Table 1_Two decades of prescription opioid-related cardiovascular safety signals: a FAERS-based pharmacovigilance study using machine learning.docx

R code for the correlation analysis was generated with the assistance of a large language model (Claude, Anthropic) and verified by the authors.</p>Results<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.</p>Conclusion<p>This study provides the first large-scale, cardiovascular adverse events-focused pharmacovigilance analysis across FDA-approved prescription opioids using over two decades 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.</p>