Keith B Hoffman1, Mo Dimbil2, Nicholas P Tatonetti3, Robert F Kyle2. 1. Advera Health Analytics, Inc., 3663 N. Laughlin Road, Suite 102, Santa Rosa, CA, 95403, USA. keith@adverahealth.com. 2. Advera Health Analytics, Inc., 3663 N. Laughlin Road, Suite 102, Santa Rosa, CA, 95403, USA. 3. Department of Biomedical Informatics, Columbia University, New York, USA.
Abstract
INTRODUCTION: Many serious drug adverse events (AEs) only manifest well after regulatory approval. Therefore, the development of signaling methods to use with post-approval AE databases appears vital to comprehensively assess real-world drug safety. However, with millions of potential drug-AE pairs to analyze, the issue of focus is daunting. OBJECTIVE: Our objective was to develop a signaling platform that focuses on AEs with historically demonstrated regulatory interest and to analyze such AEs with a disproportional reporting method that offers broad signal detection and acceptable false-positive rates. METHODS: We analyzed over 1500 US FDA regulatory actions (safety communications and drug label changes) from 2008 to 2015 to construct a list of eligible signal AEs. The FDA Adverse Event Reporting System (FAERS) was used to evaluate disproportional reporting rates, constrained by minimum case counts and confidence interval limits, of these selected AEs for 109 training drugs. This step led to 45 AEs that appeared to have a low likelihood of being added to a label by FDA, so they were removed from the signal eligible list. We measured disproportional reporting for the final group of eligible AEs on a test group of 29 drugs that were not used in either the eligible list construction or the training steps. RESULTS: In a group of 29 test drugs, our model reduced the number of potential drug-AE signals from 41,834 to 97 and predicted 73 % of individual drug label changes. The model also predicted at least one AE-drug pair label change in 66 % of all the label changes for the test drugs. CONCLUSIONS: By concentrating on AE types with already demonstrated interest to FDA, we constructed a signaling system that provided focus regarding drug-AE pairs and suitable accuracy with regard to the issuance of FDA labeling changes. We suggest that focus on historical regulatory actions may increase the utility of pharmacovigilance signaling systems.
INTRODUCTION: Many serious drug adverse events (AEs) only manifest well after regulatory approval. Therefore, the development of signaling methods to use with post-approval AE databases appears vital to comprehensively assess real-world drug safety. However, with millions of potential drug-AE pairs to analyze, the issue of focus is daunting. OBJECTIVE: Our objective was to develop a signaling platform that focuses on AEs with historically demonstrated regulatory interest and to analyze such AEs with a disproportional reporting method that offers broad signal detection and acceptable false-positive rates. METHODS: We analyzed over 1500 US FDA regulatory actions (safety communications and drug label changes) from 2008 to 2015 to construct a list of eligible signal AEs. The FDA Adverse Event Reporting System (FAERS) was used to evaluate disproportional reporting rates, constrained by minimum case counts and confidence interval limits, of these selected AEs for 109 training drugs. This step led to 45 AEs that appeared to have a low likelihood of being added to a label by FDA, so they were removed from the signal eligible list. We measured disproportional reporting for the final group of eligible AEs on a test group of 29 drugs that were not used in either the eligible list construction or the training steps. RESULTS: In a group of 29 test drugs, our model reduced the number of potential drug-AE signals from 41,834 to 97 and predicted 73 % of individual drug label changes. The model also predicted at least one AE-drug pair label change in 66 % of all the label changes for the test drugs. CONCLUSIONS: By concentrating on AE types with already demonstrated interest to FDA, we constructed a signaling system that provided focus regarding drug-AE pairs and suitable accuracy with regard to the issuance of FDA labeling changes. We suggest that focus on historical regulatory actions may increase the utility of pharmacovigilance signaling systems.
Authors: Igor Grigoriev; Wolfgang zu Castell; Philipp Tsvetkov; Alexey V Antonov Journal: Pharmacoepidemiol Drug Saf Date: 2014-02-12 Impact factor: 2.890
Authors: Matthew S Duprey; Nada S Al-Qadheeb; Nick O'Donnell; Keith B Hoffman; Jonathan Weinstock; Christopher Madias; Mo Dimbil; John W Devlin Journal: Drugs Real World Outcomes Date: 2019-09