Abstract
Background and Purpose
Fatigue is common in demyelinating disorders of the central nervous system (CNS), including multiple sclerosis (MS), neuromyelitis optica spectrum disorder (NMOSD), and myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD). We aimed to validate the usefulness of the Functional Assessment of Chronic Illness Therapy–Fatigue (FACIT-F) and the Fatigue Severity Scale (FSS) relative to the Korean version of the Modified Fatigue Impact Scale (MFIS-K) in Korean patients with MS, NMOSD, and MOGAD.
Methods
There were 294 patients with MS (n=120), NMOSD (n=103), or MOGAD (n=71) enrolled in a prospective demyelinating CNS registry. Fatigue was measured using the FACIT-F, MFIS-K, and FSS. Sleep quality, quality of life, depression, and pain were evaluated using the Pittsburgh Sleep Quality Index (PSQI), 36-item Short-Form Survey (SF-36), and Beck Depression Inventory-II (BDI-II).
Results
The MFIS-K, FACIT-F, and FSS scores showed high internal consistencies and strong correlations with each other in the MS, NMOSD, and MOGAD groups. The scores on all three fatigue scales were correlated with PSQI, SF-36, and BDI-II results in the three groups. The areas under the receiver operating characteristic curves for the FSS and FACIT-F were 0.834 and 0.835, respectively, for MS, 0.877 and 0.833 for NMOSD, and 0.925 and 0.883 for MOGAD.
Conclusions
These results suggest that the MFIS-K, FSS, and FACIT-F are useful and valuable assessment instruments for evaluating fatigue in Korean patients with MS, NMOSD, and MOGAD.
Keywords: fatigue, multiple sclerosis, neuromyelitis optica spectrum disorder, myelin oligodendrocyte glycoprotein antibody-associated disease
Graphical Abstract
INTRODUCTION
Multiple sclerosis (MS), neuromyelitis optica spectrum disorder (NMOSD), and myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) are demyelinating disorders of the central nervous system (CNS) that involve the brain, optic nerve, and spinal cord. Fatigue is defined as a state of reduced physical and/or mental energy, and it is frequently observed in MS and NMOSD.1,2 A recent study has also found that fatigue is a common symptom of MOGAD.1,2,3
Several useful tools have been used globally for characterizing fatigue in MS, such as the Fatigue Severity Scale (FSS) and Modified Fatigue Impact Scale (MFIS).4 In contrast, the Functional Assessment of Chronic Illness Therapy–Fatigue (FACIT-F) has not been commonly used in MS, but this was recently validated in Spanish patients with MS.5 Fatigue has been assessed in patients with NMOSD using the FSS, MFIS, and FACIT-F in heterogeneous studies.6,7,8,9 Only three studies conducted in the United States and United Kingdom have used the MFIS for assessing fatigue in patients with MOGAD.3,8,10 We recently validated the Korean version of the MFIS (MFIS-K) in Korean patients with MS.11 However, the Korean versions of the FSS and FACIT-F have been validated in cancers and psychiatric disorders, but not in MS and NMOSD, whereas these two scales have been used in Korean patients with MS and NMOSD.2,12,13,14 In addition, no previous studies have evaluated fatigue in Korean patients with MOGAD.
In the present study we aimed to validate the usefulness of the FACIT-F and FSS and compare them with the MFIS-K in Korean patients with MS, NMOSD, and MOGAD in order to determine their applicability in clinical practice.
METHODS
Participants
We prospectively enrolled patients with MS, NMOSD, and MOGAD in the CNS Inflammatory and Demyelinating Disease Registry between 2019 and 2022 at the Samsung Medical Center. Patients diagnosed with MS based on the 2017 McDonald criteria15 and patients with NMOSD who met the 2015 international consensus diagnostic criteria for NMOSD with anti-aquaporin-4 antibody were enrolled.16,17 Patients who were diagnosed with MOGAD in a positive cell-based assay for myelin oligodendrocyte glycoprotein–IgG and who satisfied the newly proposed criteria for MOGAD were also included.18 This selection process resulted in the final inclusion of 294 patients with MS (n=120), NMOSD (n=103), or MOGAD (n=71).
We collected demographic data including sex, age, disease activity, disease duration, type of index attack, total number of attacks, and Expanded Disability Status Scale (EDSS) scores. This study was approved by the Institutional Review Board of the Samsung Medical Center, and written informed consent was obtained from all participants (IRB no. SMC-2020–04–119).
Fatigue measures and other variables
All patients were asked to complete self-reported questionnaires for the following three fatigue scales: FSS, MFIS-K, and FACIT-F. The FSS is a 9-item questionnaire, each rated on a 7-point Likert scale from 1 (strongly disagree) to 7 (strongly agree), and the FSS score is determined as the average value of all items, with higher scores indicating greater fatigue.13,19 The MFIS-K, which was recently validated in Korean patients with MS, comprises 21 items related to cognitive, physical, and psychosocial factors. Each item is scored on a 5-point Likert scale from 0 (never) to 4 (most of the time), to give a maximum total score of 84 points, with higher scores indicating greater fatigue.11,20 The FACIT-F is a 13-item questionnaire that has recently been validated in patients with MS, and is scored on a 5-point Likert scale from 0 (a lot) to 4 (not at all); the maximum score is 52, with lower scores indicating greater fatigue.5,14
We also investigated factors that were anticipated to be associated with fatigue. To evaluate depression, sleep quality, quality of life, and pain, the patients completed the Beck Depression Inventory-II (BDI-II), Pittsburgh Sleep Quality Index (PSQI), 36-item Short-Form Survey (SF-36), and Brief Pain Inventory (BPI). The BDI-II comprises 21 items and its score ranges from 0 to 63, with higher scores indicating more-severe depression.21 The PSQI consists of seven items and its score ranges from 0 to 21, with higher scores indicating worse sleep quality.22 The SF-36 is scored from 0 to 100, with higher scores indicating better quality of life. Two summary scores of the SF-36 were used for the analysis: physical component summary (PCS) and mental component summary (MCS).23 The BPI has questions in the two categories of pain severity and pain-related interference in daily life, with the score in each category ranging from 0 to 10; the pain-severity index score evaluated by the average score for the pain-severity questions was used, with higher scores indicating more pain.24 Cognitive function was investigated with the Korean version of the Mini-Mental State Examination and the Symbol Digit Modalities Test (SDMT).25 The Mini-Mental State Examination is commonly used as a primary screening test to assess cognitive impairment. It is a 30-point scale, with lower scores indicating the presence of greater cognitive dysfunction, although it is not sufficiently sensitive to identify mild cognitive impairment in MS.25,26 The SDMT is the most-sensitive measure of MS-related cognitive dysfunction, and involves summing the number of correct substitutions during a 90-second interval; the maximum score is 110, with lower scores indicating greater cognitive dysfunction.27
Statistical analyses
All statistical analyses were performed separately for the MS, NMOSD, and MOGAD groups. For categorical variables, we compared three groups using the chi-square test or Fisher’s exact test. For continuous variables, we performed analysis of variance for comparing three groups, and Scheffe’s post-hoc analysis was conducted if p<0.05. In the case of skewed data, we compared three groups using the Kruskal–Wallis test, and conducted post-hoc analysis using the Dunn test. Internal consistency reliability was calculated using Cronbach’s α, which ranges from 0 to 1, with higher values indicating better reliability. Correlations between scores on the fatigue scales and other variables were quantified using Pearson’s correlation coefficients (r values). Variables that did not follow a normal distribution were assessed for correlations using Spearman’s rho. The absolute values of the correlation coefficients were interpreted as follows: <0.35, weak correlation; ≥0.35, to <0.65, moderate correlation; and ≥0.65, strong correlation. Binary logistic regression analysis was performed, employing FSS and FACIT-F scores as independent variables to predict fatigue, which was defined by an MFIS-K score of >38.28 Receiver operating characteristic (ROC) curves were also analyzed. Continuous variables were presented as mean±standard deviation or median [interquartile range] values, and categorical variables were presented as absolute and relative frequencies. Differences were considered statistically significant when p<0.05. All statistical analyses were performed using SPSS Statistics (version 27.0, IBM Corp., Armonk, NY, USA).
RESULTS
Demographics and clinical characteristics of patients with MS, NMOSD, and MOGAD
The demographics and clinical characteristics in the MS, NMOSD, and MOGAD groups are summarized in Table 1. The proportion of females was highest in the patients with NMOSD, followed by those with MS and MOGAD (91.3%, 78.3%, and 46.5%, respectively; p<0.001). Age was significantly higher in the patients with NMOSD than in those with MS and MOGAD (47.3±14.3, 37.7±11.0, 40.9±14.5 years, respectively; p<0.001). Disease duration was shorter in the patients with MOGAD than in those with MS and NMOSD (3.0 [1.0–5.0], 4.8 [2.3–8.6], and 4.7 [2.4–8.9] years, respectively; p=0.001), and the total number of attacks did not differ significantly among patients in the three disease groups. The EDSS score was higher in the patients with NMOSD than in those with MS and MOGAD (2.0 [1.0–3.0], 1.5 [1.0–2.5], and 1.5 [1.0–2.0], respectively; p=0.002). The SF-36 PCS score representing the physical components of the quality of life was significantly lower in the patients with NMOSD than in those with MS and MOGAD, but the scores on the sleep, depression, and pain scales did not differ between the groups.
Table 1. Demographics and clinical data of patients with multiple sclerosis (MS), neuromyelitis optica spectrum disorder (NMOSD), and myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD).
| Clinical variables and instruments | MS (n=120) | NMOSD (n=103) | MOGAD (n=71) | p | |
|---|---|---|---|---|---|
| Sex, female | 94 (78.3) | 94 (91.3) | 33 (46.5) | <0.001*†‡ | |
| Age (yr) | 37.7±11.0 | 47.3±14.3 | 40.9±14.5 | <0.001*‡ | |
| Disease duration (yr) | 4.8 [2.3–8.6] | 4.7 [2.4–8.9] | 3.0 [1.0–5.0] | 0.001†‡ | |
| Total number of attacks | 2.0 [2.0–4.0] | 2.0 [2.0–4.0] | 2.0 [1.0–5.0] | 0.809 | |
| EDSS score | 1.5 [1.0–2.5] | 2.0 [1.0–3.0] | 1.5 [1.0–2.0] | 0.002*‡ | |
| EDSS score ≥6 | 5 (4.2) | 9 (64.3) | 0 (0.0) | 0.018‡ | |
| Education duration (yr) | 16 [14–16] | 14 [12–16] | 15 [12–16] | 0.027* | |
| Unemployed | 45 (42.5) | 33 (51.6) | 16 (32.7) | 0.131 | |
| Scores on fatigue scales | |||||
| FSS score | 3.31±1.55 | 3.17±1.73 | 3.06±1.50 | 0.587 | |
| MFIS-K-physical score | 14.43±9.07 | 13.98±8.31 | 13.29±8.90 | 0.706 | |
| MFIS-K-cognitive score | 12.62±9.65 | 11.48±8.38 | 12.05±8.71 | 0.650 | |
| MFIS-K-psychosocial score | 3.48±3.81 | 3.49±3.27 | 3.00±2.41 | 0.604 | |
| MFIS-K-total score | 30.53±19.22 | 29.01±17.85 | 28.33±18.27 | 0.712 | |
| FACIT-F score | 36.97±10.02 | 35.75±10.93 | 37.01±11.40 | 0.646 | |
| Sleep quality | |||||
| PSQI score | 6.52±3.89 | 5.80±3.06 | 6.66±3.49 | 0.238 | |
| Health-related quality of life, SF-36 score | |||||
| PCS score | 68.10±20.73 | 61.38±23.28 | 68.87±19.20 | 0.028*‡ | |
| MCS score | 67.82±19.86 | 66.03±21.08 | 64.23±19.43 | 0.487 | |
| Depression, BDI-II score | 12.9±9.7 | 12.5±9.7 | 14.8±11.6 | 0.327 | |
| Pain, BPI | |||||
| PSI | 1.54±1.92 | 1.74±2.17 | 1.29±1.60 | 0.433 | |
| Cognitive function | |||||
| K-MMSE score | 30.0 [28.5–30.0] | 29.0 [28.0–30.0] | 29.0 [28.0–30.0] | 0.263 | |
| SDMT score | 58.82±13.21 | 55.65±15.16 | 54.17±11.79 | 0.107 | |
| Fatigue | |||||
| MFIS-K score >38 | 45 (38.5) | 33 (33.7) | 18 (28.6) | 0.402 | |
Data are mean±standard deviation, median [interquartile range], or n (%) values.
Post-hoc analysis: *p<0.05, MS and NMOSD; †p<0.05, MS and MOGAD; ‡p<0.05, NMOSD and MOGAD.
BDI-II, Beck Depression Inventory-II; BPI, Brief Pain Inventory; EDSS, Expanded Disability Status Scale; FACIT-F, Functional Assessment of Chronic Illness Therapy–Fatigue; FSS, Fatigue Severity Scale; K-MMSE, Korean version of the Mini-Mental State Examination; MCS, mental component summary; MFIS-K, Korean version of the Modified Fatigue Impact Scale; PCS, physical component summary; PSI, pain severity index; PSQI, Pittsburgh Sleep Quality Index; SDMT, Symbol Digit Modalities Test; SF-36, 36-item Short-Form Survey.
The FSS, MFIS-K, and FACIT-F scores did not differ between patients with MS, NMOSD, and MOGAD (Table 1). The 294 patients included 96 (32.7%) classified as having fatigue based on an MFIS-K score of >38: 45 with MS (38.5%), 33 with NMOSD (33.7%), and 18 with MOGAD (28.6%). The presence of fatigue also did not differ significantly after adjusting for confounding factors including age, sex, disease duration, total number of attacks, and EDSS score in the multivariate logistic regression analysis (data not shown).
Reliability and correlations of fatigue-scale scores in MS, NMOSD, and MOGAD
All of the fatigue scales showed high internal consistency in the MS, NMOSD, and MOGAD groups. Cronbach’s α values were 0.953, 0.938, and 0.837, for the FSS, MFIS-K, and FACIT-F, respectively, in the MS group; 0.968, 0.951, and 0.874 in the NMOSD group; and 0.950, 0.967, and 0.883 in the MOGAD group. The scores on the FSS, MFIS-K, and FACIT-F fatigue scales demonstrated strong (|r|≥0.65) correlations with each other in the patients with MS, NMOSD, and MOGAD (Supplementary Table 1 in the online-only Data Supplement). The scores on the MFIS-K subscales also revealed moderate (0.35≤|r|<0.65) to strong (|r|≥0.65) correlations with other fatigue measures. Among subscales, the MFIS-K-physical score demonstrated strong correlations with the FSS and FACIT-F scores in all MS, NMOSD, and MOGAD groups (all |r|≥0.65 and p<0.001). The MFIS-K-psychosocial score demonstrated strong correlations with the FSS score in the patients with NMOSD and MOGAD (r=0.654 and r=0.709, respectively; both p<0.001). Moreover, the MFIS-K-cognitive and MFIS-K-psychosocial scores demonstrated strong correlations with the FACIT-F score in the patients with MOGAD (r=-0.679 and r=-0.661, respectively; both p<0.001).
Correlations of fatigue-scale scores with other variables
We analyzed the correlations of the fatigue measures with other variables (Table 2). First, in the MS group, all FSS, MFIS-K, and FACIT-F scores commonly demonstrated moderate-to-strong correlations with scores on the EDSS, PSQI, SF-36 (PCS and MCS), and BDI-II (all p<0.001). Additionally, the FSS score demonstrated a moderate correlation with the BPI score (p<0.001), whereas the MFIS-K score demonstrated a moderate correlation with the SDMT score (p<0.001). Additionally, in the NMOSD group, all FSS, MFIS-K, and FACIT-F scores demonstrated moderate-to-strong correlations with scores on the PSQI, SF-36 (PCS and MCS), and BDI-II (all p<0.001). Moreover, the MFIS-K score demonstrated moderate correlations with the EDSS and SDMT scores (p<0.001 and p<0.01, respectively). Finally, in the MOGAD group, all FSS, MFIS-K, and FACIT-F scores demonstrated moderate-to-strong correlations with scores on the PSQI, SF-36 (PCS and MCS), and BDI-II (all p<0.01). The MFIS-K score demonstrated a moderate correlation with the total number of attacks (p=0.001), whereas the FACIT-F score demonstrated a moderate correlation with the BPI score (p=0.003). Disease duration was not significantly correlated with the scores on the three fatigue scales in the patients with MS, NMOSD, and MOGAD.
Table 2. Correlations of fatigue-scale scores with other variables in patients with MS, NMOSD, and MOGAD.
| Age | Disease duration | Total number of attacks | EDSS | PSQI | SF-36 (PCS) | SF-36 (MCS) | BDI-II | BPI (PSI) | K-MMSE | SDMT | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MS | ||||||||||||
| FSS | 0.236*† | -0.006 | 0.092 | 0.399***¶ | 0.472***‡ | -0.653***§ | -0.725***§ | 0.582***‡ | 0.420***‡ | -0.319**∥ | -0.240*† | |
| MFIS-K | 0.347***† | 0.094 | 0.261*∥ | 0.396***¶ | 0.380***‡ | -0.651***§ | -0.648***§ | 0.676***§ | 0.334***† | -0.324*∥ | -0.462***‡ | |
| FACIT-F | -0.208*† | 0.036 | -0.105 | -0.393***¶ | -0.536***‡ | 0.692***§ | 0.819***§ | -0.663***§ | -0.344**† | 0.275**∥ | 0.274**† | |
| NMOSD | ||||||||||||
| FSS | 0.074 | -0.002 | 0.153 | 0.179*∥ | 0.444***‡ | -0.552***‡ | -0.590***‡ | 0.604***‡ | 0.187 | -0.029 | -0.260 | |
| MFIS-K | 0.324**† | -0.010 | 0.171 | 0.438***¶ | 0.535***‡ | -0.579***‡ | -0.587***‡ | 0.681***§ | 0.316**† | -0.191 | -0.471**‡ | |
| FACIT-F | -0.194 | -0.006 | -0.214*∥ | -0.275**∥ | -0.467***‡ | 0.718***§ | 0.736***§ | -0.706***§ | -0.261*† | 0.034 | 0.275* | |
| MOGAD | ||||||||||||
| FSS | -0.298*† | -0.099 | -0.280*∥ | 0.281*∥ | 0.465***‡ | -0.450***‡ | -0.570***‡ | 0.603***‡ | 0.296*† | 0.232 | -0.020 | |
| MFIS-K | -0.113 | -0.227 | -0.402**¶ | 0.268*∥ | 0.374**‡ | -0.519***‡ | -0.625***‡ | 0.698***§ | 0.287*† | -0.027 | -0.053 | |
| FACIT-F | 0.121 | 0.207 | 0.314**∥ | -0.224 | -0.479***‡ | 0.591***‡ | 0.700***§ | -0.740***§ | -0.409**‡ | -0.150 | 0.125 | |
*p<0.05; **p<0.01; ***p<0.001; Pearson’s correlation coefficient: †0<|r|<0.35, weak correlation; ‡0.35≤|r|<0.65, moderate correlation; §0.65≤|r|<1, strong correlation; Spearman’s rho: ∥0<|ρ|<0.35, weak correlation; ¶0.35≤|ρ|<0.65, moderate correlation.
BDI-II, Beck Depression Inventory-II; BPI, Brief Pain Inventory; EDSS, Expanded Disability Status Scale; FACIT-F, Functional Assessment of Chronic Illness Therapy–Fatigue; FSS, Fatigue Severity Scale; K-MMSE, Korean version of the Mini-Mental State Examination; MCS, mental component summary; MFIS-K, Korean version of the Modified Fatigue Impact Scale; MOGAD, myelin oligodendrocyte glycoprotein antibody-associated disease; MS, multiple sclerosis; NMOSD, neuromyelitis optica spectrum disorder; PCS, physical component summary; PSI, pain severity index; PSQI, Pittsburgh Sleep Quality Index; SDMT, Symbol Digit Modalities Test; SF-36, 36-item Short-Form Survey.
Binary logistic regression model and ROC analysis
Binary logistic regression analysis was conducted based on the criterion for fatigue being present of an MFIS-K score >38, and FSS and FACIT-F scores were added as predictor variables (Table 3). In the MS group, FSS and FACIT-F scores achieved correct classification in 73.5% and 75.9% of the samples, respectively. In addition, the percentages of correct classification with the FSS and FACIT-F were 82.7% and 81.6%, respectively, in the NMOSD group, and 84.1% and 85.7% in the MOGAD group.
Table 3. Results from the binary logistic regression models for patients with and without fatigue in the MS, NMOSD, and MOGAD groups.
| B | SE | Exp(B) | 95% CI | p | ||
|---|---|---|---|---|---|---|
| MS | ||||||
| FSS | 0.989 | 0.184 | 2.689 | 1.875–3.856 | <0.001 | |
| FACIT-F | -0.154 | 0.031 | 0.857 | 0.807–0.910 | <0.001 | |
| NMOSD | ||||||
| FSS | 1.111 | 0.224 | 3.036 | 1.958–4.708 | <0.001 | |
| FACIT-F | -0.143 | 0.031 | 0.867 | 0.816–0.920 | <0.001 | |
| MOGAD | ||||||
| FSS | 1.554 | 0.391 | 4.728 | 2.196–10.182 | <0.001 | |
| FACIT-F | -0.168 | 0.042 | 0.846 | 0.778–0.919 | <0.001 | |
B, regression coefficient; CI, confidence interval; FACIT-F, Functional Assessment of Chronic Illness Therapy–Fatigue; FSS, Fatigue Severity Scale; MOGAD, myelin oligodendrocyte glycoprotein antibody-associated disease; MS, multiple sclerosis; NMOSD, neuromyelitis optica spectrum disorder; SE, standard error.
The ROC curves for the FSS and FACIT-F in the MS, NMOSD, and MOGAD groups are depicted in Fig. 1. To facilitate a direct comparison with the FSS, we subtracted the FACIT-F score from the maximum possible score when constructing the ROC curves. The areas under the ROC curves were 0.834, 0.877, and 0.925 in the MS, NMOSD, and MOGAD groups, respectively, for the FSS, and 0.835, 0.833, and 0.883 for the FACIT-F.
Fig. 1. Receiver operating characteristic (ROC) curves for the Fatigue Severity Scale (FSS) and Functional Assessment of Chronic Illness Therapy–Fatigue (FACIT-F) in patients with multiple sclerosis (MS), neuromyelitis optica spectrum disorder (NMOSD), and myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD). ROC curves for the FSS (red line) and FACIT-F (blue line) are shown for patients with MS, NMOSD, and MOGAD. The areas under the ROC curves (AUCs) were 0.834 and 0.835 for the FSS and FACIT-F, respectively, in patients with MS (p<0.001) (A), and 0.877 and 0.833 in patients with NMOSD (p<0.001) (B). AUC was highest in patients with MOGAD (C), at 0.925 for the FSS and 0.883 for the FACIT-F (p<0.001).
We determined the optimal cutoff values for the FSS and FACIT-F in the MS, NMOSD, and MOGAD groups based on the Youden index. The optimal cutoff values were 3.72, 3.50, and 3.17 in the MS, NMOSD, and MOGAD groups, respectively, for the FSS, and 36, 36, and 35 for the FACIT-F (Table 4).
Table 4. Cutoff, sensitivity, specificity, and Youden’s index for the FSS and FACIT-F in the MS, NMOSD, and MOGAD groups.
| Cutoff | Sensitivity | Specificity | Youden’s index | ||
|---|---|---|---|---|---|
| MS | |||||
| FSS | 3.72 | 0.756 | 0.792 | 0.547 | |
| FACIT-F | 36 | 0.721 | 0.812 | 0.533 | |
| NMOSD | |||||
| FSS | 3.50 | 0.818 | 0.846 | 0.664 | |
| FACIT-F | 36 | 0.788 | 0.754 | 0.542 | |
| MOGAD | |||||
| FSS | 3.17 | 0.944 | 0.822 | 0.767 | |
| FACIT-F | 35 | 0.778 | 0.844 | 0.622 | |
FACIT-F, Functional Assessment of Chronic Illness Therapy–Fatigue; FSS, Fatigue Severity Scale; MOGAD, myelin oligodendrocyte glycoprotein antibody-associated disease; MS, multiple sclerosis; NMOSD, neuromyelitis optica spectrum disorder.
DISCUSSION
We have demonstrated the usefulness of the MFIS-K and FACIT-F in evaluating fatigue in Korean patients with MS, NMOSD, and MOGAD. The internal consistency of the FSS, MFIS-K, and FACIT-F fatigue scales was high in all three patient groups. The scale scores were strongly correlated with each other and with the variables used to assess depression, sleep quality, quality of life, and pain in each group.
We observed fatigue in 38.5%, 33.7%, and 28.6% of our patients with MS, NMOSD, and MOGAD, respectively, which is consistent with the results of previous studies. A systematic review found that the prevalence of fatigue ranged from 18.2% to 78.0% in patients with MS.29 In addition, chronic fatigue was found to be equally prevalent in patients with NMOSD and MS.30 Two recent studies on MOGAD found fatigue in 29.5% and 75.6% of patients.3,8
The MFIS-K is one of the most widely used scales in MS, and has previously been validated using the FSS in Korean MS patients.11 In contrast, the FACIT-F has not been widely used in patients with MS. However, a recent study found that FACIT-F scores demonstrated high internal consistency and strong correlations with MFIS-K scores in Spanish patients with MS.5 In the present study, we also verified the usefulness of the FACIT-F in Korean patients with MS, although the correlations of the scores with age and psychomotor speed were weaker than for the MFIS-K. The MFIS-K and FACIT-F have previously been used in patients with NMOSD. In Korean patients with NMOSD, only five studies have evaluated fatigue; we used the FACIT-F in two previous studies, but the other three studies used the FSS.2,12,31,32,33 A recent clinical trial of satralizumab in NMOSD used the FACIT-F to measure fatigue, although several studies have used the MFIS in other countries.7,8,10 In our study we observed that the MFIS-K is also valid and useful for evaluating fatigue in Korean patients with NMOSD. Thus far, only three studies have evaluated fatigue in patients with MOGAD: two studies in the United Kingdom and one study in the United States used the MFIS.3,8,10 Notably, we observed that the FSS, MFIS-K and FACIT-F were useful for evaluating fatigue in Korean patients with MOGAD.
The scores on all three fatigue scales demonstrated strong correlations with sleep quality, quality of life, and depression in patients with MS, NMOSD, and MOGAD, suggesting that these conditions contribute to or have common underlying pathomechanisms of fatigue. This is consistent with our findings in previous studies of MS and NMOSD.2,11 Fatigue scores measured by the MFIS-K were correlated with disease-related disability in both the MS and NMOSD patients, but they were correlated with the total number of attacks rather than the degree of disease-related disability in MOGAD patients. In addition, the MFIS-K score was strongly correlated with cognitive function in both MS and NMOSD patients, but not in MOGAD patients. This could reflect clinical and pathophysiological differences of MOGAD from the other conditions, which need to be further elucidated. In addition, the FACIT-F score showed a strong correlation with the quality of life, particularly with the MCS score, across all diseases. The association with pain differed, with the FSS and FACIT-F scores showing moderate correlations in MS and MOGAD, respectively. These differences may be attributable to the following distinct characteristics of each fatigue scale: First, the MFIS-K has physical, psychosocial, and cognitive domains; the FSS includes items for physical exercise, psychosocial environment, and general conditions; and the FACIT-F has domains of physical, social/family, emotional, and functional well-being.11,19,20 These differences might explain the correlation of the MFIS-K score with SDMT in patients with MS and NMOSD, as well as the stronger correlation between the FACIT-F and SF-36 scores. Second, the MFIS-K score measures fatigue experienced over the previous 4 weeks, whereas the FSS and FACIT-F scores measure fatigue experienced over the previous 7 days. Therefore, the BPI score, which assesses pain over the relatively short duration of 24 hours, may be more strongly correlated with the FSS or FACIT-F score than with the MFIS-K score.
Furthermore, the present binary logistic regression models and ROC curve analyses revealed that the FSS and FACIT-F had a strong ability to correctly classify the presence and absence of fatigue in patients with MS, NMOSD, and MOGAD, supporting the usefulness of these scales in Korean patients.
The present study had several limitations. First, the single-center design meant that relatively few patients were included, which reflects that the reported prevalence of MS in South Korea is 3.23 per 100,000 population34 and that of NMOSD is 2.56–3.56 per 100,000 population.34,35 Second, fatigue can be influenced by other factors that we did not measure, such as demographics including socioeconomic level, diet, medications, genetics, exercise, and infections.36 Third, we employed the commonly used cutoff of 38 points on the MFIS-K to classify the fatigued patients.28 However, considering that demographic factors are known to influence the experience of fatigue, further investigations into developing normative data for the MFIS-K stratified by age, sex, and education duration are needed in a large Korean cohort with CNS demyelinating disorders.37 Fourth, we did not include healthy controls in this study; however, the MFIS-K, FACIT-F, and FSS are currently being used in South Korea. From this perspective our study was insightful since we directly compared the three fatigue scales that are currently used interchangeably among the three disease groups (MS, NMOSD, and MOGAD) in South Korea, and also measured other variables that may affect fatigue concurrently using these fatigue scales.
In conclusion, the MFIS-K, FSS, and FACIT-F are useful and valuable assessment instruments for evaluating fatigue in Korean patients with MS, NMOSD, and MOGAD. The present results can contribute to a better understanding of the complexities of fatigue in these demyelinating disorders of the CNS.
Footnotes
- Conceptualization: Ju-Hong Min.
- Data curation: Hyunjin Ju, Yeon Hak Chung, Soonwook Kwon, Eun Bin Cho, Kyung-Ah Park.
- Formal analysis: Hyunjin Ju.
- Funding acquisition: Ju-Hong Min.
- Investigation: Ju-Hong Min.
- Methodology: Yeon Hak Chung, Soonwook Kwon, Eun Bin Cho, Kyung-Ah Park.
- Project administration: Ju-Hong Min.
- Resources: Ju-Hong Min.
- Software: Hyunjin Ju.
- Supervision: Ju-Hong Min.
- Validation: Hyunjin Ju, Ju-Hong Min.
- Visualization: Ju-Hong Min.
- Writing—original draft: Hyunjin Ju.
- Writing—review & editing: Yeon Hak Chung, Soonwook Kwon, Eun Bin Cho, Kyung-Ah Park.
Conflicts of Interest: JH Min is funded by and has received research support from the National Research Foundation of Korea (MIST and KHIDI) and SMC Research and Development Grant. She has lectured, consulted and received honoraria from Bayer Healthcare, Merk, Biogen Idec, Sanofi, UCB, Samsung Bioepis, Mitsubishi Tanabe, Celltrion, Roche, Janssen, and Astrazeneca.
Funding Statement: This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2021R1F1A1049347) and by a grant of Patient-Centered Clinical Research Coordinating Center (PACEN) funded by the Ministry of Health & Welfare, Republic of Korea (HC23C0249).
Availability of Data and Material
The datasets generated or analyzed during the study are available from the corresponding author on reasonable request.
Supplementary Materials
The online-only Data Supplement is available with this article at https://doi.org/10.3988/jcn.2023.0328.
Correlations between fatigue scales in MS, NMOSD, and MOGAD groups
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Correlations between fatigue scales in MS, NMOSD, and MOGAD groups
Data Availability Statement
The datasets generated or analyzed during the study are available from the corresponding author on reasonable request.