RESUMEN
This is the case report of a contained ruptured aortic aneurysm presenting with acute cauda equina syndrome. The patient was a 79-year-old man. A literature search revealed various unusual presentations of abdominal aortic aneurysm (AAA), including femoral neuropathy, hip pain and others; however, there are no other reports of cauda equina-like syndrome. The present case is therefore another unusual presentation of ruptured abdominal aortic aneurysm and reiterates the utmost importance of careful history taking and clinical examination. Abdominal aortic aneurysm rupture should always be considered in the differential diagnosis of sudden onset isolated lower limb neuropathies and or pain.
Asunto(s)
Aneurisma de la Aorta Abdominal/complicaciones , Rotura de la Aorta/complicaciones , Polirradiculopatía/etiología , Anciano , Aneurisma de la Aorta Abdominal/diagnóstico por imagen , Rotura de la Aorta/diagnóstico por imagen , Angiografía por Tomografía Computarizada , Diagnóstico Diferencial , Resultado Fatal , Humanos , Imagen por Resonancia Magnética , Masculino , Polirradiculopatía/diagnósticoRESUMEN
The aim of this study was to examine the association between musculoskeletal disorders and the level of obesity (as defined by the body mass index) for a sample of the Australian population aged 20-64. A logistic regression model was used to estimate the association between musculoskeletal disorders and obesity, controlling for a range of socio-demographic characteristics. Individual-level data on obesity, musculoskeletal disorders, and various socio-demographic characteristics were extracted from the Australian Bureau of Statistics (ABS) 1995 National Health Survey (NHS). Individuals with musculoskeletal disorders were identified using ICD-9 codes 710-739 from a sample of 28,376 individuals from the non-institutionalised population. Estimates from the logistic regression equation indicate that there is a statistically significant positive relationship between the probability of having a musculoskeletal disorder and the level of obesity. Socio-demographic variables such as age, sex, origin, income level, employment status and geographic location also had a statistically significant relationship. This information can be used by public health practitioners and educators to identify those at risk and to design health strategies that target at-risk patients.