{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T05:57:25Z","timestamp":1790575045281,"version":"4.1.0"},"reference-count":42,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T00:00:00Z","timestamp":1790553600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Drug Saf. Regul."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Prescription opioids are essential for pain management but have been increasingly linked to cardiovascular adverse events. Understanding these risks is critical for informed prescribing opioid drugs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Approximately 18 million adverse event reports from 2004 Quarter 1 to 2024 Quarter 3 in the U.S. FDA Adverse Event Reporting System (FAERS) were downloaded and processed. RxNorm was used to normalize prescription opioid drug names. Empirical Bayes Geometric Mean analysis was then performed to detect signals of disproportionate reporting. Cardiovascular-related Preferred Terms from MedDRA were applied to identify drug-adverse event pairs. Unsupervised machine learning approaches, such as network analysis, hierarchical clustering, and Pearson correlation analysis were employed to study cardiovascular risk relationships among the prescription opioids. R code for the correlation analysis was generated with the assistance of a large language model (Claude, Anthropic) and verified by the authors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 79,620 prescription opioid-cardiovascular adverse event pairs were identified, comprising 15 groups of FDA-approved prescription opioids and 291 unique cardiovascular adverse events. Fentanyl and oxycodone were the top two contributing drugs, while hypertension, hypotension, cardiac arrest, cardio-respiratory arrest, and tachycardia were the top five cardiovascular adverse events. The top 10 cardiovascular adverse events for each of the 15 groups of opioids were characterized. Network analysis and hierarchical clustering revealed close associations among fentanyl-remifentanil-sufentanil-morphine, hydrocodone-tramadol, and buprenorphine-oxymorphone-pentazocine based on shared cardiovascular adverse event profiles. The AI-assisted Pearson correlation heatmap further quantified pairwise profile similarities, identifying exceptionally strong correlations between pentazocine and buprenorphine (0.96), oxymorphone and methadone (r = 0.92), and between remifentanil and sufentanil (r = 0.91), and confirming the fentanyl-remifentanil-sufentanil-morphine cluster as the most internally coherent cardiovascular risk profiling group.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study provides the first large-scale, cardiovascular adverse events-focused pharmacovigilance analysis across FDA-approved prescription opioids using over two\u00a0decades of FAERS data. The findings highlight the need for targeted cardiovascular risk assessment and monitoring, and support more refined prescribing practices and opioid safety labeling. The results showed that the integration of traditional Empirical Bayes signal detection with unsupervised machine learning may enable scalable pharmacovigilance analyses and uncover latent cardiovascular risk patterns that may be masked in broader adverse event assessments.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fdsfr.2026.1898403","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T05:45:48Z","timestamp":1790574348000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Two decades of prescription opioid-related cardiovascular safety signals: a FAERS-based pharmacovigilance study using machine learning"],"prefix":"10.3389","volume":"6","author":[{"given":"Li","family":"Ma","sequence":"first","affiliation":[{"name":"Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. FDA","place":["Jefferson, AR, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johnna","family":"Berryhill","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, Arkansas State University","place":["Jonesboro, AR, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John","family":"Ihrie","sequence":"additional","affiliation":[{"name":"Division of Surveillance and Data Integration, Office of Surveillance Strategy and Risk Prioritization, Human Foods Program, U.S. FDA","place":["College Park, MD, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ru","family":"Chen","sequence":"additional","affiliation":[{"name":"Office of New Drugs, Center for Drug Evaluation and Research, U.S. FDA","place":["Silver Spring, MD, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Beverly","family":"Lyn-Cook","sequence":"additional","affiliation":[{"name":"Division of Biochemistry and Toxicity, National Center for Toxicological Research, U.S. FDA","place":["Jefferson, AR, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul","family":"Rogers","sequence":"additional","affiliation":[{"name":"Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. FDA","place":["Jefferson, AR, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weigong","family":"Ge","sequence":"additional","affiliation":[{"name":"Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. FDA","place":["Jefferson, AR, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ningning","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Information Science, University of Arkansas at Little Rock","place":["Little Rock, AR, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huixiao","family":"Hong","sequence":"additional","affiliation":[{"name":"Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. FDA","place":["Jefferson, AR, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weida","family":"Tong","sequence":"additional","affiliation":[{"name":"Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. FDA","place":["Jefferson, AR, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen","family":"Zou","sequence":"additional","affiliation":[{"name":"Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. FDA","place":["Jefferson, AR, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,28]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"6752","DOI":"10.3390\/jcm14196752","article-title":"Artificial intelligence in predictive healthcare: a systematic review","volume":"14","author":"Al-Nafjan","year":"2025","journal-title":"J. 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