Drug-induced gastric motility disorders: A disproportionality analysis from the FAERS and CVARD databases.

Abstract

Delayed gastric emptying and gastroesophageal reflux represent critical yet frequently underrecognized complications in hospitalized patients, particularly in the context of polypharmacy. While multiple medication classes have been implicated in disrupting gastrointestinal motility, the comprehensive risk spectrum of individual drugs remains poorly characterized. This study aimed to conduct a comprehensive disproportionality analysis to identify drugs associated with delayed gastric emptying and reflux using large-scale pharmacovigilance data. We analyzed adverse event reports from the FDA Adverse Event Reporting System (FAERS; 2004-2025; n > 58 million) and validated findings against the Canada Vigilance Adverse Reaction Online Database (CVARD). Disproportionality analysis was performed using Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), and Bayesian Confidence Propagation Neural Network (BCPNN). Weibull time-to-onset analysis was conducted to characterize temporal patterns of adverse event onset. Among the top 50 drugs screened, 20 demonstrated positive signals across all three algorithms. Glucagon-like peptide-1 (GLP-1) receptor agonists exhibited the strongest associations with gastric motility disorders, with semaglutide showing the highest ROR for impaired gastric emptying (ROR: 80.27; 95% CI: 76.39-84.34), validated in CVARD (ROR: 54.17). Insulin formulations, particularly insulin degludec (ROR: 18.90), bisphosphonates, angiotensin receptor blockers, and trofinetide also demonstrated significant signals. Weibull analysis revealed divergent temporal patterns, ranging from early-onset (trofinetide: median 6.6 days) to late-onset (immunoglobulin G: median 535.1 days). This study identifies a broad spectrum of drug-associated gastric motility disorders with distinct temporal profiles. These findings provide evidence-based priorities for enhanced pharmacovigilance and inform clinical decision-making to mitigate this preventable cause of morbidity.