Antibiotics in Laboratory Medicine, 6 Ed.


Intersection of Drug Development, Challenges of Antimicrobial Resistance, and Predicting Antimicrobial Efficacy

Daniel Amsterdam and Charles William Stratton

In 2009, the World Health Organization (WHO) referred to the problem of antibiotics and antibiotic resistance, stating, “Antibiotic Resistance – one of the three greatest threats to human health.” In the 8 years since the last publication of this volume, there have been numerous and significant advances in our understanding of the effects of antimicrobial agents on the human (and animal) microbiome, the increase and recognition of new antimicrobial (i.e., antiinfective resistance mechanisms), as well as the tremendous advances in technology that have led to the detection of these multiloci mechanisms especially among the gram-negative Enterobacteriaceae. Unfortunately, progress in all these areas is dimmed by the apparent disinterest of pharmaceutical companies in the development of new, more effective compounds to combat the increasing number of drug-resistant infections. Several themes are incorporated into this perspective and limn the changing landscape of antimicrobial development and the means with associated technology for estimating efficacy in human or animal hosts. These are the constriction of the antibiotic development pipeline, origins of antibiotic resistance, developing knowledge about the human and animal microbiomes, the predictive value of antimicrobial susceptibility testing, and new technologies especially “next-generation” sequencing, which has the capability of examining the entire microbial sequence rapidly and for reasonable costs.


In the United States and around the world, the incidence of drug-resistant infections and associated morbidity have increased. WHO identified the resistance of microorganisms to antimicrobial agents as one of the three greatest threats to human health. Recent reports by the Infectious Diseases Society of America (IDSA) (1) and the European Centre for Disease Prevention and Control and the European Medicines Agency (2) document that the number of candidate drugs in the developing pipeline, which are beneficial compared to existing drugs that will be capable to treat infections due to the group of pathogens termed “ESKAPE” (Enterococcus faeciumStaphylococcus aureusKlebsiella pneumoniaeAcinetobacter baumanniiPseudomonas aeruginosa, and Enterobacter species) are few. The aforementioned six species/groups cause the majority of US hospital infections and are not always contained by the available armamentarium of antibacterial drugs (3).

It is the IDSA’s view that the antibiotic pipeline problem can be dealt with by engaging global, political, scientific, industry, economic, intellectual property, policy, medical, and philanthropic leaders to develop creative incentives that will serve to stimulate new and ongoing research and development in this area. In this regard, it has been inferred that the financial gains and advantages for major pharmaceuticals may not be particularly advantageous for the development of new antimicrobial agents because the costs for treatment regimens, and reimbursement schedules in comparison to antineoplastic, biologic respiratory, and allergy drugs. In short, the economic advantage of antimicrobial agents relative to other drug class candidates presents an economic disadvantage (4).

Despite the confluence of these negative factors, it is the IDSA’s aim that the “creation of sustainable global antibacterial drug R&D enterprise” achieve in the short term 10 new, safe, and effective antibiotics by 2020. Toward this end, IDSA (5) launched a new collaboration entitled the “IDX ‘20” initiative, which several American and European groups and societies have endorsed. This declaration is a noble effort that no doubt will be reviewed in the next few years—or by the next publication of Antibiotics in Laboratory Medicine.

The development of new antimicrobial agents is one of three strategies that have been proposed to meet the challenge of multiresistant diverse microorganism types (extended-spectrum β-lactamases [ESBLs], Klebsiella pneumoniae carbapenemase [KPC], methicillin-resistant Staphylococcus aureus [MRSA], vancomycin-resistant enterococci [VRE], etc.) collectively referred to as multidrug-resistant organisms (MDROs). The other two strategies include interrupting the cross-transmission of MDROs and effective pharmacology oversight-stewardship in the treatment of these infections. This latter strategy incorporates tactics for appropriate initiation, selection, and de-escalation of antimicrobial therapy.


The marvel of antibiotic discovery, now more than 70 years old, gave rise to an era of drug innovation and discoveries that have been tempered by the emergence of resistant microorganisms (6).

Almost every chapter in this volume addresses the detection and identification of microbes that are resistant to a particular drug or class of antiinfectives. In examining the history and development of antimicrobial resistance, should this be interpreted to mean that antibiotic resistance in clinically significant bacteria is a contemporary phenomenon? Recent studies of modern human (and environmental) commensal microbial genomes suggest that these genomes possess a greater concentration of antibiotic resistance genes than had been previously recognized (79). A highly varied collection of genes encoding resistance to β-lactam, tetracycline, and glycopeptide antibiotics was recently found in 30,000-year-old Beningian permafrost sediments in Alaska (7). D’Costa and colleagues (7) documented through structure and function studies the complete vancomycin resistance element vanA and confirmed its similarity to modern variants. In earlier work, D’Costa et al. (10) analyzed the antibiotic resistance potential of soil microorganisms. In this study, it was alarming to discover that the frequency of high-level resistance detected in this study was to antibiotics that have served as the standard therapeutic regimens for decades. No class of antibiotic natural or synthetic was spared with respect to bacterial target. A summary of the 18 antibiotics and the extent of inactivation of the 480 strains that formed the library is noted in Figure 1. In general, without exception, investigators found that every strain in the library was resistant on average to 7 or 8 antimicrobials; two strains were resistant to 15 of the 21 drugs. Several antimicrobials, including cephalexin, the synthetic dihydrofolate reductase inhibitor trimethoprim, and the more recently developed lipopeptide daptomycin were almost universally ineffective against the library of bacterial strains. This wide dissemination of antibiotic resistance elements tempers the contemporary hypothesis for the emergence of antibiotic resistance and instead implies a natural history of resistance.


This volume is dedicated to tests that estimate the interactive end point of “bug” and drug. It is noteworthy, and without alarming revelation, that diseases other than those caused by infectious agents are treated by medicating the host. In contrast, therapy for infectious disease attempts to eliminate the pathogens from the host while minimizing adverse sequelae due to host immune responses and drug side effects. The target then for drug therapy is the pathogen; however, this frequently results in collateral damage.

A wide variety of manual and automated tests are described in this volume and in particular in Chapters 2 and 3, they review the development and detail of these assays during the latter half of the last century. Results of those assays are interpreted in the form of categorical values (“S,” “I,” or “R”) or with numerical equivalents and defined by at least two major consensus groups—­Clinical and Laboratory Standards Institute (CLSI) in the United States and European Committee on Antimicrobial Susceptibility Testing (EUCAST) in the European Union. In Chapter 1, Wikler and colleagues explore and discuss the reasoning/­rationale for defining the “breakpoint,” the dividing quantitative line between susceptibility and resistance that is the underpinning of antibacterial susceptibility tests. The question posed here is how successful are these results in predicting a positive outcome in patients with infection.

When using mortality as an outcome indicator, several studies (1115) executed between 1996 and 2003 demonstrated that mortality was reduced by 40% to 60% when the first antimicrobial agent administered was “susceptible.” Resistance as determined by in vitro testing is considered in these studies to be an independent risk factor for therapeutic failure. The advent of molecular methods to detect specific resistance genes augmented by whole genome sequencing (WGS) will no doubt enhance this capability (see the following section on “Impact of New Technologies” and Chapter 9 by Hegstad et al.). Clearly, the standard, that is, phenotypic antimicrobial susceptibility tests that we use today fail to mimic the physiologic status of the host in several dimensions. First, in the “test tube,” the drug is in constant association with the host—not varying according to its pharmacokinetic construct; second, the host’s cellular and antibody entities are absent; and last, the bioburden, that is, the test system agent concentration may be at variance from the true infectious dose extant in various body compartments.

When evaluating the expected correlation between results of in vitro susceptibility tests and therapeutic response, Rex and Pfaller (16) coined the “90–60 rule,” which indicated that a susceptible result is associated with a favorable therapeutic response in 90% to 95% of patients. The formulative predictions are beclouded when one considers immunocompromised patients with polymicrobic infections.

Several pertinent and directed questions can be posed to form the essential meaning of this section. Can antimicrobial susceptibility test results as performed in the routine clinical microbiology laboratory be translated into clinical efficacy and potency? For patients with MRSA bacteremia, is there a difference in patient outcome when reported minimum inhibitory concentrations (MICs) are in the susceptible category as defined by consensus organizations? Should there be a difference in categorical interpretation (S, I, or R) for pneumococcal meningitis–associated and non–meningitis-associated disease? When documented nosocomial bacteremia is caused by P. aeruginosa, is patient outcome associated with reduced piperacillin-tazobactam MICs?

Clearly, the response in each of the previously cited cases is the critical establishment of the breakpoint concentration by regulatory oversight groups that define the chasm between susceptible and “resistant.” In Chapter 1 of this volume, Wikler and coauthors define this parameter and the necessary evidence to establish it. Simply stated, each antimicrobial agent/drug pair is dependent on the pharmacokinetic (PK) and pharmacodynamic (PD) properties of the antiinfective compound and the associated clinical outcome. Specific parameters that are pertinent to evaluating the PK/PD are the area under the curve of Cmax(peak) and above the resulted bug–drug MIC, and the time duration that Cmax is greater than the MIC (Fig. 1). The pharmacodynamics of an antimicrobial is then the sum of the antimicrobial PK plus the MIC and the clinical outcome. Generally, the application of PK/PD parameters for drugs can be categorized as those drugs which are concentration-dependent; that is, higher concentrations are required in relation to MIC to kill pathogens (e.g., fluoroquinolones and aminoglycosides) and time-dependent (concentration independent) agents whose effectiveness is measured by duration of exposure above a recognized inhibitory concentration (the MIC90) to determine killing. Examples of this latter group include cell wall–active agents such as β-lactam antibiotics (penicillins and cephalosporins) and vancomycin.

In 2004, Sakoulas and associates (17) redefined the interpretive concentration for successful (i.e., enhanced) outcome in the interpretation of vancomycin MIC for bloodstream isolates (BSIs) or MRSA. They determined that when the MIC was less than or equal to 0.5 µg/mL, successful therapy resulted in a 55.6% improved outcome as compared to 9.5% when MICs were 1 or 2 µg mL—still in the susceptible category.

In 2008, the CLSI revised the susceptible category interpretation for penicillin based on the clinical syndrome (meningitis vs. non-­meningitis) and the route of administration of penicillin. Table 1 outlines the standard pre- (“former”) and post-2008 (“new”) breakpoint interpretations. Although there was one susceptibility category (≤0.06 µg/mL) in the former standard, the new standard defined three categories based on clinical syndrome and route of administration (18).

In a 2008 study of P. aeruginosa bacteremia patients treated with piperacillin-tazobactam, Tam et al. (19) determined that when susceptibility to piperacillin-tazobactam was 32/64 µg/mL, there was a fourfold increase in 30-day mortality (85.7% vs. 22.2%) compared to isolates with susceptibility of 16 µg/mL.

In each of the previously mentioned examples, credence for the predictive value of an MIC and the associated susceptible or resistant categorical interpretation can only prove meaningful when sufficient in vitro studies have been completed along with the necessary clinical outcome evaluations.

In efforts to integrate clinical microbiologic data, that is, antimicrobial susceptibility results and pharmacologic data, programs have been developed under the banner of “antimicrobial stewardship” that are designed to “monitor and direct antimicrobial use at a health care institution, thus providing a standard evidence-based approach to judicial antimicrobial use” (20,21). Outcome measures that are desired include improved patient outcome, improved safety, reduced resistance, and reduced cost (22). These measures may be difficult to assess as well as difficult to achieve. Within the frame of “antimicrobial stewardship program” (ASP), the use of clinical pharmacists and the electronic health record offer a streamlined method for implementation (23,24). Moreover, new approaches in rapid pathogen detection and identification can prove highly effective when integrated into an ASP (25).


The microbiome, whether animal or human, can be defined as the aggregate genomes of their respective microbiota and the varied metabolic activities which they encode. The accumulated information has the potential to revolutionize the way we view contemporary therapeutics. During the past century, the information that has been gathered in the area of pharmacology, describing rates of absorption, distribution, metabolism, and excretion for hundreds of compounds—in our case, antibiotics—referred to today as xenobiotics (compound foreign to a living organism, which include antimicrobial agents as well as other therapeutic drugs). As yet a poorly understood component of xenobiotic metabolism is the effect-impact of the vast number (trillions, 107) of microorganisms that reside in the gastrointestinal tract. Although the discovery of antibiotics is decades old, spanning the past century, it appears we are at the beginning of determining the “intended” collateral damages that antibiotics can impart on the symbiotic microorganisms living in our gastrointestinal tract (26). The gathering of the varied and numerous microbial members within our person play vital roles in the maintenance of human health by freeing nutrients and/or energy from otherwise inaccessible dietary substrates, promoting the differentiation of host cells and tissues, stimulating the immune system, and protecting the host from invasion by pathogens. The assemblage of human-associated microbial communities does not generally proceed smoothly. There are several examples where some fraction of the community is removed or killed (e.g., oral hygiene). The effect of antimicrobial agents on the gut microbiota serves as a model for disturbance in human-associated communities. It is estimated, that on any given day, 1% to 3% of people in the developed world are exposed to pharmacologic doses of antibiotics (27).

Antimicrobial therapy is intended to achieve sufficient drug concentration for a sufficient duration in a particular body compartment so that the targeted pathogen is eliminated. Even if this aim were always attained, the antibiotic will also be found at varied concentrations of several locations within the body depending on the mode of administration and PK properties. When members of the microbiota are exposed to antibiotics that affect their growth rate without killing them, there is selection for resistance. The horizontal transfer of antibiotic-resistant determinants takes place in the human gut and oral communities and their reservoir serves as the starting point/place for transfer to pathogens as well as the resident microbiota. The collateral damage to the human microbiome exerted by contemporary antimicrobials through overuse and extended spectrum has likely been the driving force behind the proliferation of MDROs and members of the ESKAPE group. Understanding the balance and fragility of the human microbiome so as to use “­microbiome-sparing antimicrobial therapy,” develop techniques to restore and maintain the indigenous microbiota, as well as use protective mechanisms encoded by an intact microbiome will limit the expanding scope of resistance (28).

As we decipher the heterogeneous environment of the human body/microbiome, we recognize that microorganisms encounter these environments replaced with transient chemical and nutrient gradients. Clinically, antibiotic gradients develop when a patient begins, ends, or neglects a prescribed regimen. To simulate in vivo conditions, Zhang et al. (29) constructed a microfluidic device consisting of a tiny chamber device. The investigators then determined the effect of the microenvironment generated within the chambers on bacterial populations grown in them. They found that when the test organism Escherichia coli is grown in a heterogeneous environment that contains a steep antibiotic concentration, ciprofloxacin in their simulated experiments, they demonstrated rapid and repeatable acquisition and fixation of ciprofloxacin mutations compared with bacteria grown in homogeneous environments. If we suppose that some parts of the human body resemble heterogeneous environments rather than the in vitro containment of a Petri dish and a flask, then the environment proposed by Zhang et al. (29) would provide a more relevant model for the development of antimicrobial resistance.


More than 10 years have elapsed since the first polymerase chain reaction (PCR) assays for antimicrobial resistance was evaluated. For this event, the assay was directed to MRSA. Few targeted assays have followed but have included vancomycin resistance in Enterococcus spp and rifampin resistance in Mycobacterium tuberculosis. To date, there has not been a developed panel/array of molecular susceptibility testing for several common drug resistance mechanisms.

A barrier for molecular susceptibility testing has been the characterization of mutation(s) associated with the resistance phenotype and the subsequent development of tests specific for these markers. For example, mutants generated during in vitro selection of antimicrobial-resistant strains can differ from those that develop naturally in human populations and cause clinical disease (30).

By far, in the second decade of the 21st century, the greatest need and challenge in molecular assay development is the capability to detect resistance determinants among the gram-negative bacilli. In the family Enterobacteriaceae, several hundred mechanisms have been reported causing resistance to β-lactams, cephalosporins, monobactams, and/or carbapenems. Among Enterobacteriaceae, β-lactam resistance has been attributed to several mechanisms, which include ESBLs, AmpCs, metallo-β-lactamases (MBLs), and KPCs. The number of genotypically unique ESBLs total more than 200 (31).

Directed detection of the several and as yet uncovered resistance mechanism requires technology with efficient and specific methodology capable to be multiplexed beyond that of PCR platforms extant and will probably be based on microarrays, metabolite detection assays, or direct sequencing. Key to the widespread acceptance of these newer technologies will be the evidence to demonstrate the high negative predictive values and the associated sensitivity to detect low levels of gene expression.

Which technologies of those that are currently in use will prove meaningful for future development is unknown. Although real-time PCR has revolutionized clinical molecular diagnostics, permitting detection of targets in a closed system within a 45-minute to 2-hour time frame, an associated limitation is the number of fluorophores that can be used for simultaneous detection of multiple targets—usually six. For detection of a multiplicity of targets (more than six), liquid- and solid-phase microarrays may best suit this requirement. Examples of this technology would be XTAG (Luminex, Austin, TX) or BeadExpress (Illumina, San Diego, CA). Solid arrays which have been in use for several years in research laboratories as represented by Nanosphere, Inc. (Northbrook, IL) are an alternative. Other solid array systems such as GeneChip (Affymetrix, Santa Clara, CA) and BioFilmChip (­Autogenomics, Vista, CA) are other alternatives.

Recent advances in nucleic acid sequencing technology have made sequencing the entire human genome—or for this discussion, the microbial code—both technically and economically feasible. In clinical medicine, WGS has been heralded to clarify molecular diagnosis and guide therapy—giving rise to the concept of personalized medicine. Several benchtop, high-throughput sequencing platforms no larger than our all-in-one printer are available for this function. Among them, the 454 GS Junior (Roche, Branford, CT), MiSeq (Illumina, San Diego, CA), and the Ion Torrent PGM (Life Technologies, Carlsbad, CA) offer modest setup and operating costs. Each instrument can generate sufficient data for a draft bacterial genome (32). It is not unlikely that given the multiple bioinformatic methods available, the capability to analyze the information encoded within the complete genome sequence for determining antimicrobial resistance would be available. In the context of this discussion, reference is made to using the decoded microbial genome to identify resistance markers that would redirect health care providers from treating patients with those drugs that would predictably become ineffective when the microbial target would produce the inactivating enzymes or processes that would render the antiinfective useless. Although the time required to accomplish this is rapid (days) compared to earlier sequencing iterations, it does not meet the clinical needs for “rapid” diagnosis. Each instrument can generate the data required for a draft bacterial genome sequence suitable for identifying and characterizing pathogens.

Deciphering DNA sequences is essential for virtually all areas of biologic investigation. The classical capillary electrophoresis (CE)–based sequencing has enabled the elucidation of genetic information in almost any organism or biologic system. In order to overcome inherent barriers in this experimental system, a fundamentally different technology was developed—next-generation sequencing (NGS). NGS is especially suited for microbial systems as it has the capability to evaluate alterations throughout the genome without prior knowledge and is therefore adaptable for unculturable microorganisms. NGS has increased the rate of data output each year since its inception in 2007 so that in 2012, 1 terabase (Tb) of data is available in a single sequencing run compared to 1 gigabase (Gb) in 2007. Associated with this exponential increase in output is a 105-fold decrease in the cost of determining the genome of a microorganism. In 1995, sequencing the 1.8-megabase (Mb) genome of Haemophilus influenzae with CE technology costed approximately $1 million and took about 1 year. Sequencing the 5-Mb genome of E. coli in 2012 with NGS technology can be accomplished in 1 day at a cost of about $100.

The molecular detection of resistance is an attractive concept; however, it fails to direct or recommend any specific treatment plan, antiinfective, or course of action. Genotypic resistance approaches have been used throughout the development of antiretroviral agents to monitor the treatment of HIV. But this was necessitated as the routine phenotype testing for susceptibility (or resistance) was not readily accessible to clinical laboratories because of biosafety precautions necessary when dealing with the human immunoproliferative agent—HIV. As is the case in WGS, the issue will become for microbial targets, the capability of dedicated software to interrogate the decoded microbial sequence to identify resistant markers for extant antimicrobial agents.

Recent reports have demonstrated how high-throughput genome sequencing of bacterial genomes were used to monitor disease spread and control infections in hospital settings. These investigations were associated with an outbreak of carbapenem-resistant K. pneumonia, which occurred at the U.S. National Institute of Health Clinical Center (33); cases of group A streptococcus (GAS; Streptococcus pyogenes) isolates associated with outbreaks of puerperal sepsis in Australian hospitals (32); and an outbreak of MRSA in a neonatal intensive care unit at a hospital in Cambridge, United Kingdom (34). These three studies show the future direction of clinical laboratory studies, which enable same-day diagnosis antibiotic resistance gene profiling and virulence gene detection.

A departure from traditional molecular diagnostics for targeting either DNA or RNA that encode resistance determinants would encompass the identification of proteins responsible for resistance—the field of proteomics. Advances in mass spectrometry describe matrix-associated laser desorption-time of flight (MALDI-TOF) identification of bacteria. This technique establishes the protein signature that can fingerprint the identification of clinically significant bacteria; however, direct detection of resistance determinants has not been established because several proteins can be involved in drug resistance (35).

As antibiotic resistance mechanisms among pathogenic microorganisms, especially the Enter­obacteriaceae, are discerned, there is compelling need to rapidly and definitively identify them. Advanced diagnostics employing molecular methods is considered to be a key driver to improve therapeutic outcome—molecular arrays and NGS are key to providing the most promising opportunities.


 1.  Boucher HW, Talbot GH, Bradley JS, et al. Bad bugs, no drugs: no ESKAPE! An update from the Infectious Diseases Society of America. Clin Infect Dis 2009;48:1–12.

 2.  European Centre for Disease Prevention and Control/European Medicines Agency Joint Technical Report. The bacterial challenge: time to react. Updated September 2009. Accessed May 17, 2014.

 3.  Rice LB. Federal funding for the study of antimicrobial resistance in nosocomial pathogens: no ESKAPE. J Infect Dis 2008;197:1079–1081.

 4.  Tillotson GS. Stimulating antibiotic development. Lancet Infect Dis 2010;10:2–3.

 5.  Infectious Diseases Society of America. The 10 × ‘20 initiative pursuing a global commitment to develop 10 new antibacterial drugs by 2020. Clin Infect Dis 2010;50: 1081–1083.

 6.  Wright GD. The antibiotic resistome: the nexus of chemical and genetic diversity. Nature Rev Microbiol 2007;5: 175–186.

 7.  D’Costa VM, King CE, Kalan L, et al. Antibiotic resistance is ancient. Nature 2011;477:457–461.

 8.  Sommer MOA, Dantas G, Church GM. Functional characterization of the antibiotic resistance reservoir in the human microflora. Science 2009;325:1128–1131.

 9.  Dantas G, Sommer MOA, Oluwasegun RD, et al. Bacteria subsisting on antibiotics. Science 2008;3201:100–103.

10.  D’Costa VM, McGrann KM, Hughes DW, et al. Sampling the antibiotic resistome. Science 2006;311:374–377.

11.  Garnacho-Montero J, Garcia-Garmendia JL, Barrero-Almodora A, et al. Impact of adequate empirical antibiotic therapy on the outcome of patients admitted to the intensive care unit with sepsis. Crit Care Med 2003;31: 2742–2751.

12.  Vallees J, Rello J, Ochagavia A, et al. Community-acquired bloodstream infection in critically ill adult patients: impact of shock and inappropriate antibiotic therapy on survival. Chest 2003;123:1615–1624.

13.  Ibrahim EH, Sherman G, Ward S, et al. The influence of inadequate antimicrobial treatment of bloodstream infections on patient outcomes in the ICU. Chest 2000;118: 145–155.

14.  Rello J, Gallego M, Mariscal D, et al. The value of routine microbial investigation in ventilator-associated pneumonia. Am J Respir Crit Care Med 1997;156:196–200.

15.  Alvarez-Lerma F. Modification of empiric antibiotic treatment in patients with pneumonia acquired in the intensive care unit. ICU-Acquired Pneumonia Study Group. Intensive Care Med 1996;22:387–394.

16.  Rex JH, Pfaller MA. Has antifungal susceptibility testing come of age? Clin Infect Dis 2002;35:982–989.

17.  Sakoulas G, Moise-Broder PA, Schentag J, et al. Relationship of MIC and bactericidal activity to efficacy of vancomycin for treatment of methicillin-resistant Staphylococcus aureus bacteremia. J Clin Micro 2004;42: 2398–2402.

18.  Centers for Disease Control and Prevention. Effects of penicillin susceptibility breakpoints for Streptococcus pneumoniae. United States, 2006-2007. MMWR Morb Mortal Wkly Rep 2008;50:1353–1355.

19.  Tam VH, Gamez EA, Westan JS, et al. Outcomes of bacteremia due to Pseudomonas aeruginosa with reduced susceptibility to piperacillin-tazobactam: implications on the appropriateness of the resistance breakpoint. Clin Infect Dis 2008;46:862–867.

20.  Delit TH, Owens RC, McGowan JE Jr, et al. Infectious Diseases Society of America and the Society for Healthcare Epidemiology of America guidelines for developing an institutional program to enhance antimicrobial stewardship. Clin Infect Dis 2007;44:159–177.

21.  Tamma PD, Cosgrove SE. Antimicrobial stewardship. Infect Dis Clin North Am 2011;25:245–260.

22.  McGowan JE. Antimicrobial stewardship—the state of the art in 2011: focus on outcome and methods. Infect Control Hosp Epidemiol 2012;33:331–337.

23.  Salmasian H, Freedberg DE, Abrams JA, et al. An automated tool for detecting medication overuses based on the electronic health records. Pharmacoepidemiol Drug Saf 2013;22:183–189.

24.  Linsky A, Simon SR. Medication discrepancies in integrated electronic health records. BMJ Qual Saf 2013;22: 103–109.

25.  Perez KK, Olsen RJ, Musick WL, et al. Integrating rapid pathogen identification and antimicrobial stewardship significantly decreases hospital costs. Arch Pathol Lab Med 2013;137:1247–1254.

26.  Blaser M. Antibiotic overuse: stop the killing of beneficial bacteria. Nature 2011;476:393–394.

27.  Goossens H, Ferech M, Vander Stichele R, et al. Outpatient antibiotic use in Europe and association with resistance: a cross-national database study. Lancet 2005;365:579–587.

28.  Tosh PK, McDonald LC. Infection control in the multidrug-resistant era: tending the human microbiome. Clin Infect Dis 2012;54:707–713.

29.  Zhang Q, Lambert G, Liao D, et al. Acceleration of emergence of bacterial antibiotic resistance in connected microenvironments. Science 2011;333(6050):1764–1767.

30.  Piatek AS, Telenti A, Murray MR, et al. Genotypic analysis of Mycobacterium tuberculosis in two distinct populations using molecular beacons: implications for rapid susceptibility testing. Antimicrob Agents Chemother 2000;44:103–110.

31.  Leinberger DM, Grimm V, Rubtsova M, et al. Integrated detection of extended-spectrum-beta-lactam resistance by DNA microarray-base genotyping of TEM, SHV, and CTX-M genes. J Clin Microbiol 2010;48:460–471.

32.  Loman NJ, Misra RN, Dallman TJ, et al. Performance comparison of benchtop high-throughput sequencing platforms. Nature Biotechnol 2012;30(5):434–439.

33.  Ben Zakour NL, Venturini C, Beatson SA, et al. Analysis of Streptococcus pyogenes peripheral sepsis cluster by use of whole-genome sequencing. J Clin Microbiol 2012;50: 2224–2228.

34.  Köser CU, Holden MT, Ellington MJ, et al. Rapid whole-genome sequencing for investigation of a neonatal MRSA outbreak. N Engl J Med 2012;366:2267–2275.

35.  Carapetis JR, Steer AC, Mulholland EK, et al. The global burden of group A streptococcal disease. Lancet Infect Dis 2005;5:685–694.

36.  Shah NH, Gharbia SE, eds. Mass spectrometry for microbial proteomics. New York: John Wiley and Sons, 2010.



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