INTEGRATED ANTIBIOTIC SAFETY ASSESSMENT MODEL: CAUSALITY, SEVERITY, PREVENTABILITY AND SIGNAL DETECTION

Antibiotics are essential for the management of bacterial infections; however, their widespread and often inappropriate use is associated with adverse drug reactions (ADRs), medication errors, drug interactions, and other preventable harms. Conventional antibiotic safety assessment commonly evaluates causality, severity, preventability, and pharmacovigilance signals as separate domains, potentially limiting comprehensive interpretation and translation of safety findings into clinical action. This review proposes the Integrated Antibiotic Safety Assessment Model (IASAM), a multidimensional framework that combines these complementary domains within a unified and iterative assessment pathway. Causality assessment evaluates the likelihood that an antibiotic is responsible for an adverse event, severity assessment determines its clinical consequences, preventability assessment identifies potentially avoidable factors across the medication-use process, and signal detection examines patterns that may indicate emerging or disproportionate safety concerns at the population level. By integrating individual case assessment with population-level pharmacovigilance, IASAM aims to improve the identification, prioritization, and management of clinically significant and preventable antibiotic-related harm. The model provides a structured feedback loop linking safety assessment with risk mitigation, antimicrobial stewardship, quality improvement, and regulatory decision-making. Its potential applications extend across hospitals, pharmacovigilance centres, formulary committees, and public health programmes, including settings such as the Pharmacovigilance Programme of India. Emerging real-world data and artificial intelligence may further strengthen safety surveillance, although expert clinical judgment remains essential. IASAM represents a conceptual approach to bridge pharmacovigilance and antimicrobial stewardship; however, prospective validation is required to establish its reliability, reproducibility, clinical utility, and applicability across diverse healthcare settings and antibiotic classes.