Susan Colilla1, Elad Yom Tov2, Ling Zhang3, Marie-Laure Kurzinger4, Stephanie Tcherny-Lessenot4, Catherine Penfornis4, Shang Jen5, Danny S Gonzalez6, Patrick Caubel7, Susan Welsh3, Juhaeri Juhaeri3. 1. Pharmacoepidemiology, Global Safety Sciences, Sanofi, 55 Corporate Dr., Bridgewater, NJ, 08807, USA. susan.colilla@sanofi.com. 2. Microsoft Research, 13 Shenkar St., 4672513, Herzeliya, Israel. 3. Pharmacoepidemiology, Global Safety Sciences, Sanofi, 55 Corporate Dr., Bridgewater, NJ, 08807, USA. 4. Pharmacoepidemiology & Signal Detection, Global Safety Sciences, Sanofi, 1 Avenue Pierre Brossolette, 91385, Chilly-Mazarin, France. 5. Baxalta US, Inc., Global Drug Safety, 650 E. Kendall St., Cambridge, MA, 02142, USA. 6. US Food and Drug Administration, 10903 New Hampshire Ave., Silver Spring, MD, 20993, USA. 7. Pfizer, Worldwide Safety, 235 E. 42nd St., New York, NY, 10017, USA.
Abstract
INTRODUCTION: Post-marketing drug surveillance is largely based on signals found in spontaneous reports from patients and healthcare providers. Rare adverse drug reactions and adverse events (AEs) that may develop after long-term exposure to a drug or from drug interactions may be missed. The US FDA and others have proposed that web-based data could be mined as a resource to detect latent signals associated with adverse drug reactions. METHODS: Recently, a web-based search query method called a query log reaction score (QLRS) was developed to detect whether AEs associated with certain drugs could be found from search engine query data. In this study, we compare the performance of two other algorithms, the proportional query ratio (PQR) and the proportional query rate ratio (Q-PRR) against that of two reference signal-detection algorithms (SDAs) commonly used with the FDA AE Reporting System (FAERS) database. RESULTS: In summary, the web query methods have moderate sensitivity (80%) in detecting signals in web query data compared with reference SDAs in FAERS when the web query data are filtered, but the query metrics generate many false-positives and have low specificity compared with reference SDAs in FAERS. CONCLUSION: Future research is needed to find better refinements of query data and/or the metrics to improve the specificity of these web query log algorithms.
INTRODUCTION: Post-marketing drug surveillance is largely based on signals found in spontaneous reports from patients and healthcare providers. Rare adverse drug reactions and adverse events (AEs) that may develop after long-term exposure to a drug or from drug interactions may be missed. The US FDA and others have proposed that web-based data could be mined as a resource to detect latent signals associated with adverse drug reactions. METHODS: Recently, a web-based search query method called a query log reaction score (QLRS) was developed to detect whether AEs associated with certain drugs could be found from search engine query data. In this study, we compare the performance of two other algorithms, the proportional query ratio (PQR) and the proportional query rate ratio (Q-PRR) against that of two reference signal-detection algorithms (SDAs) commonly used with the FDA AE Reporting System (FAERS) database. RESULTS: In summary, the web query methods have moderate sensitivity (80%) in detecting signals in web query data compared with reference SDAs in FAERS when the web query data are filtered, but the query metrics generate many false-positives and have low specificity compared with reference SDAs in FAERS. CONCLUSION: Future research is needed to find better refinements of query data and/or the metrics to improve the specificity of these web query log algorithms.
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