将FDA不良事件报告系统(FAERS)描述为一个改进模式发现的网络。

IF 3.8 2区 医学 Q1 PHARMACOLOGY & PHARMACY
Raechel Davis, Oanh Dang, Suranjan De, Robert Ball
{"title":"将FDA不良事件报告系统(FAERS)描述为一个改进模式发现的网络。","authors":"Raechel Davis, Oanh Dang, Suranjan De, Robert Ball","doi":"10.1007/s40264-025-01609-7","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>In drug-safety monitoring systems, adverse events (AEs) associated with the use of medical products often consist of complex patterns of clinical events. Network analysis (NA) was used for pattern recognition and characterizing the Vaccine Adverse Event Reporting System (VAERS), but limited applications of NA to the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) left its network description incomplete.</p><p><strong>Methods: </strong>In this analysis, the network properties of FAERS were characterized and leveraged to facilitate pattern discovery. Reported AE information in FAERS is represented using preferred terms (PTs) in Medical Dictionary for Regulatory Activities terminology. The FAERS subsets were analyzed with drugs and PTs as nodes and interconnections as edges. Global characteristics, like the scale-free nature of the distribution, were examined to explore theoretical and structural considerations. Metrics that assess connectivity and edge weighting algorithms based on report co-occurrence or clustering were applied.</p><p><strong>Results: </strong>Serious AE reports from 2016 to 2023 (2,062,099) were represented as a network of 20,965 nodes (16,847 PTs and 4116 drugs) with more than four million interconnections. Characteristics of FAERS subnetworks were determined with heavy-tailed degree distributions, high local clustering, and low diameters. Complexities related to structural and evolutionary characteristics were revealed as the log-normal model fits the degree distribution better than the power law.</p><p><strong>Conclusions: </strong>Network-based techniques identified clinically relevant patterns and clustering patterns representative of known adverse drug reactions. Comparisons to VAERS reveal similarities in networks of AE reporting systems. This initial systematic application of NA to FAERS describes the overall network characteristics of the FAERS database and provides insight into the use of network applications in drug safety research.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":""},"PeriodicalIF":3.8000,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Characterizing the FDA Adverse Event Reporting System (FAERS) as a Network to Improve Pattern Discovery.\",\"authors\":\"Raechel Davis, Oanh Dang, Suranjan De, Robert Ball\",\"doi\":\"10.1007/s40264-025-01609-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>In drug-safety monitoring systems, adverse events (AEs) associated with the use of medical products often consist of complex patterns of clinical events. Network analysis (NA) was used for pattern recognition and characterizing the Vaccine Adverse Event Reporting System (VAERS), but limited applications of NA to the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) left its network description incomplete.</p><p><strong>Methods: </strong>In this analysis, the network properties of FAERS were characterized and leveraged to facilitate pattern discovery. Reported AE information in FAERS is represented using preferred terms (PTs) in Medical Dictionary for Regulatory Activities terminology. The FAERS subsets were analyzed with drugs and PTs as nodes and interconnections as edges. Global characteristics, like the scale-free nature of the distribution, were examined to explore theoretical and structural considerations. Metrics that assess connectivity and edge weighting algorithms based on report co-occurrence or clustering were applied.</p><p><strong>Results: </strong>Serious AE reports from 2016 to 2023 (2,062,099) were represented as a network of 20,965 nodes (16,847 PTs and 4116 drugs) with more than four million interconnections. Characteristics of FAERS subnetworks were determined with heavy-tailed degree distributions, high local clustering, and low diameters. Complexities related to structural and evolutionary characteristics were revealed as the log-normal model fits the degree distribution better than the power law.</p><p><strong>Conclusions: </strong>Network-based techniques identified clinically relevant patterns and clustering patterns representative of known adverse drug reactions. Comparisons to VAERS reveal similarities in networks of AE reporting systems. This initial systematic application of NA to FAERS describes the overall network characteristics of the FAERS database and provides insight into the use of network applications in drug safety research.</p>\",\"PeriodicalId\":11382,\"journal\":{\"name\":\"Drug Safety\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2025-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Drug Safety\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s40264-025-01609-7\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PHARMACOLOGY & PHARMACY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Drug Safety","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s40264-025-01609-7","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 0

摘要

在药物安全监测系统中,与医疗产品使用相关的不良事件(ae)通常由复杂的临床事件模式组成。网络分析(NA)用于模式识别和描述疫苗不良事件报告系统(VAERS),但NA在美国食品和药物管理局(FDA)不良事件报告系统(FAERS)中的应用有限,导致其网络描述不完整。方法:在本分析中,FAERS的网络特性被表征和利用,以促进模式发现。FAERS中报告的AE信息使用监管活动术语医学词典中的首选术语(PTs)表示。FAERS子集以药物和PTs为节点,以互联为边进行分析。研究了全球特征,如分布的无标度性质,以探索理论和结构方面的考虑。应用了基于报告共现性或聚类的评估连通性和边缘加权算法的指标。结果:2016年至2023年的严重AE报告(2,062,099)被表示为一个由20,965个节点(16,847个PTs和4116种药物)组成的网络,有超过400万个互连。FAERS子网络具有重尾度分布、高局部聚类和低直径的特征。由于对数正态模型比幂律模型更符合度分布,揭示了与结构和进化特征相关的复杂性。结论:基于网络的技术确定了临床相关模式和聚类模式,代表已知的药物不良反应。与VAERS的比较揭示了AE报告系统网络的相似之处。NA对FAERS的初步系统应用描述了FAERS数据库的整体网络特征,并提供了在药物安全研究中使用网络应用的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Characterizing the FDA Adverse Event Reporting System (FAERS) as a Network to Improve Pattern Discovery.

Introduction: In drug-safety monitoring systems, adverse events (AEs) associated with the use of medical products often consist of complex patterns of clinical events. Network analysis (NA) was used for pattern recognition and characterizing the Vaccine Adverse Event Reporting System (VAERS), but limited applications of NA to the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) left its network description incomplete.

Methods: In this analysis, the network properties of FAERS were characterized and leveraged to facilitate pattern discovery. Reported AE information in FAERS is represented using preferred terms (PTs) in Medical Dictionary for Regulatory Activities terminology. The FAERS subsets were analyzed with drugs and PTs as nodes and interconnections as edges. Global characteristics, like the scale-free nature of the distribution, were examined to explore theoretical and structural considerations. Metrics that assess connectivity and edge weighting algorithms based on report co-occurrence or clustering were applied.

Results: Serious AE reports from 2016 to 2023 (2,062,099) were represented as a network of 20,965 nodes (16,847 PTs and 4116 drugs) with more than four million interconnections. Characteristics of FAERS subnetworks were determined with heavy-tailed degree distributions, high local clustering, and low diameters. Complexities related to structural and evolutionary characteristics were revealed as the log-normal model fits the degree distribution better than the power law.

Conclusions: Network-based techniques identified clinically relevant patterns and clustering patterns representative of known adverse drug reactions. Comparisons to VAERS reveal similarities in networks of AE reporting systems. This initial systematic application of NA to FAERS describes the overall network characteristics of the FAERS database and provides insight into the use of network applications in drug safety research.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Drug Safety
Drug Safety 医学-毒理学
CiteScore
7.60
自引率
7.10%
发文量
112
审稿时长
6-12 weeks
期刊介绍: Drug Safety is the official journal of the International Society of Pharmacovigilance. The journal includes: Overviews of contentious or emerging issues. Comprehensive narrative reviews that provide an authoritative source of information on epidemiology, clinical features, prevention and management of adverse effects of individual drugs and drug classes. In-depth benefit-risk assessment of adverse effect and efficacy data for a drug in a defined therapeutic area. Systematic reviews (with or without meta-analyses) that collate empirical evidence to answer a specific research question, using explicit, systematic methods as outlined by the PRISMA statement. Original research articles reporting the results of well-designed studies in disciplines such as pharmacoepidemiology, pharmacovigilance, pharmacology and toxicology, and pharmacogenomics. Editorials and commentaries on topical issues. Additional digital features (including animated abstracts, video abstracts, slide decks, audio slides, instructional videos, infographics, podcasts and animations) can be published with articles; these are designed to increase the visibility, readership and educational value of the journal’s content. In addition, articles published in Drug Safety Drugs may be accompanied by plain language summaries to assist readers who have some knowledge of, but not in-depth expertise in, the area to understand important medical advances.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信