Miastenia gravis y enfermedad de Alzheimer: una asociación a estudio / Association between myasthenia gravis and Alzheimer’s disease

Introducción La miastenia gravis (MG) y la enfermedad de Alzheimer (EA) son dos de las enfermedades neurológicas en cuya fisiopatología interviene la acetilcolina en distintos niveles. En la primera, la alteración de este neurotransmisor se produce en la unión neuromuscular, y en la segunda, en el sistema nervioso central. Objetivo Analizar la posible relación entre dichas patologías estudiando la prevalencia y la odds ratio de la EA dentro de los pacientes diagnosticados de MG con respecto a la prevalencia de EA en la población general. Pacientes y métodos Se han examinado datos de las historias clínicas electrónicas del sistema de salud de Castilla-La Mancha utilizando el procesamiento de lenguaje natural a través de la plataforma clínica de inteligencia artificial Savana Manager?. Resultados Se ha identificado a 970.503 pacientes mayores de 60 años, de los que 1.028 tenían diagnóstico de MG. La proporción de pacientes con diagnóstico de EA dentro de este grupo (4,28%) es mayor que en el resto de la población (2,82%; p = 0,0047), con una odds ratio de 1,54 (intervalo de confianza al 95%: 1,13-2,08; p = 0,0051), sin que se encuentren diferencias significativas en el análisis bivariante del resto de los factores de riesgo para EA más importantes conocidos hasta ahora. Conclusiones Nuestros resultados sugieren que podría existir un aumento de la prevalencia de EA en pacientes con MG. (AU)

INTRODUCTION Myasthenia gravis (MG) and Alzheimer’s disease (AD) are two of the most important diseases where the dysregulation of acetylcholine activity plays a crucial role. In the first, this dysregulation happens at the level of the neu­romuscular junction and in the second, in the central nervous system (CNS). AIM To analyze the possible relationship between these two pathologies, analyzing the prevalence and the odds ratio of AD within patients previously diagnosed with MG. We will compare these data with respect to the prevalence of AD in the general population. PATIENTS AND METHODS We examined the data obtained by the electronic medical records of patients in the health care system of Castilla La Mancha using the Natural Language Process provided by a clinical platform of artificial intelligence known as the Savana Manager?. RESULTS We identified 970,503 patients over the age of 60 years, of which 1,028 were diagnosed with MG. The proportion of the patients diagnosed with AD within this group (4.28%) was greater than the rest of the population (2.82%) (p = 0,0047) with an odds ratio of 1.54 (confidence interval at 95% 1.13-2.08; p = 0.0051) without finding significant differences in the bivariate analysis for the rest of the most important actual known risk factors for AD. CONCLUSION. Our results suggest that there might be an increase in the prevalence of AD in patients previously diagnosed with MG. (AU)

Correlation of Global and Regional Amyloid Burden by 18 F-Florbetaben PET/CT With Cognitive Impairment Profile and Severity.

PURPOSE: To assess the correlation between profile and severity deterioration in the neuropsychological assessment and the most affected regions in amyloid PET semiquantification. The influence of vascular risk and other potential confounding factors was also evaluated. METHODS: A retrospective, observational, and multicenter study including all patients referred for amyloid PET in daily practice was conducted. Patients underwent neuropsychological assessment, and cognitive decline severity and domain(s) affected were recorded. The patients were grouped according to cognitive impairment (CI) profile and severity: (A) no CI, single-domain amnestic CI, multiple-domain amnestic CI, and nonamnestic CI; and (B) mild CI, moderate and severe dementia. An adapted Framingham Stroke Risk Profile was calculated for each individual. Depression and parkinsonism were also recorded. Standardized quantitative analysis software was used to obtain standardized uptake value ratio (SUVR) values from PET/CT images. The corresponding associations were assessed with the most appropriate statistical tests. RESULTS: One hundred twenty-nine patients were included (62 men, 67 women; 64.67 ± 7.47 years old). Significant differences in global and regional amyloid load were exclusively found in women between non-CI and moderate dementia ( P = 0.006, for total-cerebellum SUVR). Posterior and anterior cingulates and prefrontal cortex best represented CI severity ( P = 0.003, 0.006, and 0.006, respectively). No relationship between the CI profile and the regional amyloid load was shown. A significantly high positive correlation was found between age and vascular risk and between these variables and amyloid load in nearly all regions, especially in women with moderate dementia. CONCLUSION: Semiquantitative analysis of amyloid PET by SUVR values revealed a significant correlation between amyloid burden and CI severity, although only in women.

Increasing the confidence of 18F-Florbetaben PET interpretations: Machine learning quantitative approximation.

AIM: To assess the added value of semiquantitative parameters on the visual assessment and to study the patterns of 18F-Florbetaben brain deposition. MATERIALS AND METHODS: Retrospective analysis of multicenter study performed in patients with mild cognitive impairment or dementia of uncertain origin. 18F-Florbetaben PET scans were visually interpreted by two experienced observers, analyzing target regions in order to calculate the interobserver agreement. Semiquantification of all cortical regions with respect to three reference regions was performed to obtain standardized uptake value ratios (SUVRs). The ability of SUVRs to predict the visual evaluation, the possibility of preferential radiotracer deposition in some target regions and interhemisphere differences were analyzed. RESULTS: 135 patients were evaluated. In the visual assessment, 72 were classified as positive. Interobserver agreement was excellent. All SUVRs were significantly higher in positive PET scans than in negative ones. Prefrontal area and posterior cingulate were the cortical regions with the best correlations with the visual evaluation, followed by the composite region. Using ROC analysis, the SUVRs obtained in same target locations showed the best diagnostic performance. CONCLUSIONS: The derived information from target regions seems to help the visual classification, based on a preferential amyloid ß deposit, allowing machine learning. The amyloid ß deposit, although diffuse in all cortical regions, seems not to be uniform and symmetric.

Encefalopatía con lesión reversible del cuerpo calloso / Encephalopathy with reversible splenial lesion

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