Signal detection of rosuvastatin compared to other statins: data‐mining study using national health insurance claims database

To detect adverse drug reaction (ADR) signals of rosuvastatin compared to other statins with a novel data‐mining approach based on relative risk (RR) using the national health insurance claims database, and to evaluate the usefulness of this method as a tool for signal detection.

Abstract

PURPOSE: To detect adverse drug reaction (ADR) signals of rosuvastatin compared to other statins with a novel data-mining approach based on relative risk (RR) using the national health insurance claims database, and to evaluate the usefulness of this method as a tool for signal detection. METHODS: We used the Health Insurance Review & Assessment Service (HIRA) claims database (Seoul, Korea). Serious adverse events (SAE) were defined as any diagnostic code at the time of hospitalization within 12 weeks from a statin prescription date, regardless of causality. Among statin users, RRs were calculated to compare the proportion of rosuvastatin-specific SAE pairs for rosuvastatin users with the corresponding proportion of drug-SAE pairs for users of other statins. Any SAE for which the lower limit of the RR's 95% confidence interval was greater than 1 was defined as a signal. All detected signals were reviewed to determine whether the signals corresponded with published adverse events (AEs) exclusive to rosuvastatin. RESULTS: Among 96 236 elderly outpatients who received rosuvastatin, or other statins, from January 2005 to September 2005, 40 304 drug-SAE pairs and 376 SAEs were observed. Twenty-five (6.6%) SAEs were detected as signals by the RR-based data-mining approach. Among the 13 references AEs published to be exclusive to rosuvastatin, 8 (61.5%) were found to correspond with the detected signals with a positive predictive value (PPV) of 32%. CONCLUSIONS: The RR-based data-mining approach successfully detected signals for rosuvastatin using a national health insurance claims database. This approach could be useful for safety surveillance of marketed products.