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Implementation of a Modified AWaRe Guideline and Its Association with Antimicrobial Resistance Patterns in a Tertiary-Care Hospital

A retrospective study in a tertiary-care hospital found that implementing a modified AWaRe guideline was associated with a trend toward reduced antimicrobial resistance rates.

Original authors: Luis Arturo Camacho Silvas, Jorge Horacio Portillo Gallo, María Cecilia Ishida Gutiérrez, Yazmin Andrea Hernández Acosta, Jorge Manuel Sánchez González, Antonio Eugenio Rivera Cisneros, Rafael Franco

Published 2026-06-24

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CC BY 4.0

Original authors: Luis Arturo Camacho Silvas, Jorge Horacio Portillo Gallo, María Cecilia Ishida Gutiérrez, Yazmin Andrea Hernández Acosta, Jorge Manuel Sánchez González, Antonio Eugenio Rivera Cisneros, Rafael Franco Santillán, Carlos Eduardo Cañedo Figueroa

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine a hospital as a busy fortress under constant siege by invisible invaders: bacteria. For years, the fortress guards (doctors) have been using powerful weapons (antibiotics) to fight these invaders. However, the bacteria are smart; they learn to dodge the weapons, becoming "superbugs" that the old weapons can no longer defeat. This is antimicrobial resistance.

To stop this arms race from getting out of control, the World Health Organization (WHO) created a special "Weapon Guide" called AWaRe. Think of this guide as a traffic light system for antibiotics:

Green (Access): Safe, first-choice weapons for common fights.
Yellow (Watch): Stronger weapons that need careful monitoring because bacteria might start resisting them.
Red (Reserve): The "nuclear options," saved only for the most desperate, last-ditch battles.

The Experiment: Updating the Map

The researchers at a large hospital in Chihuahua, Mexico, realized that the standard WHO guide didn't perfectly match the specific "terrain" of their local fortress. The bacteria there had different weaknesses and strengths than the global average.

So, they decided to redraw the map. They created a Modified AWaRe Guideline. Instead of just following the global rules, they looked at their own local data to see exactly which antibiotics were working (Green) and which were failing (Red) against the specific bacteria in their hospital. They then taught their doctors to use this new, customized map.

The Battle Report: Before and After

The researchers looked back at the battle logs from two years:

2022 (Before the new map): They counted the bacteria and checked which weapons worked.
2023 (After using the new map): They did the same thing to see if the strategy changed the outcome.

What they found:

The Enemy Didn't Change Much: The types of bacteria attacking the hospital (like E. coli and Staph) stayed mostly the same. The "enemy lineup" was consistent.
The Weapons Got Sharper: The most exciting news was about the weapons' effectiveness. After the doctors started using the new, customized map, the bacteria became less resistant to many of the antibiotics.
- Imagine a lock that was hard to pick. After the new strategy, the lock became easier to open again for several key antibiotics.
- Specifically, the resistance rates dropped for important drugs like meropenem (a heavy-duty weapon), linezolid (a reserve weapon), and several others.
- Statistically, this improvement wasn't just luck; the numbers showed a clear link between using the new guide and the bacteria becoming easier to treat.

The "Superbugs" (ESKAPE)

The study also kept a close eye on the "Elite Six" superbugs (known as ESKAPE pathogens), which are the most notorious troublemakers in hospitals.

The number of these superbugs didn't change drastically between the two years.
However, the study noted that some of these tough bugs still held onto their resistance to certain drugs (like S. aureus resisting oxacillin), while others remained vulnerable to the best weapons (like E. coli still being weak against amikacin).

The Bottom Line

The paper concludes that customizing the rules works. By creating a local "traffic light" system based on their own hospital's specific data, the doctors were able to shift the balance back in their favor. The bacteria didn't disappear, but they became slightly less resistant to the antibiotics, suggesting that the new strategy helped preserve the power of the available weapons.

In short: The hospital didn't just follow the global rulebook; they wrote their own playbook based on their local reality, and it helped them win more battles against the bacteria.

Technical Summary: Implementation of a Modified AWaRe Guideline and Its Association with Antimicrobial Resistance Patterns

Problem Statement
Antimicrobial resistance (AMR) remains a critical global health threat, driven by the inappropriate and excessive use of antibiotics, which accelerates the emergence of resistant microorganisms and limits treatment options. While Antimicrobial Stewardship Programs (ASPs) and the World Health Organization's (WHO) Access, Watch, and Reserve (AWaRe) classification are established strategies to optimize prescribing, their implementation is uneven, particularly in low- and middle-income settings. There is a specific need to evaluate whether adapting the AWaRe framework to local resistance patterns can effectively influence antimicrobial susceptibility trends in tertiary-care environments.

Methodology
This study employed a retrospective comparative design conducted at a tertiary-care hospital in Chihuahua, Mexico. The researchers implemented a modified AWaRe guideline in 2023, adapted specifically to the institution's historical antimicrobial resistance patterns and available formulary.

Intervention: The modified guideline utilized a color-coded system (Green for susceptible, Yellow for intermediate, Red for non-susceptible) based on local susceptibility profiles to categorize antibiotics into Access, Watch, and Reserve groups.
Data Collection: The study analyzed 1,126 bacterial isolates from hospitalized patients with documented infections. Data were drawn from two periods: pre-implementation (January–December 2022) and post-implementation (January–October 2023).
Analysis: Bacterial identification and susceptibility testing were performed using the automated BD Phoenix system following Clinical and Laboratory Standards Institute (CLSI) standards. The study compared the frequency of isolated microorganisms and their susceptibility profiles between the two periods using descriptive statistics and the chi-square test to identify significant differences.

Key Contributions
The primary contribution of this work is the evaluation of a locally adapted AWaRe guideline within a specific tertiary-care setting. Unlike generic implementations, this study modified the classification based on previously reported local resistance data, creating a color-coded susceptibility tool to guide clinical decision-making. The study provides empirical data on the association between this specific stewardship intervention and changes in resistance patterns over a one-year period, focusing on both general microbial trends and specific ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterobacter species).

Results

Microorganism Prevalence: The total number of isolates decreased slightly from 568 in 2022 to 558 in 2023. The distribution of prevalent species remained largely consistent, with Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa as the dominant Gram-negatives, and Staphylococcus epidermidis, Staphylococcus aureus, and Enterococcus faecalis as the dominant Gram-positives. Notably, Acinetobacter baumannii isolates decreased significantly (from 5.5% to 1.25%), while Candida albicans increased (from 0.5% to 6.99%).
Susceptibility Trends: Analysis revealed a reduction in resistance rates for most antimicrobial agents following the implementation of the guideline.
- Statistically Significant Improvements: The chi-square test identified significant differences in resistance profiles for several agents. In the Access group, nitrofurantoin showed a significant change ( $p=0.0187$ ). In the Watch group, ceftazidime ( $p=0.0332$ ), ceftriaxone ( $p=0.0296$ ), and meropenem ( $p=0.0009$ ) demonstrated significant improvements. In the Reserve group, linezolid showed a highly significant difference ( $p<0.001$ ). Other significant changes were observed for ampicillin, cefepime, cefuroxime, and erythromycin.
- ESKAPE Pathogens: While the isolation frequency of ESKAPE pathogens did not show statistically significant differences between years, specific resistance patterns persisted. E. coli and K. pneumoniae maintained high resistance to ampicillin, S. aureus showed high resistance to oxacillin, and A. baumannii retained resistance to carbapenems. However, susceptibility to agents like linezolid, daptomycin, and colistin remained high.

Significance and Claims
The authors conclude that the implementation of the modified AWaRe guideline was associated with changes in antimicrobial susceptibility patterns and a trend toward lower levels of resistance. The study posits that local adaptation of the AWaRe classification, grounded in institutional resistance data, serves as a useful framework for monitoring susceptibility and supporting stewardship activities.

The paper emphasizes that while the most prevalent microorganisms remained consistent, the observed statistically significant shifts in resistance for specific antibiotics highlight the dynamic nature of AMR. The authors assert that these findings underscore the necessity of continuous microbiological surveillance and periodic review of treatment guidelines to facilitate evidence-based decision-making. They maintain that integrating stewardship strategies with resistance monitoring tools is essential for optimizing antimicrobial use in tertiary-care hospitals, though they modestly note that the single-center, retrospective nature of the study limits the generalizability of the findings to other institutions.

The Experiment: Updating the Map

The Battle Report: Before and After

The "Superbugs" (ESKAPE)

The Bottom Line

Technical Summary: Implementation of a Modified AWaRe Guideline and Its Association with Antimicrobial Resistance Patterns

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