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Quality of life and cognitive functions in early onset multiple sclerosis
European Journal of Paediatric Neurology, Volume 20, Issue 1, January 2016, Pages 158–163
Multiple sclerosis (MS) is a demyelinating disease of the CNS occurring in young adults and even in children in 5% of cases. Lower quality of life (QoL) and cognitive impairment (CI) (40–54%) have been reported in early-onset MS (EO-MS) patients.
To assess QoL and cognitive function in EO-MS and their relationship, also considering demographic and clinical variables.
Paediatric Quality of life inventory Version 4.0 for patients aged 13–18 and 19–25 years, Beck Depression Inventory II (BDI II) and the Rao Brief Repeatable Battery were performed in EO-MS patients (onset age ≤25years). EDSS and MSSS were performed at same time. After testing for normal distribution, group comparisons were performed through the two-tailed Student's t test, one-way analysis of variance (ANOVA) and linear or logistic regression when appropriate. The Bonferroni correction for multiple testing was used when appropriate.
59 patients were included (mean age: 20 ± 3.6; Female sex 52.54%). 34 patients had a paediatric onset (<18 years) while 20 patients had a juvenile onset (18 < age < 25 years) of disease. 5 patients were excluded for missing data.
HR-QoL was higher in paediatric than juvenile MS patients (p = 0.02), and it was inversely related to EDSS (p = 0.0005) and Multiple Sclerosis Severity score (MSSS) (p = 0.0001).
Sixtyone % of patients showed a CI at BRB. No association was found between CI and any socio-demographic and clinical data.
HR-QoL total score was not related to CI status nor to any domain-specific cognitive function score, even considering BDI as possible bias.
CI was related to social, physical functioning score and EDSS (p = 0.01) at a logistic regression backward stepwise estimation.
HR-QoL resulted to be better in paediatric than juvenile MS onset patients and was inversely related to rapidity of disability accumulation, while cognitive impairment was influenced by physical disability and poor social involvement (school, education …). Social participation, affective relations and psychological flexibility could have a protective function on CI.
- HR-QoL was higher in paediatric onset (<18 y) than juvenile onset (18 < x < 25) MS patients (p = 0.02).
- HR-QoL was inversely related to EDSS (p = 0.0005) and multiple sclerosis severity score (MSSS) (p = 0.0001).
- QoL was not related to disease duration, but to rapidity of disability accumulation.
Keywords: Cognitive impairment, Quality of life, Paediatric multiple sclerosis, Multiple sclerosis.
Multiple sclerosis (MS) is a chronic immune-mediated disease causing in inflammation, demyelination, and neurodegeneration of the Central Nervous System (CNS). Mean age at onset is 28 years old1 but paediatric onset (before 18 years of age) is increasingly recognized, representing from 3% to 5% among adults with MS.2, 3, 4, and 5
Health-related quality of life (HRQoL) is a multi-domain concept that refers to the effect of an illness and its therapy upon a patient's physical, psychological and social wellbeing, as perceived by the patients themselves. In clinical research, HRQoL measures can capture the personal and social context of the patient's experience of the disease. QoL measures have increasingly been considered to evaluate disease progression, treatment and care in MS patients.6 and 7 In addition, the US Food and Drug Administration (FDA) and the European Medicines Agency encourage the use of QoL assessment in patients with chronic illnesses,8 and 9 and there are different detailed recommendations for QoL assessment.10 and 11
Despite the acknowledgement of the need to consider QoL issues, its assessment remains under-utilized in MS clinical practice, with particular regard to early onset MS.12
Recently paediatric MS patients reported lower HRQoL scores than healthy controls,13 however little is still known about the correlations between cognitive impairment and QoL in early onset MS.
Cognitive impairment is well-documented in adult-onset cases of MS14 and has been reported in 40–54% of the patients with early-onset MS.15 Its role in affecting directly activities of daily living and overall quality of life, is still controversial in adult onset MS,16 however its presence in the early phases of disease is considered as an unfavourable prognostic factor.17
Aim of our study is to assess HRQoL and cognitive functions in a cohort of EO-MS patients and to analyse their relationship, with additional regard to possible demographic and clinical influencing variables. We also evaluated the difference between QoL in paediatric and juvenile onset MS patients.
2. Patients and methods
This is a cross-sectional study carried out on MS patients referred to MS clinic for children and young people at Federico II University (“SMAG centre). Inclusion criteria were age older than 12 and early onset (before 26 y) of RRMS or CIS, diagnosed according to 2010 McDonald’s criteria18 at least 12 months previously (to avoid the influence of recent communication of diagnosis on psychological status). Patients were all living with their parents and were not married and had no children. At the moment of evaluation, all the cases were relapse-free and had not taken steroids for at least 30 days. Socio-demographic data (age, sex and education level) and clinical data (age at onset, disease duration, expanded disability status scale [EDSS], multiple sclerosis severity score [MSSS],19 disease modifying therapy, DMT) were collected by trained neurologists (RL, AC). We classified patients into paediatric onset (<18 y) and juvenile onset (between 18 and 25 y).
HRQoL was self administered using the Paediatric Quality of life inventory (PedsQoL)Version 4.0. and than assessed by two psychologists (AN, VM).
This is a self-reported questionnaire in two versions, one for subjects aged from 13 to 18 years, the other for subjects aged between 19 and 25 years. The questionnaire consists of 23 items that constitute 4 sub-scales measuring: physical functioning (8 items), emotional functioning (5 items), social functioning (5 items) and scholastic/working functioning (5 items). Each item is accompanied by a 5-points Likert scale, where the subject has to indicate how often this situation occurred, with 0 meaning “never” and 4 meaning “almost always”. The internal consistency of the instrument is very good (for alpha = . 90).
Scores are transformed on a scale from 0 to 100. Items are reversed scored and linearly transformed to a 0–100 scale as follows: 0 = 100, 1 = 75, 2 = 50, 3 = 25, 4 = 0, so that higher scores indicate better HRQoL. The Total Score is the sum of all the items over the number of items answered on all the Scales, while the Psychosocial Health Summary Score is the Sum of the items over the number of items answered in the Emotional, Social, and School Functioning Scales.20
Cognitive function was assessed using the Brief Repeatable Battery (BRB) of neuropsychological tests for multiple sclerosis, version A, by a trained neuropsychologist (TC).
The BRB includes: the Selective Reminding Test-Long Term Storage (SRT-LTS), Selective Reminding Test-Consistent Long Term Retrieval (SRT-CLTR) and Selective Reminding Test-Delayed/SRT-D) to assess verbal memory; the 10/36 Spatial Recall Test (10/36 SPART) and 10/36 Spatial Recall Test-Delayed (10/36 SPART-D) to assess visual memory; the Symbol Digit Modalities Test (SDMT) to assess information processing speed and executive functions; the Paced Auditory Serial Addition Test 2 and 3 s (PASAT 2–3) to assess attention, information processing speed, and working memory: the Word List Generation (WLG) to assess the semantic fluency.21 We used the 5th percentile of normative data21 as cut-off score for test failure. As normative BRB data are not available in paediatric MS patients and we did not include any healthy control (HC) group, we used normative data for adult MS patients.21 Comparing normative data for adult with those of a paediatric control group,15 when applicable, we found that the 5th percentile cut-off of each test for adults was more restrictive than the paediatric HC group, thus avoiding an overestimation of the cognitive impairment in our group.
We classified patients as cognitively impaired when their scores fell below the 5th percentile of normative data in at least 2 tests (unlikely to be a chance occurrence).22
We also used Beck Depression Inventory II(BDI II),23 to avoid biases linked to concomitant depression.
2.1. Statistical analysis
After testing for normal distribution, group comparisons were performed through the two-tailed Student's t test, one-way analysis of variance (ANOVA), linear regression when appropriate. The Bonferroni correction for multiple testing was used when appropriate. Linear regression backward stepwise estimation was used to determine the best model for predictors of HRQoL including predictors that appeared to be potentially meaningful (p-value cut-off of 0.10 for exclusion). Logistic regression backward stepwise estimation was used to determine the best model for predictors of cognitive impairment including predictors that appeared to be potentially meaningful (p-value cut-off of 0.10 for exclusion).
Stata 12.0 and Microsoft Excel have been used for data processing and analysis. Each of the analyses was tested for normal distribution of residuals by using both statistical and graphical methods approaches. Results have been considered statistically significant if p < 0.05.
The study was approved by the local Carlo Romano Ethical board and written informed consent was obtained from patients or their parents as appropriate.
Fifty-nine MS patients were enrolled. Demographic and clinical features are summarized in Table 1. Age of onset was 18 or below in 34 subjects and between 18 and 25 years in 20 subjects. The two groups were comparable except for disease duration, longer in paediatric onset patients (3.59 ± 3.18 vs 1.38 ± 0.97; p = 0.003) and MSSS, lower in paediatric patients (5.51 ± 1.37 vs 6.5 ± 1.38; p = 0.01).
|Female sex, N (%)||31 (52.5)|
|Age, mean ± SD (years)||20 ± 3.6|
|Age at onset, mean ± SD (years)||17.2 ± 3.6|
|Age at onset, subgroup*|
|Paediatric (≤18 y), N (%)||34 (63)|
|Juvenile (18 y < x < 25 y), N (%)||20 (37)|
|Disease duration, median (years)||2|
|EDSS, median (Range)||2.5 (0–4.5)|
|MSSS, mean ± SD||5.88 ± 1.44|
Patients were all but one on DMT (20% patients on IFNβ1-a s.c. 22 μg, 31.5% on IFNβ1-a s.c. 44 μg, 8, 15% on IFNβ1-a i.m., 9.3% on IFNβ1-b, 20.4% on natalizumab and 1.8% on fingolimod).
Fifty-one patients (comparable to the total sample for sex, age, age at onset, disease duration, EDSS, MSSS) underwent PedsQoL (27 F, 29 paediatric patients, 22 patients). The results are shown in Table 2.
|PedQoLTOT, mean ± SD||68.45 ± 18.43|
|School functioning, mean ± SD||66.44 ± 24|
|Social functioning, mean ± SD||67.75 ± 25.28|
|Emotional functioning, mean ± SD||72.45 ± 21.78|
|Physical functioning, mean ± SD||67.44 ± 24.47|
|Psychosocial functioning, mean ± SD||68.88 ± 17.28|
|BDI-II TOT, mean ± SD (Range)||14 ± 9 (1–38)|
|BDI-II somatoaffective, mean ± SD (Range)||9 ± 5 (1–21)|
|BDI-II cognitive, mean ± SD (Range)||5 ± 5 (0–18)|
|No depression, N (%)||20 (56)|
|Mild depression, N (%)||6 (17)|
|Moderate depression, N (%)||7 (19)|
|Severe depression, N (%)||3 (8)|
Total QoL score was not related to sex, DMT and disease duration, while it was higher in paediatric MS patients than juvenile MS patients (p = 0.02) and it was inversely related to EDSS (coefficient b = −19.3; p = 0.0005) (Fig. 1a) and Multiple Sclerosis Severity Score (MSSS) (coeff. b = −7.46 p = 0.0001) (Fig. 1b). Stratifying patients in two groups according to onset age, QoL was still not related to disease duration, but only inversely to MSSS (paediatric: coefficient b = −5.53; p = 0.001; juvenile: coefficient b = −7.96; p = 0.01).
We then analysed QoL single domains.
School functioning sub-score was related to EDSS (coeff. b = −19.30, p = 0.001) and MSSS (coeff. b = −7.46, p = 0.001). Social functioning sub-score was higher in paediatric than juvenile MS patients (p = 0.01) and it was inversely related to MSSS (coeff. b = −7, p = 0.01), and treatment with DMTs (p = 0.03). The Bonferroni post hoc analysis only revealed a significant difference between Interferon β1a 44mcg and 22mcg (p = 0.04).
The emotional functioning subscore was related to age at onset (p = 0.03) and MSSS (coeff. b = −6.2, p = 0.002).
The physical functioning score was significantly related to EDSS (coeff. b = −11.22, p = 0.02) and MSSS (coeff. b = −8.31, p = 0.001).
The psychosocial functioning score was related to age at onset (p = 0.02), EDSS (coeff. b −11.3, p = 0.001) and MSSS(coeff. b = −6.89, p = 0.0001).
Thirty six patients (comparable to the total sample for sex, age, age at onset, disease duration, EDSS, MSSS) underwent cognitive evaluation (BRB). Fourteen of them showed no cognitive impairment (CI) at BRB while 22 patients (61%) were CI, as defined in methods section (17% significantly and 44% mildly). The most frequently failed tests were the word list generation test (39%), 10/36 Spatial Recall Test (19%) and PASAT 3” test (19%). Cognitive results are shown in Table 3. No association was found between CI status and any sociodemographic or clinical data (sex, age at onset, DMT, disease duration and EDSS).
|Neuropsychological tests||No. of patients (%)|
|10/36 SPART||7/36 (19%)|
|10/36 SPART-D||2/36 (6%)|
Total HRQoL score was not different between MS patients with or without CI and it was not related to any domain-specific cognitive function row score at linear regression.
BDI-II was performed by 36 over 54 (61%) (22 females, 20 paediatric and 16 juvenile MS patients). Scores are summarized in Table 2. BDI-total score was not related to sex (t-test) and to different treatments (ANOVA), while there was a significant difference between paediatric (BDI-total average10.4) and juvenile (average 18.25) patients (t-student, p = 0.01) and a significant correlation between BDI and EDSS (coefficient b = 11.67, p = 0.0001, Linear Regression) and MSSS (coefficient b = 4.89, p = 0.0001, Linear Regression). Regarding the BDI-somato-affective score, it showed the same correlations as the total score, while the BDI-cognitive score had a significant correlation only with the EDSS (coefficient b = 5.65, p = 0.001, Linear Regression) and the MSSS (coefficient b = 2.08, p = 0.001, Linear Regression).
Considering the BDI-total score and the sub-scores as continuous variables we obtained that total QoL score was related to BDI-total score (coefficient b = −1.57, p = 0.001, Linear Regression) (Fig. 2) as well as BDI-somato-affective score (coefficient b = −2.3, p = 0.001, Linear Regression) and BDI-cognitive score (coefficient b = −3.11, p = 0.001, Linear Regression). Considering BDI results as categorical data, patients without depression had a higher total QoL score (better quality of life) than depressed patients (post-hoc Bonferroni). Considering the total score and subscores as continuous variables we obtained that BDI-total, and sub-scores did not correlate with CI (ANOVA). Considering the categories of the BDI we observed a similar result. Specifically, there was no correlation between CI and classes of BDI.
To determine the variables potentially predictive of QoL, we performed a backward stepwise estimation with α = 0.10 as the Linear Regression Model, entering all significant clinical and socio-demographical variables (sex, onset age group, MSSS, EDSS, and cognitive impairment) which confirmed that total QoL score was only related to MSSS and age of onset (p = 0.0001).
Instead, using a logistic regression backward stepwise estimation including socio-demographic, and clinical data and a single domain health-related quality of life score, cognitive impairment was related to the social and physical functioning score and EDSS (p = 0.01).
We designed a cross-sectional study, including paediatric and juvenile-onset MS patients, to explore the relationship between HRQoL and cognitive functions, with particular regard to possible clinical and demographic factors interfering with this model.
Considering our main aim, total QoL score was not different between MS patients with or without CI and did not correlate with any domain-specific cognitive function row score and the linear regression model confirmed that total QoL score was only related to MSSS and age of onset. Notably, HRQoL was higher in paediatric onset than juvenile onset MS patients and it was lower in subjects presenting higher EDSS score and, more interestingly, MSSS.
Exploring mood influence on QoL and cognitive performance, we found a strong correlation of depression with poor quality of life, while cognition was not related to mood disturbance.
The main result of the present study is the significance of paediatric age of onset as a good prognostic factor and MSSS as the main negative factor for HRQoL. Although the onset of a chronic disease during the early phases of life is dramatic, our data suggests that coping with a disease with its onset in childhood or adolescence has a lower impact on quality of life and on patients mood, as self reported, with respect to adult onset.24
This might depend also on a higher integration of the disease status into ones subjectivity when it intervenes in a specific developmental phase, such as adolescent, a phase in which persons are still busy in building their personality. The best quality of life of patients with early onset may also be justified by the possibility of a progressive transformation and adaptation of life goals in this particular population.
On the other hand, we cannot exclude other social and psychological factors, such as parental intervention in mitigating and supporting disease during this phase of life, especially regarding the burden of treatment decisions, obviously more evident in children and adolescence than in adults.
Moreover MSSS, a reliable and valid measure of disability progression over time,19 resulted strongly associated with poor HRQoL, even more evidently than EDSS itself. We suppose that the rapid accumulation of disability seems to have a negative influence on the adaptation process to the pathological condition and to have a detrimental effect on the compensatory mechanisms, both from a neurological and psychosocial point of view, even more than static disability itself, as expressed by EDSS. Infact, MSSS was lower in paediatric patients, confirming a more benign disease course in this subpopulation, as already suggested in literature, with a “disconnection between relapses and disability accumulation,25 and such a slower disease progression might very probably exert a good impact on QoL.
With regard to cognition, our results showed a mild to significant cognitive impairment in 61% of EO-MS patients in the examined subgroup, using a brief neuropsychological battery of tests, including both mild and severe deficiencies. We did not find direct correlations of cognitive functions with clinical or demographic parameters, but in a stepwise estimation model, including socio-demographic, clinical and single domain health-related quality of life scores, cognitive impairment was related to social and physical functioning scores and EDSS. These results seem to suggest that cognition in the early stages of disease is influenced by physical disability and, maybe as a consequence, by the lack of social experiences (school, education, work …). Actually, a good level of social participation, promoting the maintenance of affective relations, the processes of self-regulation and psychological flexibility, seem to have an important protective function respect to cognitive impairment. This would point to the importance of promoting these behaviours in the care of young patients suffering from MS.26 and 27
The main drawback of this study regards the small number of patients, especially in the cognition subgroup, divided into two clusters of onset age, and the cross-sectional design.
Another limitation of our study might have been the lack of a control group, therefore we used adult normative values. However, we observed that, comparing to the available test performed in an healthy control paediatric group,15 the 5th percentile of each test for adults was even more restrictive than the HC group thus avoiding an overestimation of the cognitive impairment in our group.
Moreover, parental perception of QoL should be taken into account, and depression and anxiety also in parents could affect the results. In addition, the sample was not homogeneous, including also patients with onset in early adulthood, however our young adults patients still lived with their families and were not economically independent yet, therefore we could analyse the effects of MS on patients in their developmental phase of life, before reaching complete autonomy and achieving their objectives.28 However, adding an adult onset control group, might be useful in order to confirm the differences in QoL found in different age of onset populations.
In conclusion we suggest that our results can provide a snapshot of the perceived status of the disease of young MS patients. They also suggest that a rapid and effective intervention, avoiding physical disability and social retirement, using the appropriate therapies, in order to delay disability accrual, might help in improving both quality of life and cognitive outcome.
RLanzillo has received honoraria from Bayer Shering, Biogen, Merck-Serono, TEVA and Novartis for lectures or scientific boards. VBM has received honoraria from Bayer Shering, Biogen, Merck-Serono, TEVA, Genzyme and Novartis for lectures or scientific boards. ACarotenuto, AChiodi, VM, CT, AN, VBM, RLanzillo, PV, MFF, NR, are currently working at the University “Federico II” of Naples, Italy.
Funding and conflict of interest
The authors stated that there are no conflicts of interest regarding the publication of this article. Research funding played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.
- 1 B.G. Weinshenker, B. Bass, G.P. Rice, J. Noseworthy, W. Carriere, J. Baskerville, et al. The natural history of multiple sclerosis: a geographically based study. I. Clinical course and disability. Brain. 1989;112(Pt 1):133-146
- 2 T. Chitnis, B. Glanz, S. Jaffin, B. Healy. Demographics of pediatric-onset multiple sclerosis in an MS center population from the Northeastern United States. Mult Scler. 2009;15(5):627-631 Crossref
- 3 A. Boiko, G. Vorobeychik, D. Paty, V. Devonshire, D. Sadovnick. Early onset multiple sclerosis: a longitudinal study. Neurology. 2002;59(7):1006-1010 Crossref
- 4 A. Ghezzi, V. Deplano, J. Faroni, M.G. Grasso, M. Liguori, G. Marrosu, et al. Multiple sclerosis in childhood: clinical features of 149 cases. Mult Scler. 1997;3(1):43-46 Crossref
- 5 E. Sindern, J. Haas, E. Stark, U. Wurster. Early onset MS under the age of 16: clinical and paraclinical features. Acta Neurol Scand. 1992;86(3):280-284 Crossref
- 6 A.J. Mitchell, J. Benito-Leon, J.M. Gonzalez, J. Rivera-Navarro. Quality of life and its assessment in multiple sclerosis: integrating physical and psychological components of wellbeing. Lancet Neurol. 2005;4(9):556-566 Crossref
- 7 A. Solari. Role of health-related quality of life measures in the routine care of people with multiple sclerosis. Health Qual Life Outcomes. 2005;3:16 Crossref
- 8 AIFA. Guidance for industry: patient reported outcome measures: use in medical product development to support labeling claims.
- 9 EMA. Reflection paper on the regulatory guidance for the use of hrqol measures in the evaluation of medicinal products.
- 10 G. Apolone, G. De Carli, M. Brunetti, S. Garattini. Health-related quality of life (HR-QOL) and regulatory issues. An assessment of the European Agency for the evaluation of medicinal products (EMEA) recommendations on the use of HR-QOL measures in drug approval. Pharmacoeconomics. 2001;19(2):187-195 Crossref
- 11 A. Bottomley, D. Jones, L. Claassens. Patient-reported outcomes: assessment and current perspectives of the guidelines of the Food and Drug Administration and the reflection paper of the European Medicines Agency. Eur J Cancer. 2009;45(3):347-353 Crossref
- 12 C.F. Snyder, N.K. Aaronson, A.K. Choucair, T.E. Elliott, J. Greenhalgh, M.Y. Halyard, et al. Implementing patient-reported outcomes assessment in clinical practice: a review of the options and considerations. Qual Life Res. 2012;21(8):1305-1314 Crossref
- 13 E.M. Mowry, L.J. Julian, S. Im-Wang, D. Chabas, A.J. Galvin, J.B. Strober, et al. Health-related quality of life is reduced in pediatric multiple sclerosis. Pediatr Neurol. 2010;43(2):97-102 Crossref
- 14 M.P. Amato, V. Zipoli, E. Portaccio. Cognitive changes in multiple sclerosis. Expert Rev Neurother. 2008;8(10):1585-1596 Crossref
- 15 M.P. Amato, B. Goretti, A. Ghezzi, S. Lori, V. Zipoli, E. Portaccio, et al. Cognitive and psychosocial features of childhood and juvenile MS. Neurology. 2008;70(20):1891-1897 Crossref
- 16 K. Baumstarck-Barrau, M.C. Simeoni, F. Reuter, I. Klemina, V. Aghababian, J. Pelletier, et al. Cognitive function and quality of life in multiple sclerosis patients: a cross-sectional study. BMC Neurol. 2011;11:17 Crossref
- 17 M.P. Amato, G. Ponziani, G. Siracusa, S. Sorbi. Cognitive dysfunction in early-onset multiple sclerosis: a reappraisal after 10 years. Arch Neurol. 2001;58(10):1602-1606 Crossref
- 18 C.H. Polman, S.C. Reingold, B. Banwell, M. Clanet, J.A. Cohen, M. Filippi, et al. Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol. 2011;69(2):292-302 Crossref
- 19 R.H. Roxburgh, S.R. Seaman, T. Masterman, A.E. Hensiek, S.J. Sawcer, S. Vukusic, et al. Multiple sclerosis severity score: using disability and disease duration to rate disease severity. Neurology. 2005;64(7):1144-1151 Crossref
- 20 J.W. Varni, T.M. Burwinkle, M.A. Rapoff, J.L. Kamps, N. Olson. The PedsQL in pediatric asthma: reliability and validity of the pediatric quality of life inventory generic core scales and asthma module. J Behav Med. 2004;27(3):297-318 Crossref
- 21 M.P. Amato, E. Portaccio, B. Goretti, V. Zipoli, L. Ricchiuti, M.F. De Caro, et al. The Rao's brief repeatable battery and stroop test: normative values with age, education and gender corrections in an Italian population. Mult Scler (Houndmills, Basingstoke, England). 2006;12(6):787-793 Crossref
- 22 L.J. Ingraham, C.B. Aiken. An empirical approach to determining criteria for abnormality in test batteries with multiple measures. Neuropsychology. 1996;10(1):120 Crossref
- 23 R.H. Benedict, I. Fishman, M.M. McClellan, R. Bakshi, B. Weinstock-Guttman. Validity of the beck depression inventory-fast screen in multiple sclerosis. Mult Scler (Houndmills, Basingstoke, England). 2003;9(4):393-396 Crossref
- 24 S.G. Lynch, D.C. Kroencke, D.R. Denney. The relationship between disability and depression in multiple sclerosis: the role of uncertainty, coping, and hope. Mult Scler (Houndmills, Basingstoke, England). 2001;7(6):411-416
- 25 I.L. Simone, D. Carrara, C. Tortorella, M. Liguori, V. Lepore, F. Pellegrini, et al. Course and prognosis in early-onset MS: comparison with adult-onset forms. Neurology. 2002;59(12):1922-1928 Crossref
- 26 S. Vitali. Finding quality of life despite MS: harnessing resilience. Int MS J MS Forum. 2011;17(3):94-99
- 27 M.P. McCabe, E.J. O'Connor. Why are some people with neurological illness more resilient than others?. Psychol Health Med. 2012;17(1):17-34 Crossref
- 28 G. Klevan, C.O. Jacobsen, J.H. Aarseth, K.M. Myhr, H. Nyland, S. Glad, et al. Health related quality of life in patients recently diagnosed with multiple sclerosis. Acta Neurol Scand. 2014;129(1):21-26 Crossref
a Department of Neurosciences, Reproductive Science and Odontostomatology, Federico II University, Naples, Italy
b Institute of Biostructure and Bioimaging, National Research Council, Federico II University, Naples, Italy
c Department of Humanistic Study, University of Naples “Federico II”, Naples, Italy
∗ Corresponding author. Tel.: +39 0817463764; fax: +39 0815463663.
d Author who completed the statistical analysis.
© 2015 European Paediatric Neurology Society, Published by Elsevier B.V.