Video-Based Pairwise Comparison: Enabling the Development of Automated Rating of Motor Dysfunction in Multiple Sclerosis

Abstract

ObjectivesTo examine the feasibility, reliability, granularity, and convergent validity of a video-based pairwise comparison technique that uses algorithmic support to enable automated rating of motor dysfunction in patients with multiple sclerosis (MS). DesignFeasibility and larger cross-sectional cohort study. SettingThe outpatient clinic of 2 specialist university medical centers. ParticipantsSelected sample from a cohort of patients with MS participating in the Assess MS study (N=42). Videos were randomly drawn from each strata of the ataxia severity-degrees as defined in the Expanded Disability Status Scale (EDSS). In Basel: 19 videos of 17 patients (mean age, 43.4±11.6y; 10 women). In Amsterdam: 50 videos of 25 patients (mean age, 50.0±10.0y; 15 women). InterventionsNot applicable. Main Outcome MeasuresIn each center, neurologists (n=13; n=10) viewed pairs of videos of patients performing standardized movements (eg, finger-to-nose test) to assess relative performance. A comparative assessment score was calculated for each video using the TrueSkill algorithm and analyzed for intrarater (test-retest; ratio of agreement) and interrater reliability (intraclass correlation coefficient [ICC] for absolute agreement) and convergent validity (Spearman ρ). Granularity was estimated from the average difference in comparative assessment scores at which 80% of neurologists considered performance to be different. ResultsIntrarater reliability was excellent (median ratio of agreement≥0.87). The comparative assessment scores calculated from individual neurologists demonstrated good-excellent ICCs for interrater reliability (0.89; 0.71). The comparative assessment scores correlated (very) highly with their Neurostatus-EDSS equivalent (ρ=0.78, P<.001; ρ=0.91, P<.05), suggesting a more fine-grained rating. ConclusionsVideo-based pairwise comparison of motor dysfunction allows for reliable and fine-grained capturing of clinical judgment about neurologic performance, which can contribute to the development of a consistent quantified metric of motor ability in MS.