoms of somatization compared with patients with ESs and that such somatization was associated with poorer quality of life among patients with PNESs. Recognizing that somatization among patients with PNESs relates to poorer quality of life compared with patients with ESs provides one step toward understanding quality-of-life differences between these two diagnostic groups. However, the mechanisms by which somatization leads to poorer quality of life remain unclear. Given the link between alexithymia and quality of life regardless of diagnosis, potential mechanisms may lie within the way somatization relates to alexithymia in regard to difficulty understanding and expressing emotions. In fact, somatization and alexithymia are correlated in samples with epilepsy [16] and are also associated in the current study. In post hoc analyses, the current study explored whether alexithymia moderated the relationship between somatization and quality of life (Fig. 3). However, alexithymia did not prove to be a moderator of the somatization quality-of-life link. Therefore, any connection between alexithymia and somatization is likely not a mechanism by which somatization relates to poorer quality of life among patients with PNESs compared with patients with ESs. It is important to note that the current study has several limitations. For example, data on psychological treatment and trauma history were

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collected via self-report during a semistructured clinical interview with a neuropsychologist. These interview questions are asked of all patients prospectively during admission as part of their clinical neuropsychological evaluation, but we did not use a structured diagnostic clinical interview, such as the SCID, for assessing psychiatric diagnoses. Instead, we assessed a broad range of psychological areas in which patients may have been struggling. Although this may have not provided as detailed diagnostic information as a comprehensive structured diagnostic interview, we were able to prospectively collect a wide range of information during the short amount of time allowed for testing in the EMU instead of relying on retrospective chart reviews. Future research would benefit from comprehensive, diagnostic assessments of PTSD, depression, anxiety, and other relevant psychiatric diagnoses that may contribute to functioning in patients with PNESs or ESs in an attempt to evaluate if specific psychiatric diagnoses may directly explain the quality-of-life difference between diagnoses more than others and to investigate if other psychiatric problems may function as a mechanism to explain the link between somatization and quality of life. Finally, this study used a sample of patients with epilepsy from an EMU. Thus, it is not clear what the differences would be if a more representative population with epilepsy was included. Presumably, those who do not need EMU admission would have less severe disease and, therefore, potentially better quality of life.

[6]

[7]

[8] [9] [10]

[11]

[12]

[13] [14] [15]

5. Conclusions [16]

In the population of patients with severe enough disorders to be evaluated in an EMU, patients with PNESs experienced significantly poorer quality of life compared with patients with ESs. This difference was not due to alexithymia; rather, difficulty understanding and expressing emotions was associated with poorer quality of life in both diagnostic groups. Further, differences in trauma and abuse histories between patients with PNESs and those with ESs did not explain the relationship between diagnosis and quality of life. Conversely, somatization appeared to be a major factor in explaining poorer quality of life among patients with PNESs compared with patients with ESs. The strong connections between somatization and quality of life among patients with PNESs is concerning as research following patients with PNESs over time has found that patients with better outcomes often have fewer somatic complaints [39]. Treatment plans addressing alexithymia in patients with either diagnosis as well as targeting somatization among patients with PNESs specifically may be especially important to improving quality of life. Mindfulness-based treatments as well as cognitive behavioral therapy have shown promise for improving quality of life among patients with PNESs and patients with ESs [40,41]. Therefore, treatment for patients with ESs and those with PNESs would likely benefit from targeting not only seizures but also psychological symptoms that significantly impact quality of life.

[17]

[18]

[19] [20] [21]

[22]

[23]

[24]

[25]

Conflict of interest statement

[26]

We wish to confirm that there are no known conflicts of interest associated with this publication and that there has been no significant financial support for this work that could have influenced its outcome.

[27]

[28]

References [1] Lee G. Neuropsychology of epilepsy and epilepsy surgery. 1st ed. New York: Oxford University Press; 2010. [2] Testa SM, Schefft BK, Szaflarski JP, Yeh HS, Privitera MD. Mood, personality, and health-related quality of life in epileptic and psychogenic seizure disorders. Epilepsia 2007;48(5):973–82. http://dx.doi.org/10.1111/j.1528-1167.2006.00965.x. [3] Szaflarski JP, Hughes C, Szaflarski M, Ficker DM, Cahill WT, Li M, et al. Quality of life in psychogenic nonepileptic seizures. Epilepsia 2003;44(2):236–42. [4] Rosenberg HJ, Rosenberg SD, Williamson PD, Wolford II GL. A comparative study of trauma and posttraumatic stress disorder prevalence in epilepsy patients and psychogenic nonepileptic seizure patients. Epilepsia 2000;41(4):447–52. [5] van Merode T, Twellaar M, Kotsopoulos IA, Kessels AG, Merckelbach H, de Krom MC, et al. Psychological characteristics of patients with newly developed psychogenic

[29]

[30]

[31] [32]

87

seizures. J Neurol Neurosurg Psychiatry 2004;75(8):1175–7. http://dx.doi.org/10. 1136/jnnp.2003.016923. Myers L, Perrine K, Lancman M, Fleming M, Lancman M. Psychological trauma in patients with psychogenic nonepileptic seizures: trauma characteristics and those who develop PTSD. Epilepsy Behav 2013;28(1):121–6. http://dx.doi.org/10.1016/j.yebeh. 2013.03.033. Bodde NM, Brooks JL, Baker GA, Boon PA, Hendriksen JG, Mulder OG, et al. Psychogenic non-epileptic seizures — definition, etiology, treatment and prognostic issues: a critical review. Seizure 2009;18(8):543–53. http://dx.doi.org/10.1016/j. seizure.2009.06.006. Owczarek K. Anxiety as a differential factor in epileptic versus psychogenic pseudoepileptic seizures. Epilepsy Res 2003;52(3):227–32. Reuber M. Psychogenic nonepileptic seizures: answers and questions. Epilepsy Behav 2008;12(4):622–35. http://dx.doi.org/10.1016/j.yebeh.2007.11.006. LaFrance Jr WC, Syc S. Depression and symptoms affect quality of life in psychogenic nonepileptic seizures. Neurology 2009;73(5):366–71. http://dx.doi.org/10.1212/ WNL.0b013e3181b04c83. Brown RJ, Bouska JF, Frow A, Kirkby A, Baker GA, Kemp S, et al. Emotional dysregulation, alexithymia, and attachment in psychogenic nonepileptic seizures. Epilepsy Behav 2013;29(1):178–83. http://dx.doi.org/10.1016/j.yebeh.2013.07.019. Uliaszek AA, Prensky E, Baslet G. Emotion regulation profiles in psychogenic nonepileptic seizures. Epilepsy Behav 2012;23(3):364–9. http://dx.doi.org/10.1016/j. yebeh.2012.01.009. Sifneos PE. Alexithymia: past and present. Am J Psychiatry 1996;153(7 Suppl.): 137–42. Taylor G, Bagby R, Parker J. Disorders of affect regulation: alexithymia in medical and psychiatric illness. Cambridge University Press; 1997. Kaplan MJ, Dwivedi AK, Privitera MD, Isaacs K, Hughes C, Bowman M. Comparisons of childhood trauma, alexithymia, and defensive styles in patients with psychogenic non-epileptic seizures vs. epilepsy: implications for the etiology of conversion disorder. J Psychosom Res 2013;75(2):142–6. http://dx.doi.org/10.1016/j.jpsychores. 2013.06.005. Myers L, Matzner B, Lancman M, Perrine K, Lancman M. Prevalence of alexithymia in patients with psychogenic non-epileptic seizures and epileptic seizures and predictors in psychogenic non-epileptic seizures. Epilepsy Behav 2013;26(2):153–7. http://dx.doi.org/10.1016/j.yebeh.2012.11.054. Bewley J, Murphy PN, Mallows J, Baker GA. Does alexithymia differentiate between patients with nonepileptic seizures, patients with epilepsy, and nonpatient controls? Epilepsy Behav 2005;7(3):430–7. http://dx.doi.org/10.1016/j.yebeh.2005. 06.006. Reuber M, House AO, Pukrop R, Bauer J, Elger CE. Somatization, dissociation and general psychopathology in patients with psychogenic non-epileptic seizures. Epilepsy Res 2003;57(2–3):159–67. http://dx.doi.org/10.1016/j.eplepsyres.2003.11.004. Baslet G. Psychogenic non-epileptic seizures: a model of their pathogenic mechanism. Seizure 2011;20(1):1–13. http://dx.doi.org/10.1016/j.seizure.2010.10.032. Owczarek K. Somatisation indexes as differential factors in psychogenic pseudoepileptic and epileptic seizures. Seizure 2003;12(3):178–81. Cragar DE, Schmitt FA, Berry DT, Cibula JE, Dearth CM, Fakhoury TA. A comparison of MMPI-2 decision rules in the diagnosis of nonepileptic seizures. J Clin Exp Neuropsychol 2003;25(6):793–804. http://dx.doi.org/10.1076/jcen.25.6.793.16471. Locke DE, Kirlin KA, Thomas ML, Osborne D, Hurst DF, Drazkowski JF, et al. The Minnesota Multiphasic Personality Inventory-2-Restructured Form in the epilepsy monitoring unit. Epilepsy Behav 2010;17(2):252–8. http://dx.doi.org/10.1016/j. yebeh.2009.12.004. Cragar DE, Berry DT, Schmitt FA, Fakhoury TA. Cluster analysis of normal personality traits in patients with psychogenic nonepileptic seizures. Epilepsy Behav 2005;6(4): 593–600. http://dx.doi.org/10.1016/j.yebeh.2005.03.007. Testa SM, Lesser RP, Krauss GL, Brandt J. Personality Assessment Inventory among patients with psychogenic seizures and those with epilepsy. Epilepsia 2011;52(8): e84–8. http://dx.doi.org/10.1111/j.1528-1167.2011.03141.x. Galimberti CA, Ratti MT, Murelli R, Marchioni E, Manni R, Tartara A. Patients with psychogenic nonepileptic seizures, alone or epilepsy-associated, share a psychological profile distinct from that of epilepsy patients. J Neurol 2003;250(3):338–46. http://dx.doi.org/10.1007/s00415-003-1009-0. Locke DE, Kirlin KA, Wershba R, Osborne D, Drazkowski JF, Sirven JI, et al. Randomized comparison of the Personality Assessment Inventory and the Minnesota Multiphasic Personality Inventory-2 in the epilepsy monitoring unit. Epilepsy Behav 2011;21(4):397–401. http://dx.doi.org/10.1016/j.yebeh.2011.05.023. Asmussen SB, Kirlin KA, Gale SD, Chung SS. Differences in self-reported depressive symptoms between patients with epileptic and psychogenic nonepileptic seizures. Seizure 2009;18(8):564–6. http://dx.doi.org/10.1016/j.seizure.2009.05.006. Harden CL, Jovine L, Burgut FT, Carey BT, Nikolov BG, Ferrando SJ. A comparison of personality disorder characteristics of patients with nonepileptic psychogenic pseudoseizures with those of patients with epilepsy. Epilepsy Behav 2009;14(3): 481–3. http://dx.doi.org/10.1016/j.yebeh.2008.12.012. Purdom CL, Kirlin KA, Hoerth MT, Noe KH, Drazkowski JF, Sirven JI, et al. The influence of impression management scales on the Personality Assessment Inventory in the epilepsy monitoring unit. Epilepsy Behav 2012;25(4):534–8. http://dx.doi. org/10.1016/j.yebeh.2012.10.005. Bagby RM, Taylor GJ, Parker JD. The twenty-item Toronto Alexithymia Scale — II. Convergent, discriminant, and concurrent validity. J Psychosom Res 1994;38(1): 33–40. Vickery B, Perrine K, Hays R, Hermann B, Cramer J, Meador K, et al. Quality of life in epilepsy. QOLIE-31. Scoring manual. 1st ed. Santa Monica, CA: RAND; 1993. Morey L. Personality Assessment Inventory: professional manual. Lutz, FL: Psychological Assessment Resources; 1991.

88

L.D. Wolf et al. / Epilepsy & Behavior 43 (2015) 81–88

[33] Wagner MT, Wymer JH, Topping KB, Pritchard PB. Use of the Personality Assessment Inventory as an efficacious and cost-effective diagnostic tool for nonepileptic seizures. Epilepsy Behav 2005;7(2):301–4. http://dx.doi.org/10.1016/j.yebeh.2005.05. 017. [34] Cohen J. Statistical power analysis for behavioral sciences. New York: Academic Press; 1977. [35] Hayes A. Introduction to mediation, moderation, and conditional process analysis: a regression-based approach (methodology in the social sciences). New York: The Guilford Press; 2013. [36] Preacher K, Hayes A. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav Res Methods 2008; 40(3):879–91. http://dx.doi.org/10.3758/BRM.40.3.879. [37] Medford N. Dissociative symptoms and epilepsy. Epilepsy Behav 2014;30:10–3. http://dx.doi.org/10.1016/j.yebeh.2013.09.038.

[38] Mitchell JW, Ali F, Cavanna AE. Dissociative experiences and quality of life in patients with non-epileptic attack disorder. Epilepsy Behav 2012;25(3):307–12. http://dx. doi.org/10.1016/j.yebeh.2012.08.022. [39] Reuber M, Pukrop R, Bauer J, Helmstaedter C, Tessendorf N, Elger CE. Outcome in psychogenic nonepileptic seizures: 1 to 10-year follow-up in 164 patients. Ann Neurol 2003;53(3):305–11. http://dx.doi.org/10.1002/ana.3000. [40] Tang V, Michaelis R, Kwan P. Psychobehavioral therapy for epilepsy. Epilepsy Behav 2014;32(0):147–55. http://dx.doi.org/10.1016/j.yebeh.2013.12.004. [41] LaFrance Jr WC, Miller IW, Ryan CE, Blum AS, Solomon DA, Kelley JE, et al. Cognitive behavioral therapy for psychogenic nonepileptic seizures. Epilepsy Behav 2009; 14(4):591–6. http://dx.doi.org/10.1016/j.yebeh.2009.02.016.