What question did this study set out to answer?

The aim was to analyze antimicrobial resistance trends over three years and develop a Resistance Index.

May 11, 2026Infection and Drug Resistance0 citationsOpen Access

Hospital-Based Surveillance and Resistance Index Analysis of Antimicrobial Resistance Trends: A Three-Year Study from a Tertiary Hospital in Iran (2021–2023)

EMElnaz Vafadar MoradiMashhad University of Medical Sciences FHFaeze Sadat HoseiniMashhad University of Medical Sciences SMS S MousaviMashhad University of Medical Sciences

Key Points

The aim was to analyze antimicrobial resistance trends over three years and develop a Resistance Index.
Conducted a retrospective analysis of culture-positive bacterial isolates from 2021 to 2023 at a tertiary hospital.
Performed antimicrobial susceptibility testing following CLSI standards and calculated annual resistance rates for six pathogens.
Utilized linear regression to model temporal changes in resistance rates for various antibiotics.
Among 38,514 specimens, 3109 (8.1%) yielded bacterial growth with E. coli resistance declining from 45.0% to 29.7% (p=0.02).
A. baumannii resistance increased from 17.6% to 26.8% (p=0.03), with significant upward trends in resistance slopes for amikacin and ciprofloxacin (both p<0.001).
High resistance levels were noted in A. baumannii (>90%) and P. aeruginosa (>70%), while declines in resistance were seen for S. aureus and Enterobacter spp.

Abstract

Background: Antimicrobial resistance (AMR) poses an escalating global health crisis, yet institution-level temporal analyses that integrate both resistance trends and composite indices remain scarce. Methods: We performed a three-year retrospective analysis (2021– 2023) of all culture-positive bacterial isolates from a tertiary referral hospital in Iran. Antimicrobial susceptibility testing (AST) followed CLSI M02/M07/M100 standards. Annual resistance rates (%R) were calculated for six priority pathogens, and linear regression was used to model temporal changes (slopes, p-values). A composite Resistance Index (RI) was derived to capture cumulative resistance pressure. Results: Among 38,514 specimens, 3109 (8.1%) yielded bacterial growth. E. coli declined significantly (45.0%→ 29.7%, p=0.02), while A. baumannii increased (17.6%→ 26.8%, p=0.03). Regression analysis revealed pronounced upward resistance slopes in A. baumannii (eg, amikacin +8.6%/year, p 90%) and high levels in P. aeruginosa (> 70%), with moderate but variable indices in E. coli and K. pneumoniae (50– 70%). Conclusion: This single-center study demonstrates shifting AMR epidemiology with A. baumannii emerging as the dominant multidrug-resistant threat, sustained high resistance in P. aeruginosa , and encouraging declines in certain resistance patterns among E. coli, K. pneumoniae , and S. aureus . By integrating slope-based trends with a composite RI, we provide a scalable framework to convert routine antibiogram data into actionable antimicrobial stewardship programs (ASPs) and infection prevention strategies. Keywords: antimicrobial resistance, temporal trends, resistance index, hospital stewardship, infection prevention and control

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