Evidence map›Paper›PMID 26946292›Full record

ArticleDrug safety2016

A Pharmacovigilance Signaling System Based on FDA Regulatory Action and Post-Marketing Adverse Event Reports.

Keith B Hoffman, Mo Dimbil, Nicholas P Tatonetti, Robert F Kyle

Abstract read
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In one paragraph

Article in Drug safety, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Keith B HoffmanAdvera Health Analytics, Inc., 3663 N. Laughlin Road, Suite 102, Santa Rosa, CA, 95403, USA. keith@adverahealth.com.
Mo DimbilAdvera Health Analytics, Inc., 3663 N. Laughlin Road, Suite 102, Santa Rosa, CA, 95403, USA.
Nicholas P TatonettiDepartment of Biomedical Informatics, Columbia University, New York, USA.
Robert F KyleAdvera Health Analytics, Inc., 3663 N. Laughlin Road, Suite 102, Santa Rosa, CA, 95403, USA.

Funding

Drug Effect Discovery Through Data Mining and Integrative Chemical BiologyR01GM107145 · NIGMS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI TATONETTI, NICHOLAS P · 2014 to 2018
$2.5M
NIGMS NIH HHS R01 GM107145
6 · The paper itself

Abstract

introductionMany 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.

objectiveOur 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.

methodsWe 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.

resultsIn 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.

conclusionsBy 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.

Indexed as

Drug ApprovalDrug-Related Side Effects and Adverse ReactionsPharmacovigilanceAdverse Drug Reaction Reporting SystemsDatabases, FactualHumansProduct Surveillance, PostmarketingUnited StatesUnited States Food and Drug Administration

Identifiers

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.