Exploration on Adverse Drug Reaction Signal Detection, Validation and Statistics Analysis Methods: Based on the Clindamycin Injection's Renal Toxicity Adverse Reaction Monitoring Data.

Abstract

Abstract: Objective To establish the model of adverse drug reaction signal detection, validation and statistics analysis methods. Methods Using the measures of disproportionality and stratified analysis to establish the model of adverse drug reaction signal detection, validation and analysis research by the clindamycin injection's renal toxicity adverse reaction monitoring data. Results The ROR of clindamycin injection, clindamycin hydrochloride injection and clindamycin phosphate injection were 65.0, 114.0 and 14.7, respectively. The risks of renal damage of clindamycin hydrochloride injection were 6.6 times and 5.5 times than clindamycin phosphate injection after adjusting the age and single drug dose( P<0.001). It has been successfully established the model of adverse drug reaction signal detection, validation and analysis research. Conclusion There is strong relevance between clindamycin and renal toxicity adverse reaction, and the risk of renal damage of clindamycin hydrochloride injection was higher than clindamycin phosphate injection, but more researches are needed to verify it. The integrated use of data mining and statistics Methods can make more favorable evidence for adverse drug reaction signal detection, validation and analysis research.

Key words: clindamycin, adverse reaction, signal, mining, statistics

CLC Number:

R954

LU Chang-fei, TIAN Chun-hua ,TIAN Yue-jie, LIU Cui-li ,XIE Yan-jun . Exploration on Adverse Drug Reaction Signal Detection, Validation and Statistics Analysis Methods: Based on the Clindamycin Injection's Renal Toxicity Adverse Reaction Monitoring Data. [J]. Chinese Journal of Pharmacovigilance, 2013, 10(4): 222-227.

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