Integrating patient-reported outcomes and quantitative timed tasks to identify relapsing remitting multiple sclerosis...

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.

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.

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.

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.