Clinical variables influencing the perception of fatigue in people with multiple sclerosis: a cross-sectional study using FSIQ-RMS

Fatigue is perhaps the most common complaint associated with Multiple Sclerosis (MS), with prevalence estimates ranging between 70 and 90% [1, 2]. Fatigue impacts the social and working lives of people with MS (PwMS), representing one of the main factors related to the health-related quality of life (HRQoL) and a common reason for early retirement [3, 4]. Fatigue increases with age, and is more frequent in progressive MS [5]; however, its presence in the early stage of the disease, even at the time of the first episode suggestive of MS, is predictive of future disability [6, 7].

Given the subjective nature of fatigue, it is best evaluated via a patient-reported outcome (PRO) instrument. Although several available PRO instruments have been used so far in PwMS [8, 9], a review of their measurement properties suggests shortcomings in terms of current standards for PRO instrument development [10, 11]. Recently, to address limitations of existing MS-specific instruments, a new content-valid, concise PRO instrument to assess fatigue symptoms relevant to patients within the spectrum of MS, and in accordance with the Food and Drugs Administration (FDA) PRO guidance, has been developed. The instrument named the “Fatigue Symptoms and Impacts Questionnaire-Relapsing Multiple Sclerosis” (FSIQ-RMS) [12] has been proven to be a reliable PRO instrument that has demonstrated content and measurement validity for fatigue-related symptom and impact items. FSIQ-RMS examines two areas of fatigue’s physical, cognitive, and emotional impact. The first concerns the fatigue experienced in the previous 24 h. The second includes items that investigate the impact of fatigue in the previous 7 days. Its responsiveness and meaningful change of FSIQ-RMS have been analyzed in one international phase III trial, the Optimum Trial, which represents the first study to implement a validated disease-specific fatigue measure (PRO) as a prespecified endpoint [13]. Recently, FSIQ-RMS has been translated and validated for the Italian population [14]. The Italian version of the FSIQ-RMS showed excellent internal consistency (Cronbach’s alpha 0.92) and test-retest reliability (ICC 0.96) for both domains.

With the increasing numbers of multinational and multicultural studies, adequate translation, cross-cultural adaptation and psychometric testing are crucial when introducing questionnaires in new countries. Cross-cultural adaptation is a process that looks at both translational and cultural issues when a questionnaire is being prepared for use in another country [15].

Translation in Italian of the FSIQ-RMS was performed by PharmaQuest Group but, as far as we know, the assessment of the usefulness and intercultural adaptation has not yet been carried out. Thus, the aim of the study was to culturally adapt the Italian version of FSIQ-RMS.

PwMS were enrolled from May to July 2021 in two Italian MS Centers, S. Andrea Hospital and Policlinico Umberto I Hospital in Rome. The enrolment of the participants was carried out during the study of the Italian translation and validation of the FSIQ-RMS scale.

The study was approved by the Ethics Committee of the participating hospitals and was performed in accordance with the 1964 Declaration of Helsinki and its later amendments.

The inclusion criteria were: diagnosis of MS [both relapsing MS and progressive (PMS)], according to the 2017 McDonald’s criteria [16], age between 18 and 70 years and ability to participate in a 90-min face-to-face interview, medical record available for the previous 12 months or from the time of RMS diagnosis, and being fluent in Italian. Patients with the following characteristics were excluded: any condition that may cause energy-related or fatigue-related symptoms (different from MS); ongoing treatment for an autoimmune disease other than MS; history of suicide attempts and concomitant participation in trials with experimental drugs for any condition. Patients deemed by the treating neurologist to have clinically significant cognitive impairment were also excluded, as they may not have been able to comply with the study procedures [17]. .

Study subjects had to complete the FSIQ-RMS scale. This test aims to measure the level of perceived fatigue in our sample of PwMS, using its two components at 24 h (24hours_FSIQ-RMS) and 7days (7days_FSIQ-RMS). The former is the fatigue domain and includes 7 items regarding fatigue experienced in the preceding 24 h. The latter is the impact domain which encompasses 13 items assessing the physical, cognitive, emotional, and coping impacts of fatigue over the previous 7 days. The higher each score, the greater the perception and the impact of fatigue.

The total scores obtained at the 24hours_FSIQ-RMS and the 7days_FSIQ-RMS were then correlated independently with the clinical and demographic variables detected in our study group.

The variables potentially correlated with fatigue were gender, education, Body Mass Index (BMI), Expanded Disability Status Scale (EDSS) [18], disease duration, working status (Ws), physiotherapy (Ph), and disease-modifying therapies (DMTs).

Some of these variables were subdivided into categories. The demographic variables that we decided to categorize are education, according to the five levels of the Italian school system, and the BMI, creating four groups, as defined by the international guidelines [19].

Regarding the clinical variables, we divided treatment with disease-modifying treatments (DMTs) into three categories. Based on the efficacy of the drugs, we distinguish the Moderate Efficacy Therapies (METs) and High Efficacy Therapies (HETs), adding a third category for currently untreated patients (W_DMTS) [20].

The software used for the statistical analyses was SPSS Statistics version 25. To compare categories, a Student’s t-test or analysis of variance (ANOVA) was performed [21]. Independent Student’s t-test was used to compare dichotomized variables, given a sample size > 30 observations; while One-way ANOVA was used for categorized variables involving more than 2 independent groups. Parametric tests are based on assumptions about the distribution of the underlying population from which the sample was taken. The hypothesis is that, given the sample size exceeding 30 observations, the data are distributed approximately normally [22].

Finally, Pearson’s linear correlation analysis was carried out to correlate the continuous variables and the FSIQ-RMS scores [23]. Data were expressed as mean ± SD.