Optimizing Antimicrobial Susceptibility Test Reporting

JOURNAL OF CLINICAL MICROBIOLOGY, Sept. 2011, p. S15 S19 0095-1137/11/$12.00 doi:10.1128/jcm.00712-11 Copyright 2011, American Society for Microbiology. All Rights Reserved. VOL. 49, NO. 9 SUPPL. Optimizing Antimicrobial Susceptibility Test Reporting Paul C. Schreckenberger 1 * and Matthew J. Binnicker 2 Department of Pathology, Loyola University Medical Center, Maywood, Illinois 60305, 1 and Division of Clinical Microbiology, Mayo Clinic, Rochester, Minnesota 55905 2 A meeting of clinical microbiologists, representing a diverse group of practice settings, together with representatives from industry partners, discussed the pitfalls of current practices for testing and reporting antimicrobial susceptibility test results. The participants in this session identified several needs. Regarding what antibiotics to test, the discussants noted that the CLSI M100 documents must be readily accessible to all those who need them and presented in a way that is easily understood. Engineering controls (e.g., software programs) are needed that incorporate intrinsic resistance and susceptibility information and contain pathways that would allow the reporting of antibiotics for only those body locations where the antibiotic reaches therapeutic concentrations. These programs should be linked with the patient electronic medical record (EMR) and flag the physician or clinical pharmacist using an active alert messenger when testing reveals that the antibiotic(s) that the patient is receiving may not be optimal or de-escalation of the antibiotic treatment regimen is indicated. Guidelines for the practice of cascade reporting are needed and should be developed to assist laboratories in the identification and reporting of therapeutic and cost-effective antimicrobials. Personalized antibiotic reporting (PAR) software programs are needed that would deduce the optimal antibiotic for each individual patient based on a number of clinical and laboratory features. These would include the following: (i) organism identification; (ii) MIC; (iii) patient-specific factors such as weight, immune status, allergies, creatinine clearance, and albumin level; (iv) site of infection; (v) desired method of dosing (bolus versus continuous infusion); (vi) patient convenience (oral versus intravenous); (vii) drug interactions; and (viii) cost. In routine practice, the reporting of antimicrobial susceptibility test (AST) results is a passive process. Results are posted in the electronic medical record (EMR), or in some cases, as a hard copy in the patient record. These results often lie in wait to be reviewed, which may delay important clinical management decisions. In a study presented at the American Society for Microbiology (ASM) General Meeting in 1991, Byrne and colleagues (1) reported that it took an average of 3.78 days to change antibiotics when AST results from the laboratory were provided by a computer link to the ward physicians and indicated that a therapeutic change was needed. However, in a control set of patients in which a Doctor of Pharmacy (Pharm.D.) actively intervened by presenting culture data to the primary care physician as soon as they were available for review, an appropriate therapeutic intervention was made within 8 h. To say the least, reporting of AST results is not optimized for effective patient care. This session focused on the issues related to optimizing and enhancing AST result reporting. DISCUSSION POINT 1. WHAT ISSUES SHOULD BE TAKEN INTO ACCOUNT WHEN DECIDING WHAT ANTIBIOTICS TO TEST AND REPORT FOR A GIVEN ORGANISM OR SITE OF INFECTION? The discussants were asked if CLSI Table 1 provides a sufficient safeguard to ensure that only appropriate antibiotics are reported for each bacterium and site of infection. The group consensus was a firm no. Each year, a percentage of clinical * Corresponding author. Mailing address: Department of Pathology, Loyola University Medical Center, 2160 S. First Ave. Bldg. 103, Rm. 0021, Maywood, IL 60153. Phone: (708) 216-5682. Fax: (708) 216-1047. E-mail: pschrecken@lumc.edu. microbiology laboratories participating in the College of American Pathology (CAP) proficiency surveys report antibiotic results for drugs that are not indicated for the particular specimen type (e.g., reporting AST results on cerebrospinal fluid [CSF] culture isolates for antibiotics that are not indicated for treating meningitis). In a practical exercise conducted by the author (P.S.) for the November 2010 Grand Rounds in Clinical Laboratory Microbiology and Infectious Disease, 2010, sponsored by the Colorado Association for Continuing Medical Laboratory Education (CACMLE), an unknown organism was sent to the exercise participants for identification and susceptibility testing, if indicated. The case presented was that of a 24-year-old, pregnant female from whom a cleancatch, voided urine sample grew 10,000 CFU/ml of an isolate that participants were asked to identify. In addition to making the identification, participants were asked the following two questions. (i) Would your laboratory perform susceptibility testing on this organism if requested by the physician? (ii) If yes, what antibiotics would you test and report? CACMLE Grand Rounds in Microbiology is a self-evaluation/continuing education exercise in which participating laboratories receive unknown samples to identify, accompanied by a case history and a series of questions to answer. This is followed by an audioconference pertinent to the exercise. Laboratories are invited to report their answers to the exercise but are not required to do so. In this particular exercise, 15 participating laboratories returned responses (Table 1). All participating laboratories correctly identified the unknown bacterium as group B Streptococcus (GBS). Among the 15 labs which returned a response, 2 reported that they would not perform routine susceptibility testing on GBS from urine unless the patient was reported to be allergic to penicillin, in which case they would test and report clindamycin and erythromycin because they would consider a urine specimen from a pregnant S15

S16 CAMP CLIN MICRO J. CLIN. MICROBIOL. TABLE 1. Results of the November 2010 CACMLE Unknown Grand Rounds in Microbiology and Infectious Disease a Antibiotic reported No. (%) of participants reporting Ampicillin... 3 (20) Cefepime... 1 (7) Cefotaxime... 5 (33) Ceftriaxone... 6 (40) Clindamycin... 7 (47) Erythromycin... 8 (53) Gentamicin b... 1 (7) Levofloxacin... 3 (20) Penicillin...11 (73) Quinupristin-dalfopristin c... 1 (7) Tetracycline... 4 (27) Vancomycin... 8 (53) a Drugs tested and reported by participating laboratories on a group B Streptococcus isolate cultured from a clean-catch, voided urine specimen. b No CLSI breakpoint is published for gentamicin for Streptococcus spp., beta-hemolytic group (2). c Comment added to this drug in CLSI Table 2H-1 stating Report against S. pyogenes (2). woman to be a surrogate for vaginal carriage of GBS. The remaining 13 laboratories indicated that they would report full susceptibility results for the GBS isolate (Table 1). In the CLSI M100 guidelines, Table 1A provides guidance to laboratories on what antibiotics to test and report (2). CLSI Table 1A lists three drugs in group A for primary testing and reporting, including clindamycin, erythromycin, and penicillin or ampicillin. Table 1A, footnote e, states that clindamycin and erythromycin are not routinely reported for an organism isolated from the urinary tract. Furthermore, Table 1A, footnote o, states that susceptibility testing of penicillins and other -lactams for treatment of S. pyogenes or S. agalactiae is not necessary [emphasis added] for clinical purposes and need not be performed (2). In spite of this guidance, 11 (73%) respondents reported penicillin, 3 (20%) reported ampicillin, 7 (47%) reported clindamycin, and 8 (53%) reported erythromycin (Table 1). CLSI Table 1A also has a category B for drugs that are indicated for primary testing and selective reporting. These include cefepime, cefotaxime, or ceftriaxone and vancomycin. Again, Table 1A, footnote o, states that testing of other -lactams for treatment of S. pyogenes or S. agalactiae is not necessary for clinical purposes, yet 33% of labs reported cefotaxime, 40% reported ceftriaxone, and 7% reported cefepime (Table 1). Interestingly, three labs reported levofloxacin, four tetracycline, one quinupristin-dalfopristin, and one gentamicin, even though none of these drugs are indicated for primary testing with -hemolytic streptococci in CLSI Table 1A (2). The consensus of the discussion group was that CLSI Table 1 is missing the mark. This is likely due to the facts that (i) many laboratories either do not purchase the document or purchase it intermittently and (ii) for those labs that have access to the document, the footnotes are either ignored or not understood. It is easy to see why the latter is a problem, given that there are 18 footnotes for CLSI Table 1A alone. The Michigan Department of Public Health has taken a proactive approach by purchasing the CLSI M100 document and distributing one copy to every clinical laboratory in Michigan. This approach ensures that every laboratory has access to the most recent CLSI M100 document, but it does not guarantee that laboratories will refer to the document or understand the critical guidance given in the table footnotes. To address these problems, the discussion group proposed that engineering controls, in the form of computer software, should be developed to prevent reporting of antibiotics for (i) sites of infection where the drug is not concentrated or effective (e.g., daptomycin in lung tissue or macrolides in urine), (ii) bacteria known to be intrinsically resistant to certain antimicrobials (e.g., trimethoprim-sulfamethoxazole [SXT] and enterococci or imipenem and Stenotrophomonas maltophilia), or (iii) bacteria known to be universally susceptible to an antibiotic (e.g., group A and B Streptococcus to penicillins and other -lactam drugs). In addition to streamlining the process, such software would help to prevent false-susceptible or false-resistant reporting. Need Statements (i) The CLSI M100 documents must be readily accessible to all those who need them. (ii) The information in the CLSI M100 documents must be presented in a way that can be easily understood and acted upon. The sheer number of comments, notes, and footnotes is unwieldy in its present form, and this material is not utilized by many laboratories. (iii) Software programs incorporating information from the CLSI documents should be developed to automate the AST reporting process and include hard stops to prevent erroneous AST reporting. It was noted that these software programs would need to be upgraded annually as changes to the breakpoints and interpretations are made. It was suggested that ASM and CLSI begin to work with commercial and industry partners to develop and incorporate these needed software programs. (iv) Antimicrobial susceptibility information that goes beyond the FDA-approved indications for certain drugs (e.g., indications for treatment of meningitis or infection by multiple organisms) is needed and should be made available to laboratories and physicians. (v) The Food and Drug Administration (FDA) must reconsider allowing device manufacturers to accept updated CLSI information. Prior to 2006, the FDA allowed manufacturers to incorporate CLSI changes in their instrument software, but it has recently discontinued this practice. The FDA must speed its process in accepting CLSI annual updates so that AST instrument software can reflect the current recommendations. DISCUSSION POINT 2. SHOULD AST REPORTING BE CHANGED FROM A PASSIVE TO AN ACTIVE PROCESS? IF THE LABORATORY IS THE FIRST TO KNOW THE AST RESULTS, SHOULDN T IT BE THE FIRST TO ALERT THE CLINICIAN? SHOULD AST RESULT REPORTING BE LINKED TO THE CLINICAL PHARMACY? The group consensus regarding Discussion Point 2 was a strong yes. Active reporting would yield the following improvements: (i) laboratories would initiate measures to prevent reporting of antibiotics for sites where a drug is not effective (e.g., daptomycin for treatment of pneumonia), and (ii) the laboratory would develop de-escalation prompts and intrinsic

VOL. 49, NO. 9 SUPPL., 2011 ANTIMICROBIAL SUSCEPTIBILITY TESTING S17 resistance comments to ensure that proper notification is included on reports. Some participants stated that these measures have been implemented in their labs. However, concern was expressed that some laboratories may not have a director that can assist in writing these types of rule-based systems. Following discussion by the participants, it appears that current approaches are institution specific and not consistently performed. There is also a need for guidelines to be developed for postanalytic comments to help with physician and Pharm.D. interpretation of antibiotic results. Also, more partnering is needed so laboratories don t have to reinvent the same solutions. Programs like Stellara and PharmLink were developed by biomérieux and Siemens in the 1990s to link microbiology reports with pharmacy results for possible therapeutic intervention; however, these products were taken off the market due to lack of sales. Also in the 1990s, a group of clinicians from LDS Hospital in Salt Lake City developed a computer-assisted program for antibiotic management. The program uses the patient s admission diagnosis, white-cell count, temperature, surgical data, chest radiograph, and information from pathology, serology, and microbiology reports to determine the need for treatment and recommend an antimicrobial regimen(s) (4). The program also uses data on the patient s allergies, drug-drug interactions, toxicity, and drug cost in the selection of anti-infective agents. Furthermore, data on the patient s renal and hepatic function are used to calculate the dose and dosing interval for each suggested anti-infective agent (4). This program, called TheraDoc, is now commercially available (TheraDoc, Salt Lake City, UT). One limitation of this program is that it uses empirical therapy guidelines or laboratory-generated antibiotic results using published breakpoints. The discussants suggested that it is time to expand on this idea, and they proposed that ASM partner with professional organizations like the American Society of Health- System Pharmacists (ASHP) and the Infectious Diseases Society of America (IDSA) to develop and further refine these programs. Need Statement Engineering controls (e.g., software programs) are needed that incorporate intrinsic resistance and susceptibility information and describe pathways for reporting drugs only for sites where the antibiotic reaches therapeutic concentrations. These programs should be linked with the patient EMR and flag the physician or clinical pharmacist using an active alert messenger when testing reveals that the antibiotic(s) that the patient is receiving may not be optimal or de-escalation of the antibiotic treatment regimen is indicated. DISCUSSION POINT 3. SHOULD LABORATORIES PRACTICE SELECTIVE OR CASCADE REPORTING OF AST RESULTS? WHO IS RESPONSIBLE FOR DECIDING WHAT ALGORITHM TO USE? WILL THIS HELP WITH ANTIBIOTIC STEWARDSHIP? WHAT ARE THE PITFALLS OF CASCADE REPORTING? Cascade reporting is the practice of placing all the drugs of a certain drug class in rank order based on the cost of the drug and then reporting the susceptibility result for only the least expensive drug that tests as susceptible. An example would be a laboratory that tests three aminoglycosides and places them in the rank order of (i) gentamicin, (ii) tobramycin, and (iii) amikacin. In cascade reporting, if gentamicin is susceptible, it is the only antibiotic in this class that is reported because it is the least expensive. The pitfalls of this approach are 2-fold. First, the cost assigned in establishing the rank order is usually based on the acquisition cost of the drug, which may reflect only the tip of the iceberg. The total cost of a drug is really a combination of (i) the acquisition cost, (ii) the cost of therapeutic drug monitoring, (iii) the cost of treatment for adverse drug reactions, (iv) the cost of salvage therapy if the patient fails to respond clinically, and (v) the hospital costs resulting from longer hospital stays due to delays in clinical cure due to ineffective drug therapy. A second pitfall of cascade reporting involves recognition of drug resistance genes, such as AAC6, which preferentially hydrolyze tobramycin and amikacin and less so gentamicin, resulting in gentamicin testing as susceptible and tobramycin and amikacin testing as resistant. In labs practicing cascade reporting, this would result in gentamicin being reported as susceptible, while tobramycin and amikacin would be suppressed due to their higher cost. Many laboratories may not have the sophistication to recognize that this pattern is synonymous with the AAC6 gene and that gentamicin may not be appropriate for in vivo use. Studies by David Livermore have demonstrated that organisms with AAC6 resistance may test as susceptible to certain aminoglycosides in vitro but that in vivo use of these drugs should be avoided (5). Unfortunately, there are currently no published guidelines for cascade drug reporting. Need Statement Guidelines for the practice of cascade reporting are needed and should be developed to assist laboratories in the identification and reporting of therapeutic and cost-effective antimicrobials. DISCUSSION POINT 4. ARE BREAKPOINTS RELEVANT? IS IT TIME TO ELIMINATE BREAKPOINTS AND REPLACE THEM WITH PERSONALIZED ANTIBIOTIC REPORTS? Breakpoints can vary by infection type (e.g., cystitis versus sepsis versus meningitis, etc.). In other words, there may be different breakpoints for a particular organism, depending on the site of infection. In addition, breakpoints can vary by the method of antibiotic administration (e.g., oral versus intravenous [i.v.] administration) or dosage given (customary versus highest dose). Compounded by these limitations is the fact that there is inconsistency about what breakpoints should be, with CLSI, FDA, and EUCAST each using different criteria in setting breakpoints, resulting in different published breakpoints for the same drug. A recent example of changing breakpoints comes from the CLSI decision in 2010 to change the cefazolin susceptibility breakpoint from 8 g/ml to 1 g/ml. This change was based on the 1-g/8-h dose that was recommended in the manufacturer s package insert. In June 2010, a

S18 CAMP CLIN MICRO J. CLIN. MICROBIOL. complaint was made that too many urinary tract isolates were testing as intermediate to cefazolin based on the new breakpoint of 1 g/ml and that this was leading to greater use of carbapenem antibiotics for treating urinary tract infections. A presentation of pharmacokinetic/pharmacodynamic (PKPD) data from a Monte Carlo simulation showed that if a higher dose of 2 g/8 h were given, then the susceptible breakpoint could be changed from 1 g/ml to 2 g/ml, resulting in fewer intermediate isolates being reported. Of note, the cefazolin package insert does specify use of 2 g/8 h as the highest dose. The CSLI antibiotic subcommittee approved the change in the breakpoint and published this change in January 2011 with the comment that the updated breakpoint was based on a 2-g/8-h dose (3). However, CLSI does not recommend that labs include the dosing comment on the laboratory report. Consequently, labs have changed to the new breakpoint based on the maximum dose, but their reports will not reflect the fact that this is based on maximum dosing of the drug. These issues raise an important question: what is the real breakpoint for cefazolin? The answer to that question depends on several factors, including (i) is the clinician going to use the standard dose or the highest dose of cefazolin and (ii) is the infection in the bladder, where the drug is concentrated, or outside the bladder? As indicated above, there is inconsistency concerning the true breakpoint for cefazolin; EUCAST suggests that the breakpoint is 1 g, CLSI claims 2 g, and the FDA states that the breakpoint is 8 g. Furthermore, the CLSI M100-21 document now has 34 comments in its Table 2A alone (3). How are laboratories supposed to sort through this confusion? It is now becoming clear that there may never be a single breakpoint that will predict susceptibility or resistance to a drug for all patients, at all sites of infection, and under all conditions. The participants of this session suggested that the time has come to move beyond the idea of a universal breakpoint toward criteria that are more relevant to the patient. The discussants proposed the concept of developing and integrating a personalized antibiotic report (PAR). The MIC is only one piece of information that is useful in deciding if an antibiotic is going to be successful in treating an infection. The FDA and CLSI breakpoints are based only on the drug level achieved in the blood using the customary dose as specified in the drug package insert. In fact, the antibiotic reports issued by the laboratory are most often not for infections occurring in the blood but, rather, are for infections in the bladder, lungs, wounds, bone, or various tissues and organ sites. Importantly, drug concentrations at sites of infection outside the bloodstream may be higher or lower than those in the blood, rendering the breakpoint less useful in these sites. Furthermore, antibiotics are often renally excreted and the renal function of the patient will determine how long the antibiotic will remain in the blood and how much is concentrated in the bladder. In addition, all antibiotics are partially protein bound, so the albumin level of the patient affects the amount of free drug circulating. The participants of this session proposed a new approach for interpreting the results of AST, whereby results would be customized for each patient based on a number of factors, including the (i) MIC, (ii) site of infection, (iii) drug concentration achieved at the site of infection, (iv) renal function, (v) protein level, (vi) method of administration, (vii) dosing convenience, (viii) potential for drug interactions, (ix) patient allergies, and (x) cost of comparable alternatives. To achieve this goal, sophisticated computer software would have to be developed that would customize reports for each patient by mining the EMR and using data on creatinine clearance, albumin concentration, site of infection, type of infection, culture results, mode of antibiotic administration, and dosing convenience to provide a personalized antibiotic report that would specify the optimal antibiotic(s) to treat the infection. Cost features, such as the need to perform therapeutic drug monitoring, drug interactions, and inpatient or home therapy usage, could also be incorporated into the computer algorithm. Need Statements (i) A personalized antibiotic reporting (PAR) software program is needed that would deduce the optimal antibiotic for each individual patient based on a number of clinical and laboratory features. These would include the following: (i) organism identification; (ii) MIC; (iii) patient-specific factors such as weight, immune status, allergies, creatinine clearance, and albumin level; (iv) site of infection; (v) desired method of dosing (bolus versus continuous infusion); (vi) patient convenience (oral versus i.v.); (vii) drug interactions; and (viii) cost. This software program would ideally have the capacity to mine the EMR to obtain the information and determine the optimal antibiotic for each patient and, subsequently, alert the physician or clinical pharmacist in an active fashion with the customized treatment choices. In this model, there would not be a single breakpoint for determining susceptibility to an antibiotic but a continuum of antimicrobial efficacy based on the multiple parameters outlined above. A concern that was discussed by the participants was who should take the lead on developing software programs to address these needs (CLSI versus IDSA versus ASM or a combination of groups). It is apparent that the clinical microbiology community is in need of something revolutionary that doesn t exist today. Potential solutions may be incorporated into applications that could be tethered to a mobile phone or other electronic device (e.g., tablet) that physicians or pharmacists could carry and provide them with active alert messages as soon as the information becomes available. Changes of this magnitude are likely to come to fruition only if the clinical microbiology community actively partners with individuals from external groups with expertise in these areas. (ii) A coalition consisting of physicians, pharmacists, clinical microbiologists, and experts in information technology should be formed that would develop computer-based solutions, demonstrate how these solutions can be implemented effectively in hospitals, and advise hospitals on how to apply these interventions. (iii) Studies and data are needed to show that these interventions make a difference in patient care, antibiotic stewardship, and medical cost management. Session discussants: Deborah Boldt-Houle, Cary-Ann Burnham, Christian Coogan, Gina Ewald, Amanda Harrington, Sue Kehl, Mark LaRocco, Linda Mann, Elizabeth Marlowe, Alexander McAdam, Duane Newton, DeAna Paustian, Gongyi Shi, and Melvin Weinstein.

VOL. 49, NO. 9 SUPPL., 2011 ANTIMICROBIAL SUSCEPTIBILITY TESTING S19 REFERENCES 1. Byrne, K., et al. 1991. Impact of prospective Pharm.D. directed antibiotic utilization review (AUR), abstr. C-124, p. 362. Abstr. 91st Gen. Meet. Am. Soc. Microbiol. American Society for Microbiology, Washington, DC. 2. Clinical and Laboratory Standards Institute. 2010. Performance standards for antimicrobial susceptibility testing; twentieth informational supplement. CLSI document M100-S20. Clinical and Laboratory Standards Institute, Wayne, PA. 3. Clinical and Laboratory Standards Institute. 2011. Performance standards for antimicrobial susceptibility testing; twenty-first informational supplement. CLSI document M100-S21 Clinical and Laboratory Standards Institute, Wayne, PA. 4. Evans, R. S., et al. 1998. A computer-assisted management program for antibiotics and other antiinfective agents. N. Engl., J. Med. 338:232 238. 5. Livermore, D. M., T. G. Winstanley, and M. P. Shannon. 2001. Interpretative reading: recognizing the unusual and inferring resistance mechanisms from resistance phenotypes. J. Antimicrob. Chemother. 48:87 102.