Validating epilepsy diagnoses in routinely collected data. (November 2017) - Fonferko-Shadrach, Beata; Lacey, Arron S.; White, Catharine P.; Powell, H.W. Rob; Sawhney, Inder M.S.; Lyons, Ronan A.; Smith, Phil E.M.; Kerr, Mike P.; Rees, Mark I.; Pickrell, W. Owen
Highlights: Cases with and without epilepsy were linked with anonymised primary care data. Primary care diagnosis and drug codes accurately identify the cases with epilepsy. Drug codes alone can be used to identify children with epilepsy. Combining drug and diagnosis codes for adults and children increases accuracy. Abstract: 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.