Interpreting Susceptibility Results

SECTION 1

Historical Context & Motivation

The clinical need to determine whether a bacterial pathogen is vulnerable to a given antibiotic predates the formal discovery of most antibiotics themselves. When Alexander Fleming observed the antibacterial halo surrounding a Penicillium colony in 1928, he was, in essence, performing the first qualitative susceptibility test — observing a zone where bacterial growth was inhibited by a diffusing antimicrobial substance. The challenge that followed was enormous: how could clinicians move from anecdotal observation to a reproducible, standardized method for predicting therapeutic success? The answer unfolded across several decades and involved contributions from microbiologists, pharmacologists, and regulatory bodies that collectively transformed antimicrobial susceptibility testing (AST) into a cornerstone of evidence-based infectious disease management.

1928

Fleming's Penicillin Observation

Alexander Fleming notices zones of inhibition around Penicillium notatum colonies on a staphylococcal plate, establishing the conceptual foundation for diffusion-based susceptibility testing.

1947

First Serial Dilution Methods

Researchers develop broth and agar dilution protocols to determine the minimum inhibitory concentration (MIC), providing a quantitative measure of antimicrobial potency against a specific isolate.

1966

Kirby-Bauer Standardization

Bauer, Kirby, Sherris, and Turck publish their landmark paper standardizing the disk diffusion method, correlating zone diameters with MIC values and clinical outcomes. This method becomes the global standard for routine AST.

1975

NCCLS (now CLSI) Formed

The National Committee for Clinical Laboratory Standards begins publishing interpretive breakpoints, creating a consistent framework for categorizing isolates as susceptible, intermediate, or resistant.

2000s–Present

Automated & Molecular AST

Automated systems (VITEK, MicroScan) and molecular detection of resistance genes (e.g., mecA, bla_KPC) complement phenotypic AST, enabling faster turnaround and detection of emerging resistance mechanisms.

Today, susceptibility testing sits at the intersection of microbiology and pharmacology. The central question it addresses is deceptively simple: Will this antibiotic, at clinically achievable concentrations, inhibit or kill the pathogen isolated from this patient? Answering that question requires understanding how laboratory measurements — zone diameters and MIC values — are translated through interpretive breakpoints into actionable clinical categories.

SECTION 2

Core Principles & Definitions

Before diving into the mechanics of reading a susceptibility report, it is essential to establish the foundational concepts that underpin every interpretation. Susceptibility testing operates on the principle that the in vitro behavior of a bacterium exposed to an antimicrobial agent can, under carefully standardized conditions, predict in vivo therapeutic efficacy. This prediction hinges on the relationship between the pharmacokinetics of the drug (what the body does to the drug) and the pharmacodynamics of the drug-pathogen interaction (what the drug does to the organism). The following foundational ideas form the backbone of susceptibility interpretation.

Minimum Inhibitory Concentration (MIC)

The MIC is the lowest concentration of an antimicrobial agent that prevents visible growth of a bacterial isolate after overnight incubation. Expressed in µg/mL, it is the quantitative gold standard for measuring antimicrobial activity against a specific organism.

Zone of Inhibition

In disk diffusion assays, the zone of inhibition is the clear area surrounding an antibiotic-impregnated disk where bacterial growth is absent. Larger zones generally correspond to lower MICs and greater susceptibility, though the correlation depends on drug diffusion characteristics.

Interpretive Breakpoints

Breakpoints are threshold MIC values or zone diameters established by regulatory bodies (CLSI or EUCAST) that divide isolates into clinical categories: Susceptible (S), Intermediate (I), or Resistant (R). They integrate microbiological data, pharmacokinetic parameters, and clinical outcome data.

S-I-R Classification

Susceptible (S) predicts therapeutic success at standard dosing. Intermediate (I) indicates efficacy only at elevated doses or concentrated body sites. Resistant (R) predicts treatment failure regardless of dosing regimen.

Standardization Bodies

The Clinical and Laboratory Standards Institute (CLSI) (North America) and the European Committee on Antimicrobial Susceptibility Testing (EUCAST) publish and update breakpoint tables annually, ensuring interpretations reflect current resistance epidemiology and pharmacokinetic data.

✦ KEY TAKEAWAY

Think of susceptibility testing like a dose-response curve for a lock and key. The MIC tells you how much "key material" you need to open the lock (inhibit the bacterium). The breakpoint tells you whether the amount of key material your body can realistically deliver to the infection site is enough to open that lock. A "susceptible" result means the drug concentration achievable in the patient's tissues comfortably exceeds the MIC; a "resistant" result means the organism's lock requires more key material than the body can supply.

SECTION 3

Visual Explanation — Disk Diffusion & MIC Interpretation

Understanding susceptibility results is greatly aided by visualizing the two most common testing methods side by side. The diagram below illustrates a Kirby-Bauer disk diffusion plate alongside a broth microdilution panel, showing how each method generates data that is translated through breakpoint tables into clinical categories.

Left: A Kirby-Bauer disk diffusion plate showing four antibiotic disks with measured zones of inhibition. The dashed circles represent zone boundaries; larger zones correlate with greater susceptibility. Right: A broth microdilution readout showing MIC values for each antibiotic, with the twofold dilution series illustrated below. The MIC is the lowest concentration well that remains clear (no visible growth).

Notice that in the disk diffusion plate, the meropenem (MER) disk shows only a 10 mm zone — barely larger than the disk itself — indicating that the organism grew up close to the antibiotic, reflecting resistance. In contrast, the ampicillin (AMP) disk has a 28 mm zone, consistent with susceptibility. On the microdilution side, these same relationships are expressed numerically: a low MIC (e.g., 2 µg/mL for ampicillin) reflects susceptibility, while a high MIC (16 µg/mL for meropenem) signals resistance. The critical insight is that zone diameter and MIC are inversely correlated — larger zones mean lower MICs and vice versa — but the exact relationship varies by drug because each antibiotic has different diffusion properties through agar.

SECTION 4

How Breakpoints Are Established

Breakpoints are not arbitrary thresholds — they arise from the integration of three independent data streams. Understanding how CLSI and EUCAST derive these values illuminates why breakpoints differ between organisms and drugs, and why they are periodically revised. The three pillars of breakpoint determination are: microbiological data (wild-type MIC distributions), pharmacokinetic/pharmacodynamic (PK/PD) modeling, and clinical outcome data.

PK/PD Indices and Their Role

The pharmacokinetic/pharmacodynamic framework connects drug exposure in the body to antimicrobial efficacy. Three primary PK/PD indices predict outcomes for different classes of antibiotics, and breakpoints are set to ensure that the chosen category (S, I, or R) reflects whether the drug can achieve the required PK/PD target at the infection site.

TIME-DEPENDENT KILLING

%T > MIC ≥ target (e.g., 40–70%)

For β-lactams and carbapenems, efficacy correlates with the percentage of the dosing interval during which free drug concentration exceeds the MIC (ƒ%T > MIC). A susceptible breakpoint is set so that standard dosing achieves the required %T > MIC target.

CONCENTRATION-DEPENDENT KILLING

ƒC_max / MIC ≥ 8–10

For aminoglycosides, the ratio of the peak free drug concentration (ƒC_max) to the MIC best predicts bacterial killing. A ƒC_max/MIC ratio of 8–10 is associated with optimal clinical outcomes.

EXPOSURE-DEPENDENT KILLING

ƒAUC₂₄ / MIC ≥ 30–125

For fluoroquinolones and vancomycin, the area under the free drug concentration-time curve over 24 hours (ƒAUC₂₄) divided by the MIC is the critical predictor. Current vancomycin guidelines target an AUC₂₄/MIC ratio ≥ 400 (using total, not free, drug levels).

⚕ Clinical Relevance

When CLSI lowers a breakpoint (e.g., the 2010 revision of cephalosporin breakpoints for Enterobacterales), it means that pharmacokinetic modeling or clinical outcome data showed that the previous breakpoint was too generous — some isolates previously called "susceptible" were associated with treatment failures. Breakpoint changes directly impact which antibiotics clinicians choose and can alter resistance rates reported in hospital antibiograms overnight.

SECTION 5

Reading the S-I-R Report & Breakpoint Tables

A clinical susceptibility report typically lists the organism identification, the antibiotics tested, the measured MIC or zone diameter, and the S-I-R interpretation. To verify or understand these interpretations, microbiologists reference published breakpoint tables. The following diagram illustrates how a single MIC value is mapped through breakpoint criteria to determine the interpretation category for a hypothetical isolate of Escherichia coli tested against ciprofloxacin.

CLSI M100 breakpoints for ciprofloxacin versus Enterobacterales shown as dual scales. The upper bar represents MIC breakpoints, and the lower bar represents zone diameter breakpoints. Note that the scales are inversely oriented: low MIC values correspond to large zone diameters and vice versa. The worked example at the bottom shows an E. coli isolate with a ciprofloxacin MIC of 0.5 µg/mL (or zone of 18 mm), which falls in the Intermediate category.

Selected CLSI M100 breakpoints for Enterobacterales (representative values; always consult the current M100 edition)

Antibiotic	S ≤ (µg/mL)	I (µg/mL)	R ≥ (µg/mL)	S ≥ (mm)	I (mm)	R ≤ (mm)
Ampicillin	≤ 8	16	≥ 32	≥ 17	14–16	≤ 13
Ciprofloxacin	≤ 0.25	0.5	≥ 1	≥ 21	16–20	≤ 15
Gentamicin	≤ 4	8	≥ 16	≥ 15	13–14	≤ 12
Meropenem	≤ 1	2	≥ 4	≥ 23	20–22	≤ 19

When reviewing a susceptibility report, always verify that the correct organism-drug breakpoint table was used. Breakpoints for Pseudomonas aeruginosa differ from those for Enterobacterales, even for the same drug. Additionally, some drug-organism combinations have no established breakpoints, meaning the laboratory cannot provide an S-I-R interpretation and should report the raw MIC with a note indicating that breakpoints are not available.

SECTION 6

Worked Example — Interpreting a Susceptibility Report

A 58-year-old patient presents with a urinary tract infection. Urine culture grows Klebsiella pneumoniae. The microbiology lab reports the following MIC values from broth microdilution. Use CLSI M100 breakpoints for Enterobacterales to interpret the results and recommend a therapeutic agent.

Interpreting a K. pneumoniae UTI Susceptibility Report

Step 1 — Review Reported MIC ValuesThe lab reports the following MIC values: Ampicillin = ≥ 32 µg/mL, Ceftriaxone = ≤ 1 µg/mL, Ciprofloxacin = 0.5 µg/mL, Meropenem = ≤ 0.25 µg/mL, Trimethoprim-sulfamethoxazole (TMP-SMX) = ≤ 2/38 µg/mL. Note that MIC values preceded by "≤" indicate that the actual MIC is at or below the lowest concentration tested.

Five antibiotics reported with quantitative MIC data.

Step 2 — Look Up Breakpoints for Each DrugUsing the CLSI M100 Enterobacterales breakpoint table: Ampicillin — S ≤ 8, I = 16, R ≥ 32. Ceftriaxone — S ≤ 1, I = 2, R ≥ 4. Ciprofloxacin — S ≤ 0.25, I = 0.5, R ≥ 1. Meropenem — S ≤ 1, I = 2, R ≥ 4. TMP-SMX — S ≤ 2/38, R ≥ 4/76 (no intermediate category).

Breakpoints identified for all five agents.

Step 3 — Assign S-I-R CategoriesCompare each reported MIC to its breakpoint. Ampicillin: ≥ 32 µg/mL → MIC is at or above the R breakpoint of 32 → Resistant (R). Ceftriaxone: ≤ 1 µg/mL → MIC is at or below the S breakpoint of 1 → Susceptible (S). Ciprofloxacin: 0.5 µg/mL → MIC falls in the I range (between S ≤ 0.25 and R ≥ 1) → Intermediate (I). Meropenem: ≤ 0.25 µg/mL → well below the S breakpoint of 1 → Susceptible (S). TMP-SMX: ≤ 2/38 µg/mL → at the S breakpoint → Susceptible (S).

AMP = R, CRO = S, CIP = I, MEM = S, TMP-SMX = S

Step 4 — Apply Clinical ReasoningFor a UTI, clinicians prefer oral agents with high urinary concentrations when possible. Ampicillin is eliminated due to resistance. Ciprofloxacin is intermediate — but since fluoroquinolones concentrate in urine at levels far exceeding serum concentrations, some guidelines permit ciprofloxacin use for uncomplicated UTIs even at the I breakpoint. However, because K. pneumoniae is intrinsically resistant to ampicillin (most strains carry a chromosomal β-lactamase), this finding is expected. TMP-SMX is susceptible and is a preferred first-line oral agent for UTIs. Meropenem (a carbapenem) is susceptible but should be reserved for complicated infections due to antimicrobial stewardship principles.

Recommended agent: TMP-SMX (oral, narrow spectrum, S). Reserve meropenem and ceftriaxone for complicated or systemic infections.

Step 5 — Recognize Resistance FlagsReview the overall pattern for resistance mechanism clues. This isolate shows susceptibility to ceftriaxone and meropenem, suggesting it does not carry an ESBL or carbapenemase. Had the ceftriaxone MIC been elevated (e.g., 4 µg/mL), ESBL production should be considered, and the lab might perform confirmatory tests (e.g., cephalosporin/clavulanate combination disk test). Recognizing such patterns is a crucial skill that goes beyond mechanical breakpoint application.

No ESBL or carbapenemase pattern detected. Standard treatment is appropriate.

SECTION 7

Strengths & Limitations of AST Methods

No single susceptibility testing method is ideal for all clinical situations. Each approach has trade-offs in cost, turnaround time, quantitative precision, and ability to detect certain resistance mechanisms. The table below compares the most widely used phenotypic and genotypic methods encountered in clinical and research microbiology laboratories.

Comparison of commonly used antimicrobial susceptibility testing methods

Method	Strengths	Limitations
Disk Diffusion (Kirby-Bauer)	Low cost, simple equipment, flexible antibiotic panel, well-standardized (CLSI/EUCAST). Good for routine testing of non-fastidious organisms.	Semi-quantitative only (S/I/R, not exact MIC). Not suitable for slow-growing or fastidious organisms. Manual zone reading introduces inter-reader variability.
Broth Microdilution	Quantitative (provides exact MIC). Reference standard method. Can test multiple drugs on one panel. Amenable to automation.	More expensive, requires prepared panels or commercial systems. Fixed drug panels may not include all desired agents. Endpoint reading can be subjective for trailing endpoints.
Etest (Gradient Diffusion)	Provides a direct MIC reading on an agar plate. Combines convenience of disk diffusion with quantitative data. Excellent for testing individual drugs.	Expensive per strip. Impractical for large panels. Some drug-organism combinations show poor correlation with broth microdilution.
Automated Systems (VITEK, MicroScan)	Rapid turnaround (4–18 hours). Standardized reading eliminates subjectivity. Integrated software applies breakpoints automatically. Can flag expert rules.	High capital cost. May miss novel resistance mechanisms not in the algorithm database. Limited panel customization.
Molecular/Genotypic Detection	Extremely rapid (minutes to hours). Detects specific resistance genes (e.g., mecA, vanA, bla_KPC). Can be performed directly on clinical specimens.	Detects genotype, not phenotype — gene presence does not always equal expression. Cannot replace phenotypic AST for most decisions. Cannot detect novel mechanisms.

✦ KEY TAKEAWAY

Think of the relationship between genotypic and phenotypic susceptibility testing like the difference between reading a blueprint and testing a finished building. The blueprint (genotype) tells you the structure was designed with fire-resistant materials, but only a fire test (phenotype) tells you whether the building actually resists fire under real conditions. Similarly, detecting a resistance gene tells you the bacterium has the genetic blueprint for resistance, but only phenotypic testing confirms that the gene is expressed at levels sufficient to cause clinical resistance. Both types of information are valuable, but for most therapeutic decisions, phenotypic testing remains the standard.

SECTION 8

Beyond S-I-R — Advanced Interpretive Concepts

The simple S-I-R framework, while clinically powerful, has significant limitations that have driven the development of more nuanced interpretive approaches. Modern susceptibility reporting increasingly incorporates expert rules, intrinsic resistance tables, and interpretive reading — a practice in which microbiologists use susceptibility patterns to infer the underlying resistance mechanism and, in some cases, override mechanical breakpoint interpretations.

Advanced interpretive concepts in antimicrobial susceptibility testing

Concept	Description	Example
Intrinsic Resistance	Chromosomally encoded, species-level resistance that is universally present and does not need to be tested.	K. pneumoniae is intrinsically resistant to ampicillin due to SHV-1 β-lactamase. Labs should not report ampicillin as susceptible even if a testing artifact suggests it.
Expert Rules / Cascade Reporting	Algorithms that suppress or modify results to guide appropriate therapy and stewardship. Broad-spectrum agents may be suppressed if narrow-spectrum agents are susceptible.	If E. coli is susceptible to ampicillin, the lab may suppress cephalosporin results to encourage narrow-spectrum prescribing.
ESBL Detection & Interpretation	Extended-spectrum β-lactamases confer resistance to most cephalosporins. CLSI now uses revised breakpoints rather than requiring ESBL confirmatory tests before reporting.	An E. coli with ceftriaxone MIC = 4 µg/mL is reported as R using current breakpoints, regardless of ESBL confirmation.
SDD (Susceptible-Dose Dependent)	A newer CLSI category replacing I for some drug-organism combinations. Indicates the drug is effective only with modified dosing (higher dose, more frequent administration, or extended infusion).	Cefepime against Enterobacterales has SDD breakpoints; an MIC of 4–8 µg/mL is reported as SDD, prompting extended infusion protocols.

Looking forward, the integration of whole-genome sequencing (WGS) with phenotypic AST promises to enhance both the speed and depth of susceptibility interpretation. WGS can identify all known resistance genes in a single assay, predict MICs using machine learning models trained on large genotype-phenotype databases, and detect transmission clusters in real time. However, prediction accuracy varies by organism and drug, and phenotypic confirmation remains essential when WGS predictions conflict with clinical expectations. As databases grow and algorithms improve, the boundary between genotypic prediction and phenotypic testing will continue to blur, but the fundamental principles of breakpoint interpretation will remain at the center of clinical microbiology.

SECTION 9

Practice Problems

PROBLEM 1 — CONCEPTUAL

Explain why a larger zone of inhibition in a Kirby-Bauer test generally indicates greater susceptibility, and identify one factor other than intrinsic resistance that could cause two different antibiotics to produce different zone sizes against the same organism at the same MIC.

PROBLEM 2 — BASIC CALCULATION

A clinical isolate of Escherichia coli tested by broth microdilution yields a gentamicin MIC of 8 µg/mL. Using the CLSI breakpoints for Enterobacterales (S ≤ 4, I = 8, R ≥ 16 µg/mL), what is the S-I-R interpretation? If the patient has a serious bloodstream infection, would you recommend gentamicin monotherapy?

PROBLEM 3 — INTERMEDIATE

A lab reports a disk diffusion zone of 19 mm for meropenem against a Klebsiella pneumoniae isolate. The CLSI M100 breakpoints for meropenem vs. Enterobacterales are: S ≥ 23 mm, I = 20–22 mm, R ≤ 19 mm. Interpret this result and describe the next step the laboratory should take, given the clinical significance of carbapenem resistance.

PROBLEM 4 — APPLIED

You are a clinical microbiologist reviewing the annual hospital antibiogram. For E. coli urinary isolates (n = 1,200), ciprofloxacin susceptibility has declined from 82% to 68% over three years. The infectious disease team asks whether ciprofloxacin should remain on the empiric UTI treatment guideline. What susceptibility threshold is generally recommended for empiric therapy of uncomplicated UTIs, and what is your recommendation?

PROBLEM 5 — CRITICAL THINKING

An Enterobacter cloacae blood culture isolate tests susceptible to cefazolin (MIC ≤ 2 µg/mL) and susceptible to ceftriaxone (MIC ≤ 1 µg/mL) by broth microdilution. A colleague argues that ceftriaxone should be used for treatment since the isolate is susceptible. Evaluate this reasoning, considering the concept of inducible AmpC β-lactamase production, and explain what the laboratory should do with these results before releasing them.

SUMMARY

Lesson Summary

Interpreting antimicrobial susceptibility results requires integrating laboratory data with pharmacological and clinical knowledge. The two primary measurements — the minimum inhibitory concentration (MIC) from dilution methods and the zone of inhibition from disk diffusion — are translated into the clinical categories Susceptible (S), Intermediate (I), and Resistant (R) through interpretive breakpoints established by CLSI or EUCAST. These breakpoints are derived from the convergence of wild-type MIC distributions, PK/PD modeling (including indices such as %T > MIC, ƒC_max/MIC, and ƒAUC₂₄/MIC), and clinical outcome studies.

Competent interpretation goes beyond mechanical comparison of a number to a threshold. It requires awareness of intrinsic resistance (species-level resistance that overrides in vitro results), inducible resistance mechanisms (such as AmpC derepression in ESCPM organisms), expert rules and cascade reporting that guide antibiotic stewardship, and the site-of-infection context that determines whether an intermediate result might still predict clinical success (as with fluoroquinolones in urinary tract infections). Mastery of these principles enables microbiologists and clinicians to transform raw laboratory data into optimized, patient-specific therapeutic decisions.