What Is an Antibiogram and How Is It Used?

An antibiogram is a periodic report, usually updated once a year, that summarizes how well different antibiotics work against the bacteria most commonly found at a particular hospital or healthcare facility. It is the main tool clinicians reach for when a patient has a serious infection and there is no time to wait for lab results identifying which specific drug will kill the bug. In practice, it is a table: bacterial species listed down the left side, antibiotics across the top, and each cell showing the percentage of tested samples that were susceptible to that drug. The concept is straightforward, but the way antibiograms are built, interpreted, and increasingly customized has become a surprisingly rich area of clinical science.

How an Antibiogram Gets Built

Every time a hospital’s microbiology lab tests a patient’s bacterial sample against a panel of antibiotics, the result goes into a database. At the end of a reporting period, the lab compiles all of those individual test results into a single summary table. That compilation is the cumulative antibiogram, and it has been the standard format at most institutions for decades.¹ The goal is to give clinicians a snapshot of local resistance patterns rather than national averages, because the bacteria circulating in one hospital can behave very differently from those in a facility across town.

In the United States, the Clinical and Laboratory Standards Institute (CLSI) publishes a guideline document called M39 that spells out how a proper antibiogram should be assembled. The first edition came out in 2000, and the document has been revised multiple times since then to account for changes in testing methods and informatics tools.² Key rules include updating the report at least once a year, excluding organisms when fewer than a certain number of samples have been tested (because a tiny sample makes the percentage unreliable), and removing duplicate isolates from the same patient so that one person’s chronic infection does not skew the numbers for the whole hospital.

Despite the existence of these guidelines, compliance is uneven. A nationwide analysis of over 200 antibiograms collected from roughly 150 institutions found that while most met at least five of ten key M39 criteria, several clinically important elements were frequently missing. Common shortfalls included failing to note whether duplicate isolates had been removed and including organisms with fewer than ten tested samples.³ When an antibiogram is built on a small number of isolates, a single resistant sample can swing the reported susceptibility rate by ten or more percentage points, which could easily push a clinician toward the wrong drug.⁴

Guiding Empirical Antibiotic Therapy

The most common use of an antibiogram is to help a clinician pick an antibiotic before lab results come back. If you arrive at the emergency department with signs of a bloodstream infection, your doctor will order blood cultures, but it can take a day or two to grow the bacteria and test which drugs kill it. In the meantime, the patient needs treatment now. The antibiogram tells the doctor which antibiotics have the highest likelihood of working against the organisms most commonly causing that type of infection in that hospital.⁵

A typical threshold many stewardship teams use is around 80 percent: if the antibiogram shows that at least 80 percent of a given organism’s isolates were susceptible to a particular antibiotic, that drug is considered a reasonable empiric choice for less severe infections. For critical infections like sepsis, clinicians often aim higher or use combination therapy to raise coverage.

There is good evidence that this approach matters. A retrospective study of patients with bloodstream infections and sepsis found that when empiric antibiotics aligned with drugs showing at least 70 percent susceptibility on the hospital’s antibiogram, in-hospital deaths were cut roughly in half compared to when less-active drugs were chosen. Those patients also spent about a day and a half less in the intensive care unit.⁶ That said, the relationship between picking the “right” empiric drug and patient survival is not always straightforward. A study of patients with community-onset bloodstream infections found that illness severity and the source of the infection (urinary tract versus elsewhere) were stronger predictors of death than whether the initial antibiotic turned out to match the bacteria.⁷ So the antibiogram improves your odds, but it is one piece of a much bigger clinical puzzle.

Why a Single Hospital-Wide Report Is Not Enough

One of the more eye-opening findings in recent antibiogram research is just how much resistance patterns can vary within the same building. A hospital-wide antibiogram might report that 80 percent of a certain bacterium’s isolates are susceptible to a given antibiotic, but that number could mask the fact that the surgical ICU is seeing only 55 percent susceptibility while the neuroscience ICU is at 92 percent. A study comparing multiple ICUs within one hospital found exactly this kind of spread, with significant differences in susceptibility patterns for seven bacterial species across different units.⁸

The general trend is consistent: ICU isolates tend to be more resistant than non-ICU isolates across most drug classes. A large surveillance study found that every tested antibiotic showed lower susceptibility rates for ICU bacteria compared to non-ICU bacteria, with differences ranging from about 1 to 15 percentage points depending on the drug and organism.⁹ A more recent study from India quantified this starkly for several high-priority pathogens: carbapenem resistance in Klebsiella pneumoniae was about twice as high in ICU isolates as in non-ICU isolates, and methicillin-resistant Staphylococcus aureus was significantly more common in intensive care as well.¹⁰ This is why many hospitals now produce unit-specific antibiograms, especially for their ICUs, rather than relying on a single document.

Beyond the Traditional Format

The standard antibiogram tells you how often a single antibiotic works against a single organism. That is useful, but it does not answer every clinical question. Several newer formats have emerged to fill the gaps.

Syndromic Antibiograms

A syndromic antibiogram filters the data by the type of infection rather than just the organism. For example, a urinary tract infection antibiogram pools results from all urine cultures, regardless of which bacterium grew, and shows the likelihood that a given antibiotic would cover whatever pathogen is causing the infection. This is closer to the clinician’s actual decision point, because when someone walks in with signs of a UTI, the doctor does not yet know which bacterium is responsible.

A specialized version of this approach, called a weighted-incidence syndromic combination antibiogram (WISCA), goes a step further. It accounts for how common each pathogen is in a given syndrome and weights the susceptibility data accordingly, producing a single coverage probability for each antibiotic or antibiotic pair.¹¹ A study in nursing homes found that clinicians using either a traditional syndromic antibiogram or a WISCA for urinary tract infections were significantly more likely to choose an effective empiric antibiotic compared to clinicians working without either tool.¹²

Combination Antibiograms

When a pathogen is known for high resistance, clinicians sometimes use two antibiotics together. A standard antibiogram cannot tell you how well a two-drug combination would cover that organism. A combination antibiogram solves this by calculating the probability that at least one drug in a given pair would be active. One study focused on Pseudomonas aeruginosa, a notoriously resistant organism, and found that pairing a beta-lactam antibiotic with an aminoglycoside like amikacin pushed coverage rates above 98 percent, while the same beta-lactams paired with a fluoroquinolone only reached about 92 to 94 percent.¹³ That kind of granularity can steer clinicians toward the right partner drug for a critically ill patient.

Tracking Resistance Over Time

Antibiograms are not just prescription guides; they are surveillance instruments. By comparing this year’s antibiogram with last year’s, infection-control teams can spot resistance trends early. A rising percentage of resistant isolates for a particular bug-drug combination might trigger investigations into infection-control practices, prompt changes to the hospital’s empiric therapy guidelines, or lead to targeted antibiotic stewardship interventions.¹⁴

Longitudinal surveillance studies illustrate the value of this. A 15-year antibiogram-based surveillance effort in southern China tracked major Gram-positive pathogens and documented a continuous increase in methicillin resistance among staphylococci over the study period, with overall methicillin resistance rates exceeding 60 percent in S. aureus. The study also caught the first appearance of vancomycin-resistant S. aureus in the 2011–2015 period, a worrying signal that would have been invisible without years of cumulative data to compare against.¹⁵

A subtraction antibiogram is a related tool that strips out data from a previous year to isolate what is new. If a cumulative antibiogram covers 2020 through 2023, a subtraction antibiogram for 2023 would show only the isolates tested in that most recent year, giving a sharper view of current trends rather than a blended average.¹⁴

Antibiograms in Outpatient and Community Settings

Most antibiogram data comes from hospitalized patients, which means it reflects a sicker, more heavily treated population. Community-acquired infections often involve bacteria with different resistance profiles. Using a hospital antibiogram to guide antibiotic prescribing in a primary care office can lead to unnecessarily broad-spectrum choices, because the resistance rates at the hospital do not necessarily match what is circulating in the community.

Research comparing inpatient and outpatient antibiograms has confirmed that susceptibility patterns do not track together in a predictable way, reinforcing the argument that outpatient settings need their own antibiograms.¹⁶ Some health systems have started building outpatient-specific versions. The Veterans Health Administration, for example, has developed outpatient antibiograms for Staphylococcus aureus isolates using data from its nationwide network of clinics and has even explored seasonal and geographic stratification to see whether resistance patterns shift by time of year or region.¹⁷ The idea is still relatively new compared to inpatient antibiograms, but it is gaining traction as outpatient antibiotic stewardship gets more attention.

Digital Tools and Personalized Antibiograms

A traditional antibiogram is a PDF or a printed table hanging on a wall in the pharmacy. Increasingly, hospitals are building interactive digital versions directly into electronic health records. One pediatric hospital developed an online tool called Antibiogram+ that integrates susceptibility data with empiric therapy recommendations, dosing guidance, and treatment-duration suggestions, all accessible from the clinician’s workstation.¹⁸

The most ambitious evolution of the concept is the personalized antibiogram. Instead of showing population-level susceptibility rates, a personalized antibiogram uses machine learning to predict the likelihood that a specific patient’s infection will respond to each available antibiotic, based on that individual’s prior culture history, demographics, and clinical data. A study testing this approach found that antibiotic selection guided by personalized antibiograms achieved a coverage rate of about 86 percent, which was comparable to clinician performance and significantly better than random selection.¹⁹ Researchers have proposed embedding these prediction models directly into electronic health records so that a susceptibility probability score pops up in real time as a clinician is ordering antibiotics.¹⁹

Common Limitations Worth Knowing About

Antibiograms are powerful, but they have blind spots that are easy to overlook.

Selection bias: The bacteria that end up in an antibiogram are not a random sample of all infections. Cultures tend to be ordered for sicker patients, for infections that failed an initial antibiotic, or when resistance is suspected. This skews the data toward more resistant organisms, making the antibiogram look worse than what the average patient with a straightforward infection would encounter.
Aggregate masking: A hospital-wide susceptibility rate is an average. As discussed above, unit-level and infection-type differences can be large enough that the average misleads a clinician treating a patient in a specific setting.
Lag time: Because antibiograms are typically updated annually, the data may already be months old by the time a clinician uses it. A resistance trend that emerged after the reporting window closes will not show up until the next edition.
Missing combinations: Standard antibiograms test drugs individually. They cannot tell you how well two antibiotics work together, which is why the combination and WISCA formats described earlier were developed.

Understanding these limitations does not make antibiograms less useful; it just means they work best as part of a broader clinical toolkit rather than as a stand-alone prescription oracle.

Training Gaps Among Clinicians

Even when a hospital produces a high-quality antibiogram, the document is only helpful if clinicians know how to read it and apply it to patient care. A survey of physicians in Sri Lanka found that while nearly all of them recognized the value of antibiograms, most also expressed a strong need for additional training. The physicians favored interactive, small-group learning sessions over lectures, suggesting that antibiogram interpretation is not as intuitive as one might expect from a simple table of percentages.²⁰ Misreading an antibiogram can go in both directions: a clinician might choose a drug that looks effective on paper but whose susceptibility rate is inflated by a small sample size, or they might avoid a perfectly good narrow-spectrum option because the number looks lower than a broader-spectrum alternative whose slightly higher rate is driven by different patient populations.

Veterinary Medicine and the One Health Connection

Antibiograms are not exclusively a human-medicine tool. Veterinary labs produce them for livestock and companion animals, and they have become particularly important for tracking antibiotic resistance in food-producing animals. A study of Escherichia coli isolated from dairy cattle and the humans who work closely with them found high rates of resistance in both groups, underscoring the potential for resistant bacteria to move between animals and people.²¹ Research on MRSA isolates from veterinary settings has similarly demonstrated high-level resistance, with some strains showing complete resistance to multiple drug classes on susceptibility testing.²²

This cross-species relevance is why antibiograms increasingly figure into “One Health” discussions, the idea that human health, animal health, and environmental health are interconnected. Comparing antibiograms across these domains helps identify when resistance genes are spreading from agricultural settings into hospitals, or vice versa. It is low-tech surveillance, essentially just tallying which drugs still work, but that simplicity is part of what makes it scalable in resource-limited settings where genomic sequencing may not be available.

Rapid Diagnostics and the Future of Susceptibility Data

Traditional susceptibility testing requires growing bacteria in culture, which takes time. One of the more innovative intersections with antibiogram methodology involves real-time PCR techniques that can estimate a pathogen’s drug susceptibility within hours rather than days. One proof-of-concept approach monitors bacterial load in blood samples exposed to different antibiotics, generating what the researchers called a “real-time PCR antibiogram.” Rather than waiting for overnight growth, the test tracks whether the bacterial genetic signal drops after drug exposure, producing rapid susceptibility data.²³

Technologies like this have the potential to compress the gap between empiric therapy, when clinicians lean on the antibiogram, and targeted therapy, when they have culture results for the individual patient. The faster a clinician can get patient-specific susceptibility data, the less they depend on population-level estimates. But even in that future, antibiograms will retain their role as a first-pass guide for the initial treatment decision and as a surveillance barometer for the hospital and the wider community.