Polypharmacy and Drug–Drug Interaction Architecture in Hospitalized Cardiovascular Patients: Insights from Real-World Analysis

<b>Background:</b> Cardiovascular polypharmacy inherently amplifies the risk of drug-drug interactions (DDIs), yet most studies remain limited to isolated drug pairs or predefined high-risk classes, without mapping the systemic architecture through which interactions accumulate. <b>Objectives:</b> To characterize the burden, severity, and network structure of potential DDIs in a real-world cohort of hospitalized cardiovascular patients using interaction profiling combined with graph-theoretic network analysis. <b>Methods:</b> This retrospective observational study included 250 hospitalized cardiovascular patients. All home medications at admission were analyzed using the Drugs.com interaction database, and a drug interaction network was constructed to compute topological metrics (i.e., degree, betweenness, and eigenvector centrality). <b>Results:</b> Polypharmacy was highly prevalent, with a mean of 7.7 drugs per patient, and 98.4% of patients exhibited at least one potential DDI. A total of 4353 interactions were identified, of which 12.1% were classified as major, and 35.2% of patients presented high-risk profiles with ≥3 major interactions. Interaction burden showed a strong correlation with medication count (r = 0.929). Network analysis revealed a limited cluster of hub medications, particularly pantoprazole, furosemide, spironolactone, amiodarone, and perindopril, that disproportionately governed both interaction density and high-severity risk. <b>Conclusions:</b> These findings move beyond conventional pairwise screening by demonstrating how interaction risk propagates through interconnected therapeutic networks. The study supports the integration of hub-focused deprescribing, targeted monitoring strategies, and network-informed clinical decision support to mitigate DDI risk in cardiovascular polypharmacy. Future studies should link potential DDIs to clinical outcomes and validate network-based prediction models in prospective settings.