Using search data from 6 million computer users, researchers found evidence that the combination of paroxetine and pravastatin was associated with hyperglycemia.
By mining Internet search data from millions of computer users, researchers can detect adverse events associated with drug interactions before they have been reported by the FDA, according to the results of a study
published online on March 6, 2013, in the Journal of the American Medical Informatics Association
The researchers drew on data on Internet searches throughout 2010 by 6 million computer users who had consented to share their search information with Microsoft. These users performed 82 million searches concerning medications, symptoms, or medical conditions during the period in question.
To test their hypothesis that Internet searches might provide early clues about adverse drug events, the researchers focused on the pairing of the antidepressant paroxetine and the cholesterol-lowering drug pravastatin, whose interaction was reported to cause hyperglycemia through the FDA adverse event reporting system (AERS) in 2011, after the period covered by the data used in the study. To do so, the researchers analyzed the search data to see whether those searching for information on both drugs were more likely to search for hyperglycemia-associated words and phrases than those searching for only 1 of the drugs.
The results showed that approximately 10% of users who searched for both paroxetine and pravastatin over the 12-month study period also searched for terms associated with hyperglycemia, such as “high blood sugar” and “blurry vision.” By comparison, approximately 5% of paroxetine users, 4% of pravastatin users, and 0.3% of all users also searched for hyperglycemia-associated terms. The researchers note that these rates were generally consistent throughout the 12-month period.
The researchers conclude that online search data has the potential to make a positive contribution to drug safety surveillance at relatively low cost. “The prolific use of Web search to pursue information can be likened to a large-scale distributed network of sensors for identifying the potential side effects of drugs,” the researchers write. “We believe that patient search behavior directly captures aspects of patients’ concerns about sensed symptomatology and can complement more traditional sources of data for pharmacovigilance, including AERS and electronic health record data.”