Background: In recent years, β-blockers such as metoprolol have been upgraded to first-line antihypertensive drugs. However, metoprolol demonstrates poor prognosis effects on diseases such as stroke. Further clinical application may expand the possibility of its related adverse reactions. Currently, there is a lack of comprehensive research on the overall safety of metoprolol.
Research design and methods: Statistical analysis and signal mining were conducted on adverse event reports related to metoprolol obtained from the FAERS database. Signal mining was conducted using the proportional reporting ratio, the report margin method, the bayesian confidence propagation neural network, and empirical Bayesian geometric mean in the measures of disproportionality to detect potential adverse reaction signals.
Results: The results showed 16,853 reports related to metoprolol use, identifying 506 preferred terms (PTs) covering 23 system organ classes (SOCs). In addition, some new potential adverse reactions appeared among the top 30 PTs ranked by signal strength, such as 'orthostatic intolerance' (IC025 = 3.00), 'trigemino-cardiac reflex' (IC025 = 4.30), 'decorticate posture' (IC025 = 3.34), etc. Notably, there was a strong association between 'suspected suicide' (IC025 = 5.59) and the drug signal.
Conclusions: This study identified unexpected signals of serious adverse reactions, suggesting the importance of continuous post-marketing surveillance of metoprolol to understand its potential risks.
Keywords: FDA Adverse Events Reporting System; Metoprolol; adverse events; disproportionality analysis; pharmacovigilance.