INtelligent Stewardship Prompts to Improve Real-time Empiric Antibiotic Selection for Patients With Urinary Tract Infections (UTI)
Phase: NA
Conditions: Urinary Tract Infection (UTI)
Sponsor: Harvard Pilgrim Health Care

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Stewardship Prompts to Improve Antibiotic Selection 
for Urinary Tract Infection: The INSPIRE Randomized Clinical Trial 
NCT#: 03697096 
 
7/3/2018 

INSPIRE (INtelligent Stewardship Prompts to Improve Real-time Empiric Antibiotic Selection for 
Patients with Urinary Tract Infection (UTI)): The INSPIRE-ASP UTI Trial Protocol 
 
A. Background and Goals 
A.1.1 Overuse of Antibiotics in the United States 
Close to forty percent of patients admitted to U.S. hospitals receive an antibiotic to treat an 
active infection, with over half of these antibiotics used to treat just two conditions, pneumonia and 
urinary tract infection (UTI).1 In treating these infections, the Centers for Disease Control and 
Prevention (CDC) has found that antibiotic prescribing could be improved in 35-40% of patients.2 
This is critical, as there is a growing threat of antibiotic-resistant bacteria which now infect ~2 million 
people per year in U.S. hospitals and communities as well as at the global level.3-5 Concerns about 
resistance are an important driver of extended-spectrum antibiotic prescribing, which in turn 
promotes further resistance.6,7 
Rising antibiotic resistance and the slow development of novel antibiotics have fueled 
national calls to improve antibiotic choices by frontline physicians.8-13 There is a 2020 target to 
reduce “inappropriate” antibiotic prescribing in hospitals by 20%, as discussed in the 2014 “National 
Strategy for Combating Antibiotic-Resistant Bacteria” released by the White House.12 While 
physicians agree that antibiotics are overprescribed, most fail to recognize areas for self-
improvement.14,15 Therefore, hospitals have been charged with developing antimicrobial 
stewardship programs which provide ongoing education and feedback to physicians and ensure 
accountability in prescribing.10,16 To improve the success of these efforts, it is critical to develop 
evidence about best practices for improving judicious antibiotic prescribing. Evidence, engagement, 
and education are required to build a culture where it is accepted that rationale antibiotic 
prescribing is every physician’s responsibility.21 
A 1.2 Multidrug-Resistant Organisms as Drivers of Empiric Extended-Spectrum Inpatient 
Antibiotic Use 
 
There are many facets of inappropriate antibiotic prescribing, including prescribing 
antibiotics when they are not indicated, starting unnecessarily broad empiric antibiotics for a 
patient’s condition, selecting the wrong dose or duration, and failing to re-evaluate the choices that 
have been made. Empiric therapy refers to the selection of an antibiotic prior to knowledge 
regarding the specific pathogen. We focus here on improving the empiric selection of antibiotics for 
hospitalized patients that have an appropriate indication. 
When selecting initial antibiotics for common community-acquired conditions that cause 
admission, such as pneumonia and urinary tract infection (UTI), physicians vary widely in their 
adherence to national guidelines.18-20 This is often due to misperceptions that multidrug-resistant 
organisms (MDROs) are more common than they actually are, or beliefs that extended-spectrum 
antibiotics are more effective in curing infections than standard-spectrum choices, even for 
pathogens sensitive to standard-spectrum agents.21 Often, such perceptions are based on studies 
on critically ill patients and those with infections caused by methicillin-resistant Staphylococcus 
aureus (MRSA), Pseudomonas aeruginosa, and extended-spectrum beta-lactamase producing 
(ESBL) Enterobacteriaceae.22-28 
In 2005, the American Thoracic Society and the Infectious Diseases Society of America 
classified patients with recent healthcare exposure as having a higher risk of MDROs as the 
infecting pathogen in pneumonia. National guidelines recommended giving empiric extended-
spectrum antibiotics to address this potential risk.29-32  Not surprisingly, the use of extended-
spectrum antibiotics has increased.34 Despite guideline recommendations to consider the local 
prevalence of MDROs, there has been little guidance on how best to assess local MDRO 
prevalence. 
A1.3 Knowledge Gaps for Judicious Empiric Antibiotic Prescribing 
 
In understanding the appropriateness of empiric antibiotic selection, it is important to note 
that the criterion of recent healthcare exposure is a poor predictor of having an infection due to an 
MDRO.35,36 In fact, use of empiric extended-spectrum antibiotics has not been associated with 
differences in mortality in several single or small multicenter studies.35-38 Furthermore, even when 
empiric antibiotics are found to be discordant with pathogen antibiotic susceptibilities, transient use 
of discordant therapy followed by correction to appropriate therapy has not been found to have 

negative outcomes in single-center studies of non-critically ill patients.39,40 Thus, several antibiotic 
stewardship experts have advocated  reserving extended-spectrum antibiotic therapy for patients 
with culture-proven resistance, except in critically ill patients or in hospitals with a high local 
incidence of MDRO infections.41 This position may be further justified by the fact that extended-
spectrum therapy, often involving more than one drug, has been associated with a higher risk of 
Clostridium difficile infections and increased risk of colonization or subsequent infection with 
MDROs.42-44 An additional reason to initiate standard-spectrum therapy is that prescribers rarely 
modify antibiotic regimens, even after cultures reveal susceptible strains.45 
To reduce the overuse of extended-spectrum antibiotics as empiric therapy for UTI, 
hospitals have utilized both labor-intensive auditing with feedback and education, as well as point-
of-care interventions aimed at changing prescribing practices.43, 46-49 Nevertheless, successful 
interventions to improve antibiotic prescribing in hospitals have been called “the missing care 
bundle,”50 highlighting the fact that we lack certainty and generalizability in understanding which 
strategies best effect change in prescribing behavior. 
A.1.4 Incorporating Local Evidence into Decision-Making 
Knowledge of local and hospital prevalence of MDROs can be an important consideration 
when determining whether to empirically use extended-spectrum agents. This information is 
commonly obtained through antibiograms from the clinical microbiology laboratory which note the 
proportion of each pathogen that tested resistant to common antibiotics in the recent past. The 
intent of these local antibiograms are to assist physicians in their choice of empiric antibiotics,51 and 
to influence hospital guidelines for appropriate antibiotic prescribing.52-55 Nevertheless, 
antibiograms are difficult to use when the pathogen is unknown since physicians have to 
extrapolate the probability that the infection is due to a particular pathogen. As mentioned above, 
physicians are expected to use local antibiogram data from their microbiology laboratory to 
determine the risk of an MDRO infection in a given patient.51-54,92 Yet, these data are not presented 
in a manner helpful to clinical decision-making.93 For example, national data shows that over 50% 
of S. aureus are resistant to methicillin (MRSA), and 22% of E. coli and 30% of P. aeruginosa are 
resistant to fluoroquinolones.94 
These data may be misinterpreted as the probability that a patient has an MDRO infection. 
However, when accounting for the likelihood that a patient is infected with these pathogens, the risk 
of MDRO infection drops substantially. In two recent studies of patients presenting from the 
community with pneumonia, there was a 2.5-4.6% risk of MRSA, a 1.0-1.9% risk of Gram-negative 
Enterobacteriaceae, and a 0.7-1.2% risk of P. aeruginosa resistant to standard-spectrum 
antibiotics.26,94-95   
Instead of providing the MDRO risk by pathogen, physicians would be better served by 
alerts providing the local probabilities of resistance for specific types of infections. Though precision 
medicine tools in antibiotic stewardship are lacking, there is evidence that precision medicine 
approaches can substantially improve patient care.56-59 This involves reformulating a hospitals’ 
antibiogram data into disease-specific probabilities of MDRO infection to facilitate appropriate 
prescribing. 
A.1.5 Identifying Best Strategies for Ensuring Judicious Antibiotic Choices 
Best strategies for ensuring appropriate antibiotic prescribing have not been evaluated in 
clinical trials. Most evidence is based upon single center publications which are subject to unique 
hospital prescribing culture and beliefs. Much has been described about inpatient antimicrobial 
stewardship programs and the use of various electronic alerts and labor intensive physician 
feedback to influence and correct inappropriate prescribing.46-49, 60-65,-52, 68-72  Furthermore, antibiotic 
use and choice is highly variable among prescribers, particularly in community hospital settings and 
prescribing behavior is often difficult to change with conventional methods.73-75 
A.1.6 Definition of Standard-Spectrum and Extended-Spectrum Antibiotics 
National guidelines recommend selection of either a fluoroquinolone such as ciprofloxacin, 
or a 3rd generation cephalosporin such as ceftriaxone as empiric therapy for patients presenting 
from home or clinic and meeting criteria for hospitalization due to UTI.18-20 There is growing 
evidence that fluoroquinolones are associated with serious adverse drug reactions and an 
increased risk of C. difficile colitis.99-101 The Food and Drug Administration (FDA) has also issued a 

warning (May, 2016) against the routine use of fluoroquinolones, particularly for conditions in which 
resistance is known to be high, such as urinary tract infection .102,193 There is increased national 
attention to decrease the overall use of fluoroquinolones. 
 
A.1.7 Adverse Effects of Inappropriate Antibiotic Use  
 
Inappropriate use of antibiotics, particularly extended-spectrum antibiotics have been shown 
to increase a patient’s risk for future MDRO, length of stay, risk for C. difficile colitis, healthcare 
costs, and mortality.99,103  Data are emerging that there is time to await definitive culture data to 
inform antibiotic treatment and that a delay in antibiotics targeting resistant organisms is not 
necessarily harmful and may improve outcomes.103-105    
 
B. INSPIRE-ASP UTI Trial Study Design & Population 
 
B.1.1 INSPIRE-ASP UTI Trial Study Design & Population 
The INSPIRE-ASP Trial is a two-arm cluster-randomized trial in HCA hospitals comparing 
routine empiric antibiotic prescribing for patients admitted with UTI to prescribing informed by point-
of-care precision medicine computerized physician order entry (CPOE) smart prompt for adult 
patients admitted to general medical or surgical floors. The unit of randomization will be the 
hospital. The trial will begin with a six-month phase-in period from October 1, 2018-March 31, 2019, 
followed by a 15-month trial intervention period between April 1, 2019-June 30, 2020. For analysis 
purposes, the cohort will be defined as adult patients with a primary or secondary claims code 
indicating UTI that is present on admission, did not transfer directly from the ED to ICU, and is 
treated with an antibiotic within the first 3 days of hospitalization. A baseline period between April 1, 
2017-September 30, 2018 will be utilized for comparison with the intervention period at each 
hospital. Per guidance from the Office for Human Research Protections (OHRP), prisoners will not 
be included in this trial. Prescribers and antibiotic stewardship teams in intervention facilities will be 
trained (e.g., coaching calls, usual communication channels (email, flyers)) that prisoners should be 
excluded from the trial (i.e., the CPOE smart prompt should not be acted upon for these 
individuals). Data for any prisoners that may be inadvertently included in study datasets will be 
removed prior to analyses using claims data as well as admission/discharge/transfer codes 
indicating ‘Court/Law Enforcement’. 
 
B.1.2 HCA Healthcare Infrastructure for the INSPIRE-ASP Intervention Arm 
HCA Healthcare currently requires physicians to provide an indication when an antibiotic is 
ordered (see Appendix B for example CPOE indication screens). For facilities assigned to the 
intervention arm of the INSPIRE-ASP Trial, an INSPIRE CPOE smart prompt will be triggered when 
physicians prescribe an extended-spectrum (ES, Table 1) for a UTI indication in the first three days 
of a hospital stay. This CPOE smart prompt will provide clinicians with a patient-specific estimate of 
the likelihood that the UTI is due to the associated target pathogens (e.g. MRSA, VRE, 
Pseudomonas, ESBL, or CRE, Table 1). The smart prompt will also provide guidance for 
appropriate empiric antibiotic options per trial protocol and hospital policy.   
For example, if an anti-Pseudomonas antibiotic is being ordered for UTI, the clinician will 
receive a prompt during the ordering process providing the probability that the patient has UTI due 
to Pseudomonas aeruginosa. Recommendations for using standard-spectrum antibiotics (e.g. 
ceftriaxone) will be made when the probability of a given MDRO is lower than a given threshold 
(e.g., <10%). This threshold used in the trial will be finalized after evaluation of the prevalence of 
MDROs, and after discussion with the INSPIRE-ASP Steering Committee. Similarly, if an ES 
antibiotic is selected that targets a resistant Gram-negative Enterobacteriaceae, the ordering 
physician will receive information on the probability that the patient is likely to grow such organisms. 
The smart prompt will account for personal attributes of the patient as well as local 
resistance patterns. Hospitals randomized to routine care will continue their baseline stewardship 
activities, principally involving a decision support tool known as Rx VigiLanz Therapeutic Advisor,191 
which has been adopted by all HCA hospitals. This tool alerts stewardship teams of possible 
opportunities for intervention, based on microbiology results. Intervention hospitals will also 
continue these activities. 

The study population is defined at the hospital level, where the intervention will be adopted 
as part of a hospital-wide antibiotic stewardship quality improvement initiative targeting non-ICU 
adult patients admitted with UTI. Hospitals eligibility criteria are outlined in Section B.1.3.  
 
 
 
Table 1: INSPIRE Urinary Tract Infection Trial Extended-Spectrum Antibiotics 
Pathogen-Directed 
Antibiotic Category 
Extended-Spectrum Antibiotics Targeted in INSPIRE UTI CPOE Prompts 
 
Anti-MRSA 
Ceftaroline, Daptomycin, Linezolida, Vancomycinb 
Anti-VRE 
Daptomycin, Linezolid 
Antipseudomonal 
Aztreonam, Cefepime, Ceftazidime, Piperacillin/Tazobactam 
Anti-ESBL 
Ertapenem, Meropenem, Imipenem, Ceftolozane/Tazobactam 
Anti-CRE 
Ceftazidime/Avibactam, Colistin, Imipenem/Relebactam, Meropenem/Vaborbactam, 
Polymixin B, Tigecycline 
 
 
B.1.3 Recruitment and Eligibility Criteria 
Hospitals are eligible to participate if they serve adult patients with UTI, are affiliated with the 
HCA Healthcare and have had MEDITECH as their electronic health record system for at least one 
year.  Target recruitment is a minimum of 40 participating facilities. 
Recruitment will be similar to our previous HCA Healthcare trials. First, we will provide 
webinars through HCA Healthcare’s webhosting system to introduce the trial and call for 
participation. Second, a call for participation will be made by corporate leadership through 
pharmacy and antibiotic stewardship communication channels. Third, our HCA Healthcare co-
investigators, who are system-wide leaders of antibiotic stewardship, quality, and infection 
prevention, will provide direct-to-hospital endorsement for the trial. Recruitment announcements will 
utilize usual corporate and local HCA Healthcare communication channels and will be directed at 
hospital leadership as well as pharmacy and antimicrobial stewardship/infectious diseases leaders. 
Participation will be confirmed by a signed letter of participation from the individual hospitals’ CEOs. 
For each hospital participant indicating interest, enrollment criteria will be assessed by 
administering two electronic surveys. First, a contact survey will be distributed. Second, a facility 
survey will be distributed to confirm eligibility criteria and obtain information on hospital 
characteristics and antibiotic stewardship program activities.  As done in our previous HCA 
Healthcare trials, surveys will be administered through HCA Healthcare’s usual survey channels 
and results will be compiled and returned to investigators in a database format. 
 
B.1.4 Centralized IRB 
 
Centralized IRB coordination will occur through the Harvard Pilgrim Health Care (HPHC) 
IRB. HCA Healthcare’s corporate regulatory affairs and risk management liaison will facilitate 
delegation of IRB governance from each participating hospital and ensure human subjects training 
through HCA Healthcare-approved programs.  This intervention of a quality improvement strategy 
for antibiotic stewardship meets national regulatory standards for waiver of informed consent under 
the Office of Human Research Protections (OHRP) criteria 45 CFR 46.116(d), 117(c) (2) and Food 
and Drug Administration (FDA) CFR 56.109(c) (1).(38) since 1) trial activities meet minimal risk 
criteria, 2) the trial randomizes hospitals, not patients, to a quality improvement strategy, 3) the 
intervention is not designed to supplant physician judgment, but rather provide relevant information 
to prescribing physicians who will be educated to choose the treatment they deem most clinically 
appropriate for individual patients, and 4) all assigned activities will be performed according to usual 
aBoth oral and intravenous (IV) formulations 
bIV formulation only. 
Abbreviations: CPOE – Computerized Provider Order Entry, MRSA – Methicillin-Resistant Staphylococcus aureus,  
ESBL – Extended-Spectrum Beta-Lactamase Producing ESBL Enterobacterales including Acinetobacter and  
Pseudomonas species with multidrug-resistance to antipseudomonal antibiotics but can be treated with a carbapenem
or ceftolozane/tazobactam, CRE – Carbapenem-Resistant Enterobacterales, including Carbapenem-Resistant  
Acinetobacter and Pseudomonas species.

hospital quality improvement procedures. We will also collect attestations from hospital antibiotic 
stewardship programs stating that they will continue routine antibiotic stewardship efforts.   
 
B.1.5 Randomization 
While this study represents a large cluster-randomized trial of hospitals, simple 
randomization of hospitals will not ensure balance of key variables by chance alone, and without 
care could even result in very unequal numbers of hospitals in each arm. Achieving balance on key 
features of the randomization units (in this case, hospitals) is a critical task in cluster-randomized 
trials, but little literature on it exists. Unlike individually randomized trials, information about the 
clusters is often known in advance, but the number of clusters to be randomized can be relatively 
small. The existence of a priori data can mitigate the small numbers and help to obtain adequate 
balance through stratification or other methods. One attractive approach is to establish tuplets—
matched sets (pairs, for a two-arm trial) – in which one member of each tuplet is assigned to each 
arm. Schemes for constructing tuplets need not be guided by theory. A formal approach would be 
to calculate the Mahalanobis distance between hospitals across all key variables and choose the 
set of tuplets with the minimum average distance. In this approach, we could standardize the 
variables, and then multiply by values calibrated to reflect any difference in the importance of 
balancing them. Other approaches are more ad hoc, such as prioritizing broad classes of balance 
on a key variable and making pairs within these strata based on lower-priority variables. However, 
there is no “best” method of tuplet construction, only sets that come closer to meeting the varied 
needs of each trial.  
We will establish the pairs under several plausible tuplet-construction schemes, and use 
graphical methods to compare all possible realizations for balance between the arms under each 
scheme. For example, if two variables were to be balanced, we would tentatively divide the sample 
into two groups under a tuplet construction scheme and then generate a scatterplot showing the 
between-arm absolute value of the mean difference for one variable on the x-axis and the second 
on the y-axis for each possible result of the randomization. We would then divide the groups again 
under the same scheme, and find another point on the scatterplot. Repeating many times would 
show the typical and distribution of balance under a scheme. Comparing the resulting scatterplots 
from each tuplet-construction scheme can reveal the relative risks of imbalance and benefits for 
balance accruing to each randomization scheme, in a practical sense. One tuplet construction 
method may result in generally close balance on one key characteristic and very variable balance 
on the other, while a competing scheme has good median balance on both characteristics, but 
where each has a long tail implying a few bad-luck assignments with poor balance.  
We hope to consider balance on more than two factors, and for assessing the impact on 
balance in this case, we will use a parallel coordinates plot, a multivariate plot method. After 
determining key variables where balance across the arms would be highly desirable, we will plot the 
mean difference between the arms for all key variables for each potential realized randomization.  
We will focus on balancing the baseline outcome values in participating hospitals and 
certain key factors that may be associated with UTI such as facility UTI admission volume, local 
prevalence of antibiotic resistance in UTI patients, length-of-stay, ICU transfers, and facility case-
mix of patients with UTI. Hospitals that share antibiotic stewardship personnel will be treated as a 
single hospital unit since the intervention will be overseen by local antibiotic stewardship teams. 
 
B.1.6 Baseline Period Activities 
 
The baseline period will be a 18 month period from April 1, 2017 – September 30, 2018, 
prior to the phase in and will share a similar month distribution as the intervention period. This 
period will provide baseline outcome data for both arms (see Analysis Section B.1.10). In addition 
to recruitment and randomization, preparatory activities for Phase-In and Intervention Periods will 
also begin. The corporate HCA Information Technology (IT) team will develop the CPOE smart 
prompt template which will include creation of smart prompt algorithms, automated compliance 
reports, and centralized beta testing. CPOE smart prompts will use MDRO infection risk estimates 
developed in a separate retrospective cohort study.  Educational materials and training modules will 
be developed as described below.  
 
 

B.1.7 Phase-In Period Activities 
There will be a six-month Phase-In period prior to the intervention period. This Phase-In 
period will be from October 1, 2018-March 31, 2019 and is necessary for three key reasons. First, 
since the intervention will occur as a quality improvement initiative to be adopted at the hospital 
level, hospital study champions will need time to garner relevant committee approvals and support 
from key hospital stakeholders. For some hospitals, this may require in-person presentation and 
support from investigators to affirm the protocol for evidence-based empiric antibiotic 
recommendations based on local probabilities of the various MDROs from evidence developed in 
Aim 1 above. Second, we anticipate that physician behavior change will require time for feedback 
and response. Third, the implementation of the CPOE smart prompt requires both corporate and 
local IT department efforts. This will require time to accurately implement and ensure sufficient 
validation and refinement for proper function at each participating intervention arm hospital. 
During this period, educational materials will be distributed to study champions at each 
participating intervention hospital (Table 2). In addition, study site champions at intervention 
facilities will participate in coaching calls monthly during the Phase-In period, ultimately transitioning 
to every other month calls when implementation and feedback stabilizes. Special coaching calls will 
also occur at least quarterly for both arms and involve best practice stewardship recommendations 
from national guidelines and experts. All coaching calls will be led by trial investigators with active 
attendance and support by HCA Healthcare leadership. Coaching calls will involve a shared 
PowerPoint slide set followed by a question and answer session. All coaching calls will be recorded 
and placed on arm-specific INSPIRE-ASP Trial sites on the HCA Healthcare intranet for continued 
access by designated participants in each arm. Participants will be highly encouraged to share 
concerns and solutions with one another.  
 
Table 2. INSPIRE-ASP UTI Trial Educational Binder and Distributed Material 
Topic 
Description 
1. 
Trial Summary 
Goals and investigative team 
2. 
Frequently Asked 
Questions 
Answers to common questions about the trial or 
protocol.   
3. 
Roles and 
Responsibilities 
Description of expected roles and responsibilities of 
pharmacy and physician antibiotic stewardship 
champions  
4. 
Talking Points 
Talking points for common physician and pharmacist 
questions about the trial or protocol 
5. 
Committee Protocol or 
Policy Proposal 
Description of CPOE smart prompt antibiotic 
stewardship initiative for committee approval submission 
to adopt as hospital protocol or policy  
6. 
Kick-Off PowerPoint 
PowerPoint slides used in the kick-off webinar 
7. 
Physician Education 
PowerPoint 
Presentations 
Description of, and basis for, guidance on appropriate 
antibiotic use for UTI, definition of extended-spectrum 
antibiotic groups, the INSPIRE-ASP MDRO risk 
estimate and smart prompt, and compliance feedback 
reports. 
8. 
Feedback  
Process for study champions to feedback compliance 
reports to physicians  
 
 
B.1.8 Intervention Phase Activities 
The automated point-of-care precision medicine CPOE smart prompts will be implemented 
at each facility randomized to the intervention arm.  Automated compliance feedback reports 
detailing prescriber response to CPOE prompt recommendations will be generated on a rolling 3-
month basis (or sooner if available), including reasons for continuing with extended spectrum 
antibiotic prescribing.  Pharmacy/physician study champions will provide feedback to prescribers 
that show common and consistent deviation from CPOE smart prompt recommendations.  
We will have a dedicated study email and toll-free number for study questions. Hospitals 
that are not represented on coaching calls will receive an email from core staff asking for their 
responses to polling questions and directing them to the recorded link of the coaching call. 
For both study arms, study site champions will be required to confirm that a) no new hospital 
initiatives have been planned or implemented.  If a site reports a new hospital antibiotic stewardship 

intervention that may represent a direct conflict with the INSPIRE-ASP trial, they will be asked to 
report the initiative to the trial Steering Committee for determination of trial conflict. Hospitals that 
implement interventions that conflict with the trial will be given the options of either not pursuing the 
conflicting intervention or dropping from the trial.   
 
B.1.9 INSPIRE-ASP UTI Trial Outcomes 
The goal of this trial is to reduce unnecessary physician prescribing of empiric extended-
spectrum antibiotics (Table 8). Trial outcomes evaluating intervention effectiveness are outlined in 
Table 3 and will be applied to the study population of adult patients who have an ICD-10 claims 
code indicating UTI is present on admission, who received any antibiotic within 3 days of 
admission, and who were admitted to a non-ICU location. The primary trial outcome is defined as 
the summed number of different extended spectrum antibiotics received each day, measured 
repeatedly over the first three days of an admission and divided by the number of days of the 
admission within the empiric period. An empiric day is a day within the first three days of an 
admission. We define this outcome as an Extended-Spectrum Days Of Therapy (ES-DOT) per 
empiric day.  Days of therapy (DOT) is specific to each extended-spectrum antibiotic, where any 
dose is considered one day’s worth of therapy. The ES antibiotics are then aggregated to obtain the 
total ES DOT. For example, if a patient is started on vancomycin and cefepime for 1 day, this will 
count as 2 ES DOT for that day. Multiple doses of the same antibiotic received on the same day are 
only counted as one day of therapy for that extended spectrum antibiotic. This outcome will include 
antibiotics in Table1.  
 
Table 3: Primary and Secondary INSPIRE-ASP UTI Trial Outcomes 
Outcome 
Metric 
Primary Trial Outcome  
Extended-Spectrum Days Of Antibacterial 
Therapy (ES-DOT) per Empiric Day 
The summed number of different extended-spectrum antibacterials 
received each empiric day, measured repeatedly over the first three 
days of an admission and divided by the number of empiric days of 
the admission. An empiric day is a day within the first three days of 
an admission.1 
Secondary Trial Outcomes 
Vancomycin Days of Antibacterial Therapy 
per Empiric Day 
The summed number of days of Vancomycin received each empiric 
day per at-risk-day (first 3 days of admission)1 
Antipseudomonal Antibiotic Days Of 
Therapy (ES-DOT) per Empiric Day 
The summed number of different antipseudomonal antibacterials 
received each empiric day, measured repeatedly over the first three 
days of an admission and divided by the number of empiric days of 
the admission. An empiric day is a day within the first three days of 
an admission.1,2 
1E.g., if a patient is admitted for 2 days, ES-DOT will be calculated for 2 days and divided by 2 empiric days; 
conversely if a patient is admitted for 4 days, only the first 3 days will be evaluated.  
2Does not include aminoglycosides or fluoroquinolones.  
 
Safety outcomes planned for the primary manuscript are shown in Table 4. Safety outcomes will be 
analyzed separately from the main effectiveness outcomes of the trial (see Primary Statistical 
Analysis section below). 
 
Table 4: Other Pre-Specified Outcomes – Safety Trial Outcomes 
Safety Trial Outcomes (other pre-specified outcomes) 
Antibacterial Escalations [Safety 
Outcome]  
Days from start of standard-spectrum antibacterial until switch to 
extended-spectrum antibacterial during hospital stay   
ICU Transfers [Safety Outcome] 
Days from start of hospitalization until ICU transfer within hospital stay   
Length-of-stay [Safety Outcome] 
Days from hospital admission to discharge 
 
 
B.1.10 Analysis 
 
The primary trial outcome is defined as the summed number of different extended spectrum 
antibiotics received each day, measured repeatedly over the first three days of an admission and 
divided by the number of days of the admission within the empiric period. An empiric day is a day 

within the first three days of an admission. We define this outcome as an Extended-Spectrum Days 
Of Therapy (ES-DOT) per empiric day. For clarity the calculation is as follows: we define a DOT for 
a particular ES antibiotic as a day in which any number of doses of that antibiotic is given. Different 
ES antibiotics are summed across the empiric days for each patient admission, then divided by the 
empiric days for that patient-admission to determine each admission’s DOT per at-risk day. If an 
admission is less than 3 days, only the number of days the patient is admitted will contribute to the 
numerator and denominator. Antibiotics given during an associated emergency department visit on 
the date of hospital admission are counted toward the ES-DOT of the first hospital day. 
The main trial results will be based upon as-randomized, unadjusted analyses of admission-
level ES-DOT per empiric day. We define ES-DOT per empiric day at the individual admission level 
so that we can perform analyses on individuals. 
The trial will be assessed among the cohort of adult admissions who have an ICD-10 claims 
code indicating UTI is present on admission, who received any antibiotic within 3 days of 
admission, and who were admitted to a non-ICU location. For admissions initially on a non-ICU 
floor and transferred to the ICU, analysis will include only admission days on the non-ICU floor 
within 3 days of admission. 
The trial periods are defined as follows: (1) Baseline – April 1, 2017 – September 30, 2018 
(18 months); (2) Phase-in – October 1, 2018 – March 31, 2019 (6 months); (3) Intervention 
– April 1, 2019 – June 30, 2020 (15 months).
The unit of analysis will be individual admissions. Individuals can contribute more than one 
admission. The analytic model will be a generalized linear mixed effects model for differences in 
differences, with random effects accounting for correlation within cluster, period- varying random 
effects to allow for differences between hospitals from baseline to follow-up, and admission-level 
random effects to account for correlation within person and hospital. Analyses of the baseline data, 
performed before randomization, found no evidence of overdispersion in a Poisson model, and so 
we plan to use that model for analysis of outcomes. The model can be expressed as follows: 
where 𝑦𝑦𝑖𝑖𝑖𝑖𝑖𝑖ℎ is the ES-DOT per at-risk day for patient 𝑖𝑖 for admission 𝑖𝑖 in period 𝑖𝑖 at hospital ℎ, and 
𝐴𝐴𝑖𝑖𝑖𝑖𝑖𝑖ℎ = 1 if hospital ℎ is in the intervention arm and 0 if not, and 𝑇𝑇𝑖𝑖𝑖𝑖𝑖𝑖ℎ = 1 if 𝑖𝑖 is the 
intervention period and 0 if baseline period. The random effects 𝑏𝑏0ℎ and 𝑏𝑏1ℎ allow for different 
baseline mean admission-level ES-DOT per at-risk day for each hospital and each hospital per 
period, respectively, while 𝑔𝑔𝑖𝑖 allows different admissions to have different mean ES-DOT per at- 
risk day across multiple admissions. Equivalently, they allow for correlation within hospital at 
different levels at baseline and at follow-up. 
The assessment of trial success will be determined by the significance of the arm by period 
interaction term 𝛽𝛽3, which assesses whether the log relative rate of the outcome due to being in the 
intervention arm in the intervention period is different from 0. The exponentiated 
parameter estimate for 𝛽𝛽3 is the estimated relative rate of ES-DOT per at-risk day due to the 
intervention, relative to the baseline period. For example, if 𝛽𝛽3 had a negative value and a p- value 
<0.05, we would conclude that the patient-specific CPOE smart prompts generated a benefit over 
routine care. Exponentiating the parameter value would provides an estimate of the relative 
reduction due to the intervention in the expected ES-DOT per at-risk day. 
The primary trial analysis will use an as-randomized unadjusted model with two-tailed 
significance set at alpha = 0.05. Secondary outcomes also will be assessed using an as- 
randomized unadjusted model with adjustment for multiple comparisons with two-tailed significance 
set at alpha = 0.025 for the two secondary outcomes. Subsequent analyses of the primary and 
secondary outcomes will include both as-treated and adjusted models. Adjusted models will 
account for individual characteristics such as age, gender, comorbidities, and prior history of 
MDROs as well as hospital characteristics such as hospital antibiotic resistance. We will also 
account for randomization unit and for seasonality. These analyses will be reported as point 
estimates with confidence intervals without p-value. The reason for including these analyses is to 
provide additional information related to the trial outcomes for readers to assess the effects of 
potential confounders. The reason to not include them in a formal multiple comparisons adjustment 

is because these analyses are non-independent evaluations relative to the as-randomized 
unadjusted analyses. 
Safety outcomes noted in the below table will be assessed in the most conservative manner 
to identify potential safety issues. This is required because a reduction in ES-DOT might be 
achieved only by incorrectly withholding ES antibiotics when they were really needed. Each safety 
outcome will be evaluated for non-inferiority using an as-randomized unadjusted model with a one-
tailed significance set at alpha = 0.05. Analyses planned for these assessments are proportional 
hazards models with random effects (to account for hospital effects). These models are sometimes 
called frailty or shared frailty models. Because these are safety outcomes, we do not intend to 
make adjustments for multiple comparisons in testing them, further increasing conservatism. 
All analyses will be performed using current versions of SAS (Version 9.4, as of writing, SAS 
Institute, Cary NC) and/or R (Version 4.0, as of writing). 
 
B.1.11 Power Size/Sample Size Calculations 
Power assessment proceeded as follows, using a Monte Carlo approach. We used 
available data to define a baseline period of 10/1/2015-12/31/2016 in a bootstrap procedure. 
Individuals were selected with replacement from within each hospital, once to represent 
baseline data and separately to represent intervention period data. In the randomization 
portion of the Monte Carlo approach, we ordered the hospitals by “size”, meaning the 
number of admissions with UTI. Then, within strata of 6 hospitals, we ordered by ES-DOT 
per at-risk day. Then we took pairs of hospitals with adjacent ES-DOT per at-risk day and 
assigned one member of the pair to one arm. To do this, a single pseudo-random number 
uniformly distributed between 0 and 1 was generated for each pair. If it was less than 0.5, the 
arbitrary “first” member of the pair was assigned to Routine Care and the other to 
Intervention. If it was greater than or equal to 0.5, then the assignments were reversed. The 
remaining unpaired hospital of the 59 units were assigned as the “first” member of a pair with 
no match. (Three sets of hospitals each shared a stewardship program and these hospitals 
were treated as a single hospital within the routine.) 
If an admission was in the intervention arm in the intervention period, then their ES-DOT 
per at-risk day was reduced by a proportion, given below. The reduction of ES-DOT per at-risk 
day was selected with a lower bound determined by the investigators as the hoped-for effect of 
the intervention with minimal clinical significance, and the impact in the Monte Carlo calculation 
on a per-admission basis was to reduce the days of therapy proportionately between this 
number and 1, on a uniform distribution. In other words, if the effect of the intervention was to 
multiply doses by as little as .9, each person would have their doses multiplied by between .9 
and 1. Then an integer value of doses would be chosen as described above. 
We are interested in determining whether the patient-specific CPOE intervention 
reduces ES-DOT per at-risk day by at least 12.5% relative to usual care. Using the method 
above, we have 97% power (CI 91-99%) to detect this effect. Table 5 below shows the 
calculated power and confidence intervals for the primary and secondary outcomes to be 
evaluated in our primary manuscript. 
 
Table 5. Power Calculation – Primary and Secondary Outcomes 
Primary Trial Outcome 
Power (CI), % 
Extended-Spectrum Antibacterial Days Of Therapy 
(ES-DOT) per Empiric Day 
97 (91-99) 
Secondary Trial Outcomes 
 
Vancomycin Days of Therapy per Empiric Day 
92 (85-96) 
Antipseudomonal Antibiotic Days Of Therapy (ES-
DOT) per Empiric Day 
84 (77-91) 

 
 
 
References Cited 
1. Magill SS, Edwards JR, Beldavs ZG, Dumyati G, Janelle SJ, Kainer MA, Lynfield R, Nadle J, 
Neuhauser MM, Ray SM, Richards K, Rodriguez R, Thompson DL, Fridkin SK; Emerging 
Infections Program Healthcare-Associated Infections and Antimicrobial Use Prevalence Survey 
Team. Prevalence of antimicrobial use in US acute care hospitals, May-September 2011. JAMA 
2014;312(14):1438-46. 
2. Fridkin S, Baggs S, Fagan R, Magill S, Pollack LA, Malpiedi P, Slayton R, Khader K, Rubin MA, 
Jones M, Samore MH, Dumyati G, Dodds-Ashley E, Meek J, Yousey-Hindes K, Jernigan J, 
Shehab N, Herrera R, McDonald CL, Schneider A, Srinivasan A; Centers for Disease Control 
and Prevention (CDC). Vital signs: improving antibiotic use among hospitalized patients. 
MMWR Morb Mortal Wkly Rep 2014;63:194-200. 
3. Centers for Disease Control and Prevention. Antibiotic Resistance Threats in the United States, 
2013. Available at: http://www.cdc.gov/drugresistance/pdf/ar-threats-2013-508.pdf. Accessed 
on May 19, 2015. 
4. Marston HD, Dixon DM, Knisely JM, Palmore TN, Fauci AS. Antimicrobial Resistance. JAMA. 
Sep 20 2016;316(11):1193-1204. 
5. Arcilla MS, van Hattem JM, Haverkate MR, et al. Import and spread of extended-spectrum beta-
lactamase-producing Enterobacteriaceae by international travellers (COMBAT study): a 
prospective, multicentre cohort study. Lancet Infect Dis. Oct 14 2016. 
6. Harbarth S, Samore MH. Antimicrobial resistance determinants and future control. Emerg Infect 
Dis 2005;11(6):794-801. 
7. Abbo L, Sinkowitz-CochranR, Smith L, Ariza-Heredia E, Gomez-Marin O, Srinivasan A, Hooton 
TM. Faculty and resident attitudes, perceptions, and knowledge about antimicrobial use and 
resistance. Infect Control Hosp Epidemiol 2011;32:714-8. 
8. Boucher HW, Talbot GH, Bradley JS, Edwards JE, Gilbert D, Rice LB, Scheld M, Spellberg B, 
Bartlett J. Bad bugs, no drugs: No ESKAPE! An updated from the Infectious Diseases Society 
of America. Clin Infect Dis 2009;48:1-12. 
9. Boucher HW, Talbot GH, Benjamin DK Jr, Bradley J, Guidos RJ, Jones RN, Murray BE, 
Bonomo RA, Gilbert D. 10 x '20 Progress--development of new drugs active against gram-
negative bacilli: an update from the Infectious Diseases Society of America. Clin Infect Dis 
2013;56:1685-94. 
10. Centers for Disease Control and Prevention. Get Smart for Healthcare. Available at: 
http://www.cdc.gov/getsmart/healthcare/. Accessed May 8, 2015. 
11. Executive Office of the Presidents, President’s Council of Advisors on Science and Technology. 
Report to the President on Combating Antibiotic Resistance. Available at: 
https://www.whitehouse.gov/sites/default/files/microsites/ostp/PCAST/pcast_carb_report_sept2
014.pdf. Accessed on May 8, 2015. 
12. The White House. National Strategy for Combating Antibiotic-Resistant Bacteria. Available at: 
https://www.whitehouse.gov/sites/default/files/docs/carb_national_strategy.pdf. Accessed on 
May 19, 2015. 
13. Baggs J, Fridkin SK, Pollack LA, Srinivasan A, Jernigan JA. Estimating National Trends in 
Inpatient Antibiotic Use Among US Hospitals From 2006 to 2012. JAMA Intern Med. Nov 1 
2016;176(11):1639-1648. 
14. Giblin TB, Sinkowitz-Cochran RL, Harris PL, Jacobs S, Liberatore K, Palfreyman MA, Harrison 
EI, Cardo DM; CDC Campaign to Prevent Antimicrobial Resistance Team. Clinicians’ 
perceptions of the problem of antimicrobial resistance in healthcare facilities. Arch Intern Med 
2004;164:1662-8. 
15. Szymczak JE, Feemster KA, Zaoutis TE, Gerber JS. Pediatrician perceptions of an outpatient 
antimicrobial stewardship intervention. Infect Control Hosp Epidemiol 2014;35(Suppl 3):S69-78. 
16. Barlam TF, Cosgrove SE, Abbo LM, et al. Implementing an Antibiotic Stewardship Program: 
Guidelines by the Infectious Diseases Society of America and the Society for Healthcare 
Epidemiology of America. Clin Infect Dis. May 15 2016;62(10):e51-77. 
17. Provonost P, Berenholtz SM, Needham DM. Translating evidence into practice: a model for 
large scale knowledge translation. BMJ 2008;337:a1714. 

18. Mandell LA, Wunderink RG, Anzueto A, Bartlett JG, Campbell GD, Dean NC, Dowell SF, File 
TM Jr, Musher DM, Niederman MS, Torres A, Whitney CG. Infectious Diseases Society of 
America/American Thoracic Society Consensus Guidelines on the Management of Community-
Acquired Pneumonia in Adults. Clin Infect Dis 2007;44:S27-72. 
19. Gupta K, Hooton TM, Naber KG, Wullt B, Colgan R, Miller LG, Moran GJ, Nicolle LE, Raz R, 
Schaeffer AJ, Soper DE. International Clinical Practice Guidelines for the Treatment of Acute 
Uncomplicated Cystitis and Pyelonephritis in Women: A 2010 Update by the Infectious 
Diseases Society of America and the European Society for Microbiology and Infectious 
Diseases. Clin Infect Dis 2011;52:e103-20. 
20. Mazzulli T. Diagnosis and management of simple and complicated urinary tract infections 
(UTIs). Can J Urol 2012;19 Suppl 1:42-8. 
21. Sanchez GV, Roberts RM, Albert AP, Johnson DD, Hicks LA. Effects of knowledge, attitudes, 
and practices of primary care providers on antibiotic selection, United States. Emerg Infect Dis 
2014;20:2041-7. 
22. Kollef MH, Sherman G, Ward S, Fraser VJ. Inadequate antimicrobial treatment of infections: a 
risk factor for hospital mortality among critically ill patients. Chest 1999;115:462-74. 
23. Schramm GE, Johnson JA, Doherty JA, Micek ST, Kollef MH. Methicillin-resistant 
Staphylococcus aureus sterile-site infection: the importance of appropriate initial antimicrobial 
treatment. Crit Care Med 2006; 34: 2069-74. 
24. Micek ST, Lloyd AE, Ritchie DJ, Reichley RM, Fraser VJ, Kollef MH. Pseudomonas aeruginosa 
bloodstream infection: importance of appropriate initial antimicrobial treatment. Antimicrob 
Agents Chemother 2005;49:1306-11. 
25. Kang CI, Kim SH, Park WB, Lee KD, Kim HB, Kim EC, Oh MD, Choe KW. Bloodstream 
infections caused by antibiotic-resistant gram-negative bacilli: risk factors for mortality and 
impact of inappropriate initial antimicrobial therapy on outcome. Antimicrob Agents Chemother 
2005;49:760-6. 
26. Self WH, Wunderink RG, Williams DJ, et al. Staphylococcus aureus Community-acquired 
Pneumonia: Prevalence, Clinical Characteristics, and Outcomes. Clin Infect Dis. Aug 1 
2016;63(3):300-309. 
27. Cilloniz C, Gabarrus A, Ferrer M, et al. Community-Acquired Pneumonia Due to Multidrug- and 
Non-Multidrug-Resistant Pseudomonas aeruginosa. Chest. Aug 2016;150(2):415-425. 
28. Ishida T, Ito A, Washio Y, et al. Risk factors for drug-resistant pathogens in immunocompetent 
patients with pneumonia: Evaluation of PES pathogens. J Infect Chemother. Oct 8 2016. 
29. American Thoracic Society; Infectious Diseases Society of America. Guidelines for the 
management of adults with hospital-acquired, ventilator-associated, and healthcare-associated 
pneumonia. Am J Respir Crit Care Med 2005;171:388-416. 
30. Kollef MH, Shorr A, Tabak YP, Gupta V, Liu LZ, Johannes RS. Epidemiology and outcomes of 
health-care-associated pneumonia: results from a large US database of culture-positive 
pneumonia. Chest 2005;128:3854-62. 
31. Ewig S, Welte T, Chastre J, Torres A. Rethinking the concepts of community-acquired and 
health-care-associated pneumonia. Lancet Infect Dis 2010;10:279-87. 
32. Kalil AC, Metersky ML, Klompas M, et al. Management of Adults With Hospital-acquired and 
Ventilator-associated Pneumonia: 2016 Clinical Practice Guidelines by the Infectious Diseases 
Society of America and the American Thoracic Society. Clin Infect Dis. Sep 1 2016;63(5):e61-
e111. 
33. Park KH, Oh WS, Kim ES, Park SW, Hur JA, Kim YK, Moon C, Lee JH, Lee CS, Kim BN. 
Factors associated with ciprofloxacin- and cefotaxime-resistant Escherichia coli in women with 
acute pyelonephritis in the emergency department. Int J Infect Dis 2014;23:8-13. 
34. Huttner B, Jones M, Huttner A, Rubin M, Samore MH. Antibiotic prescription practices for 
pneumonia, skin and soft tissue infections and urinary tract infections throughout the US 
Veterans Affairs system. J Antimicrob Chemother 2013;68:2393-9. 
35. Chalmers JD, Rother C, Salih W, Ewig S. Healthcare-associated pneumonia does not 
accurately identify potentially resistant pathogens: a systematic review and meta-analysis. Clin 
Infect Dis 2014;58:330-9. 
36. Grenier C, Pepin J, Nault V, Nault V, Howson J, Fournier X, Poirier MS, Cabana J, Craig C, 
Beaudoin M, Valiquette L. Impact of guideline-consistent therapy on outcome of patients with 

healthcare-associated and community-acquired pneumonia. J Antimicrob Chemother 
2011;66:1617-24. 
37. Attridge RT, Frei CR, Restrepo MI, Lawson KA, Ryan L, Pugh MJ, Anzueto A, Mortensen EM. 
Guideline-concordant therapy and outcomes in healthcare-associated pneumonia. Eur Respir J 
2011;38:878-87. 
38. Troitino AX, Porhomayon J, and El-Solh AA. Guideline-concordant antimicrobial therapy for 
healthcare-associated pneumonia: a systematic review and meta-analysis. Lung 2013;191:229-
37. 
39. Lee SS, Kim Y, and Chung DR. Impact of discordant empirical therapy on outcome of 
community-acquired bacteremic acute pyelonephritis. J Infect 2011;62:159-164. 
40. Jeon JH, Kim K, Han WD, Song SH, Park KU, Rhee JE, Song KH, Park WB, Kim ES, Park SW, 
Kim NJ, Oh MD, Kim HB. Empirical use of ciprofloxacin for acute uncomplicated pyelonephritis 
caused by Escherichia coli in communities where the prevalence of fluoroquinolone resistance 
is high. Antimicrob Agents Chemother 2012;56:3043-6. 
41. Webb BJ, Dascomb K, Stenehjem E, Dean N. Predicting risk of drug-resistant organisms in 
pneumonia: Moving beyond the HCAP model. Respir Med 2015;109:1-10. 
42. Stevens V, Dumyati G, Fine LS, Fisher SG, van Wijngaarden E. Cumulative antibiotic 
exposures over time and the risk of Clostridium difficile infection. Clin Infect Dis 2011;53:42-8. 
43. Davey P, Brown E, Charani E, Fenelon L, Gould IM, Holmes A, Ramsay CR, Wiffen PJ, Wilcox 
M. Interventions to improve antibiotic prescribing practices for hospital inpatients. Cochrane 
Database Syst Rev 2013;4:CD003543. 
44. Brown K, Valenta K, Fishman D, Simor A, Daneman N. Hospital ward antibiotic prescribing and 
the risks of Clostridium difficile infection. JAMA Intern Med 2015;175:626-33. 
45. Braykov NP, Morgan DJ, Schweizer ML, Uslan DZ, Kelesidis T, Weisenberg SA, Johannsson B, 
Young H, Cantey J, Srinivasan A, Perencevich E, Septimus E, Laxminarayan R. Assessment 
of empirical antibiotic therapy optimisation in six hospitals: an observational cohort study. 
Lancet Infect Dis 2014;14:1220-7. 
46. Fowler S, Webber A, Cooper BS, Phimister A, Price K, Carter Y, Kibbler CC, Simpson AJ, 
Stone SP. Successful use of feedback to improve antibiotic prescribing and reduce Clostridium 
difficile infection: a controlled interrupted time series. J Antimicrob Chemother 2007;59:990-5. 
47. Charani E, Kyratsis Y, Lawson W, Wickens H, Brannigan ET, Moore LS, Holmes AH. An 
analysis of the development and implementation of a smartphone application for the delivery of 
antimicrobial prescribing policy: lessons learnt. Antimicrob Chemother 2013;68:960-7. 
48. Chandy SJ, Naik GS, Charles R, Jeyaseelan V, Naumova EN, Thomas K, Lundborg CS. The 
impact of policy guidelines on hospital antibiotic use over a decade: a segmented time series 
analysis. PLoS ONE 2014;9:e92206. 
49. Hamilton KW, Gerber JS, Moehring R, Anderson DJ, Calderwood MS, Han JH, Mehta JM, 
Pollack LA, ZaoutisT, Srinivasan A, Camins BC, Schwartz DN, Lautenbach E; Centers for 
Disease Control and Prevention Epicenters Program. Point-of-prescription intervention to 
improve antibiotic stewardship. Clin Infect Dis 2015;60:1252-8. 
50. Cooke FJ and Holmes AH. The missing care bundle: antibiotic prescribing in hospitals. Int J 
Antimicrob Agents 2007;30:25-9. 
51. Rodriquez-Maresca M, Sorlozano A, Grau M, Rodriquez-Castano R, Ruiz-Valverde A, 
Gutierrez-Fernandez J. Implementation of a computerized decision support system to improve 
the appropriateness of antibiotic therapy using local microbiologic data. Biomed Res Int 
2014;2014:395434. 
52. Deuster S, Roten I, Muehlebach S. Implementation of treatment guidelines to support judicious 
use of antibiotic therapy. J Clin Pharm Ther 2010;35:71-8. 
53. Pogue JM, Alaniz C, Carver PL, Pleva M, Newton D, DePestel DD. Role of unit-specific 
combination antibiograms for improving selection of appropriate empiric therapy for gram-
negative pneumonia. Infect Control Hosp Epidemiol 2011;32:289-92. 
54. Rabs N, Wieczorkiewicz SM, Costello M, Zamfirova I. Development of a urinary-specific 
antibiogram for Gram negative isolates: impact of patient risk factors on susceptibility. Am J 
Infect Control 2014;42:393-400. 
55. Karam G, Chastre J, Wilcox MH, Vincent JL. Antibiotic strategies in the era of multidrug 
resistance. Crit Care. Jun 22 2016;20(1):136. 

56. Auffray C, Caulfield T, Griffin JL, Khoury MJ, Lupski JR, Schwab M. From genomic medicine to 
precision medicine: highlights of 2015. Genome Med. Jan 29 2016;8(1):12. 
57. Chambers DA, Feero WG, Khoury MJ. Convergence of Implementation Science, Precision 
Medicine, and the Learning Health Care System: A New Model for Biomedical Research. 
JAMA. May 10 2016;315(18):1941-1942. 
58. Khoury MJ, Galea S. Will Precision Medicine Improve Population Health? JAMA. Oct 4 
2016;316(13):1357-1358. 
59. Khoury MJ, Iademarco MF, Riley WT. Precision Public Health for the Era of Precision Medicine. 
Am J Prev Med. Mar 2016;50(3):398-401. 
60. Buising KL, Thursky KA, Robertson MB, Black JF, Street AC, Richards MJ, Brown GV. 
Electronic antibiotic stewardship—reduced consumption of broad-spectrum antibiotics using a 
computerized antimicrobial approval system in a hospital setting. J Antimicrob Chemother 
2008;62:608-16. 
61. Rimawi RH, Mazer MA, Siraj DS, Gooch M, Cook PP. Impact of regular collaboration between 
infectious diseases and critical care practitioners on antimicrobial utilization and patient 
outcome. Crit Care Med 2013;41:2099-107. 
62. Boyles TH, Whitelaw A, Bamford C. Moodley M, Bonorchis K, Morris V. Rawoot N, Naicker V, 
Lusakiewicz I, Black J, Stead D, Lesosky M, Raubenheimer P, Dlamini S, Mendelson M. 
Antibiotic stewardship ward rounds and a dedicated prescription chart reduce antibiotic 
consumption and pharmacy costs without affecting  inpatient mortality or re-admission rates. 
PLoS One 2013;8:e79747. 
63. Schulz L, Osterby K, Fox B. The use of best practice alerts with the development of an 
antimicrobial stewardship navigator to promote antibiotic de-escalation in the electronic medical 
record. Infect Control Hosp Epidemiol 2013;34:1259-65. 
64. Lee TC, Frenette C, Jayaraman D, Green L, Pilote L. Antibiotic self-stewardship: trainee-led 
structured antibiotic time-outs to improve antimicrobial use. Ann Intern Med 2014;161(10 
Suppl):S53-8. 
65. Lesprit P, de Pontfarcy A, Esposito-Farese M, Ferrand H, Mainardi JL, Lafauri M, Parize P, 
Rioux C, Tubach F, Lucet JC. Postprescription review improves in-hospital antibiotic use: a 
multicenter randomized controlled trial. Clin Microbiol Infect 2015;21:180.e1-7 
66. Platt R. Predictors of response to therapy for infections caused by Pseudomonas aeruginosa. 
Rev Infect Dis 1984;6 Suppl 3:S759-68. 
67. Huang SS, Platt R. Risk of methicillin-resistant Staphylococcus aureus infection after previous 
infection or colonization. Clin Infect Dis 2003;36:281-5. 
68. Shojania KG, Yokoe D, Platt R, Fiskio J, Ma'luf N, Bates DW. Reducing vancomycin use 
utilizing a computer guideline: results of a randomized controlled trial. J Am Med Inform Assoc 
1998;5:554-62. 
69. Zanetti G, Flanagan HL Jr, Cohn LH, Giardina R, Platt R. Improvement of intraoperative 
prohylaxis in prolonged cardiac surgery by automated alerts in the operating room. Infect 
Control Hosp Epidemiol 2003;24:13-6. 
70. Simon SR, Smith DH, Feldstein AC, Perrin N, Yang X, Zhou Y, Platt R, Soumerai SB. 
Computerized prescribing alerts and group academic detailing to reduce the use of potentially 
inappropriate medications in older people. J Am Geriatr Soc 2006;54:963-8. 
71. Smith DH, Perrin N, Feldstein A, Yang X, Kuang D, Simon SR, Sittig DF, Platt R, Soumerai SB. 
The impact of prescribing safety alerts for elderly persons in an electronic medical record: an 
interrupted time series evaluation. Arch Intern Med 2006;166:1098-104. 
72. Langford BJ, Seah J, Chan A, Downing M, Johnstone J, Matukas LM. Antimicrobial 
Stewardship in the Microbiology Laboratory: Impact of Selective Susceptibility Reporting on 
Ciprofloxacin Utilization and Susceptibility of Gram-Negative Isolates to Ciprofloxacin in a 
Hospital Setting. J Clin Microbiol. Sep 2016;54(9):2343-2347. 
73. Stenehjem E, Hersh AL, Sheng X, et al. Antibiotic Use in Small Community Hospitals. Clin 
Infect Dis. Nov 15 2016;63(10):1273-1280. 
74. Goldstein EJ, Goff DA, Reeve W, et al. Approaches to Modifying the Behavior of Clinicians Who 
Are Noncompliant With Antimicrobial Stewardship Program Guidelines. Clin Infect Dis. Aug 15 
2016;63(4):532-538. 

75. Emanuel EJ, Ubel PA, Kessler JB, et al. Using Behavioral Economics to Design Physician 
Incentives That Deliver High-Value Care. Ann Intern Med. Jan 19 2016;164(2):114-119. 
76. Madaras-Kelly KJ, Remington RE, Fan VS, Sloan KL. Predicting antibiotic resistance to 
community-acquired pneumonia antibiotics in culture-positive patients with healthcare-
associated pneumonia. J Hosp Med. Mar 2012;7(3):195-202. 
77. Goodman KE, Lessler J, Cosgrove SE, et al. A Clinical Decision Tree to Predict Whether a 
Bacteremic Patient Is Infected With an Extended-Spectrum beta-Lactamase-Producing 
Organism. Clin Infect Dis. Oct 1 2016;63(7):896-903. 
78. Anesi JA, Lautenbach E, Nachamkin I, et al. Clinical and Molecular Characterization of 
Community-Onset Urinary Tract Infections Due to Extended-Spectrum Cephalosporin-Resistant 
Enterobacteriaceae. Infect Control Hosp Epidemiol. Sep 28 2016:1-7. 
79. Jacobson KL, Cohen SH, Inciardi JF, et al. The relationship between antecedent antibiotic use 
and resistance to extended-spectrum cephalosporins in group I beta-lactamase-producing 
organisms. Clin Infect Dis. Nov 1995;21(5):1107-1113. 
80. Aliberti S, Reyes LF, Faverio P, et al. Global initiative for meticillin-resistant Staphylococcus 
aureus pneumonia (GLIMP): an international, observational cohort study. Lancet Infect Dis. Sep 
1 2016. 
81. Hudepohl NJ, Cunha CB, Mermel LA. Antibiotic Prescribing for Urinary Tract Infections in the 
Emergency Department Based on Local Antibiotic Resistance Patterns: Implications for 
Antimicrobial Stewardship. Infect Control Hosp Epidemiol. Mar 2016;37(3):359-360. 
82. Jacobs DM, Kuper K, Septimus E, Arafat R, Garey KW. Assessment of Antimicrobial 
Stewardship Activities in a Large Metropolitan Area. J Pharm Pract 2014 (Epub ahead of print). 
83. Septimus EJ, Kuper K. Clinical Challenges in Addressing Antimicrobial Resistance in the 21st 
Century. Cl Pharm and Therap 2009; 86:336-339. 
84. Septimus E, Owens R.  Need and Potential of Antimicrobial Stewardship in Community 
Hospitals. Clin Infect Dis 2011;53:S8-S14. 
85. Fishman N, Patterson J, Saiman L, Srinivasan A, Trivedi KK, van Schooneveld T, Lynfield R, 
Gerding D, Septimus E, Schwartz D, Daum R, Eglund JA, Harrison CJ, Bradley JS, Newland J. 
Policy statement on antimicrobial stewardship by the Society for Healthcare Epidemiology of 
America (SHEA), the Infectious Diseases Society of America (IDSA), and the Pediatric 
Infectious Diseases Society  (PIDS). Infect Control Hosp Epidemiol 2012;33:322-7. 
86. Moody J, Cosgrove SE, Olmsted R, Septimus E, Aureden K, Oriola S, Patel GW, Trivedi KK. 
Antimicrobial stewardship: a collaborative partnership between infection preventionists and 
healthcare epidemiologists. Infect Control Hosp Epidemiol 2012;33:328-30. 
87. Septimus EJ. Antimicrobial Stewardship—Qualitative and Quantitative Outcomes: The Role of 
Measurement. Curr Infect Dis Rep 2014;16:433-438. 
88. Calderwood MS. Role of the Hospital Epidemiologist in Supporting Antimicrobial Stewardship. 
In: Mylonakis E, Rice L, Drusano G, eds. Antimicrobial Stewardship: Principles and Practices. 
2015 (in press). 
89. McKinnell J, Miller LG, Eells S, Cui E, Huang SS. A Systematic Literature Review and Meta-
analysis of Factors Associated with MRSA Colonization at Time of Hospital or ICU Admission. 
Infect Control Hosp Epidemiol 2013;34:1077-86. 
90. Datta R, Shah A, Huang SS, Cui E, Nguyen V, Welbourne S, Quan K, Thrupp L. High nasal 
burden of methicillin-resistant Staphylococcus aureus increases risk for invasive disease. J Clin 
Microbiol 2014;52:312-4. 
91. Datta R, Kleinman K, Rifas-Shiman S, Placzek H, Lankiewicz J, Platt R, Huang SS. 
Confounding by indication affects antimicrobial risk factors for methicillin-resistant 
Staphylococcus aureus (MRSA) but not vancomycin-resistant enterococci (VRE) acquisition. 
Antibio Resist Infect Control 2014;3:19. 
92. Pakyz AL. The utility of hospital antibiograms as tools for guiding empiric therapy and tracking 
resistance. Insights from the Society of Infectious Diseases Pharmacists. Pharmacotherapy 
2007;27:1306-12. 
93. Perla RJ and Belliveau PP. Antibiogram-derived radial decision trees: An innovative approach 
to susceptibility data display. Am J Infect Dis 2005;1:124-7. 

94. The Center for Disease Dynamics, Economics & Policy. Resistance Map. Available at: 
http://www.cddep.org/projects/resistance_map/resistance_overview_0. Accessed on May 24, 
2015. 
95. Falcone M, Russo A, Gianella M, Cangemi R, Scarpellini MG, Bertazzoni G, Alarcon JM, Taliani 
G, Palange P, Farcomeni A, Vestri A, Bouza E, Violi F, Venditti M. Individualizing risk of 
multidrug-resistant pathogens in community-onset pneumonia. PLoS One 2015;10:e0119528. 
96. Self WH, Wunderink RG, Williams DJ, Barrett TW, Baughman AH, Grijalva CG. Comparison of 
clinical prediction models for resistant bacteria in community-onset pneumonia. Acad Emerg 
Med 2015;22(6):730-40. 
97. Elixhauser A, Steiner C. Harris DR, Coffey RM. Comorbidity measures for use with 
administrative data. Med Care 1998;36:8-27. 
98. Healthcare Cost and Utilization Project. Comorbidity Software, Version 3.7. Available at: 
https://www.hcup-us.ahrq.gov/toolssoftware/comorbidity/comorbidity.jsp#overview. Accessed 
on May 27, 2015. 
99. Cosgrove SE. The relationship between antimicrobial resistance and patient outcomes: 
mortality, length of hospital stay, and health care costs. Clin Infect Dis. Jan 15 2006;42 Suppl 
2:S82-89. 
100. 
Pepin J, Saheb N, Coulombe MA, et al. Emergence of fluoroquinolones as the predominant 
risk factor for Clostridium difficile-associated diarrhea: a cohort study during an epidemic in 
Quebec. Clin Infect Dis. Nov 01 2005;41(9):1254-1260. 
101. 
Dhalla IA, Mamdani MM, Simor AE, Kopp A, Rochon PA, Juurlink DN. Are broad-spectrum 
fluoroquinolones more likely to cause Clostridium difficile-associated disease? Antimicrob 
Agents Chemother. Sep 2006;50(9):3216-3219. 
102. 
FDA Drug Safety Communication: FDA advises restricting fluoroquinolone antibiotic use for 
certain uncomplicated infections; warns about disabling side effects that can occur together. 
May 12, 2016. U.S. Food and Drug Administration. 
http://www.fda.gov/downloads/Drugs/DrugSafety/UCM500591.pdf. 
103. 
Kang CI, Kim SH, Park WB, et al. Bloodstream infections due to extended-spectrum beta-
lactamase-producing Escherichia coli and Klebsiella pneumoniae: risk factors for mortality and 
treatment outcome, with special emphasis on antimicrobial therapy. Antimicrob Agents 
Chemother. Dec 2004;48(12):4574-4581. 
104. 
Queen MA, Myers AL, Hall M, et al. Comparative effectiveness of empiric antibiotics for 
community-acquired pneumonia. Pediatrics. Jan 2014;133(1):e23-29. 
105. 
Postma DF, van Werkhoven CH, van Elden LJ, et al. Antibiotic treatment strategies for 
community-acquired pneumonia in adults. N Engl J Med. Apr 2 2015;372(14):1312-1323.