RESUMO
Amyloid-ß transmission has been described in patients both with and without iatrogenic Creutzfeldt-Jakob disease; however, there is little information regarding the clinical impact of this acquired amyloid-ß pathology during life. Here, for the first time, we describe in detail the clinical and neuroimaging findings in 3 patients with early onset symptomatic amyloid-ß cerebral amyloid angiopathy following childhood exposure to cadaveric dura (by neurosurgical grafting in 2 patients and tumor embolization in a third). Our observations provide further in vivo evidence that cerebral amyloid angiopathy might be caused by transmission of amyloid-ß seeds (prions) present in cadaveric dura and have diagnostic relevance for younger patients presenting with suspected cerebral amyloid angiopathy. Ann Neurol 2019; 1-7 ANN NEUROL 2019;85:284-290.
Assuntos
Peptídeos beta-Amiloides/metabolismo , Angiopatia Amiloide Cerebral/diagnóstico por imagem , Dura-Máter/transplante , Adulto , Idade de Início , Cadáver , Sobreviventes de Câncer , Angiopatia Amiloide Cerebral/metabolismo , Angiopatia Amiloide Cerebral/patologia , Angiopatia Amiloide Cerebral/fisiopatologia , Craniotomia , Dura-Máter/metabolismo , Embolização Terapêutica , Feminino , Hemangioma Cavernoso do Sistema Nervoso Central/terapia , Humanos , Doença Iatrogênica , Imageamento por Ressonância Magnética , Masculino , Pessoa de Meia-Idade , Papiloma do Plexo Corióideo/cirurgia , Neoplasias Parotídeas/terapia , Fraturas Cranianas/cirurgiaRESUMO
PURPOSE: Anonymised, routinely-collected healthcare data is increasingly being used for epilepsy research. We validated algorithms using general practitioner (GP) primary healthcare records to identify people with epilepsy from anonymised healthcare data within the Secure Anonymised Information Linkage (SAIL) databank in Wales, UK. METHOD: A reference population of 150 people with definite epilepsy and 150 people without epilepsy was ascertained from hospital records and linked to records contained within SAIL (containing GP records for 2.4 million people). We used three different algorithms, using combinations of GP epilepsy diagnosis and anti-epileptic drug (AED) prescription codes, to identify the reference population. RESULTS: Combining diagnosis and AED prescription codes had a sensitivity of 84% (95% ci 77-90) and specificity of 98% (95-100) in identifying people with epilepsy; diagnosis codes alone had a sensitivity of 86% (80-91) and a specificity of 97% (92-99); and AED prescription codes alone achieved a sensitivity of 92% (70-83) and a specificity of 73% (65-80). Using AED codes only was more accurate in children achieving a sensitivity of 88% (75-95) and specificity of 98% (88-100). CONCLUSION: GP epilepsy diagnosis and AED prescription codes can be confidently used to identify people with epilepsy using anonymised healthcare records in Wales, UK.