Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia · 2026
Risk factors for drug-related ischemic stroke: an analysis of the FDA adverse event reporting system (FAERS).
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Cao Weili, Zhu Bo, Jia Xiaotao, Gu Naibing, Zhang Quanzeng, Wang Beilei, Shi Miao, Liu Zhiqin, Di Zhengli
Аннотация
BACKGROUND: Ischemic stroke (IS) is a leading cause of global morbidity and mortality. While certain drugs are implicated in IS, systematic real-world analyses across all drug classes are scarce.
OBJECTIVE: To evaluate drug-IS associations using the FDA Adverse Event Reporting System (FAERS) database, identify pharmacological risk factors, and explore temporal onset patterns.
METHODS: A retrospective pharmacovigilance study was conducted using FAERS data (Q1 2004-Q1 2025). IS cases were identified using Medical Dictionary for Regulatory Activities (MedDRA) Preferred Terms. Disproportionality analyses employed four algorithms (Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), Multi-item Gamma Poisson Shrinker (MGPS). Drugs with >100 cases, ROR_05 > 1, and False Discovery Rate (FDR)-p < 0.01 were further analyzed using Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate logistic regression to identify independent risk factors. Time-to-onset (TTO) was assessed.
RESULTS: Among 59,869 drug-related IS reports, disproportionality analysis identified 66 significant drugs across 22 categories, most frequently antineoplastics, anticoagulants, and antiplatelets. Multivariate regression confirmed 64 independent risk factors (7 demographic, 57 drugs), including Andexanet alfa (Adjusted Odds Ratio (aOR) = 138.80), Rofecoxib (27.45), and combined oral contraceptives. The prediction model achieved an Area Under the Curve (AUC) of 0.770. The median TTO was 77 days (IQR: 10-365).
CONCLUSIONS: This large-scale FAERS analysis systematically characterized drug-IS associations, identifying multiple strong and independent pharmacological risk signals and revealing temporal patterns. The findings provide real-world evidence to guide clinical decision-making and targeted pharmacovigilance.
OBJECTIVE: To evaluate drug-IS associations using the FDA Adverse Event Reporting System (FAERS) database, identify pharmacological risk factors, and explore temporal onset patterns.
METHODS: A retrospective pharmacovigilance study was conducted using FAERS data (Q1 2004-Q1 2025). IS cases were identified using Medical Dictionary for Regulatory Activities (MedDRA) Preferred Terms. Disproportionality analyses employed four algorithms (Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), Multi-item Gamma Poisson Shrinker (MGPS). Drugs with >100 cases, ROR_05 > 1, and False Discovery Rate (FDR)-p < 0.01 were further analyzed using Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate logistic regression to identify independent risk factors. Time-to-onset (TTO) was assessed.
RESULTS: Among 59,869 drug-related IS reports, disproportionality analysis identified 66 significant drugs across 22 categories, most frequently antineoplastics, anticoagulants, and antiplatelets. Multivariate regression confirmed 64 independent risk factors (7 demographic, 57 drugs), including Andexanet alfa (Adjusted Odds Ratio (aOR) = 138.80), Rofecoxib (27.45), and combined oral contraceptives. The prediction model achieved an Area Under the Curve (AUC) of 0.770. The median TTO was 77 days (IQR: 10-365).
CONCLUSIONS: This large-scale FAERS analysis systematically characterized drug-IS associations, identifying multiple strong and independent pharmacological risk signals and revealing temporal patterns. The findings provide real-world evidence to guide clinical decision-making and targeted pharmacovigilance.