INtelligent Stewardship Prompts to Improve Real-time Empiric Antibiotic Selection for Patients With Pneumonia
Phase: NA
Conditions: Pneumonia
Sponsor: Harvard Pilgrim Health Care

             
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Stewardship Prompts to Improve Antibiotic Selection for Pneumonia: 
The INSPIRE Randomized Clinical Trial 
NCT#: 03697070 
 
7/3/2018 

             
 
INSPIRE (INtelligent Stewardship Prompts to Improve Real-time Empiric Antibiotic Selection for 
Patients with Pneumonia): The INSPIRE-ASP Pneumonia 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. 
A.1.3 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 
pneumonia, 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 
levofloxacin, 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 
pneumonia.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 for acute bacterial exacerbation of chronic bronchitis.102 There is increased 
national attention to decrease the overall use of fluoroquinolones. 
 
A1.7 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 Pneumonia Trial Study Design & Population 
 
B.1.1 INSPIRE-ASP Pneumonia Trial Study Design & Population 
The INSPIRE-ASP Pneumonia Trial is a two-arm cluster-randomized trial in HCA 
hospitals comparing routine empiric antibiotic prescribing for patients admitted with pneumonia 
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 pneumonia 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 Pneumonia Trial, an INSPIRE CPOE smart prompt 
will be triggered when physicians prescribe an extended-spectrum (ES, Table 1) for a 

             
 
pneumonia 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 pneumonia is due to 
the associated target pathogens (e.g. MRSA, 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-MRSA antibiotic is being ordered for pneumonia, the clinician will 
receive a prompt during the ordering process providing the probability that the patient has 
pneumonia due to MRSA. 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 or 
Pseudomonas aeruginosa, 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, 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 pneumonia. Hospitals eligibility criteria are outlined in 
Section B.1.3.  
 
Table 1: INSPIRE Pneumonia Trial Extended-Spectrum Antibiotics 
Pathogen-Directed 
Antibiotic Category 
Extended-Spectrum Antibiotics Targeted in  
INSPIRE Pneumonia CPOE Prompts 
Anti-MRSA 
Ceftaroline, Daptomycina, Linezolidb, Vancomycinc 
Anti-VRE 
Daptomycin, Linezolidb 
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 
aDaptomycin not an accepted pneumonia treatment but included in trial analysis in case ordered. 
bBoth oral and intravenous (IV) formulations. 
cIV formulation only. 
Abbreviations: CPOE – Computerized Provider Order Entry, MRSA – Methicillin-Resistant Staphylococcus 
aureus, ESBL – Extended-Spectrum Beta-Lactamase Producing 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

 
  
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B.1.3 Recruitment and Eligibility Criteria 
Hospitals are eligible to participate if they serve adult patients with pneumonia, 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 hospital 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 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 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.  We submit that 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 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, 

 
  
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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 pneumonia such as facility pneumonia admission volume, local prevalence 
of antibiotic resistance in pneumonia patients, length-of-stay, ICU transfers, and facility case mix of patients 
with pneumonia. 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 Healthcare 
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 Pneumonia 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.  

 
  
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Table 2. INSPIRE-ASP Pneumonia 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 pneumonia, 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 Pneumonia 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 Pneumonia 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 pneumonia 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 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 

 
  
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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 Table 1.  
 
Table 3: Primary and Secondary INSPIRE-ASP Pneumonia 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 pneumonia 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.  

 
  
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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 y_ijph is the ES-DOT per at-risk day for patient i for admission j in period p at hospital h, and A_ijph=1 if 
hospital h is in the intervention arm and 0 if not, and T_ijph=1 if p is the intervention period and 0 if baseline 
period. The random effects b_0h and b_1h allow for different baseline mean admission-level ES-DOT per at-
risk day for each hospital and each hospital per period, respectively, while g_j 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 β_3, 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 β_3 is the estimated 
relative rate of ES-DOT per at-risk day due to the intervention, relative to the baseline period. For example, if 
β_3  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 

 
  
11 
 
 
intervention period data. In the randomization portion of the Monte Carlo approach, we ordered the hospitals 
by “size”, meaning the number of admissions with pneumonia. 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) 
 
 
 
 
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