Health-Related Quality of Life in Pediatric Patients With Demyelinating Diseases: Relevance of Disability, Relapsing Presentation, and Fatigue

Health-Related Quality of Life in Pediatric Patients With Demyelinating Diseases: Relevance of... Abstract Objective Decreased health-related quality of life (HRQOL) in pediatric patients with multiple sclerosis is established, but little research has examined HRQOL in the broader pediatric demyelinating disease population, and predictors of reduced HRQOL are largely unexplored. We sought to (1) compare generic HRQOL and fatigue of pediatric patients with relapsing (i.e., multiple sclerosis and neuromyelitis optica) versus monophasic demyelinating diseases (i.e., acute disseminated encephalomyelitis, optic neuritis, transverse myelitis, clinically isolated syndrome) and (2) examine the extent to which disability, relapsing disease, and fatigue predict HRQOL. Methods Child and/or parent-proxy reports of generic and fatigue-related HRQOL were collected for 64 pediatric patients with demyelinating diseases. HRQOL of the sample was compared with published healthy child norms. Independent samples t-tests compared HRQOL and fatigue for children with monophasic versus relapsing diseases. Regression analyses examined disability, disease presentation, and fatigue as potential predictors of HRQOL. Results Compared with healthy child norms, generic HRQOL was significantly lower for the demyelinating disorder group, for both child and parent reports across multiple domains. As hypothesized, the relapsing disease group reported lower overall HRQOL and more fatigue than the monophasic group. Disability and relapsing disease predicted lower HRQOL for both parents and children, whereas fatigue was only predictive per the child perspective. Conclusions Children with demyelinating diseases evidence significantly lower HRQOL than healthy peers, supporting need for intervention. Those with relapsing disease appear particularly at risk; targeting disability and fatigue may be fruitful areas for intervention. health-related quality of life, pediatric demyelinating diseases, pediatric multiple sclerosis Introduction Demyelinating diseases are a class of central nervous system diseases affecting the optic nerve(s), spinal cord, cerebrum, cerebellum, and/or brainstem (Chabas, Green, & Waubant, 2006). Presentation of symptoms depends on the location of the demyelinated white matter, or lesions (Chabas et al., 2006). Wide-ranging symptoms can include motor impairments, sensory impairments, bowel and bladder dysfunction, visual impairments, cognitive difficulties, and fatigue (Patel, Bhise, & Krupp, 2009). The terms monophasic and relapsing are used to discriminate demyelinating diseases, typically presenting with single attacks (i.e., acute disseminated encephalomyelitis [ADEM], optic neuritis [ON], transverse myelitis [TM], and clinically isolated syndrome [CIS]) from those typically presenting with multiple attacks over time (i.e., multiple sclerosis [MS] and neuromyelitis optica [NMO]). Though typically known as affecting adults, demyelinating diseases may also present in childhood or adolescence, with incidence rate ranging from 0.5 to 1.66 per 100,000 children across studies (Banwell et al., 2009; Ketelslegers et al., 2012; Langer-Gould et al., 2011; Reinhardt, Weiss, Rosenbauer, Gärtner, & Kries, 2014). Approximately 2%–5% of adults with MS experience symptoms before age 16, and 28% of pediatric patients with MS experience symptoms before 12 years of age (Belman et al., 2016; Boiko et al., 2002; Duquette et al., 1987; Ghezzi et al., 1997; Harding et al., 2013; Waldman et al., 2014). Despite growing recognition that demyelinating diseases can present in childhood, knowledge about disease course and psychosocial sequelae in the pediatric population is still developing (Hanefeld, 2007). Psychosocial functioning in patients with demyelinating diseases has been predominantly examined in adults with MS because of relatively higher prevalence compared with other demyelinating diseases and with pediatric presentation. Compared with the general population or healthy controls, adults with MS report reduced health-related quality of life (HRQOL) and greater difficulties in various domains such as physical functioning and ambulation, pain, and cognition, whereas only small differences are typically found for psychological and social functioning (Jones, Pohar, Warren, Turpin, & Warren, 2008; Pittock et al., 2004; Rudick et al., 2001). However, some studies of psychological health in adult patients with MS report increased prevalence of affective disorders, such as major depression, compared with those without MS (Papuć & Stelmasiak, 2012; Patten, Beck, Williams, Barbui, & Metz, 2003; Patten, Svenson, & Metz, 2005). Historically, literature concerning pediatric MS largely consisted of case studies, qualitative research, retrospective reviews, and commentaries (Banwell, Ghezzi, Bar-Or, Mikaeloff, & Tardieu, 2007). More recently, a few studies of children with demyelinating diseases have descriptively characterized HRQOL, a multidimensional construct capturing both physical and psychosocial dimensions and increasingly recognized as an essential patient-reported health outcome measure (McCabe, Ebacioni, Simmons, McDonald, & Melton, 2015; Reeve et al., 2013; Uzark et al., 2013; Uzark et al., 2016; Varni, Seid, & Rode, 1999; Varni, Seid, Knight, Uzark, & Szer, 2002). Although a small body of literature, findings consistently indicate reduced HRQOL for children with MS compared with healthy children, with greater fatigue, difficulties with sleep, physical limitations, and cognitive and academic difficulties (Holland, Graves, Greenberg, & Harder, 2014; MacAllister et al., 2009). Although HRQOL in children and adolescents with MS has received some examination, HRQOL in other pediatric demyelinating diseases is largely unexamined, and research comparing HRQOL for children with monophasic versus relapsing demyelinating diseases is scant. Suppiej et al.(2014) examined quality of life in pediatric patients with ADEM (i.e., monophasic), reporting good overall HRQOL that is generally comparable with healthy children, perhaps suggesting those with monophasic diseases fare better. Mowry et al. (2010) examined HRQOL in a combined group of children with MS (i.e., relapsing) or CIS (i.e., monophasic), reporting worse HRQOL compared with healthy sibling controls, but no comparison was conducted between those with MS versus CIS. Ketelslegers et al.(2010) compared HRQOL in a small sample of children with MS with children with monophasic demyelinating disease (i.e., ADEM, ON, or TM), reporting some domains of HRQOL to be lower for the children with MS. While this single small (N = 32) Dutch study comparing pediatric monophasic versus relapsing demyelinating diseases is additive to the literature, further examination is warranted. Identification of predictors of reduced HRQOL may also inform potential interventions. Conceptual models of HRQOL posit that disease-specific symptoms and functional status are causal indicators of overall HRQOL (Fayers & Hand, 1997; Fayers, Hand, Bjordal, & Groenvold, 1997; Wilson & Cleary, 1995) and that identification of specific, potentially modifiable predictors of HRQOL can facilitate targeted interventions to improve HRQOL. Specifically, a patient’s symptom status is conceptualized as directly influencing functional status, which in turn affects one’s general health perception and subsequent HRQOL (Wilson & Cleary, 1995). Studies with adult patients with MS support this conceptual model, reporting that degree of disability and associated functional limitation is related to decreased HRQOL (Benedict et al., 2005; Krysko & O’Connor, 2016; Papuć & Stelmasiak, 2012; Tepavcevic et al., 2014). Specifically, adult literature identifies neurological impairment, routinely captured in clinical practice using the physician-rated Expanded Disability Status Scale (EDSS; Kurtzke, 1983) as one of the strongest predictors of HRQOL (Berrigan et al., 2016a; Lobentanz et al., 2004). The limited pediatric literature also supports this relation (MacAllister et al., 2009; Mowry et al., 2010). Supporting the relevance of examining monophasic versus relapsing disease presentation, Baumstarck and colleagues (2015) identified at least one relapse to predict lower physical HRQOL scores in adult patients with MS. To our knowledge, EDSS has not been examined as a predictor of HRQOL in pediatric demyelinating diseases. Often pervasive but not directly observable, fatigue is another likely symptom contributor to reduced HRQOL in those with demyelinating diseases. Fatigue affects the majority of adult patients with MS, often experienced as the most problematic symptom with pronounced interference in routine functioning (Krupp, Alvarez, LaRocca, & Scheinberg, 1988). Not surprisingly, fatigue is an established predictor of decreased HRQOL in adults with MS (Benedict et al., 2005; Berrigan et al., 2016; Krysko & O’Connor, 2016; Nourbakhsh, Julian, & Waubant, 2016; Papuć & Stelmasiak, 2012; Yamout et al., 2013). Also, a commonly occurring bothersome symptom in pediatric-onset MS, fatigue, often interferes with daily functioning and is associated with difficulties with cognitive/executive function (Goretti et al., 2012; Ketelslegers et al., 2010; Krupp, Alvarez, LaRocca, & Scheinberg, 1988; MacAllister et al., 2009). Fatigue may also occur with other demyelinating diseases, though examination has been limited. Parrish et al. (2013) reported fatigue in both pediatric MS (i.e., relapsing) and ADEM (i.e., monophasic) samples as higher than healthy peers but not different than each other. Though Ketelslegers et al. (2010) also reported their sample of children with a monophasic demyelinating disease did not significantly differ from MS patients in an overall fatigue score, children with MS did report more subjective fatigue than those with monophasic disease. In sum, existing literature concerning HRQOL and fatigue in pediatric patients with a broader range of demyelinating diseases (i.e., other than MS) and potential differences between relapsing versus monophasic disease presentations is limited. Therefore, after descriptive examination of HRQOL in our sample as compared with published healthy child norms, we sought to compare generic HRQOL and fatigue of patients with relapsing (i.e., MS and NMO) versus monophasic (i.e., ADEM, ON, TM, CIS) demyelinating diseases. We hypothesized that pediatric patients with relapsing conditions would exhibit decreased HRQOL and more problems with fatigue compared with peers with a monophasic presentation. Given the limited literature examining potential predictors of HRQOL in pediatric patients with demyelinating diseases, we also sought to address this aim. Based on the conceptual model that disease-specific symptoms and functional status are causal indicators of generic HRQOL (Fayers & Hand, 1997; Fayers et al., 1997; Wilson & Cleary, 1995) and existing adult and pediatric MS research, we hypothesized that neurological impairment (i.e., EDSS), symptoms of fatigue (i.e., General, Sleep, and Cognitive fatigue domains), and relapsing versus monophasic presentation would be significantly associated with generic HRQOL. We hypothesized that increased disability, fatigue, and relapsing disease would be associated with reduced generic HRQOL. Methods Participants Demyelinating Diseases Sample Parents of pediatric patients between ages 2 and 18 years along with their children ages 5–18 years attending a regularly scheduled outpatient appointment at a clinic for pediatric demyelinating diseases at Texas Children’s Hospital in Houston, TX, were invited to participate. Participants were excluded if they lacked fluency in either English or Spanish, had a physical or intellectual disability that precluded questionnaire completion (e.g., significant developmental delay), or had insufficient reading ability. Successive patients meeting these inclusion/exclusion criteria were approached in clinic from 2005 to 2009. Participating children included 64 pediatric outpatients (total sample mean age = 12.46 years; range = 3–18.75 years; SD = 3.91; 23% male). Participants were identified as Hispanic (35.9%), White non-Hispanic (28.1%), and Black non-Hispanic (26.6%). Diagnoses included MS (43.8%), ADEM (15.6%), ON (12.5%), TM (10.9%), NMO (10.9%), and CIS (6.3%). Chart review provided information about neurological attack history and current physician-rated disability as measured through the Kurtzke EDSS (Total M = 1.36). Participants were grouped according to relapsing demyelinating disease (i.e., MS or NMO, n = 35) or monophasic demyelinating disease (i.e., ADEM, ON, TM, or CIS, n = 29); descriptive statistics for these groups are provided in Table I. According to EDSS descriptors, the relapsing group would fall in the range of “No disability, minimal signs on more than one Functional System” and the monophasic group would fall in the range of “No disability, minimal signs on one Functional System.” Table I. Descriptive Statistics for Pediatric Patients With Relapsing and Monophasic Demyelinating Disease Presentation (N = 64) Demographic  Relapsing N (%)  Monophasic N (%)  Gender       Female  28 (80%)  21 (72%)   Male  7 (20%)  8 (28%)  Mean age (SD)  13.97 (3.29)  10.64 (3.87)  Race       Black non-Hispanic  11 (31%)  6 (20%)   Hispanic  13 (37%)  10 (35%)   White non-Hispanic  8 (23%)  10 (35%)   Other  3 (9%)  3 (10%)  Mean number of attacks (SD)Mean EDSS (SD)  3.52 (3.24)1.61 (2.44)  1.05 (2.10)  Demographic  Relapsing N (%)  Monophasic N (%)  Gender       Female  28 (80%)  21 (72%)   Male  7 (20%)  8 (28%)  Mean age (SD)  13.97 (3.29)  10.64 (3.87)  Race       Black non-Hispanic  11 (31%)  6 (20%)   Hispanic  13 (37%)  10 (35%)   White non-Hispanic  8 (23%)  10 (35%)   Other  3 (9%)  3 (10%)  Mean number of attacks (SD)Mean EDSS (SD)  3.52 (3.24)1.61 (2.44)  1.05 (2.10)  Note. EDSS = Kurtzke Expanded Disability Scale score (Kurtzke, 1983). Healthy Children Normative Reference Group To offer context for our demyelinating diseases sample, results from our study were first compared with published data, offering a large normative reference group of healthy children (Varni, Burwinkle, Seid, & Skarr, 2003). The existing healthy children sample was derived from the PedsQL™ healthy children database that included data from the PedsQL™ 4.0 initial field test in (N = 730) (Varni, Seid, & Kurtin, 2001) and a statewide Children’s Health Insurance Program evaluation in 2001 (N = 8,836) both conducted in California (Varni et al., 2003). These children were assessed either in physicians’ offices during well-child visits, by telephone, or via a statewide mailing. Procedures After obtaining informed parental consent and child assent, parents and children completed the PedsQL™ 4.0 Generic Core Scales and the PedsQL™ Multidimensional Fatigue Scale before their regularly scheduled outpatient visit with their pediatric neurologist (T.L.). Children received age-appropriate PedsQL™ instruments (ages 5–7, 8–12, and 13–18); for children aged <5 years, only parent-proxy measures were obtained. Children 5–18 years old completed child self-report (N = 59), and parents of children 2–18 years completed parent-proxy report to describe parent perceptions of the child’s HRQOL (N = 62). Both parent- and self-report forms were available for 58 dyads. A review of patients’ medical charts yielded information on patient diagnosis, attack history, and current EDSS. The Baylor College of Medicine institutional review board approved this research protocol. Statistical analyses were conducted using SPSS version 22. Measures Kurtzke Expanded Disability Status Scale The EDSS is a physician-rated scale of disability based on neurological examination that ranges from 0 to 10, with “0” defined as within normal limits and “10” defined as death (Kurtzke, 1983). The EDSS score is considered the gold-standard assessment of neurological impairment in clinical practice and clinical trials related to demyelinating diseases. Functional systems contributing to the score include pyramidal, cerebellar, brainstem, sensory, bowel/bladder, visual, cerebral, and other. All EDSS scores were assigned by the same pediatric neurologist (T.L.) at the time of clinic visit. PedsQL™ 4.0 Generic Core Scales Generic HRQOL was assessed using the 23-item PedsQL™ 4.0 Generic Core Scales, which yields a total score and the following subscales: (1) Physical Functioning (eight items, e.g., “It is hard for me to run.”), (2) Emotional Functioning (five items, e.g., “I feel sad or blue”), (3) Social Functioning (five items, e.g., “Other teens do not want to be my friend”), and (4) School Functioning (five items, e.g., “I have trouble keeping up with my schoolwork”) in parallel child self-report and parent proxy-report formats (Varni et al., 2001). Respondents were asked to report how much of a problem each item has been during the past 1 month. Items are reverse-scored and linearly transformed to a 0–100 scale such that higher PedsQL™ scores indicate better HRQOL. The Total Scale Score (23 items) is computed as the sum of the items divided by the number of items answered in the Physical, Emotional, Social, and School Functioning Scales. The Psychosocial Health Summary Score (15 items) is computed as the sum of the items divided by the number of items answered in the Emotional, Social, and School Functioning Scales, accounting for any missing data (Varni, Burwinkle, Seid, & Skarr, 2003). Although other strategies exist for imputing missing values, this PedsQL™ scoring method is established and consistent with other peer-reviewed publications. PedsQL™ Multidimensional Fatigue Scale The 18-item PedsQL™ Multidimensional Fatigue Scale encompasses three subscales: (1) General Fatigue (six items, e.g., “I feel too tired to do things that I like to do.”), (2) Sleep/Rest Fatigue (six items, e.g., “I feel tired when I wake up in the morning.”), and (3) Cognitive Fatigue (six items, e.g., “It is hard for me to keep my attention on things.”) (Varni, Burwinkle, & Szer, 2004). The format, instructions, Likert response scale, and scoring method are identical to the PedsQL™ 4.0 Generic Core Scales, with higher scores indicating better HRQOL (i.e., lower fatigue symptoms). Total scores are computed as the sum of items divided by the number of items answered on all the scales. Scale scores are computed as the sum of items divided by the number of items answered. Statistical Analysis To offer descriptive context, one-sample t-tests were computed to compare PedsQL™ 4.0 Generic Core Scale scores for our sample of children with demyelinating diseases to a previously published normative reference group of healthy children. Independent sample t-tests compared PedsQL™ 4.0 Generic Core Scale scores and Multidimensional Fatigue Scale scores of children with relapsing demyelinating diseases (i.e., MS and NMO) to those of children with monophasic demyelinating diseases (i.e., ADEM, ON, TM, CIS). To examine relationships among intended regression predictor variables (i.e., the three Multidimensional Fatigue Scales [General, Sleep, and Cognitive Fatigue], EDSS, and disease presentation [relapsing or monophasic]) and outcome variables (i.e., child-reported and parent-proxy reported PedsQLTM 4.0 Generic Core Scales Total Score), Pearson correlation coefficients were computed for continuous variables and point biserial correlations for dichotomous variables (gender: 0 = male, 1 = female; disease presentation: 0 = relapsing, 1 = monophasic). Finally, gender, disease presentation, EDSS, and either the child or parent-proxy report fatigue scales were entered into two multiple regression models, one predicting child report of generic HRQOL and one predicting proxy report of generic HRQOL. Results Descriptive Comparison of Generic HRQOL of Demyelinating Diseases Sample to a Healthy Child Normative Reference Group Results reflected significantly lower HRQOL for children with demyelinating diseases across domains for both parent and child reports, with the exception of child report of social functioning, which did not differ between groups. Effect sizes (i.e., Cohen’s d) for significant differences were medium to large (Table II). Table II. One-Sample t-tests Comparing PedsQL™ 4.0 Generic Core Scales of Children With Demyelinating Diseases With a Published Healthy Child Normative Reference Group Scale  Healthy M (SD)  Demyelinating M (SD)  t  p  Cohen’s d  Child self-report (N = 59)             Total Score  83.9 (12.5)  73.2 (18.7)  −4.4  .000  0.67    Physical Health  87.8 (13.1)  72.0 (25.4)  −4.8  .000  0.78    Psychosocial Health  81.8 (14.0)  73.9 (18.1)  −3.4  .001  0.49     Emotional Functioning  79.2 (18.0)  68.9 (23.7)  −3.4  .001  0.57     Social Functioning  85.0 (16.7)  83.5 (19.2)  −0.6  ns  0.08     School Functioning  81.3 (16.1)  69.2 (22.1)  −4.2  .000  0.63  Parent-proxy report (N = 62)             Total Score  82.3 (15.6)  61.4 (21.2)  −7.8  .000  1.12    Physical Health  84.1 (19.7)  55.5 (29.2)  −7.7  .000  1.14    Psychosocial Health  81.2 (15.34)  65.2 (19.3)  −6.6  .000  0.92     Emotional Functioning  81.2 (16.4)  61.4 (21.9)  −7.1  .000  1.02     Social Functioning  83.1 (19.7)  71.7 (21.4)  −4.2  .000  0.55     School Functioning  78.3 (19.6)  62.6 (24.5)  −4.8  .000  0.71  Scale  Healthy M (SD)  Demyelinating M (SD)  t  p  Cohen’s d  Child self-report (N = 59)             Total Score  83.9 (12.5)  73.2 (18.7)  −4.4  .000  0.67    Physical Health  87.8 (13.1)  72.0 (25.4)  −4.8  .000  0.78    Psychosocial Health  81.8 (14.0)  73.9 (18.1)  −3.4  .001  0.49     Emotional Functioning  79.2 (18.0)  68.9 (23.7)  −3.4  .001  0.57     Social Functioning  85.0 (16.7)  83.5 (19.2)  −0.6  ns  0.08     School Functioning  81.3 (16.1)  69.2 (22.1)  −4.2  .000  0.63  Parent-proxy report (N = 62)             Total Score  82.3 (15.6)  61.4 (21.2)  −7.8  .000  1.12    Physical Health  84.1 (19.7)  55.5 (29.2)  −7.7  .000  1.14    Psychosocial Health  81.2 (15.34)  65.2 (19.3)  −6.6  .000  0.92     Emotional Functioning  81.2 (16.4)  61.4 (21.9)  −7.1  .000  1.02     Social Functioning  83.1 (19.7)  71.7 (21.4)  −4.2  .000  0.55     School Functioning  78.3 (19.6)  62.6 (24.5)  −4.8  .000  0.71  Note. Healthy child population means from published article (Varni, Burwinkle, Seid, & Skarr, 2003); Cohen’s d benchmarks: small = 0.2, medium = 0.5, large = 0.8 (Cohen, 1988). Comparison of Relapsing Versus Monophasic Demyelinating Disease Groups on Generic HRQOL Child self-report results indicated the relapsing diseases group reported significantly lower total scores (p = .015), physical health scores (p = .003), and psychosocial health summary scores (p = .042), than the monophasic demyelinating diseases group, though differences for the psychosocial component subscales of Emotional, Social, and School did not reach statistical significance. For parent-proxy report, total score (p < .001), Physical Health (p < .001), and Psychosocial Health (p = .008; including Social and Emotional Functioning but not School Functioning) scores were significantly lower for children with relapsing versus monophasic demyelinating diseases (Table III). Table III. Independent Samples t-tests Comparing PedsQL™ 4.0 Generic Core Scales and Multidimensional Fatigue Scales for Children With Relapsing Versus Monophasic Demyelinating Diseases Scale  Relapsing M  Monophasic M  t  p  Cohen’s d  Lower 95% CI of difference  Upper 95% CI of difference  HRQOL child self-report                 Total Score  67.3 (18.1)  80.3 (17.2)  −2.8  .007  0.74  −22.2  −3.7    Physical Health  63.0 (26.0)  82.5 (20.6)  −3.2  .003  0.83  −31.9  −7.1    Psychosocial Health  69.5 (18.8)  79.1 (16.0)  −2.1  .042  0.55  −18.8  −0.4     Emotional Functioning  64.1 (25.7)  74.6 (20.0)  −1.7  ns  0.46  −22.7  1.6     Social Functioning  79.2 (19.6)  88.7 (17.6)  −1.9  ns  0.51  −19.3  0.3     School Functioning  65.2 (23.7)  73.9 (19.4)  −1.5  ns  0.40  −19.9  2.6  HRQOL parent-proxy report                 Total Score  52.9 (18.6)  71.1 (20.1)  −3.7  .000  0.94  −28.0  −8.4    Physical Health  42.6 (27.1)  69.7 (24.8)  −4.1  .000  1.04  −40.5  −13.7    Psychosocial Health  59.3 (17.1)  72.0 (19.7)  −2.7  .008  0.69  −22.0  −3.4     Emotional Functioning  55.8 (21.5)  67.8 (21.0)  −2.2  .031  0.56  −22.7  −1.2     Social Functioning  64.9 (19.8)  79.1 (21.0)  −2.7  .008  0.70  −24.7  −3.8     School Functioning  57.1 (21.5)  69.2 (26.6)  −1.9  ns  0.50  −24.9  0.7  Fatigue child self-report                 Total Fatigue  68.6 (18.9)  76.9 (17.6)  −1.7  ns  0.46  −18.0  1.3    General Fatigue  68.6 (23.6)  78.9 (19.4)  −1.8  ns  0.48  −21.8  1.1    Sleep Fatigue  60.9 (24.3)  75.7 (18.7)  −2.6  .013  0.68  −26.3  −3.2    Cognitive Fatigue  76.3 (20.3)  75.9 (23.9)  0.1  ns  −0.02  −11.1  12.1  Fatigue parent-proxy report                 Total Fatigue  54.9 (22.6)  71.8 (21.6)  −3.1  .003  0.77  −15.6  8.2    General Fatigue  49.4 (25.3)  69.7 (23.4)  −3.3  .002  0.83  −15.7  11.1    Sleep Fatigue  53.2 (26.4)  73.1 (21.1)  −3.3  .002  0.79  −12.8  13.4    Cognitive Fatigue  61.0 (28.3)  72.7 (24.7)  −1.7  ns  0.44  −22.2  5.0  Scale  Relapsing M  Monophasic M  t  p  Cohen’s d  Lower 95% CI of difference  Upper 95% CI of difference  HRQOL child self-report                 Total Score  67.3 (18.1)  80.3 (17.2)  −2.8  .007  0.74  −22.2  −3.7    Physical Health  63.0 (26.0)  82.5 (20.6)  −3.2  .003  0.83  −31.9  −7.1    Psychosocial Health  69.5 (18.8)  79.1 (16.0)  −2.1  .042  0.55  −18.8  −0.4     Emotional Functioning  64.1 (25.7)  74.6 (20.0)  −1.7  ns  0.46  −22.7  1.6     Social Functioning  79.2 (19.6)  88.7 (17.6)  −1.9  ns  0.51  −19.3  0.3     School Functioning  65.2 (23.7)  73.9 (19.4)  −1.5  ns  0.40  −19.9  2.6  HRQOL parent-proxy report                 Total Score  52.9 (18.6)  71.1 (20.1)  −3.7  .000  0.94  −28.0  −8.4    Physical Health  42.6 (27.1)  69.7 (24.8)  −4.1  .000  1.04  −40.5  −13.7    Psychosocial Health  59.3 (17.1)  72.0 (19.7)  −2.7  .008  0.69  −22.0  −3.4     Emotional Functioning  55.8 (21.5)  67.8 (21.0)  −2.2  .031  0.56  −22.7  −1.2     Social Functioning  64.9 (19.8)  79.1 (21.0)  −2.7  .008  0.70  −24.7  −3.8     School Functioning  57.1 (21.5)  69.2 (26.6)  −1.9  ns  0.50  −24.9  0.7  Fatigue child self-report                 Total Fatigue  68.6 (18.9)  76.9 (17.6)  −1.7  ns  0.46  −18.0  1.3    General Fatigue  68.6 (23.6)  78.9 (19.4)  −1.8  ns  0.48  −21.8  1.1    Sleep Fatigue  60.9 (24.3)  75.7 (18.7)  −2.6  .013  0.68  −26.3  −3.2    Cognitive Fatigue  76.3 (20.3)  75.9 (23.9)  0.1  ns  −0.02  −11.1  12.1  Fatigue parent-proxy report                 Total Fatigue  54.9 (22.6)  71.8 (21.6)  −3.1  .003  0.77  −15.6  8.2    General Fatigue  49.4 (25.3)  69.7 (23.4)  −3.3  .002  0.83  −15.7  11.1    Sleep Fatigue  53.2 (26.4)  73.1 (21.1)  −3.3  .002  0.79  −12.8  13.4    Cognitive Fatigue  61.0 (28.3)  72.7 (24.7)  −1.7  ns  0.44  −22.2  5.0  Note. Relapsing group = MS and NMO; Monophasic group = ADEM, ON, TM, and CIS; Cohen’s d benchmarks: Small = 0.2, Medium = 0.5, Large = 0.8 (Cohen, 1988). Comparison of Relapsing Versus Monophasic Demyelinating Disease Groups on Multidimensional Fatigue For child self-report, significant group differences were found only for the Sleep Fatigue subscale (p = .013), with the relapsing diseases group reporting more sleep-related fatigue. Significant differences did not emerge for the General Fatigue and Cognitive Fatigue child-report subscales. Parent-proxy report indicated the relapsing diseases group to have significantly greater Total Fatigue (p = .003), General Fatigue (p = .002), and Sleep Fatigue (p = .002) than the monophasic group. Groups did not significantly differ for parent-proxy-reported Cognitive Fatigue (Table III). Predictors of Child Report and Parent-Proxy Report Generic HRQOL Correlations For child report, results indicated significant correlations between Total HRQOL and EDSS (r = −.40, p < .01), disease presentation (r = .35, p < .05; lower HRQOL positively correlated with relapsing), gender (r = −.30, p < .05; lower HRQOL associated with female gender), general fatigue (r = .72, p < .01), sleep fatigue (r = .55, p < .01), and cognitive fatigue (r = .54, p < .01). Parent-proxy-report results indicated significant correlations between Total HRQOL and EDSS (r = −.36, p < .01) and disease presentation (r = .43, p < .01; lower HRQOL positively correlated with relapsing). Child age was not significantly correlated with either parent-proxy or child report generic HRQOL and so was not included in the subsequent regression models (Table IV). Table IV. Correlations Among Demographics, Disease Presentation, HRQOL, and Fatigue Variables  1  2  3  4  5  6  7  8  9  10  11  1 Gender (Male-0, Female-1)  –                      2 Age  .17  –                    3 Disease presentation (Relapsing-0, Monophasic-1)  −.09  −.42**  –                  4 EDSS  .11  .02  −.12  –                5 Child-report Total HRQOL  −.30*  −.20  .35*  −.40**  –              6 Proxy-report Total HRQOL  −.17  −.03  .43**  −.36**  .75**  –            7 Child-report General Fatigue  .17  −.16  .24  −.45**  .72**  .55**  –          8 Child-report Sleep Fatigue  −.24  −.20  .32*  −.06  .55**  .48**  .60**  –        9 Child-report Cognitive Fatigue  −.04  .01  −.01  .01  .54**  .34**  .55**  .50**  –      10 Proxy-report General Fatigue  −.02  .18  .04  .04  −.02  .03  −.04  −.11  −.12  –    11 Proxy-report Sleep Fatigue  −.13  −.03  −.01  .10  −.12  −.13  −.16  −.26*  −.16  .79**  –  12 Proxy-report Cognitive Fatigue  −.01  .00  .16  .04  .12  .08  .08  .08  −.08  .69**  .59**  Variables  1  2  3  4  5  6  7  8  9  10  11  1 Gender (Male-0, Female-1)  –                      2 Age  .17  –                    3 Disease presentation (Relapsing-0, Monophasic-1)  −.09  −.42**  –                  4 EDSS  .11  .02  −.12  –                5 Child-report Total HRQOL  −.30*  −.20  .35*  −.40**  –              6 Proxy-report Total HRQOL  −.17  −.03  .43**  −.36**  .75**  –            7 Child-report General Fatigue  .17  −.16  .24  −.45**  .72**  .55**  –          8 Child-report Sleep Fatigue  −.24  −.20  .32*  −.06  .55**  .48**  .60**  –        9 Child-report Cognitive Fatigue  −.04  .01  −.01  .01  .54**  .34**  .55**  .50**  –      10 Proxy-report General Fatigue  −.02  .18  .04  .04  −.02  .03  −.04  −.11  −.12  –    11 Proxy-report Sleep Fatigue  −.13  −.03  −.01  .10  −.12  −.13  −.16  −.26*  −.16  .79**  –  12 Proxy-report Cognitive Fatigue  −.01  .00  .16  .04  .12  .08  .08  .08  −.08  .69**  .59**  Note. *p < .05; **p < .01; HRQOL = health-related quality of life; EDSS = Expanded Disability Status Scale. Effect sizes for Pearson r: small = 0.10, medium = 0.30, large = 0.50. Regression The child-report model accounted for 61% of the variability in generic HRQOL, F(6, 51) = 15.8, p < .01, representing a large effect. Significant individual predictors included disease presentation (β = .2, p < .05), EDSS (β = −.2, p < .05), general fatigue (β = .3, p < .05), and cognitive fatigue (β = .3, p < .05). As hypothesized, higher disability, increased fatigue, and relapsing disease presentation predicted reduced HRQOL (Table V). Table V. Multiple Regression Analyses Predicting Generic Health-Related Quality of Life in Pediatric Demyelinating Diseases   Child-reported HRQOL N = 57   Parent-reported HRQOL N = 60   Variable  B  SE B  β  B  SE B  Β  Gender  −4.8  4.0  −.1  −8.7  6.0  −.2  Monophasic or Relapsing  8.4  3.4  .2*  15.4  4.8  .4**  EDSS  −2.0  0.9  −.2*  −2.7  1.0  −.3*  General Fatigue  0.3  0.1  .3*  0.2  0.2  .1  Sleep Fatigue  0.1  0.1  .1  −0.3  0.2  −.3  Cognitive Fatigue  0.3  0.1  .3*  0.1  0.1  .1  Adjusted R2    0.61      0.28    F    15.8**      4.9**      Child-reported HRQOL N = 57   Parent-reported HRQOL N = 60   Variable  B  SE B  β  B  SE B  Β  Gender  −4.8  4.0  −.1  −8.7  6.0  −.2  Monophasic or Relapsing  8.4  3.4  .2*  15.4  4.8  .4**  EDSS  −2.0  0.9  −.2*  −2.7  1.0  −.3*  General Fatigue  0.3  0.1  .3*  0.2  0.2  .1  Sleep Fatigue  0.1  0.1  .1  −0.3  0.2  −.3  Cognitive Fatigue  0.3  0.1  .3*  0.1  0.1  .1  Adjusted R2    0.61      0.28    F    15.8**      4.9**    Note. *p < .05; ** p < .01. HRQOL = health-related quality of life; EDSS = Expanded Disability Status Scale. Adjusted R2effect sizes: small = 0.02, medium = 0.13, large = 0.26. The parent-report model accounted for 28% of the variability in generic HRQOL, F(6, 54) = 4.9, p < .01, representing a large effect. Significant individual predictors included disease presentation (β = .4, p < .01) and EDSS (β = −.3, p < .05), and as predicted, relapsing disease presentation and higher disability were associated with reduced HRQOL. Contrary to prediction, for parent-proxy report, fatigue scales were not significantly associated with generic HRQOL (Table V). Discussion A wide spectrum of physical, psychological, and social factors associated with pediatric demyelinating diseases have the potential to negatively affect HRQOL. The unpredictable nature of the disease course with potential for relapse, intermittent or ongoing physical symptoms, and associated impairment (e.g., fatigue, motor or visual impairments, bowel or bladder dysfunction), necessary treatment regimens with their associated burden or side effects (Boyd & MacMillan, 2000; MacAllister et al., 2007), potential cognitive deficits (e.g., difficulties with executive functioning, attention, and working memory; Banwell & Anderson, 2005; Banwell et al., 2007; MacAllister et al., 2007), and patient and family adjustment to having a demyelinating disease all may impact physical, emotional, academic, and social functioning for these children. This study supplements the limited literature examining HRQOL in pediatric patients with demyelinating diseases, contributing a novel comparison of HRQOL and fatigue in monophasic versus relapsing demyelinating diseases and examination of predictors of HRQOL. In our sample of children with demyelinating diseases, both parent-proxy report and child self-report reflect significantly poorer generic HRQOL than healthy child normative data across multiple domains, converging with prior studies and supporting need for intervention. Optimal care should attend to the physical, psychosocial, and treatment-related factors that negatively impact HRQOL of individual patients. Both children and parents reported lower total HRQOL as well as Physical and Psychosocial subscales, highlighting the potential for concern across domains of functioning. Of note, child report of social functioning was the only nonsignificant group comparison, suggesting peer relationships may be less affected. Compared with extant pediatric literature, our sample scored similarly on the neurological exam for disability (EDSS), with a mean EDSS comparable with previous pediatric studies (Ketelslegers et al., 2010; MacAllister et al., 2009). However, it should be noted that EDSS scores were determined by a single rater, precluding ability to assess reliability of the ratings. As predicted, our pattern of results generally suggested patients with relapsing demyelinating diseases experience greater impairments in generic HRQOL and, to a less consistent extent, fatigue than those with monophasic diseases. This may relate to the sequelae of experiencing recurrent neurological attacks, as well as potential psychosocial effects of anticipating potential future attacks and the associated uncertainty about disease course and impact. That said, group differences were more pronounced for parent than child report such that parents expressed more concern about psychosocial functioning and fatigue. This highlights the importance of assessing and better understanding both parent and child perspectives. Future research with larger samples should examine HRQOL in specific diagnostic subgroups of demyelinating diseases, assess the relative contribution of accumulated disability and illness uncertainty to reduced HRQOL, and explore divergence in parent and child perspectives. Our study also extends the limited pediatric demyelinating diseases literature by examining predictors of HRQOL, which can inform clinical care and intervention development. Our hypotheses were largely confirmed in the child-report model, with EDSS, relapsing presentation, general fatigue, and cognitive fatigue all negatively related to HRQOL. Interestingly, sleep fatigue was not associated with HRQOL. Although adult literature reflects that demyelinating diseases are associated with sleep disturbance (Hughes et al., 2017; Nociti et al., 2017), the limited pediatric literature suggests sleep may be less problematic (Zafar, Ness, Dowdy, Avis, & Bashir, 2012). Our results suggest interventions targeting general and cognitive fatigue may be more salient in the pediatric population. For the parent-report model, however, only disability and relapsing disease status were significant predictors of HRQOL, whereas fatigue was not significantly associated. Given that fatigue is a subjective symptom that may not be directly observable, one might suspect that parents could be under-aware of this concern; however, parent report of fatigue reflects that parents in our sample described their children as having notable difficulty across the fatigue subscales, more so than the children themselves. This converges with prior studies indicating parents often report more fatigue for children with MS than children self-report (Goretti et al., 2012; Holland et al., 2014; MacAllister et al., 2009), but the lack of relation between fatigue and generic HRQOL per the parent perspective is surprising. The consistent finding of cross-informant variance substantiates the need to assess the perspectives of both parent and child when assessing HRQOL and fatigue in pediatric patients with demyelinating diseases. The cross-sectional design of our study precludes causal interpretations. Indeed, while some literature with adult MS patients finds EDSS to predict HRQOL (Berrigan et al., 2016), other longitudinal literature reflects that baseline HRQOL may predict subsequent change in EDSS scores (Baumstarck et al., 2015). Pediatric longitudinal studies are needed to understand the relationship between HRQOL and clinical outcomes over time, and examination of indirect effects may also be informative. Multiple analyses were conducted with the p-value of .05; given the current state of the literature, no statistical corrections were employed. Further, our sample size was not sufficient to allow comparison of individual demyelinating disease groups, which would be a fruitful area of future study. Given the wide-ranging nature of potential symptoms both within and across demyelinating diseases, a more detailed examination of multiple specific symptoms beyond fatigue (e.g., visual symptoms, bowel and bladder dysfunction), as well as additional research related to sleep, would also be valuable. Collecting data in the context of a routine health care visit in a busy neurology clinic prevented capturing recruitment rate information and also limited the constructs that could be measured. The absence of a measure of depression is a limitation, as in adults with MS, depression is an identified predictor of HRQOL and has been associated with fatigue, with potential for bidirectional or mediation effects (Berrigan et al., 2016b; Lobentanz et al., 2004; Morrison & Stuifbergen, 2016; Nourbakhsh, Julian, & Waubant, 2016; Papuć & Stelmasiak, 2012; Tepavcevic et al., 2014; Yamout et al., 2013). Incorporation of a measure of depression, as well as information on cognitive functioning and its relationship with fatigue, would benefit future research (Goretti et al., 2012; Holland et al., 2014). In adults, fatigue commonly interferes with work and productivity (Smith & Arnett, 2005), meriting consideration of interference with school attendance and academic functioning in the pediatric population. While reducing physical disability may be a potent intervention to improve HRQOL, this is not always modifiable, and targeting other symptoms potentially amenable to change may be a valuable and achievable way to improve quality of life for patients with demyelinating diseases (Berrigan et al., 2016). Fatigue and HRQOL were strongly related from the children’s perspective, and though the cross-sectional design precludes causative conclusions, optimal management of fatigue may be helpful in improving overall HRQOL. Cognitive behavioral therapy techniques (e.g., behavioral activation or activity pacing), which have been moderately efficacious in the adult chronic fatigue population (Malouff, Thorsteinsson, Rooke, Bhullar, & Schutte, 2008), may be helpful. Multidisciplinary team-based care including psychologists, neurologists, and physical/occupational therapists may serve to optimize HRQOL of children with demyelinating diseases. Conflicts of interest: None declared. 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Health-Related Quality of Life in Pediatric Patients With Demyelinating Diseases: Relevance of Disability, Relapsing Presentation, and Fatigue

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Oxford University Press
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© The Author 2017. Published by Oxford University Press on behalf of the Society of Pediatric Psychology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com
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0146-8693
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10.1093/jpepsy/jsx093
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Abstract

Abstract Objective Decreased health-related quality of life (HRQOL) in pediatric patients with multiple sclerosis is established, but little research has examined HRQOL in the broader pediatric demyelinating disease population, and predictors of reduced HRQOL are largely unexplored. We sought to (1) compare generic HRQOL and fatigue of pediatric patients with relapsing (i.e., multiple sclerosis and neuromyelitis optica) versus monophasic demyelinating diseases (i.e., acute disseminated encephalomyelitis, optic neuritis, transverse myelitis, clinically isolated syndrome) and (2) examine the extent to which disability, relapsing disease, and fatigue predict HRQOL. Methods Child and/or parent-proxy reports of generic and fatigue-related HRQOL were collected for 64 pediatric patients with demyelinating diseases. HRQOL of the sample was compared with published healthy child norms. Independent samples t-tests compared HRQOL and fatigue for children with monophasic versus relapsing diseases. Regression analyses examined disability, disease presentation, and fatigue as potential predictors of HRQOL. Results Compared with healthy child norms, generic HRQOL was significantly lower for the demyelinating disorder group, for both child and parent reports across multiple domains. As hypothesized, the relapsing disease group reported lower overall HRQOL and more fatigue than the monophasic group. Disability and relapsing disease predicted lower HRQOL for both parents and children, whereas fatigue was only predictive per the child perspective. Conclusions Children with demyelinating diseases evidence significantly lower HRQOL than healthy peers, supporting need for intervention. Those with relapsing disease appear particularly at risk; targeting disability and fatigue may be fruitful areas for intervention. health-related quality of life, pediatric demyelinating diseases, pediatric multiple sclerosis Introduction Demyelinating diseases are a class of central nervous system diseases affecting the optic nerve(s), spinal cord, cerebrum, cerebellum, and/or brainstem (Chabas, Green, & Waubant, 2006). Presentation of symptoms depends on the location of the demyelinated white matter, or lesions (Chabas et al., 2006). Wide-ranging symptoms can include motor impairments, sensory impairments, bowel and bladder dysfunction, visual impairments, cognitive difficulties, and fatigue (Patel, Bhise, & Krupp, 2009). The terms monophasic and relapsing are used to discriminate demyelinating diseases, typically presenting with single attacks (i.e., acute disseminated encephalomyelitis [ADEM], optic neuritis [ON], transverse myelitis [TM], and clinically isolated syndrome [CIS]) from those typically presenting with multiple attacks over time (i.e., multiple sclerosis [MS] and neuromyelitis optica [NMO]). Though typically known as affecting adults, demyelinating diseases may also present in childhood or adolescence, with incidence rate ranging from 0.5 to 1.66 per 100,000 children across studies (Banwell et al., 2009; Ketelslegers et al., 2012; Langer-Gould et al., 2011; Reinhardt, Weiss, Rosenbauer, Gärtner, & Kries, 2014). Approximately 2%–5% of adults with MS experience symptoms before age 16, and 28% of pediatric patients with MS experience symptoms before 12 years of age (Belman et al., 2016; Boiko et al., 2002; Duquette et al., 1987; Ghezzi et al., 1997; Harding et al., 2013; Waldman et al., 2014). Despite growing recognition that demyelinating diseases can present in childhood, knowledge about disease course and psychosocial sequelae in the pediatric population is still developing (Hanefeld, 2007). Psychosocial functioning in patients with demyelinating diseases has been predominantly examined in adults with MS because of relatively higher prevalence compared with other demyelinating diseases and with pediatric presentation. Compared with the general population or healthy controls, adults with MS report reduced health-related quality of life (HRQOL) and greater difficulties in various domains such as physical functioning and ambulation, pain, and cognition, whereas only small differences are typically found for psychological and social functioning (Jones, Pohar, Warren, Turpin, & Warren, 2008; Pittock et al., 2004; Rudick et al., 2001). However, some studies of psychological health in adult patients with MS report increased prevalence of affective disorders, such as major depression, compared with those without MS (Papuć & Stelmasiak, 2012; Patten, Beck, Williams, Barbui, & Metz, 2003; Patten, Svenson, & Metz, 2005). Historically, literature concerning pediatric MS largely consisted of case studies, qualitative research, retrospective reviews, and commentaries (Banwell, Ghezzi, Bar-Or, Mikaeloff, & Tardieu, 2007). More recently, a few studies of children with demyelinating diseases have descriptively characterized HRQOL, a multidimensional construct capturing both physical and psychosocial dimensions and increasingly recognized as an essential patient-reported health outcome measure (McCabe, Ebacioni, Simmons, McDonald, & Melton, 2015; Reeve et al., 2013; Uzark et al., 2013; Uzark et al., 2016; Varni, Seid, & Rode, 1999; Varni, Seid, Knight, Uzark, & Szer, 2002). Although a small body of literature, findings consistently indicate reduced HRQOL for children with MS compared with healthy children, with greater fatigue, difficulties with sleep, physical limitations, and cognitive and academic difficulties (Holland, Graves, Greenberg, & Harder, 2014; MacAllister et al., 2009). Although HRQOL in children and adolescents with MS has received some examination, HRQOL in other pediatric demyelinating diseases is largely unexamined, and research comparing HRQOL for children with monophasic versus relapsing demyelinating diseases is scant. Suppiej et al.(2014) examined quality of life in pediatric patients with ADEM (i.e., monophasic), reporting good overall HRQOL that is generally comparable with healthy children, perhaps suggesting those with monophasic diseases fare better. Mowry et al. (2010) examined HRQOL in a combined group of children with MS (i.e., relapsing) or CIS (i.e., monophasic), reporting worse HRQOL compared with healthy sibling controls, but no comparison was conducted between those with MS versus CIS. Ketelslegers et al.(2010) compared HRQOL in a small sample of children with MS with children with monophasic demyelinating disease (i.e., ADEM, ON, or TM), reporting some domains of HRQOL to be lower for the children with MS. While this single small (N = 32) Dutch study comparing pediatric monophasic versus relapsing demyelinating diseases is additive to the literature, further examination is warranted. Identification of predictors of reduced HRQOL may also inform potential interventions. Conceptual models of HRQOL posit that disease-specific symptoms and functional status are causal indicators of overall HRQOL (Fayers & Hand, 1997; Fayers, Hand, Bjordal, & Groenvold, 1997; Wilson & Cleary, 1995) and that identification of specific, potentially modifiable predictors of HRQOL can facilitate targeted interventions to improve HRQOL. Specifically, a patient’s symptom status is conceptualized as directly influencing functional status, which in turn affects one’s general health perception and subsequent HRQOL (Wilson & Cleary, 1995). Studies with adult patients with MS support this conceptual model, reporting that degree of disability and associated functional limitation is related to decreased HRQOL (Benedict et al., 2005; Krysko & O’Connor, 2016; Papuć & Stelmasiak, 2012; Tepavcevic et al., 2014). Specifically, adult literature identifies neurological impairment, routinely captured in clinical practice using the physician-rated Expanded Disability Status Scale (EDSS; Kurtzke, 1983) as one of the strongest predictors of HRQOL (Berrigan et al., 2016a; Lobentanz et al., 2004). The limited pediatric literature also supports this relation (MacAllister et al., 2009; Mowry et al., 2010). Supporting the relevance of examining monophasic versus relapsing disease presentation, Baumstarck and colleagues (2015) identified at least one relapse to predict lower physical HRQOL scores in adult patients with MS. To our knowledge, EDSS has not been examined as a predictor of HRQOL in pediatric demyelinating diseases. Often pervasive but not directly observable, fatigue is another likely symptom contributor to reduced HRQOL in those with demyelinating diseases. Fatigue affects the majority of adult patients with MS, often experienced as the most problematic symptom with pronounced interference in routine functioning (Krupp, Alvarez, LaRocca, & Scheinberg, 1988). Not surprisingly, fatigue is an established predictor of decreased HRQOL in adults with MS (Benedict et al., 2005; Berrigan et al., 2016; Krysko & O’Connor, 2016; Nourbakhsh, Julian, & Waubant, 2016; Papuć & Stelmasiak, 2012; Yamout et al., 2013). Also, a commonly occurring bothersome symptom in pediatric-onset MS, fatigue, often interferes with daily functioning and is associated with difficulties with cognitive/executive function (Goretti et al., 2012; Ketelslegers et al., 2010; Krupp, Alvarez, LaRocca, & Scheinberg, 1988; MacAllister et al., 2009). Fatigue may also occur with other demyelinating diseases, though examination has been limited. Parrish et al. (2013) reported fatigue in both pediatric MS (i.e., relapsing) and ADEM (i.e., monophasic) samples as higher than healthy peers but not different than each other. Though Ketelslegers et al. (2010) also reported their sample of children with a monophasic demyelinating disease did not significantly differ from MS patients in an overall fatigue score, children with MS did report more subjective fatigue than those with monophasic disease. In sum, existing literature concerning HRQOL and fatigue in pediatric patients with a broader range of demyelinating diseases (i.e., other than MS) and potential differences between relapsing versus monophasic disease presentations is limited. Therefore, after descriptive examination of HRQOL in our sample as compared with published healthy child norms, we sought to compare generic HRQOL and fatigue of patients with relapsing (i.e., MS and NMO) versus monophasic (i.e., ADEM, ON, TM, CIS) demyelinating diseases. We hypothesized that pediatric patients with relapsing conditions would exhibit decreased HRQOL and more problems with fatigue compared with peers with a monophasic presentation. Given the limited literature examining potential predictors of HRQOL in pediatric patients with demyelinating diseases, we also sought to address this aim. Based on the conceptual model that disease-specific symptoms and functional status are causal indicators of generic HRQOL (Fayers & Hand, 1997; Fayers et al., 1997; Wilson & Cleary, 1995) and existing adult and pediatric MS research, we hypothesized that neurological impairment (i.e., EDSS), symptoms of fatigue (i.e., General, Sleep, and Cognitive fatigue domains), and relapsing versus monophasic presentation would be significantly associated with generic HRQOL. We hypothesized that increased disability, fatigue, and relapsing disease would be associated with reduced generic HRQOL. Methods Participants Demyelinating Diseases Sample Parents of pediatric patients between ages 2 and 18 years along with their children ages 5–18 years attending a regularly scheduled outpatient appointment at a clinic for pediatric demyelinating diseases at Texas Children’s Hospital in Houston, TX, were invited to participate. Participants were excluded if they lacked fluency in either English or Spanish, had a physical or intellectual disability that precluded questionnaire completion (e.g., significant developmental delay), or had insufficient reading ability. Successive patients meeting these inclusion/exclusion criteria were approached in clinic from 2005 to 2009. Participating children included 64 pediatric outpatients (total sample mean age = 12.46 years; range = 3–18.75 years; SD = 3.91; 23% male). Participants were identified as Hispanic (35.9%), White non-Hispanic (28.1%), and Black non-Hispanic (26.6%). Diagnoses included MS (43.8%), ADEM (15.6%), ON (12.5%), TM (10.9%), NMO (10.9%), and CIS (6.3%). Chart review provided information about neurological attack history and current physician-rated disability as measured through the Kurtzke EDSS (Total M = 1.36). Participants were grouped according to relapsing demyelinating disease (i.e., MS or NMO, n = 35) or monophasic demyelinating disease (i.e., ADEM, ON, TM, or CIS, n = 29); descriptive statistics for these groups are provided in Table I. According to EDSS descriptors, the relapsing group would fall in the range of “No disability, minimal signs on more than one Functional System” and the monophasic group would fall in the range of “No disability, minimal signs on one Functional System.” Table I. Descriptive Statistics for Pediatric Patients With Relapsing and Monophasic Demyelinating Disease Presentation (N = 64) Demographic  Relapsing N (%)  Monophasic N (%)  Gender       Female  28 (80%)  21 (72%)   Male  7 (20%)  8 (28%)  Mean age (SD)  13.97 (3.29)  10.64 (3.87)  Race       Black non-Hispanic  11 (31%)  6 (20%)   Hispanic  13 (37%)  10 (35%)   White non-Hispanic  8 (23%)  10 (35%)   Other  3 (9%)  3 (10%)  Mean number of attacks (SD)Mean EDSS (SD)  3.52 (3.24)1.61 (2.44)  1.05 (2.10)  Demographic  Relapsing N (%)  Monophasic N (%)  Gender       Female  28 (80%)  21 (72%)   Male  7 (20%)  8 (28%)  Mean age (SD)  13.97 (3.29)  10.64 (3.87)  Race       Black non-Hispanic  11 (31%)  6 (20%)   Hispanic  13 (37%)  10 (35%)   White non-Hispanic  8 (23%)  10 (35%)   Other  3 (9%)  3 (10%)  Mean number of attacks (SD)Mean EDSS (SD)  3.52 (3.24)1.61 (2.44)  1.05 (2.10)  Note. EDSS = Kurtzke Expanded Disability Scale score (Kurtzke, 1983). Healthy Children Normative Reference Group To offer context for our demyelinating diseases sample, results from our study were first compared with published data, offering a large normative reference group of healthy children (Varni, Burwinkle, Seid, & Skarr, 2003). The existing healthy children sample was derived from the PedsQL™ healthy children database that included data from the PedsQL™ 4.0 initial field test in (N = 730) (Varni, Seid, & Kurtin, 2001) and a statewide Children’s Health Insurance Program evaluation in 2001 (N = 8,836) both conducted in California (Varni et al., 2003). These children were assessed either in physicians’ offices during well-child visits, by telephone, or via a statewide mailing. Procedures After obtaining informed parental consent and child assent, parents and children completed the PedsQL™ 4.0 Generic Core Scales and the PedsQL™ Multidimensional Fatigue Scale before their regularly scheduled outpatient visit with their pediatric neurologist (T.L.). Children received age-appropriate PedsQL™ instruments (ages 5–7, 8–12, and 13–18); for children aged <5 years, only parent-proxy measures were obtained. Children 5–18 years old completed child self-report (N = 59), and parents of children 2–18 years completed parent-proxy report to describe parent perceptions of the child’s HRQOL (N = 62). Both parent- and self-report forms were available for 58 dyads. A review of patients’ medical charts yielded information on patient diagnosis, attack history, and current EDSS. The Baylor College of Medicine institutional review board approved this research protocol. Statistical analyses were conducted using SPSS version 22. Measures Kurtzke Expanded Disability Status Scale The EDSS is a physician-rated scale of disability based on neurological examination that ranges from 0 to 10, with “0” defined as within normal limits and “10” defined as death (Kurtzke, 1983). The EDSS score is considered the gold-standard assessment of neurological impairment in clinical practice and clinical trials related to demyelinating diseases. Functional systems contributing to the score include pyramidal, cerebellar, brainstem, sensory, bowel/bladder, visual, cerebral, and other. All EDSS scores were assigned by the same pediatric neurologist (T.L.) at the time of clinic visit. PedsQL™ 4.0 Generic Core Scales Generic HRQOL was assessed using the 23-item PedsQL™ 4.0 Generic Core Scales, which yields a total score and the following subscales: (1) Physical Functioning (eight items, e.g., “It is hard for me to run.”), (2) Emotional Functioning (five items, e.g., “I feel sad or blue”), (3) Social Functioning (five items, e.g., “Other teens do not want to be my friend”), and (4) School Functioning (five items, e.g., “I have trouble keeping up with my schoolwork”) in parallel child self-report and parent proxy-report formats (Varni et al., 2001). Respondents were asked to report how much of a problem each item has been during the past 1 month. Items are reverse-scored and linearly transformed to a 0–100 scale such that higher PedsQL™ scores indicate better HRQOL. The Total Scale Score (23 items) is computed as the sum of the items divided by the number of items answered in the Physical, Emotional, Social, and School Functioning Scales. The Psychosocial Health Summary Score (15 items) is computed as the sum of the items divided by the number of items answered in the Emotional, Social, and School Functioning Scales, accounting for any missing data (Varni, Burwinkle, Seid, & Skarr, 2003). Although other strategies exist for imputing missing values, this PedsQL™ scoring method is established and consistent with other peer-reviewed publications. PedsQL™ Multidimensional Fatigue Scale The 18-item PedsQL™ Multidimensional Fatigue Scale encompasses three subscales: (1) General Fatigue (six items, e.g., “I feel too tired to do things that I like to do.”), (2) Sleep/Rest Fatigue (six items, e.g., “I feel tired when I wake up in the morning.”), and (3) Cognitive Fatigue (six items, e.g., “It is hard for me to keep my attention on things.”) (Varni, Burwinkle, & Szer, 2004). The format, instructions, Likert response scale, and scoring method are identical to the PedsQL™ 4.0 Generic Core Scales, with higher scores indicating better HRQOL (i.e., lower fatigue symptoms). Total scores are computed as the sum of items divided by the number of items answered on all the scales. Scale scores are computed as the sum of items divided by the number of items answered. Statistical Analysis To offer descriptive context, one-sample t-tests were computed to compare PedsQL™ 4.0 Generic Core Scale scores for our sample of children with demyelinating diseases to a previously published normative reference group of healthy children. Independent sample t-tests compared PedsQL™ 4.0 Generic Core Scale scores and Multidimensional Fatigue Scale scores of children with relapsing demyelinating diseases (i.e., MS and NMO) to those of children with monophasic demyelinating diseases (i.e., ADEM, ON, TM, CIS). To examine relationships among intended regression predictor variables (i.e., the three Multidimensional Fatigue Scales [General, Sleep, and Cognitive Fatigue], EDSS, and disease presentation [relapsing or monophasic]) and outcome variables (i.e., child-reported and parent-proxy reported PedsQLTM 4.0 Generic Core Scales Total Score), Pearson correlation coefficients were computed for continuous variables and point biserial correlations for dichotomous variables (gender: 0 = male, 1 = female; disease presentation: 0 = relapsing, 1 = monophasic). Finally, gender, disease presentation, EDSS, and either the child or parent-proxy report fatigue scales were entered into two multiple regression models, one predicting child report of generic HRQOL and one predicting proxy report of generic HRQOL. Results Descriptive Comparison of Generic HRQOL of Demyelinating Diseases Sample to a Healthy Child Normative Reference Group Results reflected significantly lower HRQOL for children with demyelinating diseases across domains for both parent and child reports, with the exception of child report of social functioning, which did not differ between groups. Effect sizes (i.e., Cohen’s d) for significant differences were medium to large (Table II). Table II. One-Sample t-tests Comparing PedsQL™ 4.0 Generic Core Scales of Children With Demyelinating Diseases With a Published Healthy Child Normative Reference Group Scale  Healthy M (SD)  Demyelinating M (SD)  t  p  Cohen’s d  Child self-report (N = 59)             Total Score  83.9 (12.5)  73.2 (18.7)  −4.4  .000  0.67    Physical Health  87.8 (13.1)  72.0 (25.4)  −4.8  .000  0.78    Psychosocial Health  81.8 (14.0)  73.9 (18.1)  −3.4  .001  0.49     Emotional Functioning  79.2 (18.0)  68.9 (23.7)  −3.4  .001  0.57     Social Functioning  85.0 (16.7)  83.5 (19.2)  −0.6  ns  0.08     School Functioning  81.3 (16.1)  69.2 (22.1)  −4.2  .000  0.63  Parent-proxy report (N = 62)             Total Score  82.3 (15.6)  61.4 (21.2)  −7.8  .000  1.12    Physical Health  84.1 (19.7)  55.5 (29.2)  −7.7  .000  1.14    Psychosocial Health  81.2 (15.34)  65.2 (19.3)  −6.6  .000  0.92     Emotional Functioning  81.2 (16.4)  61.4 (21.9)  −7.1  .000  1.02     Social Functioning  83.1 (19.7)  71.7 (21.4)  −4.2  .000  0.55     School Functioning  78.3 (19.6)  62.6 (24.5)  −4.8  .000  0.71  Scale  Healthy M (SD)  Demyelinating M (SD)  t  p  Cohen’s d  Child self-report (N = 59)             Total Score  83.9 (12.5)  73.2 (18.7)  −4.4  .000  0.67    Physical Health  87.8 (13.1)  72.0 (25.4)  −4.8  .000  0.78    Psychosocial Health  81.8 (14.0)  73.9 (18.1)  −3.4  .001  0.49     Emotional Functioning  79.2 (18.0)  68.9 (23.7)  −3.4  .001  0.57     Social Functioning  85.0 (16.7)  83.5 (19.2)  −0.6  ns  0.08     School Functioning  81.3 (16.1)  69.2 (22.1)  −4.2  .000  0.63  Parent-proxy report (N = 62)             Total Score  82.3 (15.6)  61.4 (21.2)  −7.8  .000  1.12    Physical Health  84.1 (19.7)  55.5 (29.2)  −7.7  .000  1.14    Psychosocial Health  81.2 (15.34)  65.2 (19.3)  −6.6  .000  0.92     Emotional Functioning  81.2 (16.4)  61.4 (21.9)  −7.1  .000  1.02     Social Functioning  83.1 (19.7)  71.7 (21.4)  −4.2  .000  0.55     School Functioning  78.3 (19.6)  62.6 (24.5)  −4.8  .000  0.71  Note. Healthy child population means from published article (Varni, Burwinkle, Seid, & Skarr, 2003); Cohen’s d benchmarks: small = 0.2, medium = 0.5, large = 0.8 (Cohen, 1988). Comparison of Relapsing Versus Monophasic Demyelinating Disease Groups on Generic HRQOL Child self-report results indicated the relapsing diseases group reported significantly lower total scores (p = .015), physical health scores (p = .003), and psychosocial health summary scores (p = .042), than the monophasic demyelinating diseases group, though differences for the psychosocial component subscales of Emotional, Social, and School did not reach statistical significance. For parent-proxy report, total score (p < .001), Physical Health (p < .001), and Psychosocial Health (p = .008; including Social and Emotional Functioning but not School Functioning) scores were significantly lower for children with relapsing versus monophasic demyelinating diseases (Table III). Table III. Independent Samples t-tests Comparing PedsQL™ 4.0 Generic Core Scales and Multidimensional Fatigue Scales for Children With Relapsing Versus Monophasic Demyelinating Diseases Scale  Relapsing M  Monophasic M  t  p  Cohen’s d  Lower 95% CI of difference  Upper 95% CI of difference  HRQOL child self-report                 Total Score  67.3 (18.1)  80.3 (17.2)  −2.8  .007  0.74  −22.2  −3.7    Physical Health  63.0 (26.0)  82.5 (20.6)  −3.2  .003  0.83  −31.9  −7.1    Psychosocial Health  69.5 (18.8)  79.1 (16.0)  −2.1  .042  0.55  −18.8  −0.4     Emotional Functioning  64.1 (25.7)  74.6 (20.0)  −1.7  ns  0.46  −22.7  1.6     Social Functioning  79.2 (19.6)  88.7 (17.6)  −1.9  ns  0.51  −19.3  0.3     School Functioning  65.2 (23.7)  73.9 (19.4)  −1.5  ns  0.40  −19.9  2.6  HRQOL parent-proxy report                 Total Score  52.9 (18.6)  71.1 (20.1)  −3.7  .000  0.94  −28.0  −8.4    Physical Health  42.6 (27.1)  69.7 (24.8)  −4.1  .000  1.04  −40.5  −13.7    Psychosocial Health  59.3 (17.1)  72.0 (19.7)  −2.7  .008  0.69  −22.0  −3.4     Emotional Functioning  55.8 (21.5)  67.8 (21.0)  −2.2  .031  0.56  −22.7  −1.2     Social Functioning  64.9 (19.8)  79.1 (21.0)  −2.7  .008  0.70  −24.7  −3.8     School Functioning  57.1 (21.5)  69.2 (26.6)  −1.9  ns  0.50  −24.9  0.7  Fatigue child self-report                 Total Fatigue  68.6 (18.9)  76.9 (17.6)  −1.7  ns  0.46  −18.0  1.3    General Fatigue  68.6 (23.6)  78.9 (19.4)  −1.8  ns  0.48  −21.8  1.1    Sleep Fatigue  60.9 (24.3)  75.7 (18.7)  −2.6  .013  0.68  −26.3  −3.2    Cognitive Fatigue  76.3 (20.3)  75.9 (23.9)  0.1  ns  −0.02  −11.1  12.1  Fatigue parent-proxy report                 Total Fatigue  54.9 (22.6)  71.8 (21.6)  −3.1  .003  0.77  −15.6  8.2    General Fatigue  49.4 (25.3)  69.7 (23.4)  −3.3  .002  0.83  −15.7  11.1    Sleep Fatigue  53.2 (26.4)  73.1 (21.1)  −3.3  .002  0.79  −12.8  13.4    Cognitive Fatigue  61.0 (28.3)  72.7 (24.7)  −1.7  ns  0.44  −22.2  5.0  Scale  Relapsing M  Monophasic M  t  p  Cohen’s d  Lower 95% CI of difference  Upper 95% CI of difference  HRQOL child self-report                 Total Score  67.3 (18.1)  80.3 (17.2)  −2.8  .007  0.74  −22.2  −3.7    Physical Health  63.0 (26.0)  82.5 (20.6)  −3.2  .003  0.83  −31.9  −7.1    Psychosocial Health  69.5 (18.8)  79.1 (16.0)  −2.1  .042  0.55  −18.8  −0.4     Emotional Functioning  64.1 (25.7)  74.6 (20.0)  −1.7  ns  0.46  −22.7  1.6     Social Functioning  79.2 (19.6)  88.7 (17.6)  −1.9  ns  0.51  −19.3  0.3     School Functioning  65.2 (23.7)  73.9 (19.4)  −1.5  ns  0.40  −19.9  2.6  HRQOL parent-proxy report                 Total Score  52.9 (18.6)  71.1 (20.1)  −3.7  .000  0.94  −28.0  −8.4    Physical Health  42.6 (27.1)  69.7 (24.8)  −4.1  .000  1.04  −40.5  −13.7    Psychosocial Health  59.3 (17.1)  72.0 (19.7)  −2.7  .008  0.69  −22.0  −3.4     Emotional Functioning  55.8 (21.5)  67.8 (21.0)  −2.2  .031  0.56  −22.7  −1.2     Social Functioning  64.9 (19.8)  79.1 (21.0)  −2.7  .008  0.70  −24.7  −3.8     School Functioning  57.1 (21.5)  69.2 (26.6)  −1.9  ns  0.50  −24.9  0.7  Fatigue child self-report                 Total Fatigue  68.6 (18.9)  76.9 (17.6)  −1.7  ns  0.46  −18.0  1.3    General Fatigue  68.6 (23.6)  78.9 (19.4)  −1.8  ns  0.48  −21.8  1.1    Sleep Fatigue  60.9 (24.3)  75.7 (18.7)  −2.6  .013  0.68  −26.3  −3.2    Cognitive Fatigue  76.3 (20.3)  75.9 (23.9)  0.1  ns  −0.02  −11.1  12.1  Fatigue parent-proxy report                 Total Fatigue  54.9 (22.6)  71.8 (21.6)  −3.1  .003  0.77  −15.6  8.2    General Fatigue  49.4 (25.3)  69.7 (23.4)  −3.3  .002  0.83  −15.7  11.1    Sleep Fatigue  53.2 (26.4)  73.1 (21.1)  −3.3  .002  0.79  −12.8  13.4    Cognitive Fatigue  61.0 (28.3)  72.7 (24.7)  −1.7  ns  0.44  −22.2  5.0  Note. Relapsing group = MS and NMO; Monophasic group = ADEM, ON, TM, and CIS; Cohen’s d benchmarks: Small = 0.2, Medium = 0.5, Large = 0.8 (Cohen, 1988). Comparison of Relapsing Versus Monophasic Demyelinating Disease Groups on Multidimensional Fatigue For child self-report, significant group differences were found only for the Sleep Fatigue subscale (p = .013), with the relapsing diseases group reporting more sleep-related fatigue. Significant differences did not emerge for the General Fatigue and Cognitive Fatigue child-report subscales. Parent-proxy report indicated the relapsing diseases group to have significantly greater Total Fatigue (p = .003), General Fatigue (p = .002), and Sleep Fatigue (p = .002) than the monophasic group. Groups did not significantly differ for parent-proxy-reported Cognitive Fatigue (Table III). Predictors of Child Report and Parent-Proxy Report Generic HRQOL Correlations For child report, results indicated significant correlations between Total HRQOL and EDSS (r = −.40, p < .01), disease presentation (r = .35, p < .05; lower HRQOL positively correlated with relapsing), gender (r = −.30, p < .05; lower HRQOL associated with female gender), general fatigue (r = .72, p < .01), sleep fatigue (r = .55, p < .01), and cognitive fatigue (r = .54, p < .01). Parent-proxy-report results indicated significant correlations between Total HRQOL and EDSS (r = −.36, p < .01) and disease presentation (r = .43, p < .01; lower HRQOL positively correlated with relapsing). Child age was not significantly correlated with either parent-proxy or child report generic HRQOL and so was not included in the subsequent regression models (Table IV). Table IV. Correlations Among Demographics, Disease Presentation, HRQOL, and Fatigue Variables  1  2  3  4  5  6  7  8  9  10  11  1 Gender (Male-0, Female-1)  –                      2 Age  .17  –                    3 Disease presentation (Relapsing-0, Monophasic-1)  −.09  −.42**  –                  4 EDSS  .11  .02  −.12  –                5 Child-report Total HRQOL  −.30*  −.20  .35*  −.40**  –              6 Proxy-report Total HRQOL  −.17  −.03  .43**  −.36**  .75**  –            7 Child-report General Fatigue  .17  −.16  .24  −.45**  .72**  .55**  –          8 Child-report Sleep Fatigue  −.24  −.20  .32*  −.06  .55**  .48**  .60**  –        9 Child-report Cognitive Fatigue  −.04  .01  −.01  .01  .54**  .34**  .55**  .50**  –      10 Proxy-report General Fatigue  −.02  .18  .04  .04  −.02  .03  −.04  −.11  −.12  –    11 Proxy-report Sleep Fatigue  −.13  −.03  −.01  .10  −.12  −.13  −.16  −.26*  −.16  .79**  –  12 Proxy-report Cognitive Fatigue  −.01  .00  .16  .04  .12  .08  .08  .08  −.08  .69**  .59**  Variables  1  2  3  4  5  6  7  8  9  10  11  1 Gender (Male-0, Female-1)  –                      2 Age  .17  –                    3 Disease presentation (Relapsing-0, Monophasic-1)  −.09  −.42**  –                  4 EDSS  .11  .02  −.12  –                5 Child-report Total HRQOL  −.30*  −.20  .35*  −.40**  –              6 Proxy-report Total HRQOL  −.17  −.03  .43**  −.36**  .75**  –            7 Child-report General Fatigue  .17  −.16  .24  −.45**  .72**  .55**  –          8 Child-report Sleep Fatigue  −.24  −.20  .32*  −.06  .55**  .48**  .60**  –        9 Child-report Cognitive Fatigue  −.04  .01  −.01  .01  .54**  .34**  .55**  .50**  –      10 Proxy-report General Fatigue  −.02  .18  .04  .04  −.02  .03  −.04  −.11  −.12  –    11 Proxy-report Sleep Fatigue  −.13  −.03  −.01  .10  −.12  −.13  −.16  −.26*  −.16  .79**  –  12 Proxy-report Cognitive Fatigue  −.01  .00  .16  .04  .12  .08  .08  .08  −.08  .69**  .59**  Note. *p < .05; **p < .01; HRQOL = health-related quality of life; EDSS = Expanded Disability Status Scale. Effect sizes for Pearson r: small = 0.10, medium = 0.30, large = 0.50. Regression The child-report model accounted for 61% of the variability in generic HRQOL, F(6, 51) = 15.8, p < .01, representing a large effect. Significant individual predictors included disease presentation (β = .2, p < .05), EDSS (β = −.2, p < .05), general fatigue (β = .3, p < .05), and cognitive fatigue (β = .3, p < .05). As hypothesized, higher disability, increased fatigue, and relapsing disease presentation predicted reduced HRQOL (Table V). Table V. Multiple Regression Analyses Predicting Generic Health-Related Quality of Life in Pediatric Demyelinating Diseases   Child-reported HRQOL N = 57   Parent-reported HRQOL N = 60   Variable  B  SE B  β  B  SE B  Β  Gender  −4.8  4.0  −.1  −8.7  6.0  −.2  Monophasic or Relapsing  8.4  3.4  .2*  15.4  4.8  .4**  EDSS  −2.0  0.9  −.2*  −2.7  1.0  −.3*  General Fatigue  0.3  0.1  .3*  0.2  0.2  .1  Sleep Fatigue  0.1  0.1  .1  −0.3  0.2  −.3  Cognitive Fatigue  0.3  0.1  .3*  0.1  0.1  .1  Adjusted R2    0.61      0.28    F    15.8**      4.9**      Child-reported HRQOL N = 57   Parent-reported HRQOL N = 60   Variable  B  SE B  β  B  SE B  Β  Gender  −4.8  4.0  −.1  −8.7  6.0  −.2  Monophasic or Relapsing  8.4  3.4  .2*  15.4  4.8  .4**  EDSS  −2.0  0.9  −.2*  −2.7  1.0  −.3*  General Fatigue  0.3  0.1  .3*  0.2  0.2  .1  Sleep Fatigue  0.1  0.1  .1  −0.3  0.2  −.3  Cognitive Fatigue  0.3  0.1  .3*  0.1  0.1  .1  Adjusted R2    0.61      0.28    F    15.8**      4.9**    Note. *p < .05; ** p < .01. HRQOL = health-related quality of life; EDSS = Expanded Disability Status Scale. Adjusted R2effect sizes: small = 0.02, medium = 0.13, large = 0.26. The parent-report model accounted for 28% of the variability in generic HRQOL, F(6, 54) = 4.9, p < .01, representing a large effect. Significant individual predictors included disease presentation (β = .4, p < .01) and EDSS (β = −.3, p < .05), and as predicted, relapsing disease presentation and higher disability were associated with reduced HRQOL. Contrary to prediction, for parent-proxy report, fatigue scales were not significantly associated with generic HRQOL (Table V). Discussion A wide spectrum of physical, psychological, and social factors associated with pediatric demyelinating diseases have the potential to negatively affect HRQOL. The unpredictable nature of the disease course with potential for relapse, intermittent or ongoing physical symptoms, and associated impairment (e.g., fatigue, motor or visual impairments, bowel or bladder dysfunction), necessary treatment regimens with their associated burden or side effects (Boyd & MacMillan, 2000; MacAllister et al., 2007), potential cognitive deficits (e.g., difficulties with executive functioning, attention, and working memory; Banwell & Anderson, 2005; Banwell et al., 2007; MacAllister et al., 2007), and patient and family adjustment to having a demyelinating disease all may impact physical, emotional, academic, and social functioning for these children. This study supplements the limited literature examining HRQOL in pediatric patients with demyelinating diseases, contributing a novel comparison of HRQOL and fatigue in monophasic versus relapsing demyelinating diseases and examination of predictors of HRQOL. In our sample of children with demyelinating diseases, both parent-proxy report and child self-report reflect significantly poorer generic HRQOL than healthy child normative data across multiple domains, converging with prior studies and supporting need for intervention. Optimal care should attend to the physical, psychosocial, and treatment-related factors that negatively impact HRQOL of individual patients. Both children and parents reported lower total HRQOL as well as Physical and Psychosocial subscales, highlighting the potential for concern across domains of functioning. Of note, child report of social functioning was the only nonsignificant group comparison, suggesting peer relationships may be less affected. Compared with extant pediatric literature, our sample scored similarly on the neurological exam for disability (EDSS), with a mean EDSS comparable with previous pediatric studies (Ketelslegers et al., 2010; MacAllister et al., 2009). However, it should be noted that EDSS scores were determined by a single rater, precluding ability to assess reliability of the ratings. As predicted, our pattern of results generally suggested patients with relapsing demyelinating diseases experience greater impairments in generic HRQOL and, to a less consistent extent, fatigue than those with monophasic diseases. This may relate to the sequelae of experiencing recurrent neurological attacks, as well as potential psychosocial effects of anticipating potential future attacks and the associated uncertainty about disease course and impact. That said, group differences were more pronounced for parent than child report such that parents expressed more concern about psychosocial functioning and fatigue. This highlights the importance of assessing and better understanding both parent and child perspectives. Future research with larger samples should examine HRQOL in specific diagnostic subgroups of demyelinating diseases, assess the relative contribution of accumulated disability and illness uncertainty to reduced HRQOL, and explore divergence in parent and child perspectives. Our study also extends the limited pediatric demyelinating diseases literature by examining predictors of HRQOL, which can inform clinical care and intervention development. Our hypotheses were largely confirmed in the child-report model, with EDSS, relapsing presentation, general fatigue, and cognitive fatigue all negatively related to HRQOL. Interestingly, sleep fatigue was not associated with HRQOL. Although adult literature reflects that demyelinating diseases are associated with sleep disturbance (Hughes et al., 2017; Nociti et al., 2017), the limited pediatric literature suggests sleep may be less problematic (Zafar, Ness, Dowdy, Avis, & Bashir, 2012). Our results suggest interventions targeting general and cognitive fatigue may be more salient in the pediatric population. For the parent-report model, however, only disability and relapsing disease status were significant predictors of HRQOL, whereas fatigue was not significantly associated. Given that fatigue is a subjective symptom that may not be directly observable, one might suspect that parents could be under-aware of this concern; however, parent report of fatigue reflects that parents in our sample described their children as having notable difficulty across the fatigue subscales, more so than the children themselves. This converges with prior studies indicating parents often report more fatigue for children with MS than children self-report (Goretti et al., 2012; Holland et al., 2014; MacAllister et al., 2009), but the lack of relation between fatigue and generic HRQOL per the parent perspective is surprising. The consistent finding of cross-informant variance substantiates the need to assess the perspectives of both parent and child when assessing HRQOL and fatigue in pediatric patients with demyelinating diseases. The cross-sectional design of our study precludes causal interpretations. Indeed, while some literature with adult MS patients finds EDSS to predict HRQOL (Berrigan et al., 2016), other longitudinal literature reflects that baseline HRQOL may predict subsequent change in EDSS scores (Baumstarck et al., 2015). Pediatric longitudinal studies are needed to understand the relationship between HRQOL and clinical outcomes over time, and examination of indirect effects may also be informative. Multiple analyses were conducted with the p-value of .05; given the current state of the literature, no statistical corrections were employed. Further, our sample size was not sufficient to allow comparison of individual demyelinating disease groups, which would be a fruitful area of future study. Given the wide-ranging nature of potential symptoms both within and across demyelinating diseases, a more detailed examination of multiple specific symptoms beyond fatigue (e.g., visual symptoms, bowel and bladder dysfunction), as well as additional research related to sleep, would also be valuable. Collecting data in the context of a routine health care visit in a busy neurology clinic prevented capturing recruitment rate information and also limited the constructs that could be measured. The absence of a measure of depression is a limitation, as in adults with MS, depression is an identified predictor of HRQOL and has been associated with fatigue, with potential for bidirectional or mediation effects (Berrigan et al., 2016b; Lobentanz et al., 2004; Morrison & Stuifbergen, 2016; Nourbakhsh, Julian, & Waubant, 2016; Papuć & Stelmasiak, 2012; Tepavcevic et al., 2014; Yamout et al., 2013). Incorporation of a measure of depression, as well as information on cognitive functioning and its relationship with fatigue, would benefit future research (Goretti et al., 2012; Holland et al., 2014). In adults, fatigue commonly interferes with work and productivity (Smith & Arnett, 2005), meriting consideration of interference with school attendance and academic functioning in the pediatric population. While reducing physical disability may be a potent intervention to improve HRQOL, this is not always modifiable, and targeting other symptoms potentially amenable to change may be a valuable and achievable way to improve quality of life for patients with demyelinating diseases (Berrigan et al., 2016). Fatigue and HRQOL were strongly related from the children’s perspective, and though the cross-sectional design precludes causative conclusions, optimal management of fatigue may be helpful in improving overall HRQOL. Cognitive behavioral therapy techniques (e.g., behavioral activation or activity pacing), which have been moderately efficacious in the adult chronic fatigue population (Malouff, Thorsteinsson, Rooke, Bhullar, & Schutte, 2008), may be helpful. Multidisciplinary team-based care including psychologists, neurologists, and physical/occupational therapists may serve to optimize HRQOL of children with demyelinating diseases. Conflicts of interest: None declared. 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Journal of Pediatric PsychologyOxford University Press

Published: Mar 1, 2018

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