CLSI M39-guided cumulative susceptibility matrix for empiric antimicrobial selection
A cumulative antibiogram is only as trustworthy as the isolate collection process behind it. CLSI document M39-A4 ("Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data") lays out strict rules to avoid two classic statistical traps: sampling too few isolates to trust a percentage, and double-counting the same resistant organism from one patient's repeat cultures, which would artificially inflate apparent resistance.
CLSI M39 requires a minimum of 30 isolates of a given species tested against a given antibiotic before that percentage is reported on the facility antibiogram. This is not an arbitrary cutoff — it reflects basic binomial confidence interval mathematics.
Why it matters: • With n=10 isolates and 8 susceptible, the reported 80% susceptibility carries a 95% CI of roughly 44–97% — clinically useless for guiding therapy • With n=30, the same 80% point estimate narrows to a 95% CI of about 61–92% • With n=100+, the CI tightens further to roughly 71–87%, approaching a number a clinician can actually act on
Organism-antibiotic combinations that fail to reach 30 isolates in a given year are suppressed from the published antibiogram (shown as "insufficient data") rather than reported with a falsely precise percentage. Some laboratories footnote low-volume organisms (e.g., Acinetobacter baumannii in a facility with few ICU beds) as "combined with prior year" to reach threshold.
A hospital lab that reports 100% ciprofloxacin susceptibility for Pseudomonas aeruginosa based on 4 isolates is not being helpful — it is being statistically misleading. CLSI explicitly instructs suppression below n=30 for exactly this reason.
The single most important rule in M39 is deceptively simple: include only the FIRST isolate of a given species from a given patient within the analysis period, regardless of anatomic source or specimen type.
Why duplicates distort the picture: • A patient with a persistent multidrug-resistant UTI may have 6 urine cultures over 3 weeks, all growing the same resistant E. coli clone • Counting all 6 as independent isolates would make the organism appear far more resistant hospital-wide than it truly is — the antibiogram would reflect one patient's clinical failure, not population-level resistance • Conversely, repeated susceptible cultures from patients who clear infection easily would artificially inflate apparent susceptibility
Implementation in the microbiology LIS (laboratory information system): • Deduplication logic keys on patient medical record number + organism species (not exact strain typing, which is rarely available) • "First" is typically defined by collection date, not report date • Separate site-specific antibiograms (e.g., blood-only, urine-only) may be built if sample sizes allow, since resistance patterns can differ meaningfully by source — blood isolates trend more resistant than urine isolates for the same organism because they often originate from healthcare-associated or breakthrough infections
Modern antibiogram construction pulls from automated identification and susceptibility platforms integrated with the hospital LIS:
• VITEK 2 / BD Phoenix / MicroScan: automated broth microdilution systems generate organism ID and MIC values within 6-18 hours for most Gram-negative and Gram-positive organisms • MALDI-TOF mass spectrometry: rapid organism identification (minutes, not days) from colony material, feeding species-level classification into the antibiogram grouping logic • CLSI M100 breakpoint tables: MIC values are converted to Susceptible / Intermediate / Resistant categories using the current-year M100 breakpoint document, which is revised annually — a strain's categorical result can change year to year even if its MIC is unchanged, because breakpoints themselves are periodically lowered as resistance mechanisms are better understood • Infection control exclusion: isolates known to represent colonization/surveillance screening (e.g., MRSA nasal screens, VRE rectal swabs) are excluded from the clinical antibiogram, which is meant to reflect isolates from actual infections driving treatment decisions
The 2024 dataset compiled here totals 1,066 non-duplicate clinical isolates across five organism groups (E. coli, K. pneumoniae, P. aeruginosa, S. aureus, Enterococcus spp.), each exceeding the 30-isolate CLSI threshold for every reported antibiotic column.
Once the qualifying isolate set is finalized, the laboratory computes %Susceptible for every organism-antibiotic pair and arranges the results into the grid every clinician recognizes as "the antibiogram" — organisms down the rows, antibiotics across the columns, colored or shaded by susceptibility tier.
The core antibiogram calculation is: %Susceptible = (number of susceptible isolates) ÷ (total isolates tested against that agent) × 100.
Handling of the "Intermediate" category: • CLSI M39 specifies that %S is calculated using ONLY the susceptible count in the numerator — intermediate and resistant isolates are both excluded from the numerator • Some institutions additionally report a "%Susceptible + Intermediate" column for agents where intermediate results still predict reasonable clinical response at higher dosing (e.g., extended-infusion beta-lactams), but the headline number clinicians use for empiric selection is strict %S • A drug with 70% S and 15% I looks meaningfully different from one with 70% S and 15% R — the stewardship team reviews both when %S alone sits in a gray zone (60-80%)
Cross-resistance and cascade reporting: • Cascade reporting suppresses susceptibility results for broader-spectrum agents (e.g., meropenem) when a narrower agent (e.g., ceftriaxone) already tests susceptible, to nudge prescribers toward narrower therapy — this is itself a stewardship intervention embedded in the microbiology report • The antibiogram matrix is typically built from ALL tested isolates regardless of cascade suppression, so the full picture remains available for antibiogram purposes even though individual patient reports show cascaded results
This dashboard's matrix (see canvas heatmap) reflects five organism rows against six antibiotic columns, using the standard three-tier color convention:
• Green (≥80% S): reliable empiric choice for that organism • Amber (60-79% S): borderline — acceptable only if no better option exists, or combined with source control / de-escalation plan • Red (<60% S): unreliable for empiric use; should not be chosen blind • Gray/dash: not clinically applicable (e.g., vancomycin is not tested against E. coli because it lacks intrinsic Gram-negative activity; ceftriaxone is not meaningfully active against Enterococcus)
Key patterns visible in the 2024 data: piperacillin-tazobactam and meropenem retain strong Gram-negative coverage (85-99% S) across E. coli and K. pneumoniae, while ceftriaxone susceptibility has slipped into the 70s for both organisms — a classic signature of rising ESBL (extended-spectrum beta-lactamase) prevalence, explored further in Stage 5.
The entire purpose of the antibiogram is realized at the bedside: a febrile patient needs antibiotics NOW, before any culture result is available, and the clinician must guess the most probable pathogen and the antibiotic most likely to cover it. The antibiogram converts that guess from folklore into a locally validated, data-driven decision.
Empiric selection using the antibiogram follows a three-step chain that every antimicrobial stewardship curriculum teaches:
Step 1 — Identify the likely syndrome and probable pathogen(s): • Uncomplicated cystitis / febrile UTI → E. coli (65-75% of cases), K. pneumoniae, Proteus • Purulent cellulitis / abscess → S. aureus (including MRSA), less commonly Streptococcus • Late-onset hospital-acquired or ventilator-associated pneumonia → P. aeruginosa, K. pneumoniae, S. aureus • Line-associated bacteremia → coagulase-negative Staph, S. aureus, occasionally Gram-negatives
Step 2 — Go to that organism's row on the antibiogram and scan across antibiotic columns for the highest %S among agents that are clinically appropriate (right spectrum, right tissue penetration, right toxicity profile for that patient)
Step 3 — Apply an institutional threshold, commonly ≥80-90% S, below which an agent is not considered reliable for blind empiric use in a seriously ill patient. If no single agent clears the threshold, combination empiric therapy or a broader-spectrum agent is chosen deliberately, pending de-escalation once culture data return (see the companion "Antibiotic De-Escalation Decision Support" simulator for that downstream process).
Worked example from this hospital's 2024 data: for suspected pyelonephritis (E. coli row), piperacillin-tazobactam (93% S) and meropenem (99% S) both clear threshold; ceftriaxone (78% S) falls into the amber "use with caution" zone and would not be first-line empiric choice for a hemodynamically unstable patient, even though it remains a perfectly reasonable step-down agent once susceptibility is confirmed.
The antibiogram is a population-level tool and has real limitations clinicians must respect:
• It reflects the AVERAGE patient in the hospital population, not necessarily the patient in front of you — a patient transferred from a nursing home with recent carbapenem exposure carries individually higher resistance risk than the pooled hospital rate suggests • It is retrospective by definition — a full calendar year of data compiled and published the following January means the matrix can lag emerging resistance trends by up to 12-18 months • It does not account for combination effects, prior culture history for that specific patient, or local unit-level "hot spots" (e.g., an ICU with a higher MRSA burden than the hospital-wide average) — many institutions now publish unit-specific or ward-specific antibiograms (ICU vs. general medicine) for this reason • It cannot substitute for source control (draining an abscess, removing an infected line) which often matters more than antibiotic choice alone
A single hospital's antibiogram gains additional meaning when placed alongside regional and national resistance surveillance data. The CDC's National Healthcare Safety Network (NHSN) Antimicrobial Resistance (AR) Option aggregates susceptibility data from thousands of participating facilities, providing a pooled-mean benchmark that helps a stewardship committee decide whether their institution's resistance pattern is typical or an outlier demanding local investigation.
A hospital antibiogram in isolation cannot tell you whether 78% ceftriaxone susceptibility in E. coli is "normal" or a red flag. Benchmarking against CDC NHSN AR-tracking and regional public health department aggregates provides that context.
Uses of benchmarking: • Outlier detection: if local %S for a key drug falls meaningfully (typically >5 percentage points) below the regional/national pooled mean, it triggers a root-cause review — is this a true reservoir of resistant organisms circulating in the facility, a recent outbreak, or a testing/reporting artifact? • Regional risk stratification: NHSN and state health department dashboards allow comparison to peer facilities of similar size, patient acuity, and geography — comparing a large tertiary academic center to a small community hospital using raw national averages would be misleading, so peer-group stratification is preferred when available • Trend corroboration: if a hospital sees rising carbapenem-resistant Enterobacterales (CRE) locally at the same time regional/national CRE surveillance also shows an uptick, it strengthens confidence that a true epidemiologic shift is occurring rather than a local data artifact
This hospital's 2024 ceftriaxone susceptibility for E. coli (78%) sits about 4.2 percentage points below the CDC NHSN pooled national mean (~82%) — inside the "monitor" zone but not yet triggering a full outbreak investigation under this institution's stewardship policy (which sets the escalation trigger at a >5 point gap sustained across two consecutive reporting periods).
Antimicrobial stewardship programs accredited under The Joint Commission and CMS Conditions of Participation are required to review facility-specific resistance trends at least annually and to document comparison against external benchmarks such as NHSN AR Option data as part of core stewardship activities.
The CDC's Core Elements of Hospital Antibiotic Stewardship Programs frames antibiogram benchmarking as one pillar within a larger structure:
1. Leadership commitment — dedicated financial and human resources 2. Accountability — a single physician or pharmacy leader responsible for program outcomes 3. Pharmacy expertise — a pharmacist co-leader empowered to improve antibiotic use 4. Action — implementing interventions such as prospective audit-and-feedback, prior authorization for restricted agents, and antibiogram-driven order set defaults 5. Tracking — monitoring antibiotic prescribing, resistance patterns, and outcomes (this is where the annual antibiogram lives) 6. Reporting — regularly sharing antibiogram and stewardship metrics with prescribers, unit leadership, and hospital administration 7. Education — training clinical staff on resistance trends and updated empiric guidance
Benchmarking against national NHSN data operationalizes element 5 (Tracking) and directly feeds element 6 (Reporting), closing the loop between surveillance data and frontline prescribing behavior.
A single year's antibiogram is a snapshot; overlaying five consecutive years reveals the trajectory that matters most for forward-looking policy. The most consequential pattern in this hospital's dataset is a steady, multi-year decline in ceftriaxone susceptibility among E. coli and K. pneumoniae — the fingerprint of increasing ESBL (extended-spectrum beta-lactamase) enzyme prevalence.
Extended-spectrum beta-lactamases (ESBLs) are plasmid-encoded enzymes (commonly CTX-M-type in contemporary U.S. epidemiology) that hydrolyze third-generation cephalosporins such as ceftriaxone and ceftazidime, along with aztreonam and penicillins, while classically sparing carbapenems and cephamycins.
What a rising ESBL burden looks like on a multi-year antibiogram: • A gradual, sustained decline in %S to ceftriaxone/ceftazidime for E. coli and K. pneumoniae, often 1-3 percentage points per year • A WIDENING gap between cephalosporin susceptibility and carbapenem/pip-tazo susceptibility for the same organisms, since ESBL producers typically remain susceptible to those agents • Little to no corresponding change in Gram-positive organism susceptibility (S. aureus, Enterococcus) — the trend is specific to Gram-negative beta-lactamase-mediated resistance, not a generalized institutional decline
This hospital's reconstructed five-year trend shows ceftriaxone %S in E. coli falling from approximately 87% (2020) to 78% (2024) — a 9-percentage-point decline — while meropenem susceptibility has remained essentially flat (97-99%) across the same period, precisely the divergence pattern expected from expanding ESBL prevalence rather than a broader resistance crisis.
CLSI M100 recommends that laboratories no longer perform routine ESBL confirmatory testing when using current cephalosporin breakpoints, because the lowered breakpoints already capture most ESBL producers as "resistant" or "susceptible-dose-dependent" without a separate confirmatory step — meaning the %S column itself is the primary surveillance signal for stewardship teams tracking ESBL drift over time.
Leading stewardship programs increasingly apply statistical process control (SPC) methods — control charts originally developed for manufacturing quality assurance — to serial antibiogram data, distinguishing normal year-to-year sampling noise from a true, statistically significant shift in resistance.
Practical approach: • Plot %S for each key organism-drug pair across at least 5 consecutive years • Calculate a control limit band (e.g., ±2 standard deviations around the historical mean) using the isolate counts and binomial variance for each year • A single year's dip within the control band may be sampling noise; two or more consecutive years trending in the same direction, or any single year breaching the control limit, warrants formal review • This hospital's ceftriaxone/E. coli trend (87% → 84% → 82% → 79% → 78%) shows a consistent monotonic decline across all five years — a pattern control-chart methodology would flag as a true special-cause signal, not random variation, justifying the policy response described in Stage 6
Data without action is merely documentation. The final and most consequential stage of the antibiogram cycle is translating observed resistance trends into concrete changes to hospital empiric therapy guidelines, computerized order sets, and restricted-antibiotic criteria — the mechanism by which population-level surveillance data actually changes what happens at the bedside.
Once the annual antibiogram and its multi-year trend analysis are finalized, the Pharmacy & Therapeutics (P&T) Committee and Antimicrobial Stewardship Program (ASP) jointly review findings and decide on concrete interventions. For this hospital's 2024 cycle, the declining ceftriaxone susceptibility trend in E. coli/K. pneumoniae prompted the following changes:
1. Sepsis order set default: empiric Gram-negative coverage for suspected severe sepsis/septic shock of urinary or intra-abdominal origin shifted from ceftriaxone monotherapy to piperacillin-tazobactam as the new pathway default, reserving ceftriaxone for confirmed susceptible organisms or low-severity presentations 2. Febrile UTI/pyelonephritis pathway: outpatient step-down criteria updated to require confirmed culture susceptibility before continuing oral cephalosporin therapy, rather than defaulting to it empirically 3. Restricted-agent criteria: no new restrictions were added to carbapenems this cycle, since carbapenem susceptibility remained stable (≥97%) — restricting a still-reliable agent prematurely can paradoxically accelerate resistance to whatever replaces it 4. Education rollout: a hospital-wide grand rounds and pharmacy-led unit in-services communicated the updated empiric recommendations and the data supporting them
This full cycle — from raw isolate data to a go-live order set change — took approximately 8-10 weeks, consistent with typical P&T committee review and IT build timelines for clinical decision support updates.
Antimicrobial stewardship order-set changes driven directly by antibiogram trend data have been shown in multiple published quality-improvement studies to reduce inappropriate empiric antibiotic selection by 15-30% and to measurably slow further resistance drift over subsequent reporting cycles — turning a surveillance document into an active clinical intervention.
Policy change is not the end of the cycle — it is the beginning of the next one. The updated order sets and restricted-agent criteria themselves become inputs the stewardship program monitors going forward:
• Prospective audit-and-feedback: pharmacists review empiric antibiotic selections against the new order set defaults, providing real-time feedback to prescribers who deviate without documented justification • Compliance metrics: percentage of eligible sepsis cases receiving pathway-concordant empiric therapy is tracked monthly and reported to unit medical directors • Next-year antibiogram as the outcome measure: the ultimate test of whether the policy change was effective is whether ceftriaxone and pip-tazo susceptibility trends stabilize or reverse in the following year's antibiogram — closing the surveillance-to-action-to-re-surveillance loop that defines a mature antimicrobial stewardship program
This cyclical structure — collect, analyze, benchmark, trend, act, remeasure — repeats annually in perpetuity, making the hospital antibiogram not a static report but a living instrument of institutional infection-control policy.