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Susceptibility Testing Data—New Guidance for Developing Antibiograms

DOI: http://dx.doi.org/10.1309/LMK4KIG76DQEAYPH 459-462 First published online: 1 August 2009

Laboratory professionals are often at the forefront in collaborative activities with other health care providers including clinicians, infection control managers, and pharmacists. One critical cooperative effort that has significant impact on patient outcomes is the development and distribution of the facility-specific antibiogram. Whether the information is distributed as a pocket-size directory or through a facility-wide information system, clinicians find that having the antimicrobial susceptibility data readily available can help guide them in making appropriate and timely treatment decisions. (See Figure 1.)

Clinical and Laboratory Standards Institute (CLSI) recently published Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data; Approved Guideline—Third Edition (M39-A3). Replacing the 2005 second edition, M39-A3 presents specific recommendations for the collection, analysis, and presentation of cumulative antibiograms. It describes methods for recording and analyzing antimicrobial susceptibility test data, using both cumulative and ongoing summaries of susceptibility patterns of clinically significant microorganisms.1

“The objective of an antibiogram is to present useful, validated information in a consistent way to clinicians and policy makers,” explained John Stelling, MD, MPH, a member of the working group of the Subcommittee on Antimicrobial Susceptibility Testing that revised the guideline. “The document is important,” he stated, “because it helps laboratorians understand local bacterial populations, and present information to clinicians to aid in treatment decisions. By tapping into this rich, though underutilized, data source generated by routine culture and sensitivity testing, the laboratory can make substantial contributions to patient outcomes without additional testing.”

Figure 1

Appendix E1. Cumulative antimicrobial susceptibility report example—antimicrobial agents listed alphabetically (hypothetical data).

According to Stephen Jenkins, PhD, also a member of the working group, M39-A3 “provides practical guidance to clinical laboratories and hospital pharmacies on managing and using antimicrobial susceptibility data.” Further, the document provides suggestions for effective use of a facility’s cumulative susceptibility statistics, such as illustrating trending in susceptibility within a facility over time. M39-A3 also addresses the way in which multiple isolates from the same patient are handled, the species to be included or combined in a statistic, data analysis frequency, and data presentation formats.

Dr. Stelling added that the guideline provides microbiologists insight into managing selection, sampling, and testing bias. It also offers detailed recommendations for organizing specific organisms and organism groups when developing antibiograms.

M39-A3 includes expanded recommendations for calculating percent susceptible for subsets of isolates, suggesting that laboratories provide clinicians with information that takes into consideration patient care areas, patient types, and body sites. Before developing antibiograms for specific populations and/or sample types, such as urine cultures from patients on the geriatric floor, a laboratory may want to consult with the medical staff and clinicians to determine the usefulness of cumulative data reports. When developing antibiograms derived from data subsets, it is important to qualify any data that may be misleading before sending information to clinicians.2

The guideline also includes various options for presenting data when the incidence of a specific multidrug-resistant organism such as Pseudomonas aeruginosa is significant in an institution. There are recommendations for calculating the percentage of isolates susceptible to either or both of two antimicrobial agents for clinicians considering combination therapy.

Some Trends in Emerging Resistance Not Revealed

Antibiograms help direct clinicians in selecting initial empirical antimicrobial therapy for infections. To help ensure appropriate treatment decisions, the guideline recommends that the data set should include only the first isolate of a given species from an individual patient. With this in mind, the cumulative antibiogram generated may not reveal some trends in emerging resistance. Janet Hindler, MCLS, MT(ASCP), chairholder of the working group, reminds laboratorians that “one cannot substitute careful analysis of all susceptibility test data derived from individual patient results when evaluating treatment options.”2

“Resistance usually emerges when the patient is in the hospital or nursing home for an extended period of time,” explained working group member Judith Johnston, MS. “Since only the first isolate is included in the cumulative anti-biogram, emerging resistance is harder to detect. Most of the first isolates will be more susceptible than the fifth or tenth isolate of the same organism from the same patient.”

For a number of years, many public health organizations have been requesting that isolate data are submitted either voluntarily, or in some cases, to meet regulatory requirements. These statistics can provide critical information on emerging resistance to health care agencies so they can secure appropriate resources for treatment regimes and successfully manage prevalent organisms in their locale or region.

Selection of Empirical Antimicrobial Therapy for Initial Infections

Dr. Jenkins emphasized that the importance of annual reporting of susceptibility data “is to maximize the chances of detecting changing patterns of antimicrobial resistance within an institution as soon as possible in order that modifications to algorithms and individual prescribing patterns for empiric therapy can be considered.” Furthermore, “Preliminary identification and susceptibility results may change during testing, so it is vital to include only final, verified results,” explained Dr. Jenkins. This will “minimize the chances that errors might impact the statistic.”

Also, laboratories should only include diagnostic isolates, not resistant ones, when analyzing data. “If one is specifically seeking out resistant, colonizing organisms through screening of patient samples, the rates of resistance for applicable genera and species will be artificially elevated by including such isolates in the database,” Dr. Jenkins stated. “This would impact the antibiogram significantly and potentially imply that a drug useful for treatment of infection is problematic due to a high (artificial) rate of resistance.”

Ms. Johnston added, “[Laboratories] may see some small differences year to year, but by looking at subsets of the data, [for example] methicillin-resistant Staphylococcus aureus (MRSA) over three to five years, they can see trends in susceptibility or emerging resistance.”

M39-A3 Useful in the Development of Software

Dr. Stelling points out that M39-A3 is intended for use by clinical microbiologists, epidemiologists, physicians, pharmacists, infectious disease specialists, and public health officials. Laboratory information system vendors and manufacturers of diagnostic products whose systems include epidemiology software packages find M39-A3 particularly useful as they develop products to meet customer requirements for producing the antimicrobial susceptibility reports.

Johnston indicated that M39-A3 has been used in the development of system software. “The medical device manufacturers have developed software for data management on their automated testing platforms. Customers (clinical laboratories, reference laboratories) can set up queries and generate the data needed for the cumulative antibiogram,” she explained.

Collaborative Tool

Although the use of M39-A3 is entirely voluntary, the working group members urge laboratories to use the guideline to develop antibiograms that will help clinicians choose the appropriate empirical antimicrobial therapy for treating infections. As microbiologists collaborate on developing or updating the facility antibiogram, they and the other members of the team may find useful information on how to communicate the data following the example in Appendix G: Steps for Presenting Local Cumulative Antibiogram Report to Health Care Professionals. This tool is just one of many useful examples and step-by-step guides that the entire team can use to construct the customized, environment-specific antibiogram for the institution.

Appendix

The Experts

Janet F. Hindler, MCLS, MT(ASCP), is Sr. Specialist, Clinical Microbiology, at UCLA Medical Center in Los Angeles.

Stephen G. Jenkins, PhD, is Director of Microbiology at New York Presbyterian Hospital, Weill Cornell Medical Center, and professor of pathology and laboratory medicine at Weill Cornell Medical College in New York.

Judith Johnston, MS, is Director of Microbiology Product Development at Siemens Healthcare Diagnostics in West Sacramento, CA.

John Stelling, MD, MPH, is professor of infectious diseases in the Department of Medicine at Brigham and Women’s Hospital in Boston.

Notes

Examples in M39-A3 for Preparing Antibiograms and Managing Antimicrobial Susceptibility Data

  • Suggestions for verification of antimicrobial susceptibility test results and confirmation of organism identification.

  • Using a line listing to verify susceptibility rates determined by analysis software.

  • Supplemental analyses—Stratifying cumulative antibiogram data by various parameters.

  • Cumulative antimicrobial susceptibility reports.

  • Steps for presenting local cumulative antibiogram reports to health care professionals.

CLSI is a global, nonprofit organization that promotes the development and use of voluntary consensus standards and guidelines within the health care community.

In addition to the M39-A3 document, the Web teleconference Antibiograms: Developing Cumulative Reports for Your Clinicians is available at www.aphl.org/clsi.

References

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