Antibiograms and Antimicrobial Stewardship: A Practical Guide for Public Health and Clinical Care

Antimicrobial resistance is reshaping how we treat infections at every level of health care, from community clinics to intensive care units. One of the most practical and underused tools in our arsenal is the antibiogram, a data-driven summary that helps clinicians, stewardship teams, and health departments make smarter decisions about empiric therapy. This guide walks through what an antibiogram is, how hospitals and health departments build one, how clinicians use it at the bedside, and where the field is heading.

Key Takeaways

• An antibiogram summarizes resistance patterns for selected pathogens across a defined population and time period, showing the percentage of isolates susceptible to each antimicrobial agents tested, particularly among gram negative organisms like E. coli and Klebsiella from urine cultures and blood cultures.
• The Clinical and Laboratory Standards Institute M39 guideline provides the standard methodology for building valid facility and regional antibiograms, including minimum isolate counts of 30 per organism–drug pair and strict deduplication rules.
• Antibiograms are used to inform initial empiric therapy in clinical settings, but they do not account for individual patient history or factors such as allergies, recent antibiotic exposures, or site-specific pharmacokinetics.
• Effective antibiogram use requires collaboration among microbiology laboratories, antimicrobial stewardship programs, IT and EHR teams, and front-line clinicians in hospitals and health departments.
• Key limitations include bias toward sicker or hospitalized patients, lack of patient-level context, and the potential for breakpoints changes to shift reported susceptibility percentages independently of actual resistance trends.

Introduction: Why Antibiograms Matter in 2026

Antimicrobial resistance now ranks among the leading threats to patient safety worldwide. The WHO’s 2025 Global Antimicrobial Resistance and Use Surveillance System report documented over 23 million confirmed bacterial infections across more than 90 countries, revealing persistent and rising resistance in common pathogens. In the United States, an estimated 80 to 90 percent of antibiotic use occurs in outpatient settings, where clinicians often prescribe empirically without the benefit of culture data. When that empiric antimicrobial therapy is mismatched to local resistance, the consequences compound: treatment failures, prolonged illness, increased hospitalizations, and further acceleration of resistance.

The antibiogram addresses this gap directly. By aggregating susceptibility testing results from a facility or region, it gives clinicians a probability-based view of which antimicrobials are most likely to work against common pathogens locally. Antibiograms can lead to improved patient outcomes by facilitating effective antibiotic selection, and they are a cornerstone of antimicrobial stewardship programs that optimize antibiotic use. This article serves as a practical, step-by-step guide for clinicians, hospital leaders, and health department staff who want to build, interpret, and deploy antibiogram data more effectively, drawing on real examples from U.S. health systems and city and state programs between 2021 and 2025.

What Is an Antibiogram?

An antibiogram is a cumulative summary of in vitro antimicrobial susceptibility results for bacterial (and sometimes fungal) isolates obtained from clinical cultures over a defined period among a defined population. Most commonly, hospitals generate annual antibiograms for bacterial isolates from patients, covering a full calendar year of data. The report displays the percentage of isolates susceptible to each antibiotic for every organism with sufficient data, giving clinicians a percentage-based snapshot of antibiotic effectiveness against bacteria in their local setting.

It is important to distinguish among several types. A routine or facility antibiogram aggregates data from a single health facility and is the most common form. Abbreviated antibiograms focus on common organisms and treatments, trimming the display to the most clinically relevant pathogen–drug pairs. Enhanced antibiograms guide empiric therapy for specific populations, such as pediatric patients or ICU admissions. Rolling antibiograms provide current data over a shifting time frame, such as a trailing 12-month window updated quarterly. Advanced antibiograms include combination and escalation types that model multi-drug regimens or step-up approaches for complex infections. All are compiled using consensus guidelines to ensure accuracy and reliability.

The organisms typically reported include common gram negative pathogens such as E. coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa, along with gram positive organisms such as Staphylococcus aureus (including methicillin resistant staphylococcus aureus) and Enterococcus species. Specimen types typically include urine, blood, and respiratory cultures. Regional antibiograms, such as those published by New York City or Wisconsin, combine data from multiple individual facilities to offer a broader geographic view and are especially useful where local facility data are sparse.

The “percent susceptible” metric is what makes the antibiogram actionable. If a facility antibiogram shows 94% of E. coli urine isolates are susceptible to nitrofurantoin, a clinician treating a straightforward cystitis can feel confident in that choice. If fluoroquinolone susceptibility has dropped to 61%, that drug becomes a poor empiric bet. The metric supports empiric therapy decisions, guideline development, and benchmarking of antimicrobial resistance trends over time.

Core Principles: CLSI M39 and Methodologic Foundations

The primary reference for building cumulative antibiograms in clinical microbiology is the Clinical and Laboratory Standards Institute M39 guideline, now in its 5th edition (M39-Ed5, 2022). This document, produced by the clinical and laboratory standards institute, standardizes how susceptibility testing data are collected, deduplicated, analyzed, and presented so that antibiograms are comparable across facilities and over time.

Key CLSI M39 recommendations include the following. First, a minimum of 30 isolates must be tested for any organism–antibiotic pair to appear in the report. This threshold reduces the risk of misleading percentages driven by small samples. Second, deduplication requires that only the first isolate per patient per species per analysis period is included, regardless of specimen source or resistance profile, to avoid overcounting resistant organisms from repeatedly cultured patients. Third, only clinically significant diagnostic isolates should be included; screening or surveillance cultures are excluded. Fourth, intermediate results are reported separately from susceptible, and intrinsic resistance (where an organism is inherently not susceptible to a drug) must be noted or excluded.

A critical but often overlooked element involves breakpoints, the MIC thresholds that determine whether an isolate is called susceptible, intermediate, or resistant. These are set and periodically updated through CLSI M100, and when a breakpoint is lowered, the same population of bacteria may suddenly appear less susceptible on the antibiogram even though the organisms themselves have not changed. Laboratories must annotate when breakpoint revisions occur so that year-to-year trends are interpreted with appropriate caution.

Building a valid antibiogram requires collaboration between the microbiology laboratory, infectious diseases physicians, pharmacists, and data analysts. Each plays a role in ensuring the method aligns with CLSI M39 and that the final product is both statistically sound and clinically meaningful. Public health agencies may adapt M39 when aggregating data across institutions, applying consistent rules so that regional antibiograms are comparable.

How Hospitals Build a Facility-Specific Antibiogram

Consider a typical 500-bed academic medical center preparing its 2025 antibiogram. The process begins by defining the analysis period, usually January 1 through December 31 of the calendar year. From the laboratory information system, the microbiology team extracts all final, verified susceptibility testing results for diagnostic isolates collected during that window. They then apply CLSI M39 deduplication, retaining only the first isolate of each species per patient for the year. Surveillance-only cultures, such as rectal swabs for vancomycin-resistant Enterococcus screening, are filtered out.

For each organism–drug pair meeting the 30-isolate threshold, the team calculates percent susceptible. Decisions about which organisms and antimicrobials to display are guided by clinical utility: common pathogens encountered in the facility and the antimicrobial agents most frequently used for empiric therapy are prioritized, while rarely prescribed or niche drugs may be omitted to improve usability.

Stratifications add granularity. Hospitals often produce separate views for inpatient versus outpatient populations, ICU versus non-ICU settings, and adult versus pediatric patients. When isolate counts allow, unit-specific antibiograms for high-risk areas like hematology-oncology or neonatal ICU can reveal resistance patterns that differ markedly from the facility average. Focused or “syndromic” antibiograms built from urine cultures only or respiratory cultures only are increasingly common and are discussed in detail below.

Quality checks are essential before publication. The stewardship team compares the new antibiogram against prior years, flags any abrupt shifts, and investigates whether those shifts reflect true changes in local resistance or artifacts of breakpoint revisions, changes in the testing panel, or laboratory errors. Annual antibiograms are generated by hospitals for bacterial isolates in this fashion, and the process is replicated across thousands of facilities nationwide. Wisconsin hospitals submitted data for 2025 statewide antibiograms as part of a coordinated state effort, with 89 percent of hospitals reporting antibiotic resistance data for all 12 months. Wisconsin’s antibiograms include data from 134 hospitals for 2025, representing broad coverage of the state’s health system.

How Health Departments Develop Regional and Citywide Antibiograms

Local and state health departments play a distinct role by aggregating antibiogram data across multiple facilities to create regional antibiograms that reveal broader resistance patterns. The method typically involves soliciting facility-level antibiograms or raw susceptibility data, applying standardized rules for deduplication and organism–drug pair inclusion, and pooling results to produce a combined report.

Data sources vary. Some departments collect antibiogram PDFs directly from hospital labs, while others rely on electronic submissions through the National Healthcare Safety Network Antimicrobial Resistance Option or specialized surveillance platforms. The latest NYC antibiogram includes data from 33 health facilities, with borough-level stratifications and emergency department and pediatric-specific views where data permit. Wisconsin’s statewide antibiogram draws from 134 hospitals submitting through NHSN, covering general acute care, critical access, and specialty facilities.

Harmonizing data across laboratories is one of the most significant challenges. Individual facilities may use differing breakpoints, test different panels of antimicrobials, apply inconsistent deduplication practices, or classify specimen sources differently. These inconsistencies must be addressed, or at minimum clearly documented with footnotes, before pooling.

Regional antibiograms serve several purposes. They inform outpatient empiric therapy guidance in the region when a clinic or small hospital lacks its own facility data. They help public health agencies track gram negative resistance trends in urine cultures across a state over several years. And they support policy decisions, such as determining where to target infection prevention resources or whether carbapenem-resistant Enterobacterales prevalence is rising in a particular area. However, clear caveats are essential: regional antibiograms are often less suitable for individual bedside decisions but are valuable for policy, guideline development, and identifying hotspots of resistance.

Reading an Antibiogram: Practical Interpretation for Clinicians

An antibiogram is organized as a grid. Rows list organisms (E. coli, K. pneumoniae, P. aeruginosa, S. aureus, and others), while columns list antimicrobials (fluoroquinolones, TMP-SMX, cephalosporins, carbapenems, and so on). For each cell, two numbers matter: N, the number of isolates tested, and percent susceptible (%S). A larger N means more statistical confidence. A higher %S means the drug is more likely to cover that organism empirically.

Clinicians aim for a susceptibility rate of greater than 80 percent when selecting antibiotics for empiric therapy. For severe infections such as sepsis or ventilator-associated pneumonia, many stewardship programs set the bar higher, preferring agents with 90 to 95 percent susceptibility. For lower-risk conditions like uncomplicated cystitis, thresholds of 80 to 85 percent may be acceptable depending on patient factors and drug safety profiles.

Consider a practical example. A clinician evaluating empiric treatment for community-onset pyelonephritis reviews the urine-specific antibiogram. E. coli fluoroquinolone susceptibility is reported at 61 percent, TMP-SMX at 74 percent, and nitrofurantoin at 96 percent. Nitrofurantoin achieves excellent urinary concentrations but is not appropriate for upper tract infections. The clinician may instead select ceftriaxone, which the antibiogram shows at 91 percent susceptibility for E. coli, recognizing that this choice aligns with the target threshold for a moderately severe infection.

Antibiograms guide empiric therapy decisions while awaiting test results, but they complement rather than replace clinical judgment, individual patient culture history, and knowledge of infection site pharmacokinetics. When the antibiogram shows lower susceptibility for a drug, that information should prompt consideration of alternatives, not automatic exclusion if patient-specific data support its use.

Empiric Therapy in Common Clinical Scenarios

Antibiograms come alive in clinical decision-making through scenario-based application. Three common situations illustrate the approach.

Community-onset urinary tract infection. An otherwise healthy 32-year-old woman presents with classic cystitis symptoms. The urine-specific antibiogram for this outpatient population shows E. coli susceptibility to nitrofurantoin at 96 percent, TMP-SMX at 75 percent, and ciprofloxacin at 62 percent. Given that fluoroquinolone resistance among E. coli isolates reported through the CDC’s Antibiotic Resistance and Patient Safety Portal reached 39.1 percent nationally in 2024, nitrofurantoin is the clear first-line choice. TMP-SMX falls below the 80 percent threshold and warrants caution unless the patient has no risk factors for resistance.

Hospital-onset pneumonia in an ICU patient. A 68-year-old mechanically ventilated patient develops fever and new infiltrates. The ICU-specific antibiogram shows Pseudomonas aeruginosa susceptibility to piperacillin-tazobactam at 82 percent, cefepime at 78 percent, and meropenem at 91 percent. Methicillin resistant staphylococcus aureus prevalence on the unit is 42 percent of all S. aureus isolates. The stewardship-endorsed empiric regimen for this scenario combines meropenem with vancomycin, covering both resistant organisms pending culture results.

Diabetic foot infection. A patient with a deep wound and concern for osteomyelitis requires empiric IV therapy. The antibiogram reveals mixed gram negative and gram positive flora; E. coli ESBL-positivity is found at 8 percent locally, and MRSA rates are significant. The clinician selects a carbapenem plus vancomycin while awaiting deep tissue cultures.

In all cases, antibiograms should be re-evaluated once patient-specific culture and susceptibility results are available.