Antimicrobial utilization decision support in the critical care unit: is the glass half-empty or half-full?*.

Recently, the Centers for Disease Control and Prevention released an alarming report noting that more than two million Americans are sickened yearly by antibioticresistant pathogens resulting in at least 23,000 deaths (1). Unfortunately, antibiotic use is the key driver of resistance, and the more antibiotics are used, the more antibiotic resistance ensues (2). In critically ill persons, it is imperative to quickly identify those patients with infection and promptly initiate appropriate anti-infectives. Numerous studies have demonstrated that inappropriate initial antimicrobial therapy (largely due to antibiotic resistance) is associated with increased mortality and poor outcome (3–5). Herein lies the rub—in order to effectively treat critically ill patients with infections, clinicians are increasingly forced to use combinations of antibiotics with broad-spectrum activity, which further fuels the emergence of resistance and subsequent use of more broad-spectrum antibiotics. The solution to this spiraling problem is to quickly and accurately identify patients with infections due to resistant pathogens, promptly initiate appropriate antibiotics, avoid unnecessary antibiotic exposure in uninfected persons, and stop antibiotics as soon as an infection has been adequately treated. Unfortunately, each of these steps is complex and difficult to achieve. Two broad strategies exist to improve empiric antimicrobial use: 1) rapid identification and determination of the susceptibility of the pathogen and 2) development of prediction tools to identify those at greatest risk of infection due to resistant pathogens. Although great strides have been made in clinical microbiology to speed the identification of pathogens through a variety of techniques (polymerase chain reaction, matrixassisted laser desorption ionization–time of flight, etc.), these methods require technical expertise and capital investment and are not universally available. Another approach to assist clinicians in making better therapeutic decisions is to use traditional epidemiologic techniques, such as prediction rules, to identify patients who are at greater risk of infection due to resistant pathogens. A drawback of prediction rules is they require the analysis of large amounts of data to produce widely applicable results. The introduction of electronic health records holds promise as they allow easy access to vast amounts of clinical data and the potential to generate real-time clinical alerts. Previous studies have explored the promise of electronic records and clinical alerts. In one of the seminal studies on this topic, Evans et al (6) described the introduction of a computerized system which combined microbiologic, clinical, and Copyright © 2014 by the Society of Critical Care Medicine and Lippincott Williams & Wilkins DOI: 10.1097/CCM.0000000000000388 Mark E. Rupp, MD Trevor C. Van Schooneveld, MD Department of Internal Medicine University of Nebraska Medical Center Omaha, NE At the crossroads of the quest for quality and data overload, a priori rational examination of what is to become standard of practice at large expense to our already stretched healthcare system, one must humbly concede that some important events still cannot be counted with a high degree of accuracy. The study by Brown et al (6) at the very least helps clarify the other half of the chiasmus, or what it is that should not be counted, even though it can.