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Class-level drug safety surveillance in hospital practice: A pharmacovigilance analysis of 84 therapeutic subclasses from a South Asian tertiary care center

Research output: Contribution to journalArticlepeer-review

Abstract

ObjectivePost-marketing drug surveillance is critical for strengthening pharmacovigilance in low- and middle-income settings. This study provides the first class-level pharmacovigilance analysis from a South Asian tertiary hospital, generating safety signals across 84 drug subclasses and evaluating concordance between Proportional Reporting Ratio (PRR) and Reporting Odds Ratio (ROR) methods.MethodsWe conducted a retrospective cross-sectional analysis of electronic health records (2018–2022), comprising 718,088 drug administration events. Adverse drug reactions (ADRs) were identified using ICD-9-CM/ICD-10-CM codes. Disproportionality analyses using PRR and ROR were applied to each subclass.ResultsPRR identified positive signals in 5 subclasses, while ROR identified 7. The seven high-risk subclasses included antitubercular agents (PRR 3.24; ROR 4.14), anesthetic muscle relaxants (2.44; 2.84), aminoglycosides (2.24; 2.55), immunosuppressants (2.19; 2.47), neurologic sedatives (2.06; 2.29), antineoplastic cytotoxic agents (1.95; 2.15), and general anesthetic agents (1.88; 2.06). Concordance between PRR and ROR was high (96.4% agreement; κ ≈ 0.80; McNemar p = 0.48). Signal strength correlated inversely with overall drug exposure (ρ = −0.34).ConclusionThis analysis establishes baseline class-level risk estimates in a developing pharmacovigilance environment, identifies disproportionately high-risk drug classes, and supports the robustness of PRR and ROR as complementary screening tools for regional drug safety monitoring.

Original languageEnglish (US)
Article number100711
JournalExploratory Research in Clinical and Social Pharmacy
Volume21
DOIs
Publication statusPublished - Mar 2026

Keywords

  • Adverse drug reactions
  • Disproportionality analysis
  • Drug safety
  • Pharmacovigilance
  • Signal detection algorithms
  • South Asia

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