ArticleDrug safety2020
Towards Automating Adverse Event Review: A Prediction Model for Case Report Utility.
Article in Drug safety, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Charting and Sidestepping the Pitfalls of Disproportionality Analysis.Drug safety · 2026Review
- Recommendations to Enable Broader Use of Real-World Evidence to Inform Decision-Making Throughout Pharmacovigilance Signal Management.Pharmacoepidemiology and drug safety · 2025Review
- An Evaluation of Duplicate Adverse Event Reports Characteristics in the Food and Drug Administration Adverse Event Reporting System.Drug safety · 2025Article
- FDA Adverse Event Reporting System (FAERS) Essentials: A Guide to Understanding, Applying, and Interpreting Adverse Event Data Reported to FAERS.Clinical pharmacology and therapeutics · 2025Article
- Long-term hepatobiliary disorder associated with trastuzumab emtansine pharmacovigilance study using the FDA Adverse Event Reporting System database.Scientific reports · 2024Article
- Trust but Verify: Lessons Learned for the Application of AI to Case-Based Clinical Decision-Making From Postmarketing Drug Safety Assessment at the US Food and Drug Administration.Journal of medical Internet research · 2024Article
- Article
- BERT based natural language processing for triage of adverse drug reaction reports shows close to human-level performance.PLOS digital health · 2023Article
- Article
- Signals of Adverse Drug Reactions Communicated by Pharmacovigilance Stakeholders: A Scoping Review of the Global Literature.Drug safety · 2023Article
- Natural language processing for automated triage and prioritization of individual case safety reports for case-by-case assessment.Frontiers in drug safety and regulation · 2023Article
- Leveraging Machine Learning to Facilitate Individual Case Causality Assessment of Adverse Drug Reactions.Drug safety · 2022Article
- "Artificial Intelligence" for Pharmacovigilance: Ready for Prime Time?Drug safety · 2022Article
- Clinical Information Systems Research in the Pandemic Year 2020.Yearbook of medical informatics · 2021Review
- Article
- An Evaluation of Postmarketing Reports with an Outcome of Death in the US FDA Adverse Event Reporting System.Drug safety · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe rapidly expanding size of the Food and Drug Administration's (FDA) Adverse Event Reporting System database requires modernized pharmacovigilance practices. Techniques to systematically identify high utility individual case safety reports (ICSRs) will support safety signal management.
objectivesThe aim of this study was to develop and validate a model predictive of an ICSR's pharmacovigilance utility (PVU).
methodsPVU was operationalized as an ICSR's inclusion in an FDA-authored pharmacovigilance review's case series supporting a recommendation to modify product labeling. Multivariable logistic regression models were used to examine the association between PVU and ICSR features. The best performing model was selected for bootstrapping validation. As a sensitivity analysis, we evaluated the model's performance across subgroups of safety issues.
resultsWe identified 10,381 ICSRs evaluated in 69 pharmacovigilance reviews, of which 2115 ICSRs were included in a case series. The strongest predictors of ICSR inclusion were reporting of a designated medical event (odds ratio (OR) 1.93, 95% CI 1.54-2.43) and positive dechallenge (OR 1.67, 95% CI 1.50-1.87). The strongest predictors of ICSR exclusion were death reported as the only outcome (OR 2.72, 95% CI 1.76-4.35), more than three suspect products (OR 2.69, 95% CI 2.23-3.24), and > 15 preferred terms reported (OR 2.69, 95% CI 1.90-3.82). The validated model showed modest discriminative ability (C-statistic of 0.71). Our sensitivity analysis demonstrated heterogeneity in model performance by safety issue (C-statistic range 0.58-0.74).
conclusionsOur model demonstrated the feasibility of developing a tool predictive of ICSR utility. The model's modest discriminative ability highlights opportunities for further enhancement and suggests algorithms tailored to safety issues may be beneficial.
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