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Unified Huntington's disease rating scale for advanced patients: validation and follow-up study.

ABSTRACT: The Unified Huntington's Disease Rating Scale (UHDRS) adequately measures decline in patients at early and moderate stages of Huntington's disease (HD). In advanced patients, floor effects hamper the evaluation, thus calling for an adjusted scale. We designed the UHDRS-For Advanced Patients (UHDRS-FAP), in order to improve longitudinal assessment of patients at advanced disease stage. Sixty-nine patients with a Total Functional Capacity (TFC) ≤ 5 were recruited in France and in the Netherlands. Among them, 45 patients were followed longitudinally (mean 1.6 ± 1.2 years) with the UHDRS-FAP; 30 were also assessed with the UHDRS. Cross-sectional analyses evaluated psychometric properties and interrater reliability of the scale. Longitudinal analyses evaluated the sensitivity to decline compared to the UHDRS. Internal consistency was higher for motor and cognitive scores than for somatic and behavioral scores (0.84, 0.91, 0.70, and 0.49, respectively). Interrater reliability was ≥ 0.88 in all scores. The somatic score, specific to the UHDRS-FAP, declined over time, as well as motor and cognitive performance with both scales. Although performance with the 2 scales correlated, the UHDRS-FAP appeared more sensitive to change and was the only scale that detected decline in patients with a TFC ≤ 1. Neither scale detected a significant decline in behavioral scores. The UHDRS-FAP is reliable and more sensitive to change than the original UHDRS for cognitive and motor domains. It offers items relevant for daily care. Behavioral scores tended to decline but this may reflect the decline in the communicative abilities of the patients.

SUBMITTER: Youssov K

PROVIDER: S-EPMC5071381 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe diagnosis of Parkinson's disease (PD) and evaluation of its symptoms require in-person clinical examination. Remote evaluation of PD symptoms is desirable, especially during a pandemic such as the coronavirus disease 2019 pandemic. One potential method to remotely evaluate PD motor impairments is video-based analysis. In this study, we aimed to assess the feasibility of predicting the Unified Parkinson's Disease Rating Scale (UPDRS) score from gait videos using a convolutional neural network (CNN) model.MethodsWe retrospectively obtained 737 consecutive gait videos of 74 patients with PD and their corresponding neurologist-rated UPDRS scores. We utilized a CNN model for predicting the total UPDRS part III score and four subscores of axial symptoms (items 27, 28, 29, and 30), bradykinesia (items 23, 24, 25, 26, and 31), rigidity (item 22) and tremor (items 20 and 21). We trained the model on 80% of the gait videos and used 10% of the videos as a validation dataset. We evaluated the predictive performance of the trained model by comparing the model-predicted score with the neurologist-rated score for the remaining 10% of videos (test dataset). We calculated the coefficient of determination (R2) between those scores to evaluate the model's goodness of fit.ResultsIn the test dataset, the R2 values between the model-predicted and neurologist-rated values for the total UPDRS part III score and subscores of axial symptoms, bradykinesia, rigidity, and tremor were 0.59, 0.77, 0.56, 0.46, and 0.0, respectively. The performance was relatively low for videos from patients with severe symptoms.ConclusionsDespite the low predictive performance of the model for the total UPDRS part III score, it demonstrated relatively high performance in predicting subscores of axial symptoms. The model approximately predicted the total UPDRS part III scores of patients with moderate symptoms, but the performance was low for patients with severe symptoms owing to limited data. A larger dataset is needed to improve the model's performance in clinical settings.

Project description:ObjectivesTo investigate both cross-sectional and time-related changes of striatal and whole-brain microstructural properties in different stages of Huntington's disease (HD) using diffusion tensor imaging.Experimental designFrom the TRACK-HD study, premanifest gene carriers (preHD), early manifest HD and controls were scanned at baseline and 2-year follow-up. Stratification of the preHD group into a far (preHD-A) and near (preHD-B) to predicted disease onset was performed. Age-corrected histograms of whole-brain white matter (WM), gray matter (GM) and striatal diffusion measures were computed and normalised by the number of voxels in each subject's data set.Principle observationsHigher cross-sectional mean, axial and radial diffusivities were found in both WM (P ≤ 0.001) and GM (P ≤ 0.001) of the manifest HD compared to the preHD and control groups. In preHD, only WM axial diffusivity (AD) was higher than in controls (P ≤ 0.01). This finding remained valid only in preHD-B (P ≤ 0.001). AD was also higher in the striatum of preHD-B compared to controls and preHD-A (P ≤ 0.01). Fractional anisotropy (FA) lacked sensitivity in differentiating between the groups. Histogram peak heights were generally lower in manifest HD compared to the preHD and control groups. No longitudinal differences were found in the degree of diffusivity change between the groups in the two year follow-up. There was a significant relationship between diffusivity and neurocognitive measures.ConclusionsAlterations in cross-sectional diffusion profiles between manifest HD subjects and controls were evident, both in whole-brain and striatum. In the preHD stage, only AD alterations were found, a finding suggesting that this metric is a sensitive marker for early change in HD prior to disease manifestation. The individual diffusivities were superior to FA in revealing pathologic microstructural brain alterations. Diffusion measures were well related to clinical functioning and disease stage.

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Unified Huntington's disease rating scale for advanced patients: validation and follow-up study.

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