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A susceptibility percentage can look straightforward. If an organism was 86 percent susceptible to an antimicrobial last year and 78 percent susceptible this year, it is reasonable to wonder whether resistance is increasing. Sometimes that is exactly what the data are showing. But the percentage by itself does not explain why the change occurred. 

In clinical microbiology, that is where context becomes important. Cumulative susceptibility data bring together many individual antimicrobial susceptibility testing (AST) results and can help guide empirical therapy and antimicrobial stewardship. Before comparing one year with another, however, we need to know that the underlying data were generated and analyzed in a comparable way. A change may reflect a true shift in resistance, but it can also result from changes in breakpoints, testing methods, reporting practices, or the population represented in the data. 

Start with the breakpoint 

One of the first questions to ask is whether the breakpoints changed. 

Clinical and Laboratory Standards Institute (CLSI) breakpoints are updated as new evidence becomes available. They allow laboratories to interpret an MIC or zone diameter using categories such as susceptible, intermediate, susceptible dose-dependent, or resistant, depending on the organism and antimicrobial. When a breakpoint is revised, the same MIC or zone diameter may be interpreted differently. The organism has not suddenly changed; the criteria used to interpret the result have.^1,2 

That difference can affect an antibiogram. Published work on CLSI breakpoint revisions has shown that newer breakpoints can change the percentage reported as susceptible for some organism-antimicrobial combinations.^2,3 If susceptibility falls from one year to the next, it is therefore worth checking whether both periods were interpreted using comparable breakpoints before concluding that resistance has increased. 

What changed in the laboratory? 

Breakpoints are only one part of the picture. A laboratory may introduce a different AST system, update a testing panel, change which antimicrobials are routinely tested, or revise reporting practices. Selective or cascade reporting can also influence which results appear on patient reports and how cumulative data are handled.¹ 

These changes are a normal part of laboratory practice, but they matter when data are compared over time. If a susceptibility trend looks unexpected, it helps to review what else changed during the same period. Was a new method introduced? Did the testing panel change? Were reporting rules revised? Was the data extraction from the laboratory information system handled differently? 

This is where laboratory knowledge adds important context. A spreadsheet can show the final percentage, but the laboratory team understands how the results behind that percentage were generated and reported. 

Are we comparing similar data? 

Another important question is which isolates were included in the analysis. 

For routine antibiograms, CLSI M39 recommends using final, verified results from diagnostic isolates rather than surveillance isolates. It also recommends a standardized first-isolate approach so that repeated isolates from the same patient do not have too much influence on the cumulative result.¹ 

The population represented in the data matters as well. A hospital-wide susceptibility percentage may differ from one calculated for a specific unit or patient group. Changes in specimen sources or patient populations can also change which organisms and resistance patterns are represented. 

The point is not to explain away every change as a data issue. It is to make sure we are comparing similar data before deciding that the resistance pattern itself has changed. 

Keep the denominator in view 

The number of isolates behind a percentage is easy to overlook, but it affects how confidently the result can be interpreted. A change from 90 percent to 80 percent may look substantial, yet an 80 percent result based on a small number of isolates does not carry the same weight as the same percentage based on a large, stable dataset. 

For routine antibiograms, CLSI recommends including only species with testing data from at least 30 isolates during the analysis period.1 Even at or above that threshold, the isolate count still provides useful context. Published work has shown that smaller isolate numbers are associated with less reliable cumulative susceptibility estimates.⁴ Looking at the denominator alongside the percentage helps prevent normal variation in a limited dataset from being mistaken for a major resistance shift. 

It is a simple check, but an important one: before focusing on how much the percentage changed, look at how many isolates produced that percentage. 

When the change still looks real 

After reviewing the breakpoints, testing process, isolate selection, patient population, and isolate count, the change may still appear meaningful. At that point, the finding deserves attention beyond the laboratory. 

Microbiology data are most useful when they are discussed with teams that can place them in clinical context. Depending on the setting, this may include antimicrobial stewardship, infectious diseases, pharmacy, and infection prevention. A change that persists over time, appears in a particular patient population, or is supported by other resistance findings may deserve closer review. 

CDC includes tracking resistance patterns and regularly reporting information on antibiotic use and resistance as part of hospital antibiotic stewardship.⁵ The microbiology laboratory contributes by making sure the resistance data used in those discussions are reliable and by explaining important testing or reporting changes that may affect interpretation. 

Look beyond the percentage 

Antibiograms make a large amount of microbiology data easier to use. That is one of their greatest strengths. At the same time, a single percentage can hide many decisions and changes that occurred before the number appeared in the final report. 

When a susceptibility percentage shifts, a useful first question is not simply, “Is resistance getting worse?” It is, “What changed, and could any part of our testing or data explain it?” 

Sometimes the answer will be a true change in resistance. In other situations, the explanation may be a breakpoint revision, a testing change, a different patient or isolate mix, or a limited denominator. Distinguishing between these possibilities requires reliable data, knowledge of the laboratory process, and careful interpretation. 

For laboratory professionals, our role does not end when an individual AST result is released. We also help ensure that when thousands of those results are brought together, the larger pattern is interpreted accurately and communicated in a way that clinicians and stewardship teams can use with confidence. 

References 

1. Clinical and Laboratory Standards Institute. Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data. 5th ed. CLSI guideline M39. Clinical and Laboratory Standards Institute; 2022. 

2. Clinical and Laboratory Standards Institute. Performance Standards for Antimicrobial Susceptibility Testing. 36th ed. CLSI supplement M100. Clinical and Laboratory Standards Institute; 2026. 

3. Humphries RM, Abbott AN, Hindler JA. Understanding and addressing CLSI breakpoint revisions: a primer for clinical laboratories. Journal of Clinical Microbiology. 2019;57(6):e00203-19. doi:10.1128/JCM.00203-19. 

4. Tran C, Hargy J, Hess B, Pettengill MA. Estimated impact of low isolate numbers on the reliability of cumulative antibiogram data. Microbiology Spectrum. 2023;11(1):e03939-22. doi:10.1128/spectrum.03939-22. 

5. Centers for Disease Control and Prevention. Core Elements of Hospital Antibiotic Stewardship Programs. Updated September 15, 2025.