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A systematic analysis and data mining of opioid-related adverse events submitted to the FAERS database.
Le, Huyen; Hong, Huixiao; Ge, Weigong; Francis, Henry; Lyn-Cook, Beverly; Hwang, Yi-Ting; Rogers, Paul; Tong, Weida; Zou, Wen.
Afiliación
  • Le H; Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
  • Hong H; Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
  • Ge W; Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
  • Francis H; Retired, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD 20993, USA.
  • Lyn-Cook B; Division of Biochemical Toxicology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
  • Hwang YT; Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
  • Rogers P; Department of Statistics, National Taipei University, New Taipei City 23148, Taiwan.
  • Tong W; Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
  • Zou W; Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
Exp Biol Med (Maywood) ; 248(21): 1944-1951, 2023 11.
Article en En | MEDLINE | ID: mdl-38158803
ABSTRACT
The opioid epidemic has become a serious national crisis in the United States. An indepth systematic analysis of opioid-related adverse events (AEs) can clarify the risks presented by opioid exposure, as well as the individual risk profiles of specific opioid drugs and the potential relationships among the opioids. In this study, 92 opioids were identified from the list of all Food and Drug Administration (FDA)-approved drugs, annotated by RxNorm and were classified into 13 opioid groups buprenorphine, codeine, dihydrocodeine, fentanyl, hydrocodone, hydromorphone, meperidine, methadone, morphine, oxycodone, oxymorphone, tapentadol, and tramadol. A total of 14,970,399 AE reports were retrieved and downloaded from the FDA Adverse Events Reporting System (FAERS) from 2004, Quarter 1 to 2020, Quarter 3. After data processing, Empirical Bayes Geometric Mean (EBGM) was then applied which identified 3317 pairs of potential risk signals within the 13 opioid groups. Based on these potential safety signals, a comparative analysis was pursued to provide a global overview of opioid-related AEs for all 13 groups of FDA-approved prescription opioids. The top 10 most reported AEs for each opioid class were then presented. Both network analysis and hierarchical clustering analysis were conducted to further explore the relationship between opioids. Results from the network analysis revealed a close association among fentanyl, oxycodone, hydrocodone, and hydromorphone, which shared more than 22 AEs. In addition, much less commonly reported AEs were shared among dihydrocodeine, meperidine, oxymorphone, and tapentadol. On the contrary, the hierarchical clustering analysis further categorized the 13 opioid classes into two groups by comparing the full profiles of presence/absence of AEs. The results of network analysis and hierarchical clustering analysis were not only consistent and cross-validated each other but also provided a better and deeper understanding of the associations and relationships between the 13 opioid groups with respect to their adverse effect profiles.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Oxicodona / Analgésicos Opioides Tipo de estudio: Systematic_reviews País/Región como asunto: America do norte Idioma: En Revista: Exp Biol Med (Maywood) Asunto de la revista: BIOLOGIA / FISIOLOGIA / MEDICINA Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Oxicodona / Analgésicos Opioides Tipo de estudio: Systematic_reviews País/Región como asunto: America do norte Idioma: En Revista: Exp Biol Med (Maywood) Asunto de la revista: BIOLOGIA / FISIOLOGIA / MEDICINA Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Suiza