infection control & hospital epidemiology july 2017, vol. 38, no. 7 concise communication


A Simple Microsoft Excel Method to Predict Antibiotic Outbreaks and Underutilization


Cristina Miglis, PharmD;1,2 Nathaniel J. Rhodes, PharmD, MSc;1,2 Sean N. Avedissian, PharmD;1,2 Teresa R. Zembower, MD, MPH;3 Michael Postelnick, RPh;2 Richard G. Wunderink, MD;4 Sarah H. Sutton, MD;3 Marc H. Scheetz, PharmD, MSc1,2


Benchmarking strategies are needed to promote the appropriate use of antibiotics. We have adapted a simple regressive method in Microsoft Excel that is easily implementable and creates predictive indices. This method trends consumption over time and can identify periods of over- and underuse at the hospital level.


Infect Control Hosp Epidemiol 2017;38:860–862


by these organisms are associated with a significant clinical and financial burden worldwide and are responsible for at least 2 million illnesses and 23,000 attributable deaths annually in the United States alone.2 Moreover, the Centers for Diseases Control and Prevention (CDC) estimates that up to 50% of antibiotic prescriptions in the United States are unnecessary.2 As a result of inappropriate overuse, various calls to action


The emergence of antibiotic-resistant bacteria is an increas- ingly serious threat to global public health.1 Infections caused


for antimicrobial stewardship have been sounded. While a need to curtail inappropriate use through stewardship exists, defining this in the hospital setting can be difficult and requires standardization.3,4 One mechanism aimed at benchmarking consumption and evaluating national trends is the CDC Antibiotic Use and Resistance (AUR) module for reporting data to the National Healthcare Safety Network (NHSN). We recently described a methodology to identify potential “antibiotic outbreaks” and periods of underutilization using data compiled by the NHSN AU method. Antibiotic use (ie, consumption) was measured in days of therapy (DOT), which was standardized to days present (DP) in the hospital (ie, DOT/1,000 DP). Over- and under-utilization was defined as a monthly rate of use outside the trend-adjusted prediction window.5 One barrier to implementing our previous metho- dology is the requirement for users to have technical statistical software (eg, Stata, StataCorp LP, College Station, TX) and statistical expertise. In this manuscript, we translate our methodology into a “plug and play strategy” using Microsoft Excel. By using well-known and readily available software, most hospital users should be able to employ these strategies. This method is easy to implement in any hospital that generates longitudinal antimicrobial utilization data, including those who participate in the NHSN AUR module.


For method illustration, data were obtained from North-


western Memorial Hospital, an 897-bed, tertiary-care, aca- demic medical center in Chicago, Illinois. Data regarding intravenous administration of piperacillin-tazobactam were extracted from our medical intensive care unit (MICU) monthly from January 1, 2012, to December 31, 2015. Antibiotic consumption was calculated as antimicrobial days (AD) per 1,000 DP in the ICU and compiled according to the NHSN AU methodology.6 Antimicrobial days and DP facility wide were extracted from the electronic medication record (eMAR) and tallied. Microsoft Excel (2016) standard formulae were used for all calculations, and priority for formulae/code was based on compatibility with older Microsoft Excel versions. A database is supplied as SupplementaryMaterial with formulae for each calculated text box. Interested readers can transfer their own data and generate similar graphics and predictions. Our data were extracted from the NHSN portal as follows.


The “Analysis” portal within the Patient Safety component was accessed, and the “Output options” section was selected. From this portal, the Antimicrobial Use and Resistance module was chosen and directed to Antimicrobial Use data. Using the “CDC defined output,”“Line Listing-All Submitted AU Data by Location” was downloaded (although whole-hospital [FACWIDEIN] is also an option). By modifying the output to comma-separated-value format (*.csv), the data were immediately available in Microsoft Excel.Date data (ie, month of use) are defined in theNHSNdata as “summaryYM.” Numerator data (ie, consumption) were obtained from “IV_Count.” Denominator data (ie, days present) were obtained from “numDaysPresent.” After sorting for location and antibiotic of choice, sequentialmonths were enumerated. Consumption data were then standardized as DOT/1,000 DP in the adjacent columnfor each correspondingmonth using the formula (=cell defining “IV_Count”/cell defining “numDaysPresent” *1,000; see SupplementaryMaterial). Because NHSN data are not universally available to all institutions, any antibiotic consumption data following a


similar format (eg, DOT/1,000 DP) can be modeled using the file available in the Supplementary Material. That is, this document only requires that users insert institution-specific consumption data standardized to 1,000 DP. Formulae are included in the Supplementary Material, and derivations are detailed in Table 1. To investigate antibiotic outbreak thresholds and periods


of underutilization, prediction intervals were defined for each individual time period.5 In this case, an 80% prediction interval was arbitrarily chosen to define potential antibiotic outbreaks; however, percentages can be set to any threshold at the discretion of each institution. We propose that this value has greater utility than the more generally reported 95% confidence interval for the mean, which predicts the


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