A systematic review of validated methods for identifying seizures, convulsions, or epilepsy using administrative and claims data.

PURPOSE: To systematically review algorithms to identify seizure, convulsion, or epilepsy cases in administrative and claims data, with a focus on studies that have examined the validity of the algorithms.
METHODS: A literature search was conducted using PubMed and the Iowa Drug Information Service database. Reviews were conducted by two investigators to identify studies using data sources from the USA or Canada because these data sources were most likely to reflect the coding practices of Mini-Sentinel data partners.
RESULTS: Eleven studies that validated seizure, convulsion, or epilepsy cases were identified. All algorithms included International Classification of Diseases, Ninth Revision, Clinical Modification code 345.X (epilepsy) and either code 780.3 (convulsions) or code 780.39 (other convulsions). Six studies included 333.2 (myoclonus). In populations that included children, 779.0 (convulsions in newborn) was also fairly common. Positive predictive values (PPVs) ranged from 21% to 98%. Studies that used nonspecific indicators such as presence of an electroencephalogram or anti-epileptic drug (AED) level monitoring had lower PPVs. In studies focusing exclusively on epilepsy as opposed to isolated seizure events, sensitivity ranged from 70% to 99%.
CONCLUSIONS: Algorithm performance was highly variable, so it is difficult to draw any strong conclusions. However, the PPVs were generally best in studies where epilepsy diagnoses were required. Using procedure codes for electroencephalograms or prescription claims for drugs possibly used for epilepsy or convulsions in the absence of a diagnostic code is not recommended. Many newer AEDs require no drug level monitoring, so requiring an AED level monitoring procedure in algorithms to identify epilepsy is not recommended.