Multiple-Sclerosis-Partners-Advancing-Technology-Health-Solutions (MSPATHS) is an international multicentre digital database that collects clinical information provided directly by patients together with standardized MRI and biomarkers.
We identify a Benign multiple sclerosis (BMS) population using Patient-Determined-Disease-Steps (PDDS) as a proxy for EDSS. We describe its physical and non-physical characteristics, and explore the features that best discriminate BMS.
Cross-sectional study of MSPATHS patients (Feb 2019). In patients with disease duration ≥10 years, BMS was considered when PDDS score<2. We compared BMS and non-BMS in terms of (1)socio-demographic and clinical characteristics, (2)physical status (lower and upper extremity function by Neuro-QoL (LUEF-NQ) and neurological performance tests: walking speed test (WST), manual dexterity test (MDT), processing speed test (PST), contrast sensitivity test (CST)) and non-physical symptoms (anxiety, depression, fatigue, among other NQ domains), and (3)MRI (gadolinium enhancement and new T2 lesions). We built a random forest model to estimate the importance of each variable. Cohen’s d was used for descriptive statistics to categorize differences in small (d=0.2-0.5), medium (d=0.5-0.8) and large (d>0.8). A sensitivity analysis with a 1:1 matched cohort by disease duration was performed.
From 15,257 patients included, 8,349 had a disease duration ≥10 years and 3,852 (46.1%) were classified as BMS. (1)BMS and non-BMS patients were similar for gender, age at disease onset and diagnosis, ethnicity, years of education and smoking status. Compared to non-BMS, BMS had small differences in disease duration (median, 17.2 (12,9-23,4) vs. 20.9 (15,1-28,8 years); d=0.39) but medium/large differences in (2)physical status (LUEF-NQ d=2.06 and 1.53, WST d=0.81, MDT d=0.97, PST d=0.82 and CST d=0.56), as well as, in all non-physical symptoms evaluated by NQ (anxiety d=0.53, depression d=0.69, fatigue d=0.84, stigma d=1.32, cognition d=0.69, social role satisfaction (SRS) d=1.11 and participation (SRP) d=1.19). (3)No differences were found on MRI activity. With 0.88 sensitivity and 0.86 specificity, LUEF-NQ was the most contributing variable for the random forest followed by stigma, SRP, WST, and SRS. The sensitivity analysis showed similar results.
PDDS seems to be a useful disability proxy to identify BMS when using digital technology. LUEF-NQ, stigma, SRP and SRS seem to better discriminate BMS.