Pharmacoepidemiology and drug safety · 2007
Identification of abacavir hypersensitivity reaction in health care claims data.
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Nordstrom Beth L, Norman Heather S, Dube Timothy J, Wilcox Marsha A, Walker Alexander M
Аннотация
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.
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.