PURPOSE: Abacavir is associated with an infrequent but potentially serious 
hypersensitivity reaction (HSR) that can include a wide range of signs and 
symptoms. Identification of this reaction through medical insurance claims could 
provide a simple and efficient means of monitoring the incidence of abacavir 
hypersensitivity in large populations of patients.
METHODS: Using data from a safety study of 948 abacavir users with 22 
hypersensitivity events identified from claims and validated through medical 
record review, we used a recursive partitioning analysis to construct an 
algorithm to differentiate between patients with and without validated adverse 
events. Bootstrap resampling techniques provided validation for the analysis.
RESULTS: The analysis produced a classification tree with three decision nodes 
that comprised the best indicators of HSRs. The predictors included any one of 
several specific symptoms commonly found with this reaction, a claims diagnosis 
of adverse effect of drug, anaphylactic shock or unspecified allergy, and a 
discontinuation in abacavir prior to completing a 90-day course of therapy. The 
algorithm demonstrated 95% sensitivity and 90% specificity when tested using a 
bootstrap resampling approach with the current data.
CONCLUSIONS: A sensitive and specific algorithm for identifying abacavir 
hypersensitivity from claims was created. This algorithm would permit efficient 
identification of charts for medical review. Further testing of the algorithm 
with additional medical claims data for abacavir users will be required to 
ascertain its validity across databases.