1 research outputs found
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 77β90) and
specificity of 98% (95β100) in identifying people with epilepsy; diagnosis codes alone had a sensitivity of
86% (80β91) and a specificity of 97% (92β99); and AED prescription codes alone achieved a sensitivity of
92% (70β83) and a specificity of 73% (65β80). Using AED codes only was more accurate in children
achieving a sensitivity of 88% (75β95) and specificity of 98% (88β100).
Conclusion: GP epilepsy diagnosis and AED prescription codes can be confidently used to identify people
with epilepsy using anonymised healthcare records in Wales, U