Early onset cerebral amyloid angiopathy following childhood exposure to cadaveric dura.

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.

Annotation of epilepsy clinic letters for natural language processing.

BACKGROUND: Natural language processing (NLP) is increasingly being used to extract structured information from unstructured text to assist clinical decision-making and aid healthcare research. The availability of expert-annotated documents for the development and validation of NLP applications is limited. We created synthetic clinical documents to address this, and to validate the Extraction of Epilepsy Clinical Text version 2 (ExECTv2) NLP pipeline. METHODS: We created 200 synthetic clinic letters based on hospital outpatient consultations with epilepsy specialists. The letters were double annotated by trained clinicians and researchers according to agreed guidelines. We used the annotation tool, Markup, with an epilepsy concept list based on the Unified Medical Language System ontology. All annotations were reviewed, and a gold standard set of annotations was agreed and used to validate the performance of ExECTv2. RESULTS: The overall inter-annotator agreement (IAA) between the two sets of annotations produced a per item F1 score of 0.73. Validating ExECTv2 using the gold standard gave an overall F1 score of 0.87 per item, and 0.90 per letter. CONCLUSION: The synthetic letters, annotations, and annotation guidelines have been made freely available. To our knowledge, this is the first publicly available set of annotated epilepsy clinic letters and guidelines that can be used for NLP researchers with minimum epilepsy knowledge. The IAA results show that clinical text annotation tasks are difficult and require a gold standard to be arranged by researcher consensus. The results for ExECTv2, our automated epilepsy NLP pipeline, extracted detailed epilepsy information from unstructured epilepsy letters with more accuracy than human annotators, further confirming the utility of NLP for clinical and research applications.

Validating epilepsy diagnoses in routinely collected data.

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.