Back

Development and validation of the Dysarthria Impact Scale

Vogel, A. P.; Graf, L.; Gauss, M.; Chan, J. S. J.; Hepworth, G.; Synofzik, M.

2025-07-05 neurology
10.1101/2025.07.02.25330604 medRxiv
Show abstract

BackgroundImpaired speech due to dysarthria significantly impacts quality of life. Patient-reported outcomes (PROs) offer critical insight into the lived experience of communication disability and are increasingly central to regulatory frameworks for patient-focused drug development. ObjectivesTo develop and validate the Dysarthria Impact Scale (DIS), a brief PRO designed to assess the impact of motor speech disorders on quality of life across neurological conditions. MethodsA multi-site, cross-sectional study was conducted with 244 participants, including individuals with Huntingtons disease, Parkinsons disease, hereditary ataxias, and head and neck cancer, along with healthy controls. The 22-item DIS was developed using expert input and patient feedback and evaluated alongside reference tools (Voice Handicap Index and SF-36). Item reduction procedures yielded two shorter versions (DIS-17 and DIS-6). Validity, reliability, and sensitivity/specificity analyses were performed, and minimal clinically important differences (MCIDs) were estimated using distribution-based methods. ResultsAll DIS versions showed strong convergent validity with the VHI (r = -0.85) and SF-36 (r = 0.72) and were correlated with blinded perceptual speech ratings. DIS-17 and DIS-6 achieved comparable sensitivity (0.93 and 0.88) and specificity (0.84 and 0.86, respectively). Test-retest reliability was high (r = 0.98), with WSSD estimates ranging from 4.0 to 10.6 across groups. Estimated MCIDs ranged from 5 to 15 points. Group differences were observed, with lower DIS scores in ataxia and Parkinsons disease compared to Huntingtons disease. ConclusionsThe DIS is a valid, reliable, and practical PRO for quantifying the impact of dysarthria on quality of life. It is suitable for clinical monitoring and clinical trial use, with sensitivity to disease-related variation and change.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.