Evidence map›Paper›PMID 41704838›Full record

ArticleExploratory research in clinical and social pharmacy2026

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

Abdullah Umer, Adam Khan Mohmand, Naimal Mouzam Khan, Ainan Arshad, Sher M Sethi

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Article in Exploratory research in clinical and social pharmacy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Abdullah UmerMedical College, Aga Khan University, Karachi, Pakistan.
Adam Khan MohmandMedical College, Aga Khan University, Karachi, Pakistan.
Naimal Mouzam KhanMedical College, Aga Khan University, Karachi, Pakistan.
Ainan ArshadDepartment of Medicine, Aga Khan University Hospital, Karachi, Pakistan.
Sher M SethiDepartment of Medicine, Aga Khan University Hospital, Karachi, Pakistan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Post-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. Methods: We 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. Results: PRR 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 Conclusion: This 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.

Indexed as

Adverse drug reactionsDisproportionality analysisDrug safetyPharmacovigilanceSignal detection algorithmsSouth Asia

Identifiers

PMID41704838
PMCPMC12907660

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