This document provides guidance on antibiograms, which are profiles of antimicrobial susceptibility testing results that summarize percentages of microorganisms susceptible to various drugs. It discusses generating antibiograms from aggregate laboratory data and including only clinically useful drugs. Methods for measuring susceptibility like disc diffusion and broth dilution are outlined. Recommendations include analyzing data annually and only including common species and diagnostic isolates. Antibiograms help guide empirical treatment and detect resistance trends. Limitations include potential biases and small isolate numbers reducing significance.

1. By: Dr Mostafa Mahmoud PhD,
Consultant Microbiologist, Riyadh, MOH
Assist. Professor of Medical Microbiology &
Immunology, Faculty of Medicine, ASU, Cairo,
Egypt.

2. What is an Antibiogram (ABG)?
 An antibiogram is an overall profile of antimicrobial
susceptibility testing (AST) results of a specific
microorganism to a battery of antimicrobial drugs.
 This profile is generated by the laboratory using
aggregate data from a hospital or healthcare system;
data are summarized periodically and presented
showing percentages (%) of organisms tested that
are susceptible to a particular antimicrobial drug.
 Only results for antimicrobial drugs that are
routinely tested and clinically useful should be
presented to clinicians.

3. How to measure the antimicrobial
susceptibility in the micro lab?
1- Disc Diffusion (DD) method and E-test (Manual
labs).
2- The broth dilution (MIC) method either macro or
microdilution (Automated labs).

4. 1- Disc Diffusion method (Manual labs)
Disc Diffusion Ring Disc Diffusion E- Test.

5. 2- Broth Microdilution (Microscan)

6. 2- Broth Microdilution (Phoenix)

7. 2- Broth Microdilution (Vitek 2)

8. Recommendations for Antibiograms:
 Analyze/present cumulative antibiogram report at least
annually;
 Include only final, verified test results;
 Include only species with testing data for ≥ 30 isolates.
 Include only diagnostic (not surveillance) isolates
 Eliminate duplicates by including only the first isolate of a
species/patient/analysis period, irrespective of body site or
antimicrobial profile
 Include only antimicrobial agents routinely tested; do not
report supplemental agents selectively tested on resistant
isolates only.
 Report %S (Susceptible) and do not include %I
(intermediate) in the statistic.

9. Antibiogram Uses:
1- Antibiograms help guide the clinician and
pharmacist in selecting the best empirical
antimicrobial treatment in the event of pending
microbiology culture and susceptibility results.
2- They are also useful tools for detecting and
monitoring trends in antimicrobial resistance.

10. Scope of applications of ABG:
1- Staff working in analysis and presentation of AST data
(e.g. clinical microbiologists, pharmacists, physicians).
2- Staff utilizing cumulative AST data to make clinical
decisions (e.g. clinical microbiologists, infectious
disease specialists and other clinicians, infection
control practitioners, pharmacists, epidemiologists,
other health care personnel, and public health
officials).
3- For designing information systems for the storage and
analysis of AST data (e.g. laboratory information system
[LIS] vendors, manufacturers of diagnostic products
that include epidemiology software packages).

11. Data Required to perform ABG:
1. Patient:
- Required: ID,
- Desirable: Age, Sex, Location (Ward),
Admission date;
2. Specimen information:
- Required: number, type & date of
collection.
- Desirable: Body site e.g. right or left

12. 3. Organism information:
- Required: identification up to the genus or species
level (genus can be satisfactory).
- Desirable: Isolate number, change in name of
isolate (if happen), Infection control data e.g.
colonization or infection, community-
acquired, or healthcare associated CA or HAI
4. AST information:
- Required: final MIC or zone diameter (ZD) used,
method used, special tests for detection of B-
lactamase, mecA gene, PBP2a by agar
screening , PCR or latex testing respectively.
- Desirable: detailed MIC or ZD

13. Frequency of Performance
 At least once yearly.
 More frequent with high number of isolates,
new antimicrobial introduced, or presence of
important medical changes.
Facility:
 ABG is performed based upon local institution-
specific susceptibility data.

14. Data presentation:
 In a tabular form.
 No universal formats
 Separate tables for Gram-positive, and Gram-
negative, also for anaerobes and yeasts if
applicable.
 Arrange the organisms within the table
alphabetically, by organism group of by the
prevalence.

15.  Some labs may present data by body site e.g.
urine gram-negative or gram-positive.
 Antibiogram for critical care units (more
resistant) e.g. ICUs better to be separated to
compare %S with the total hospital.
 Comments upon the table must be included to
explain it.
 Recommended species to be included (even if
<30 isolates) in the tables are the following:

16. Gram-negative species:
 Acinetobacter baumannii • Citrobacter freundii
 Enterobacter aerogenes • Enterobacter cloacae
 Escherichia coli • Haemophilus influenzae
 Klebsiella pneumoniae • Morganella morganii
 Proteus mirabilis • Providencia spp.
 Pseudomonas aeruginosa • Salmonella spp.
 Serratia marcescens • Shigella spp.
 Stenotrophomonas maltophilia

17. Example for Gram-negative ABG
- Comments upon the table must be included.
- Denoting of abbreviations must be included e.g. AMK= Amikacin.

18. Gram-positive species:
 Enterococcus spp. (preferable to be in separate species).
 Staphylococcus aureus
 Coagulase-negative Staphylococcus spp. (exclude S.
lugdunensis and S. saprophyticus, separately if in
sufficient numbers).
 Streptococcus pneumoniae.
 Viridans group Streptococcus spp. (from usually sterile
body sites only).

19. Example for Gram-Positive ABG
- Comments upon the table must be included.
- Denoting of abbreviations must be included e.g. CLI= Clindamycin

20. Example for Gram-Positive ABG

21. Example for Gram-Positive ABG

22. Anaerobes
 Bacteroides fragilis
 Clostridium perfringens

23. Example of ABG data Report by alphabetically
listing of Antimicrobial Agents.

24. Example of Antibiogram data Report by class of
Antimicrobial Agents.

25. Presenting Emerging Resistance Trends:
 Presentation of resistant organisms over years can be
presented in tables or graphs e.g.:
 MRSA (S. aureus – %S for oxacillin - inpatient and
outpatient);
 VRE (Enterococcus spp. – %S to vancomycin isolates
from sterile body sites);
 E. coli – %S to trimethoprim-sulfamethoxazole (urine
isolates) and fluoroquinolones;
 ESBLs (K. pneumoniae and E. coli – % of isolates that
produce ESBLs);
 P. aeruginosa – %S to fluoroquinolones and/or
imipenem.

26. Presenting trends in resistance to Oxacillin
(MRSA):

27. Different ABG presentation formats
 By nursing unit or care sits: i.e. patient location e.g.
ICU, Burn unit, Ward, OPD, nursing home for the
better selection of site specific empirical therapy.
 By organism’s resistance character: especial for
MDROs e.g. MRSA, VRE, MDR Acineto., ESBLs.
 By specimen type or infection site: e.g. urine
isolates, blood isolates).
 By clinical service or patient population: to guide
initial empirical antimicrobial therapy for specific
patient types (e.g. surgical, pediatric, cystic fibrosis,
transplant, burn).

28. Data presentation in tables:
1- Date of the period of the Report.
2- Name of the Laboratory of the facility.
3- Comments upon methodology used in
preparation of data.
4- Organism name and numbers

29. Calculations
 Report the first isolates of pathological
organisms only not surveillance one.
 Only report organism with more than 30
isolates.
 Use CLSI recommended antimicrobials to each
species.
 Report only the percentage of susceptible (S%)
not intermediate.
 Give in the form of tables and graphs.

30. Number of isolates:
 Typically should > 30 isolates to be statistically
significant.
 If less than 30 you have to put comment upon
the table.
 You can group similar species together to
increase the numbers e.g. salmonella or shigella
species.

31. Distribution Formats:
 In easy accessible formats to all prescribing
Physicians, Infection control staffs, Pharmacists,
Epidemiologists & Microbiology Staffs‘:-
 Small cards in coat pocket
 Or in laminated sheets.
 In the institution website (Intranet). As
application or PDF.
 Printed formats especially in special areas like
ICUs.

32. Antibiogram
cards

33. Antibiogram laminated sheet

34. Antibiogram laminated sheet GP & Anaer.

35. Antibiogram laminated sheet GN

36. Methods of calculations:
1- Confidence Intervals (CI): it gives the
precision of susceptibility % and depends upon
the numbers of isolates.
2- Statistical Significance of Changes in
Susceptibility Rates: use Chi-square test to
compare different S% in different years. A P
value of ≤ 0.05 is accepted for being significantly
different.

37. Limitations of Data, Data Analysis, and
Data Presentation
 Culturing Practices: sample collection,
transport and storage. Bias in treatment e.g. in
OPD, change in culturing technique in the lab.
 Influence of Small Numbers of Isolates:
number of isolates to be > 30.

38. Antibiogram Limitations:
1. Minimum inhibitory concentrations (MICs) are
not included; as a result subtle trends below
the resistance threshold (known as “MIC
creep”) are not reflected.
2. Data do not take into account patient factors
such as history of infection or past
antimicrobial use. Resistance patterns for
certain drugs vary significantly by age, and a
patient’s underlying medical condition may
affect how well an antimicrobial works

39. 3. Data are the result of single organism-
antimicrobial combinations, therefore do
not show trends in cross-resistance of an
organism to other drugs, nor do they reveal
synergistic properties of antimicrobials used
in combination.
4. Data may not be generalizable to specific
patient populations or locations of a
healthcare facility if the antibiogram is
compiled using hospital- or healthcare
system-wide data.

40. Examples of Accepted Antibiogram (KSH)

41. KSH

42. KKHK

43. Iman

44. (Iman)

45. Defective Antibiograms

46. No Comments!!!!!

48. Graphs not from our hospitals
Klebsiella - Percentage of sensitive
Isolates (178).
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
80%
37.60%
29.20%
37.10%
34.30%
61.20%
35.40%
32%
43.30%
75.30%
54.50%
87.60%
62.40%
83.70%
30.30%
68.50%68.50%
39.30%
29.30%
Series1

49. Pseudomonas Aeruginosa -
Percentage of sensitive Isolates (127).
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
AKCEFPCTXCAZCIPFOSFGNIPMLEVOMEROPIPTAZTOB
60.60%
48.80%
3.90%
48%
52.80%
38.60%
52.80%
55.90%
54.30%
58.30%
55.90%
66.90%
54.30%
Series1

50. Acinaetobacter - Percentage of sensitive
Isolates(20).
0%
10%
20%
30%
40%
50%
60%
70%
80%
AKCEFPCTXCAZCIPGNIPMLEVOMEROPIPTAZTOBSXT
25%
10%10%
20%
15%
35%
10%10%10%10%
30%
25%
70%
Series1

51. Staph.aureus - Percentage of sensitive
Isolates (67).
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%97%
3%
97%
89.60%
70%
95.50%
98.50%
91%
95.50%
100%100%
3%
100%100%100%
98.50%
100%
Series1

54. References
 CLSI. Analysis and Presentation of Cumulative Antimicrobial
Susceptibility Test Data; Approved Guideline—Third Edition.
CLSI document M39-A3. Wayne, PA: Clinical and Laboratory
Standards Institute; 2012.
 King Fahd Medical City Antibiograms.
 King Khaled Hospital Hafr Al-Baten

55. Thank you