Polypharmacy in Cardiovascular: When More Drugs Mean More Risks

Abstract

Background: Drug–drug interactions (DDIs) are a major concern in cardiovascular polypharmacy. Tools like Lexicomp categorize interactions (A–X), but these are often misinterpreted — overestimating risk can lead to unnecessary therapy changes, while underestimating risk may cause harm. This study aimed to quantify the burden and predictors of clinically significant DDIs in cardiovascular patients, emphasizing the need for clinical judgment beyond rigid categorization for balanced prescribing.

Methods: A retrospective analysis was conducted on 395 cardiovascular discharge prescriptions. The highest-risk interaction per prescription was identified, and total interactions were also quantified. Associations between polypharmacy categories, comorbidities, length of hospital stay, and highest-risk interaction (C/D/X) were analyzed using chi-square, correlation, and regression models using Jamovi software.

Results: In this cohort of 395 prescriptions, most flagged interactions were Category C (n = 148, 37.5%), requiring monitoring rather than avoidance, while Categories D (n = 20, 5.1%) and X (n = 11, 2.8%) were less frequent. This shows that the majority of interactions were not absolute contraindications but signals for clinical vigilance. In regression analysis, the number of comorbidities (β = 0.28, p = 0.01) and the length of hospital stay (β = 0.31, p = 0.004) were independent predictors of high-risk DDIs. Notably, the likelihood of clinically significant interactions increased with polypharmacy category, comorbidity burden, and length of stay. Correlation analysis confirmed a positive moderate relationship between the total number of prescribed drugs and drug interaction counts (Spearman’s r = 0.42, p < 0.001), supporting the stepwise rise in DDI risk with higher drug counts.

Conclusion: Polypharmacy in cardiovascular patients is strongly linked to DDIs, but most interactions (Category C/D) require monitoring, not avoidance. Risk is multifactorial — shaped by drug count, comorbidities, and hospital stay — underscoring the need for clinical judgment in interpreting DDI alerts.