Year: 2025
Type: preprint
Abstract: Introduction: Pacemakers are high-risk medical devices implanted in over 700,000 patients annually worldwide, yet a significant proportion of patients experience complications. There is a lack of research systematically identifying and analyzing the broader spectrum of associated clinical adverse events through post-market surveillance data. Objective: This study aimed to identify adverse events associated with pacemakers and evaluate their statistical significance as safety signals using disproportionality analysis. Methods: A retrospective analysis was conducted on the FDA MAUDE(Manufacturer and User Facility Device Experience) database reports (January 2020 – December 2024). Disproportionality was assessed via the Proportional Reporting Ratio (PRR) and the Reporting Odds Ratio (ROR), and the derived signals were visualized on a two-dimensional scatter plot partitioned into quadrants delimited by the median of log10(count) on the x-axis and by the reference line log2(PRR)=1 on the y-axis. Results: Among 2900 reports analyzed, 16 and 19 signals reached significance for PRR and ROR, respectively. And quadrant analysis stratified the signals into four distinct groups, which were prioritized into three risk tiers: (1) High-priority signals (upper-right quadrant: high frequency and strength; e.g., asystole); (2) Potential critical signals (upper-left quadrant: low frequency but high strength; e.g., coma); and (3) Lower-priority signals (both lower quadrants: high frequency with low strength, or low frequency and strength; e.g., discomfort and vegetation, respectively), which likely represent background noise or very rare benign events. Conclusion: The four-quadrant analysis provides a mechanistic hierarchy of risk for pacemaker adverse events, moving beyond statistical significance to offer a practical, risk-based prioritization strategy that is vital for informed regulatory oversight and proactive patient safety monitoring.
Authors: Die FU, Ling SU
Institution: Jinan University
Language: English
Subfield: Toxicology
Domain: Life Sciences
Open Access status: gold
OpenAlex ID: W4413851431