The Adverse Event Reporting System (AERS) is a computerized information database designed to support the FDA's post-marketing safety surveillance program for all approved drug and therapeutic biologic products. The FDA uses AERS to monitor for new adverse events and medication errors that might occur with these marketed products. Reporting of adverse events from the point of care is voluntary in the United States. FDA receives some adverse event and medication error reports directly from health care professionals (such as physicians, pharmacists, nurses and others) and consumers (such as patients, family members, lawyers and others). Healthcare professionals and consumers may also report these events to the products' manufacturers. If a manufacturer receives an adverse event report, it is required to send the report to FDA as specified by regulations. The files listed on this page contain raw data extracted from the AERS database for the indicated time ranges and are not cumulative. Users of these files need to be familiar with creation of relational databases using applications such as ORACLE, Microsoft Office Access, MySQL and IBM DB2 or the use of ASCII files with SAS analytic tools. A simple search of AERS data cannot be performed with these files by persons who are not familiar with creation of relational databases.

Comparison of open-access web-resources that mine FDA Adverse Events data.

Includes data files and supplemental information. Supplemental information includes a reproducible RMarkdown file, an Excel sheet with metadata, and complete webpage files.From the FAERS Quarterly Data Extract Files Website:The files listed on this page contain raw data extracted from the AERS database for the indicated time ranges and are not cumulative. Users of these files need to be familiar with creation of relational databases using applications such as ORACLE®, Microsoft Office Access, MySQL® and IBM DB2 or the use of ASCII files with SAS® analytic tools. A simple search of FAERS data cannot be performed with these files by persons who are not familiar with creation of relational databases. However, you can get a summary FAERS report for a product by sending a Freedom of Information Act (FOIA) request to FDA. You can also request individual case reports by submitting a FOIA request listing case report numbers. The quarterly data files, which are available in ASCII or SGML formats, include:demographic and administrative information and the initial report image ID number (if available);drug information from the case reports;reaction information from the reports;patient outcome information from the reports;information on the source of the reports;a "README" file containing a description of the files.Additional fields will appear in the 2014 Q3 date files below.For more details: Summary of Changes for the 2014 Q3 Quarterly Date Extract (PDF -71 KB)

This dataset tracks the updates made on the dataset "Adverse Event Reporting System (AERS)" as a repository for previous versions of the data and metadata.

AERS – Adverse Events Reporting System; AEs – Adverse Events; PR – Prescribing Ratio.*“total cases” for each item refers to the number of cases found in the AERS database using the respective search term.

Available openFDA front-ends.

Pharmacovigilance contributes to health care. However, direct access to the underlying data for academic institutions and individual physicians or pharmacists is intricate, and easily employable analysis modes for everyday clinical situations are missing. This underlines the need for a tool to bring pharmacovigilance to the clinics. To address these issues, we have developed OpenVigil FDA, a novel web-based pharmacovigilance analysis tool which uses the openFDA online interface of the Food and Drug Administration (FDA) to access U.S. American and international pharmacovigilance data from the Adverse Event Reporting System (AERS). OpenVigil FDA provides disproportionality analyses to (i) identify the drug most likely evoking a new adverse event, (ii) compare two drugs concerning their safety profile, (iii) check arbitrary combinations of two drugs for unknown drug-drug interactions and (iv) enhance the relevance of results by identifying confounding factors and eliminating them using background correction. We present examples for these applications and discuss the promises and limits of pharmacovigilance, openFDA and OpenVigil FDA. OpenVigil FDA is the first public available tool to apply pharmacovigilance findings directly to real-life clinical problems. OpenVigil FDA does not require special licenses or statistical programs.

Naming of cells in the 2x2 contingency table.

Metadata and data derived from Agricultural Economics Research Series. A series of documents focused on agricultural economics produced by University of Idaho researchers.

A linked data version of the AERS (currently FAERS) dataset of the US Food and Drug administration for the years 2005-2012. The dataset has been linked to Drugbank, DBPedia (v3.7), Diseaseome, Sider, the NCI Thesaurus and the CTCAE ontology. Code as part of the raw2ld v0.1 library (in aersld)Original data CC0

Unique values and counts of metadata subject fields.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Time-based metadata formatted for TimelineJS or other applications.

Unique values and counts of metadata facet fields.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This study aims to analyze the adverse event reports (AERs) to vericiguat using data from the Food and Drug Administration Adverse Event Reporting System (FAERS) and provide evidence for the clinical use. AERs due to vericiguat from 2021Q1 to 2024Q1 identified as the primary suspect were screened, with duplicate reports subsequently eliminated. Various quantitative signal detection methods, including reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network, and multi-item gamma poisson shrinker, were then employed for data mining and analysis. Signal strength is represented by the 95% confidence interval, information component (IC), and empirical Bayesian geometric mean (EBGM). A total of 617 vericiguat-related AERs were identified. Strong signals were observed in 21 system organ classes. Furthermore, the most frequently reported preferred terms (PT) was hypotension (n = 86, ROR 25.92, PRR 24.11, IC 4.59, EBGM 24.07), followed by dizziness (n = 52, ROR 6.44, PRR 6.20, IC 2.63, EBGM 6.20), malaise (n = 25, ROR 3.59, PRR 3.54, IC 1.82, EBGM 3.54), blood pressure decreased (n = 23, ROR 20.00, PRR 19.64, IC 4.29, EBGM 19.61), and anemia (n = 21, ROR 6.67, PRR 6.57, IC 2.72, EBGM 6.57). This study extended the adverse reactions documented in the FDA instruction and provided supplementary evidence regarding the clinical safety of vericiguat.

BackgroundRipretinib has been approved for the treatment of gastrointestinal stromal tumors (GIST). As a novel therapy, several adverse reactions remain unidentified, necessitating a thorough safety evaluation. This study analyzes real-world data from the US Food and Drug Administration Adverse Event Reporting System (FAERS) to investigate adverse events (AEs) associated with ripretinib.MethodsAdverse event reports (AERs) related to ripretinib were extracted from FAERS ASCII data spanning from the second quarter of 2020 to the second quarter of 2024. Following standardization, various disproportionality analyses, including the reporting odds ratio (ROR), proportional reporting ratio (PRR), bayesian confidence propagation neural network (BCPNN), and empirical bayes geometric mean (EBGM), were employed to identify potential safety signals linked to ripretinib. The data provided by medical professionals underwent sensitivity analysis to assess the robustness of the results.ResultsA total of 3,105 ripretinib-related AERs were identified, categorized into 22 system organ classes (SOCs) and 84 preferred terms (PTs). Common AEs, such as alopecia, constipation, and muscle spasms, were consistent with the drug label and clinical trial findings. Notably, the risk of skin cancer associated with ripretinib was further elucidated. Additionally, new signals, including liver abscess and prostatomegaly, were detected. Despite their lower frequency, these signals demonstrated significant strength. A substantial proportion of adverse reactions (n = 322, 39.80%) occurred within the first month of treatment, although a smaller fraction emerged after one year. The sensitivity analysis revealed that most PTs related to skin and subcutaneous tissue maintained high signal values, with 8 cases of skin squamous cell carcinoma-related AEs still reported.ConclusionThe findings of this study align with established drug guidance and uncover new adverse event signals for ripretinib, thereby enhancing clinical monitoring and facilitating risk identification.