Differentiation of Cognitive Deficit Profiles in Multiple Sclerosis Patients: Latent Profile Analysis

Italian adaptation and psychometric validation of the Fatigue Impact Scale (FIS) and its modified versions in adults with multiple sclerosis: a Rasch analysis study

Purpose: Several outcome measures are available to assess the severity of fatigue in people with multiple sclerosis (MS). The aim of this study was to adapt the Italian version of the Fatigue Impact Scale (FIS-40) and its modified versions: a 21-item Modified scale (MFIS-21), its 5-item short version (MFIS-5), and an 8-item version for daily use (DFIS-8) and investigate their measurement properties through classical theory-test (CTT) and Rasch analysis (RA).Methods: 229 Italian-speaking adults with MS were included. Questionnaires were cross-culturally translated and subjected to CTT (i.e. internal consistency through Cronbach's alpha and unidimensionality through confirmatory factor analysis [CFA]) and RA. (i.e. internal construct validity, reliability, and targeting).Results: Internal consistency was high for all scales (>0.850). Final CFAs reported issues in the unidimensionality for all scales except for FIS-40. Baseline RA revealed a misfit for all scales. After adjusting for local dependency, FIS-40, MFIS-21, and MFIS-5 fitted the Rasch model (RM). MFIS-21 and D-FIS-8 required a structural modification, i.e. item deletions to satisfy the RM.Conclusion: The FIS-40, MFIS-21, MFIS-5, and DFIS-8 achieved the fit to the RM after statistical and structural modifications. The fit to the RM allowed for providing ordinal-to-interval measurement conversion tables for all the questionnaires.

Differences in metacognition between multiple sclerosis phenotypes: cognitive impairment and fatigue are key factors

Background

Cognitive impairment is present in 40-65% of patients with multiple sclerosis (pwMS). Objectively measured cognitive performance often does not match patients' subjective perception of their own performance.

Objective

We aimed to compare cognitive performance and subjective perception of cognitive deficits between pwMS and healthy controls (HCs), as well as the accuracy of subjective perception.

Methods

In total, 54 HC and 112 pwMS (relapsing-remitting, RRMS, and progressive PMS) underwent neuropsychological evaluation and completed perceived deficit, fatigue, and anxiety-depression scales. Participants were classified according to their consistency between subjective self-evaluation of cognitive abilities and objective cognitive performance to assess accuracy. Regression models were used to compare cognitive performance between groups and explore factors explaining inaccuracy in the estimation of cognitive performance.

Results

PMS showed greater and more widespread cognitive differences with HC than RRMS. No differences were found between pwMS and HC in the perception of deficit. PMS had higher ratios of overestimators. In explaining inaccuracy, fatigue and cognitive preservation were found to be risk factors for underestimation, whereas physical disability and cognitive impairment were risk factors for overestimation.

Conclusion

PwMS have metacognitive knowledge impairments. This study provides new information about metacognition, data on the prevalence of impairments over a relatively large sample of PwMS, and new insights into factors explaining it. Anosognosia, related to cognitive impairment, may be present in pwMS. Fatigue is a key factor in underestimating cognition.

Evaluation of the determinants of cognitive dysfunction in patients with multiple sclerosis

Background

Cognitive dysfunction is common among patients with multiple sclerosis (MS); however, the determinants of cognitive dysfunction are still unknown. This study aimed to investigate the determinants of cognitive dysfunction in a relatively large sample of patients with MS for rapid screening.

Results

Fifty-three patients (33.6%) had cognitive dysfunction. According to the Wechsler Memory Scale, patients with relapsing-remitting MS (RRMS) and patients with progressive MS (PMS) had significantly lower scores than the control group. Patients with RRMS compared to the control group were 76.73 ± 8.50 versus 105.58 ± 8.71 (P < 0.01), and patients with PMS compared to the control group were 72.56 ± 6.44 versus 105.58 ± 8.71 (P < 0.01). In patients with RRMS, the factors affecting the emergence of cognitive dysfunction included disability, fatigue, depression, and duration of illness, whereas in patients with PMS, just the disability variable was related to the presence or absence of cognitive dysfunction.

Conclusions

Our findings showed that disability, fatigue, depression, and duration of illness were factors associated with cognitive dysfunction in patients with RRMS. Proper identification of these factors can be helpful in the screening of cognitive dysfunction in this population.

Integrating patient-reported outcomes and quantitative timed tasks to identify relapsing remitting multiple sclerosis patient subgroups: a latent profile analysis

BACKGROUND

Multiple sclerosis (MS) patients experience wide-ranging symptoms with varied severity, and approaches that integrate patient-reported outcomes and objective quantitative measures will present opportunities for advancing clinical profiling. The primary objective of the current study was to conduct exploratory data analysis using latent variable modeling to empirically identify clusters of relapsing remitting (RR) MS patients with shared impairment patterns across three patient-reported outcomes and two timed task measures.

METHODS

Latent profile analyses and impairment data for 2,012 RRMS patients identified distinct patient clusters using timed task measures of upper and lower limb performance, and patient-reported outcomes measuring quality of life, depression symptom severity, and perceived global disability. Multinomial logistic regression models were used to characterize associations between socio-demographic attributes and assignment to the patient clusters.

RESULTS

There were 6 distinct clusters of RRMS patients that differed by symptom patterns, and by their socio-demographic attributes. Most notable were were no differences in age, sex, or disease duration between the least and most impaired classes, representing 14% and 4% of patients, respectively. Patients in the most impaired class were much more likely to be Black American, have a history of smoking, have a higher body mass index, and be of lower socioeconomic status than the least impaired class. There were positive relationships between age and classification to clusters of increasing moderately severe impairment but not the most severe clusters.

CONCLUSION

We present a framework for discerning phenotypic impairment clusters in RRMS. The results demonstrate opportunities for advancing clinical profiling, which is necessary for optimizing personalized MS care models and clinical research.