ABSTRACT
INTRODUCTION: Ataxia and hereditary spastic paraplegia are rare neurodegenerative syndromes. We aimed to determine the prevalence of these disorders in Spain in 2019. PATIENTS AND METHODS: We conducted a cross-sectional, multicentre, retrospective, descriptive study of patients with ataxia and hereditary spastic paraplegia in Spain between March 2018 and December 2019. RESULTS: We gathered data from a total of 1933 patients from 11 autonomous communities, provided by 47 neurologists or geneticists. Mean (SD) age in our sample was 53.64 (20.51) years; 938 patients were men (48.5%) and 995 were women (51.5%). The genetic defect was unidentified in 920 patients (47.6%). A total of 1371 patients (70.9%) had ataxia and 562 (29.1%) had hereditary spastic paraplegia. Prevalence rates for ataxia and hereditary spastic paraplegia were estimated at 5.48 and 2.24 cases per 100 000 population, respectively. The most frequent type of dominant ataxia in our sample was SCA3, and the most frequent recessive ataxia was Friedreich ataxia. The most frequent type of dominant hereditary spastic paraplegia in our sample was SPG4, and the most frequent recessive type was SPG7. CONCLUSIONS: In our sample, the estimated prevalence of ataxia and hereditary spastic paraplegia was 7.73 cases per 100 000 population. This rate is similar to those reported for other countries. Genetic diagnosis was not available in 47.6% of cases. Despite these limitations, our study provides useful data for estimating the necessary healthcare resources for these patients, raising awareness of these diseases, determining the most frequent causal mutations for local screening programmes, and promoting the development of clinical trials.
Subject(s)
Cerebellar Ataxia , Spastic Paraplegia, Hereditary , Male , Humans , Female , Middle Aged , Spastic Paraplegia, Hereditary/epidemiology , Spastic Paraplegia, Hereditary/genetics , Cross-Sectional Studies , Retrospective Studies , Spain/epidemiologyABSTRACT
INTRODUCTION: Ataxia and hereditary spastic paraplegia are rare neurodegenerative syndromes. We aimed to determine the prevalence of these disorders in Spain in 2019. PATIENTS AND METHODS: We conducted a cross-sectional, multicentre, retrospective, descriptive study of patients with ataxia and hereditary spastic paraplegia in Spain between March 2018 and December 2019. RESULTS: We gathered data from a total of 1.809 patients from 11 autonomous communities, provided by 47 neurologists or geneticists. Mean (SD) age in our sample was 53.64 (20.51) years; 920 patients were men (50.8%) and 889 were women (49.2%). The genetic defect was unidentified in 920 patients (47.6%). A total of 1371 patients (70.9%) had ataxia and 562 (29.1%) had hereditary spastic paraplegia. Prevalence rates for ataxia and hereditary spastic paraplegia were estimated at 5.48 and 2.24 cases per 100 000 population, respectively. The most frequent type of dominant ataxia in our sample was SCA3, and the most frequent recessive ataxia was Friedreich ataxia. The most frequent type of dominant hereditary spastic paraplegia in our sample was SPG4, and the most frequent recessive type was SPG7. CONCLUSIONS: In our sample, the estimated prevalence of ataxia and hereditary spastic paraplegia was 7.73 cases per 100 000 population. This rate is similar to those reported for other countries. Genetic diagnosis was not available in 47.6% of cases. Despite these limitations, our study provides useful data for estimating the necessary healthcare resources for these patients, raising awareness of these diseases, determining the most frequent causal mutations for local screening programmes, and promoting the development of clinical trials.
ABSTRACT
BACKGROUND AND PURPOSE: The unanticipated detection by magnetic resonance imaging (MRI) in the brain of asymptomatic subjects of white matter lesions suggestive of multiple sclerosis (MS) has been named radiologically isolated syndrome (RIS). As the difference between early MS [i.e. clinically isolated syndrome (CIS)] and RIS is the occurrence of a clinical event, it is logical to improve detection of the subclinical form without interfering with MRI as there are radiological diagnostic criteria for that. Our objective was to use machine-learning classification methods to identify morphometric measures that help to discriminate patients with RIS from those with CIS. METHODS: We used a multimodal 3-T MRI approach by combining MRI biomarkers (cortical thickness, cortical and subcortical grey matter volume, and white matter integrity) of a cohort of 17 patients with RIS and 17 patients with CIS for single-subject level classification. RESULTS: The best proposed models to predict the diagnosis of CIS and RIS were based on the Naive Bayes, Bagging and Multilayer Perceptron classifiers using only three features: the left rostral middle frontal gyrus volume and the fractional anisotropy values in the right amygdala and right lingual gyrus. The Naive Bayes obtained the highest accuracy [overall classification, 0.765; area under the receiver operating characteristic (AUROC), 0.782]. CONCLUSIONS: A machine-learning approach applied to multimodal MRI data may differentiate between the earliest clinical expressions of MS (CIS and RIS) with an accuracy of 78%.
Subject(s)
Brain/diagnostic imaging , Demyelinating Diseases/diagnostic imaging , Gray Matter/diagnostic imaging , Machine Learning , Multiple Sclerosis/diagnostic imaging , White Matter/diagnostic imaging , Adult , Bayes Theorem , Brain/pathology , Demyelinating Diseases/pathology , Female , Gray Matter/pathology , Humans , Image Processing, Computer-Assisted , Magnetic Resonance Imaging/methods , Male , Middle Aged , White Matter/pathologyABSTRACT
No disponible
Subject(s)
Humans , Tremor/chemically induced , Volatile Organic Compounds/adverse effects , Movement Disorders , Trauma, Nervous System/chemically inducedABSTRACT
No disponible
