A systematic review of validated methods for identifying uveitis using administrative or claims data

• Validation of algorithms applied to identify uveitis cases has not often been conducted.
• No studies evaluated individual codes for accuracy in case identification.
• Positive predictive value in studies using groups of codes or text mining was 52.1%, 24.8%, and 52.6%.
• Further research, with case and code validation, is needed to determine appropriate algorithms.

PurposeTo review algorithms used to identify uveitis in administrative and claims databases.MethodsWe searched the MEDLINE database via PubMed from 1991 to September 2012 using vocabulary and key terms related to uveitis. We also searched the reference lists of included studies. Two investigators independently assessed studies against pre-determined inclusion criteria. The same two investigators independently extracted data regarding participant and algorithm characteristics and assessed a study's methodological rigor using a pre-defined approach.ResultsSeven studies met inclusion criteria. Variability exists among algorithms employed in these studies for finding cases of uveitis and related conditions as well as in use and implementation of validation methods. Of the seven included studies, three involved case validation. One used a narrow algorithm in addition to text mining of electronic medical records to identify incident cases and found a positive predictive value of 52.1%. The other two, which used broader uveitis definitions and included both incident and prevalent cases, found positive predictive values of 24.8% and 52.6%.ConclusionsFurther research, with case as well as individual code validation, is needed to determine appropriate uveitis algorithms for purposes of active surveillance in administrative data. Decisions about which algorithm to use will depend on the desired balance of sensitivity and specificity.