Parkinson hastalığı için subtalamik çekirdek derin beyin stimülasyon programlamasında hasta tarafından bildirilen görsel analog ölçeklerin psikometrik güvenilirliği.

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Subthalamic nucleus deep brain stimulation is an established therapy for Parkinson's disease, yet its programming relies heavily on subjective patient feedback. Visual analogue scales have been proposed to structure patient-reported outcome measures during programming, but their psychometric reliability has not been systematically evaluated. In this study, fifteen patients with bilateral subthalamic nucleus deep brain stimulation completed four structured experiments to assess the reliability of visual analogue scales: test-retest consistency, the effect of stimulation duration (15, 60, 120 s), the impact of unilateral deep brain stimulation withdrawal intervals (0, 10, 30 min), and contralateral stimulation ON versus OFF. Across all experiments, patients provided over 3000 visual analogue scale ratings, which were analyzed using correlation, regression, and Bland-Altman methods, with subgroup analyses examining motor phenotype, cognition, and disease burden. Visual analogue scale ratings demonstrated strong test-retest reliability (r = 0.70, R 2 = 0.53), with 83% of repeated scores within ±2 points. Reliability was lower in patients with tremor-onset compared to non-tremor onset (P = 0.04) but was unaffected by cognitive status or quality of life. Stimulation duration influenced absolute scores, with 15 s ratings systematically lower than 60-120 s (P r = 0.31, R 2 = 0.13), suggesting hemispheric interactions in subjective perception. These findings indicate that visual analogue scale ratings provide reproducible and quantifiable feedback during subthalamic nucleus deep brain stimulation programming. Exploratory analyses suggest that reliability may vary with motor phenotype, stimulation duration, and bilateral context. Incorporating structured visual analogue scale feedback could enhance programming workflows, support remote care models, and inform future multimodal closed-loop deep brain stimulation strategies.

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