Risk of hepatotoxicity associated with the use of telithromycin: a signal detection using data mining algorithms.
BACKGROUND
With the exception of case reports, limited data are available regarding the risk of hepatotoxicity associated with the use of telithromycin.


OBJECTIVE
To detect the safety signal regarding the reporting of hepatotoxicity associated with the use of telithromycin using 4 commonly employed data mining algorithms (DMAs).


METHODS
Based on the Adverse Events Reporting System (AERS) database of the Food and Drug Administration, 4 DMAs, including the reporting odds ratio (ROR), the proportional reporting ratio (PRR), the information component (IC), and the Gamma Poisson Shrinker (GPS), were applied to examine the association between the reporting of hepatotoxicity and the use of telithromycin. The study period was from the first quarter of 2004 to the second quarter of 2006. The reporting of hepatotoxicity was identified using the preferred terms indexed in the Medical Dictionary for Regulatory Activities. The drug name was used to identify reports regarding the use of telithromycin.


RESULTS
A total of 226 reports describing hepatotoxicity associated with the use of telithromycin were recorded in the AERS. A safety problem of telithromycin associated with increased reporting of hepatotoxicity was clearly detected by 4 algorithms as early as 2005, signaling the problem in the first quarter by the ROR and the IC, in the second quarter by the PRR, and in the fourth quarter by the GPS.


CONCLUSIONS
A safety signal was indicated by the 4 DMAs suggesting an association between the reporting of hepatotoxicity and the use of telithromycin. Given the wide use of telithromycin and serious consequences of hepatotoxicity, clinicians should be cautious when selecting telithromycin for treatment of an infection. In addition, further observational studies are required to evaluate the utility of signal detection systems for early recognition of serious, life-threatening, low-frequency drug-induced adverse events.
Chen
Yan
Y
Division of Pharmacy Practice and Administrative Sciences, James L Winkle College of Pharmacy, University of Cincinnati Academic Health Center, Cincinnati, OH 45267, USA. yance@email.uc.edu
Guo
Jeff J
JJ
Healy
Daniel P
DP
Lin
Xiaodong
X
Patel
Nick C
NC
D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data*; D000465:Algorithms; D000900:Anti-Bacterial Agents / Q000009:adverse effects*; D056486:Chemical and Drug Induced Liver Injury; D003627:Data Interpretation, Statistical; D006801:Humans; D048628:Ketolides / Q000009:adverse effects*; D008107:Liver Diseases / Q000453:epidemiology; D016017:Odds Ratio; D016012:Poisson Distribution; D014481:United States / Q000453:epidemiology; D014486:United States Food and Drug Administration
D000900:Anti-Bacterial Agents; D048628:Ketolides; C106791:telithromycin