Drugs associated the most with Retroperitoneal Fibrosis: assessment of the 1969-2025 Food and Drug Administration (FDA) pharmacovigilance database

Introduction and Objectives Retroperitoneal fibrosis (RPF) is a rare fibroinflammatory disorder characterized by the development of fibrotic tissue around the abdominal aorta, often leading to ureteral entrapment and renal dysfunction. Several pharmacological agents have been implicated as potential triggers. Despite scattered case reports and limited series, comprehensive quantitative assessments of drug-related RPF signals remain scarce. This study aimed to identify and quantify signals of disproportionate reporting of RPF associated with specific medications using data from a large pharmacovigilance database. Methods The FDA Adverse Event Reporting System (FAERS) was queried from 1969 to 2025 to identify RPF-related adverse event reports. Drugs associated with more than twenty RPF reports were included in a disproportionality analysis; this threshold was arbitrarily defined to focus on medications with a relatively higher volume of reports. This selection aimed to ensure adequate statistical power and reduce noise from underreported associations. For each, the Reporting Odds Ratio (ROR), 95% Confidence Intervals (CI), Proportional Reporting Ratio (PRR), chi-square values, and p-values were calculated. A signal was considered statistically significant when PRR > 2, chi-square > 4, and p < 0.05. The total number of reports in the FAERS database was 30,668,520. Results A total of 1,013 RPF cases were identified. Of these, 392 (38.7%) were linked to twelve drugs, each associated with more than twenty RPF reports. Methysergide showed the strongest association (ROR: 5332.28; 95% CI: 4292.96–6623.21; PRR: 4604.65; chi-square: 429546.30; p < 0.0001), followed by pergolide (ROR: 923.85; 95% CI: 686.60–1243.09; PRR: 897.76; chi-square: 40148.64; p < 0.0001) and bromocriptine (ROR: 311.30; 95% CI: 224.78–431.13; PRR: 308.26; chi-square: 11201.91; p < 0.0001). Several widely used medications such as bisoprolol, atenolol, and metformin also showed significant, though less pronounced, associations (p < 0.0001). The remaining drugs included etanercept, prednisone, rituximab, propranolol, and cyclophosphamide, all of which exhibited significant levels of disproportionate reporting (p < 0.0001). Conclusions This pharmacovigilance analysis identified multiple medications, spanning different therapeutic classes, that demonstrated a disproportionate association with reports of RPF. Notably, some drugs with well-established safety profiles also emerged as potential signals, warranting increased clinical awareness. While this analysis reveals pharmacovigilance signals suggestive of drug-induced RPF, the observational nature of FAERS data limits causal inference. These findings underscore the need for further clinical research and mechanistic studies to better understand the etiopathogenic role of these agents in RPF development.