Background: Clinical scales such as the Scale for the Assessment and Rating of Ataxia (SARA) cannot be used to study ataxia at home or to assess daily fluctuations. The objective of the current study was to develop a video-based instrument, SAR-A home , for measuring ataxia severity easily and independently at home. Methods: Based on feasibility of self-application, we selected 5 SARA items (gait, stance, speech, nosefinger test, fast alternating hand movements) for SAR-A home (range, 0-28). We compared SARA home items with total SARA scores in 526 patients with spinocerebellar ataxia types 1, 2, 3, and 6 from the EUROSCA natural history study. To prospectively validate the SARA home , we directly compared the selfapplied SARA home and the conventional SARA in 50 ataxia patients. To demonstrate feasibility of independent home recordings in a pilot study, 12 ataxia patients were instructed to obtain a video each morning and evening over a period of 14 days. All videos were rated offline by a trained rater. Results: SARA home extracted from the EUROSCA baseline data was highly correlated with conventional SARA (r = 0.9854, P < 0.0001). In the prospective validation study, the SARA home was highly correlated with the conventional SARA (r = 0.9254, P < 0.0001). Five of 12 participants of the pilot study obtained a complete set of 28 evaluable videos. Seven participants obtained 13-27 videos. The intraindividual differences between the lowest and highest SARA home scores ranged from 1 to 5.5. Conclusion: The SARA home and the conventional SARA are highly correlated. Application at home is feasible. There was a considerable degree of intraindividual variability of the SARA home scores.

A BS TRACT: Background: Sporadic adult-onset ataxias without known genetic or acquired cause are subdivided into multiple system atrophy of cerebellar type (MSA-C) and sporadic adult-onset ataxia of unknown etiology (SAOA). Objectives: To study the differential evolution of both conditions including plasma neurofilament light chain (NfL) levels and magnetic resonance imaging (MRI) markers. Methods: SPORTAX is a prospective registry of sporadic ataxia patients with an onset >40 years. Scale for the Assessment and Rating of Ataxia was the primary outcome measure. In subgroups, blood samples were taken and MRIs performed. Plasma NfL was measured via a single molecule assay. Regional brain volumes were automatically measured. To assess signal changes, we defined the pons and middle cerebellar peduncle abnormality score (PMAS). Using mixed-effects models, we analyzed changes on a time scale starting with ataxia onset. Results: Of 404 patients without genetic diagnosis, 130 met criteria of probable MSA-C at baseline and 26 during follow-up suggesting clinical conversion to MSA-C. The remaining 248 were classified as SAOA. At

Most individuals with rare diseases initially consult their primary care physician. For a subset of rare diseases, efficient diagnostic pathways are available. However, ultra-rare diseases often require both expert clinical knowledge and comprehensive genetic diagnostics, which poses structural challenges for public healthcare systems. To address these challenges within Germany, a novel structured diagnostic concept, based on multidisciplinary expertise at established university hospital centers for rare diseases (CRDs), was evaluated in the three year prospective study TRANSLATE NAMSE. A key goal of TRANSLATE NAMSE was to assess the clinical value of exome sequencing (ES) in the ultra-rare disease population. The aims of the present study were to perform a systematic investigation of the phenotypic and molecular genetic data of TRANSLATE NAMSE patients who had undergone ES in order to determine the yield of both ultra-rare diagnoses and novel gene-disease associations; and determine whether the complementary use of machine learning and artificial intelligence (AI) tools improved diagnostic effectiveness and efficiency. ES was performed for 1,577 patients (268 adult and 1,309 pediatric). Molecular genetic diagnoses were established in 499 patients (74 adult and 425 pediatric). A total of 370 distinct molecular genetic causes were established. The majority of these concerned known disorders, most of which were ultra-rare. During the diagnostic process, 34 novel and 23 candidate genotype-phenotype associations were delineated, mainly in individuals with neurodevelopmental disorders. To determine the likelihood that ES will lead to a molecular diagnosis in a given patient, based on the respective clinical features only, we developed a statistical framework called YieldPred. The genetic data of a subcohort of 224 individuals that also gave consent to the computer-assisted analysis of their facial images were processed with the AI tool Prioritization of Exome Data by Image Analysis (PEDIA) and showed superior performance in variant prioritization. The present analyses demonstrated that the novel structured diagnostic concept facilitated the identification of ultra-rare genetic disorders and novel gene-disease associations on a national level and that the machine learning and AI tools improved diagnostic effectiveness and efficiency for ultra-rare genetic disorders.