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Quantification of discrete behavioral components of the MDS-UPDRS.

Brooks, Chris; Eden, Gabrielle; Chang, Andrew; Demanuele, Charmaine; Kelley Erb, Michael; Shaafi Kabiri, Nina; Moss, Mark; Bhangu, Jaspreet; Thomas, Kevin.
J Clin Neurosci; 61: 174-179, 2019 Mar.
Artículo en Inglés | MEDLINE | ID: mdl-30385169

Resumen

INTRODUCTION: The Movement Disorder Society's Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is the current gold standard means of assessing disease state in Parkinson's disease (PD). Objective measures in the form of wearable sensors have the potential to improve our ability to monitor symptomology in PD, but numerous methodological challenges remain, including integration into the MDS-UPDRS. We applied a structured video coding scheme to temporally quantify clinical, scripted, motor tasks in the MDS-UPDRS for the alignment and integration of objective measures collected in parallel. METHODS: 25 PD subjects completed two video-recorded MDS-UPDRS administrations. Visual cues of task performance reliably identifiable in video recordings were used to construct a structured video coding scheme. Postural transitions were also defined and coded. Videos were independently coded by two trained non-expert coders and a third expert coder to derive indices of inter-rater agreement. RESULTS: 50 videos of MDS-UPDRS performance were fully coded. Non-expert coders achieved a high level of agreement (Cohen's κ > 0.8) on all postural transitions and scripted motor tasks except for Postural Stability (κ = 0.617); this level of agreement was largely maintained even when more stringent thresholds for agreement were applied. Durations coded by non-expert coders and expert coders were significantly different (p < 0.05) for only Postural Stability and Rigidity, Left Upper Limb. CONCLUSIONS: Non-expert coders consistently and accurately quantified discrete behavioral components of the MDS-UPDRS using a structured video coding scheme; this represents a novel, promising approach for integrating objective and clinical measures into unified, longitudinal datasets.