Detecting poststroke epilepsy in nationwide administrative data: A validation study using Swedish registers - André Idegård; David Larsson (PLoS ONE, 2025)
Objective Healthcare administrative data often rely on the International Classification of Diseases (ICD) system, which lacks specific codes to identify etiological subgroups of epilepsy. Combining indicators for epilepsy and potential etiologies is possible, but such approaches require validation. This study aimed to validate methods for identifying poststroke epilepsy (PSE) in Swedish administrative data. Methods The algorithms were based on combinations of ICD-10 codes for stroke and seizures, with some also incorporating antiseizure medication prescriptions. We focused on positive predictive values (PPVs), using medical records as the reference standard. We identified individuals in the National Patient Register with a primary inpatient diagnostic code for stroke (I61 or I63) during 2005–2010 and a first-ever seizure-related code (G40, G41, or R56.8), occurring more than seven days post-stroke. To facilitate access to medical records, only patients who were deceased at data extraction (Jan 16, 2021) were eligible. A nationwide random sample of 500 patients was selected, with the intended sample for medical record review being 250. Medical records were reviewed before processing the administrative data. Results Records were obtained for 321 patients (median age 78; 56% males), with no significant differences in characteristics between those included and the rest of the sample. Across different algorithms, PPVs ranged from 84.1% (95% CI: 79.2–88.3) to 92.5% (95% CI: 87.3–96.1). Relative coverage ranged from 60% to 89% compared to the most inclusive algorithm. Significance Our findings demonstrate the potential of administrative data to reliably identify PSE cases, supporting the use of these algorithms for large-scale studies of treatment and outcomes. Stricter algorithms, limited to G40 codes for epilepsy or requiring ASM prescriptions, improve accuracy but at the cost of missing more cases. Limitations include the inability to calculate sensitivity due to study design, and the need for local validation before use in other healthcare systems.