Cumulative Antibiogram data should be accurate and validated. The final antibiogram should be consistent with guidelines from the Clinical
Laboratory Standards Institute (CLSI, formerly NCCLS) found in a document named M39-A2 (Analysis and Presentation of Cumulative
Antimicrobial Susceptibility Test Data: Approved Guidelines). The M39-A2 provides guidance on standardizing the approach to antibiogram
development and its clinical use. The tables in this checklist are based on the updated M39-A recommendations and specifications.
Recommended Data Elements for an Antibiogram: Quick Checklist
The recommendations are from M39-A2 documents. Data on the antibiogram should be consistent from year to year. Data elements analyzed and
reported are those with the greatest impact on appropriate empiric antibiotic selection.
The antibiogram is annual
Explanation: Antibiogram period is no longer than one year and more frequent analysis is performed only on bacterial species which include large numbers
of isolates or when clinically important changes occur.
Inclusive dates for the antibiogram report are indicated
The minimum number of isolates of each species listed is 30, and the total number of isolates is listed
Explanation: Small numbers potentiate large interval changes in percent susceptible. Possible ways to express data when the number is <30 includes:
a) combine data from several years; b) combine data from several institutions in a defined geographic area; c) combine data for more than one species
within a genus (i.e., Shigella spp, or when further speciation is not performed, i.e., coagulase-negative staphylococci); and, d) include data from published
summaries and guides. Footnote the specific manipulations employed.
Duplicate isolates are removed and the first isolate per patient is employed
Explanation: Include only the first isolate of a given species per patient per analysis period, irrespective of body site, susceptibility test result, or phenotypic resistance profile. The ratio of isolates of a species to the total number of unique patients should be 1.00, assuming the absence of duplicate isolates.
Values provided in the antibiogram grid represent percent susceptible; except notation for intermediate susceptibility of Streptococcus
pneumoniae and viridans group streptococci for penicillin
Cumulative data is not combined from more than one institution without explanation or subanalysis
Data represents bacterial isolates collected for diagnostic purposes
Explanation: No surveillance culture data should be included on the antibiogram (i.e., fecal VRE, nasopharyngeal swabs for MRSA, etc.).
Isolates are stratified according to infection site, such as urine versus non-urine, adult versus pediatric, and ICU versus non-ICU, when appropriate
Explanation: Separate data from different demographic origins when % susceptibility results vary significantly. Separate reporting illustrates that
resistance is higher in the ICU and lower in non-ICU areas. Patients move between units, and it is important to follow the migration of resistance pathogens
within an institution. Stratification may reveal distinct difference in % susceptibility between the same species depending on culture site (blood, urine,
respiratory) and location (TCU, SNF/LTCF using hospital as reference lab, etc.).
Tables are separate for gram-negatives and gram-positives
Specific drugs listed to represent a particular antibiotic class are footnoted
A legend is used to associate full names with standardized abbreviations, as found in M39
Explanation: Standardized abbreviations should be the same as listed in patient reports.
Clinically ineffective drugs do not have empty cells and are represented by a dash or other symbol
Explanation: There may be drugs tested that are inappropriate even if they test as susceptible (e.g., Bactrim®- P.aeruginosa and Cephamycins-Salmonella).
Even if the drug is appropriate in combination therapy, it should not be reported (e.g., rifampin-S.aureus).
MRSA and MSSA are treated as separate line listings
Streptococcus pneumoniae percent susceptibilities for CSF isolates reflect meningitis breakpoints
Susceptibility testing methods are listed
Explanation: If different methods of susceptibility testing become available and yield different values compared to previous results or within a specific
species (e.g., P.aeruginosa), these should be noted.
Selectively tested drugs are reported separately, as footnotes or unique line items, as applicable
Explanation: Selective testing (“cascade testing”) is often applied to multi-drug resistant isolates when primary agents demonstrate resistance. Note the
number of uniquely tested isolates. Including result from selective testing will bias the cumulative data towards higher resistance. Likewise, report
separately results of agents supplementally tested against multi-drug resistant isolates if the results are clinically important. The number of isolates and
the software rules for selective testing should be listed. For antimicrobials tested against isolates from urine only, restate or footnote the number of urine
isolates tested against the specific agent. Footnote the selective tests (e.g., nitrofurantoin is tested on urine isolates only).
Essential Data Elements within the Retrievable Database
It is essential that data is exported from the laboratory’s primary data management system to an external analysis software and the data consists of all
verified and finalized antimicrobial susceptibility test results. The external data system must have the ability to analyze data for a defined time period to
generate cumulative statistics and line listings and to remove or edit incorrect data in the database. Antibiograms should be produced for single healthcare
facilities. From this database, several queries will be made to populate the antibiogram template.
Patient demographics
Required: unique ID number, healthcare facility, age, sex, bed location of isolate obtained, and admission date
Optional (every attempt is made to capture or link the following patient demographic information): clinical service of admission, admitting diagnosis and
ICD-9/10 or DRG codes, infectious disease diagnosis, patient’s clinical picture or situation, co-morbidities, severity of illness scores, current antimicrobial
therapy, previous antimicrobial therapy, and nursing home/long-term care association
Specimen information
Required: specimen number or unique identifier, date of specimen collection, specimen type (blood, CSF, urine, wound, etc.)
Organism information
Required: genus and species, and trends of name changes are linked
Optional (every attempt is made to capture the following organism information): 1) does the isolate represent colonization or infection, and 2) does the
isolate represent community or nosocomial acquisition?
Antimicrobial susceptibility test information
Required: in separate fields, quantitative test results (MIC, zone size) and/or qualitative interpretations (S, I, R) of all agents tested, regardless of whether
they are reported, from final and verified results. This includes supplemental testing against multi-drug resistant pathogens (e.g., P.aeruginosa and
polymyxin B)
Required: validate all test results using quality indicators to screen for many unusual results. Verification protocols may include rare or unusual phenotypes, and provide alerts of outbreaks, emergence of a new resistant strain of epidemiologic importance, or technical error. Rules include: a) vancomycin
and S.aureus should be 100%; b) inconsistent beta-lactam susceptibilities for S.aureus; c) E.coli and imipenem not 100% S; and d) K.pneumoniae with any
ampicillin susceptibility > 0%. (+) Other examples are provided on p. 5-6 of M39-A2.
Optional: an appropriate subset of data is validated by manual calculation from raw data
Optional: specific susceptibility test system used (MBD, agar diffusion, E-test, specific panel, commercial system) and original quantitative results
retained following “expertised” interpretations*
(+) See Zapantis (11)
* For ESBL-producing Klebsiella, the susceptibility results of all cephalosporins may be modified to "resistant", but original MIC or zone size should be retained for reference
Factors that Can Influence Antibiogram Data and Interpretation of Resistance Causes
Development of antibiotic resistance is inherently intertwined between three major factors: infection control, antibiotic use, and patient demographics.
Infection Control
Antibiotic Use
Demographics
✓ Noncompliance with infection control policies
✓ Over-utilization of a specific class
✓ Average length of stay
✓ Spread of resistant clones through contact
with colonized healthcare personnel
✓ Suboptimal antibiotic dosing
✓ Number of long-term care facilities in
service area
✓ Entry of resistant pathogens into hospital
✓ Changes in resistance patterns within the
community
✓ Over-the-counter antibiotic use (border areas)
and foreign travel
✓ Improper antibiotic selection
✓ Use of unnecessary antibiotic combinations
✓ Excessive antibiotic duration
✓ Changes in case-mix index, severity of
diseases, number of ICU beds
✓ Addition of transplantation services, oncology
care, burn unit, etc.
✓ Changes in patient populations who are
predisposed to nosocomial pathogens
✓ Acute care versus ICU beds
✓ Interhospital transfers
Design and Presentation of Antibiogram
The design style, presentation, and stratification of data will dictate the clinical usefulness of the cumulative antimicrobial susceptibility report. Allotment
of space should be considered for other data of clinical relevance.
Design is user friendly, readable, and well-organized
Antibiogram should be published in a readable font size (at least 8)
Double design antibiogram to include a pocketsize fold-out and medical chart version
Special sections are clearly readable and contain relevant clinical information
Organisms are listed in tables by prevalence (preferable) or alphabetically
There is a section for footnotes which are relevant, i.e., prevalence of beta-lactamase in H.influenzae.
A section for analysis of longitudinal antibiogram data (trends) may focus on emerging resistance trends
Clindamycin inducibility in MRSA with its erythromycin-R clinda-S isolates
Gentamicin and streptomycin synergy testing for systemic enterococcal isolates
Formulary (and restricted or non-formulary) antibiotics may be noted by italicizing, footnoting, etc.
Ideal/adjusted body weight and Cockcroft-Gault (creatinine clearance) equations
Empiric therapy guidelines (based on antibiogram data) for medicine and specialty areas
Dosing nomograms (i.e., aminoglycosides) and renal and hepatic dosing adjustments
Automatic substitution policies
Absolute or relative cost data is included
Dissemination and Use of Antibiogram for Education: After the Antibiogram
An overlooked aspect of antibiogram development and surveillance is the decision of what to do with the antibiogram data and analyses – how are
recommendations to be conveyed to prescribers, how is education to be conducted, and how will impacts of the education be assessed. Not infrequently, the
development and publication of the antibiogram marks the endpoint of the process.
Ensure a process of identification, preferably as part of the day-to-day function of data verification, of new patterns of resistance during or after treatment,
since the first isolate per patient is insensitive to second or later isolates which may develop resistance
Educational efforts to present charts to highlight resistance trends
Disseminate information on appearances of rare phenotypes which may have been missed through the “first isolate” approach
Teaching to facilitate understanding and use of the antibiogram
Describe by case examples how the antibiogram may be used as a general guide to empiric therapy
Include antibiogram and education on institution’s intranet or website application
Include copies of antibiograms in manuals at nursing stations in binders
Supply printed versions to microbiology, infection control and epidemiology, quality improvement departments, nursing, and pharmacy personnel
Discuss development of chart dividers, empiric therapy guidelines by disease state
Disseminate information on the web which is useful for antibiogram analysis
Become familiar with Chi-square analysis tables (in M39) to interpret significance of trend changes
Analyze culture positivity and improve culturing practice
Three Scenarios in which Antibiogram Data is Useful in Epidemiologic Research Investigations, Guideline Development,
Antibiotic Pathways, and Physician Education
Antibiogram-specific
situation
Research Question
Indicators for audit
(MUE/DUE, investigation)
A nosocomial pneumonia treatment
pathway is being devised
What should be the primary
antibiotic regimens of choice to
treat nosocomial pneumonia?
✓ Compare the institution’s pathogens with the incidence cited in
reference studies on pathogen distribution
✓ Analyze the cases of VAP according to early-onset and late-onset
✓ Examine drug susceptibilities for VAP-associated pathogens
✓ Define antibiotic susceptibility issues and recent outbreaks in the ICU
MRSA rates have increased ~5%
per year over last 5 years
What factors are important in the
increase of MRSA at the hospital?
✓ MRSA isolates taken from clinics, ED, ward, and ICU;
adult versus pediatric
✓ MRSA isolates collected from patients <48 hours (community-acquired)
versus >48 hours following admission (nosocomial-acquired)
✓ Specimen types, e.g., skin, soft tissue, wound, blood, urine, sputum, etc.
✓ Patient demographics according to MRSA isolates (risk factor): patients
from LTCF, treatment of diabetic foot, ventilator-associated pneumonia,
surgical wound infection
✓ Density of antibiotic use or exposure within 4 weeks prior to
isolation of MRSA
✓ Patient length of stay, # procedures, and severity of illness prior
to +MRSA
Fluoroquinolone-resistant E.coli is
25% of all E.coli isolates this year,
but five years ago it was only 5%
What patient-, institution-, or
antibiotic-related factors are most
significant for this change?
✓ Distribution of FQ-R E.coli from clinics, ED, ward, and ICU
✓ Percentage of FQ-R E.coli from urinary versus non-urinary specimens
✓ Male-to-female and age group ratios
✓ % FQ-R E.coli from patients with cystitis, acute pyelonephritis, sepsis
syndrome, or other diagnosis
✓ Percentage of FQ-R E.coli from patients with chronic indwelling
Foley catheters
✓ Antibiotic exposure history within previous 4 weeks prior to isolation
✓ Phenotypic resistance of FQ-R E.coli (summarized as 1-drug, 2-drug,
3-drug, resistance patterns)
The following can be obtained at www.clsi.org:
1. Analysis and presentation of cumulative antimicrobial susceptibility test data. Wayne, Pa: Clinical and Laboratory Standards Institute; 2005. 2nd ed. CLSI Publication M39-A2.
2. Ernst EJ, Diekema DJ, Boots Miller BJ, et al. Are United States hospitals following national guidelines for the analysis and presentation of cumulative antimicrobial susceptibility data?
Diagn Microbiol Infect Dis. 2004;49:141-145.
3. Halstead DC, Gomez N, McCarter YS. Reality of developing a community-wide antibiogram. J Clin Microbiol. 2004;42:1-6.
4. Horvat RT, Klutman NE, Lacy MK, Grauer D, Wilson M. Effect of duplicate isolates of methicillin-susceptible and methicillin-resistant Staphylococcus aureus on antibiogram data.
J Clin Microbiol. 2003;41:4611-4616.
5. Lee SO, Cho YK, Kim SY, Lee ES, Park SY, Seo YH. Comparison of trends of resistance rates over 3 years calculated from results for all isolates and for the first isolate of a given species
from a patient. J Clin Microbiol. 2004;42:4776-4779.
6. Poupard J, Brown J, Gagnon R, Stanhope MJ, Stewart C. Methods for data mining from large multinational surveillance studies. Antimicrob Agents Chemother. 2002;46:2409-2419.
7. Rodriguez JC, Sirvent E, Lopez-Lozano JM, Royo G. Criteria of time and antibiotic susceptibility in the elimination of duplicates when calculating resistance frequencies.
J Antimicrob Chemother. 2003;52:132-134.
8. Shannon KP, French GL. Antibiotic resistance: effect of different criteria for classifying isolates as duplicates on apparent resistance frequencies. J Antimicrob Chemother. 2002;49:201-204.
9. Van Beneden CA, Lexau C, Baughman W, et al. Aggregated antibiograms and monitoring of drug-resistant Streptococcus pneumoniae. Emerg Infect Dis. 2003;9:1089-1095.
10. White RL, Friedrich LV, Burgess DS, Brown EW, Scott LE. Effect of removal of duplicate isolates on cumulative susceptibility reports. Diagn Microbiol Infect Dis. 2001;39:251-256.
11. Zapantis A, Lacy MK, Horvat RT, et al. Nationwide antibiogram analysis using NCCLS M39-A guidelines. J Clin Microbiol. 2005;43:2629-2634.
WHONET and BACLINK
WHONET is a free software available from the World Health Organization for the laboratory-based surveillance of infectious diseases and antimicrobial resistance. BacLink is an interface tool
for capturing data from existing databases, laboratory information systems, and susceptibility test instruments.