Clinical trial NCT02063867
Conditions: Healthcare Associated Infections Methicillin Resistant Staphylococcus Aureus Multi Drug Resistant Organisms
Keywords: HAI MRSA VRE
Sponsor: University of California, Irvine
Start date: 2014-04-01; Completion date: 2019-02-01
Enrollment: 53 participants; Allocation: RANDOMIZED; Intervention model: PARALLEL

Brief summary:
The ABATE Infection Project is a cluster randomized trial of hospitals to compare two quality improvement strategies to reduce multi-drug resistant organisms and healthcare-associated infections in non-critical care units. The two strategies to be evaluated are:

* Arm 1: Routine Care Routine policy for showering/bathing
* Arm 2: Decolonization Use of chlorhexidine as routine soap for showering or bed bathing for all patients Mupirocin x 5 days if MRSA+ by history, culture, or screen

Note that enrolled "subjects" represents 53 individual HCA Hospitals (representing \~190 non-critical care units) that have been randomized.

Detailed description:

Arms: arm 1: Routine policy for showering or bathing non-critical care patients arm 2: Daily chlorhexidine (CHG) shower or CHG cloth bath for all non-critical care patients.

Topical intranasal mupirocin ointment (bilateral nares, twice daily) x5 days if non-critical care patients are MRSA+ by history, culture, or screen.
Interventions: intervention 1: Arm 2: Decolonization
Intervention descriptions: intervention 1: Daily chlorhexidine (CHG) shower or CHG cloth bath for all non-critical care patients.

Topical intranasal mupirocin ointment (bilateral nares, twice daily) x5 days if non-critical care patients are MRSA+ by history, culture, or screen.

Protocol documents:
Prot_000.pdf: 
Active Bathing to Eliminate Infection (ABATE Infection) Trial 
NCT02063867 
Study Protocol 
June 30, 2015 


1 
Pragmatic Clinical Trials Demonstration Project 
Title: Decreasing Bioburden to Reduce Healthcare-Associated Infections and Readmissions 
ABATE (Addressing Bioburden while Admitted To Eliminate) Infection Trial 
Project Narrative 
Healthcare-associated infections are one of the 10 most frequent causes of death in the United States and 
incur over $6.5 billion dollars of healthcare costs each year. Although most prevention trials have focused on 
intensive care unit (ICUs), where the daily risk for infection is the highest, the majority of healthcare-associated 
infections occur outside of ICUs. This cluster-randomized controlled trial will evaluate whether bathing non-ICU 
patients with antimicrobial soap prevents healthcare-associated infections and the readmissions they cause.  
Abstract 
Healthcare-associated infections (HAIs) are a leading cause of preventable morbidity and mortality. 
Prevention of HAIs is a national priority for patient safety and best practice because of their high morbidity, 
mortality, and cost, incurring over $6.5 billion dollars of healthcare costs each year. Most infections result from 
common bacteria that normally live on the skin or in the nose and which overcome the body’s normal defenses 
because of invasive medical devices, surgical incisions, or the physiologic effects of hospitalization. Studies in 
intensive care units (ICUs) indicate that decolonization of patients’ skin with chlorhexidine, and nares with 
mupirocin can prevent many HAIs. However, evidence is lacking about the effectiveness of decolonization in 
non-ICU settings, where the majority of HAIs occur, and where medical care, risk of infection, patient-to-patient 
interactions, pathogen transmission, and bathing practices differ considerably from ICU settings. 
Decolonization is thus rarely used in these settings, despite its potential to meaningfully decrease the HAI rate. 
The ABATE Infection Trial (Addressing Bioburden while Admitted To Eliminate Infection) will efficiently 
evaluate the impact of decolonization on HAIs in the general patient population outside ICUs. This cluster-
randomized trial will randomize 54 hospitals treating over 400,000 patients to evaluate 1) universal daily 
chlorhexidine bathing to prevent infections from all pathogens, combined with 2) nasal decolonization with 
mupirocin for known carriers of methicillin-resistant Staphylococcus aureus (MRSA), one of the most common 
causes of HAIs. While decolonization has been successful in short-stay high risk areas, such as ICUs, this trial 
will address the much larger problem of HAIs in non-ICU medical and surgical wards. This patient population 
has typically not been evaluated because the complexity and cost of sufficiently large randomized trials to 
demonstrate effectiveness have been beyond the reach of conventional hospital-based trials.   
This trial will provide a critically needed evaluation of decolonization to reduce hospital infection risk 
and infectious readmissions in nearly all hospitalized patients. It will provide essential information to determine 
whether routine decolonization through daily bathing with chlorhexidine should become standard practice for 
40 million patients hospitalized each year in the United States alone. Alternatively, it will suggest that tailored 
strategies distinct from those effective in ICU settings are needed for these patients outside ICUs. 
This trial will also illustrate the strengths of a new model of clinical effectiveness research that quickly 
and efficiently addresses critical management questions by embedding research into the usual delivery of 
health care, and using the organizational and informatics strengths of a large hospital system.  


2 
 
Specific Aims – UH2 
Pragmatic Clinical Trial – Planning Phase 
 
It is a national priority to reduce the morbidity, mortality, and cost of healthcare-associated infections 
(HAIs) and their associated readmissions to their lowest practicable level. There have been notable successes 
in preventing HAIs in intensive care units, where the risk per patient is highest. The focus now needs to 
address the larger fraction of HAIs that occur in general medical, surgical, and oncology units, where medical 
care, risk of infection, patient-to-patient interactions, pathogen transmission, and bathing practices differ 
considerably from ICU settings. To that end, we propose the ABATE Infection Trial (Addressing Bioburden 
while Admitted To Eliminate Infection) to reduce the reservoirs of bacteria on the skin and in the nose that are 
the major causes of these infections. This cluster-randomized trial will assess whether the strategy of body 
decolonization with chlorhexidine plus nasal mupirocin will significantly reduce the rate of HAIs and 
readmissions among patients in general hospital units when compared to routine bathing care.   
We recently completed a successful ICU based cluster-randomized trial involving 43 hospitals, 72 
ICUs, and 75,000 patients using interventions similar to those proposed here. We propose to use our 
experience and collective knowledge to conduct the ABATE Infection Trial in general hospital units. In the 
planning phase of this trial, we will: 
 
1) Recruit hospitals for a cluster-randomized trial of 1) routine daily bathing with chlorhexidine for all 
patients plus 2) routine nasal decolonization with mupirocin for patients harboring methicillin-resistant 
Staphylococcus aureus (MRSA) in general medical and surgical units to reduce infection and 
readmissions.  
 
2) Obtain Institutional Review Board (IRB) approval for the cluster-randomized trial and maximize 
efficiency by having most hospitals cede to a central IRB. 
 
3) Develop trial educational materials and routine electronic nursing queries for daily bathing and 
decolonization in general medical and surgical inpatient areas. Additionally, begin baseline strain 
collection activity to assess resistance. 
 
4) Obtain baseline data on HAI infection rates for participating hospitals to lay the foundation for trial 
outcomes. These will be obtained using corporate data warehouse capabilities of Hospital Corporation 
of America (HCA).  
 
 
 
 


3 
 
Specific Aims – UH3 
Pragmatic Clinical Trial – Trial Intervention Phase 
The majority of healthcare-associated infections (HAIs) occur outside of ICUs, in general medical, 
surgical, and oncology units. The ABATE Infection Trial (Addressing Bioburden while Admitted To Eliminate 
Infection) seeks to reduce these HAIs and the readmissions they cause to their lowest practicable level by 
reducing the reservoirs of the pathogens that cause most HAIs. This cluster-randomized trial will target all 
pathogens through bathing with chlorhexidine and additionally target carriers of methicillin-resistant 
Staphylococcus aureus (MRSA) through nasal decolonization with mupirocin. This intervention strategy will be 
compared to routine care in non-ICU medical, surgical, and oncology units.   
This trial will assess a high yield strategy that is relevant to the large majority of hospitalized patients. If 
successful, this decolonization strategy would become best practice for preventing infection among 40 million 
patients hospitalized each year in the United States alone.  
Specific aims for the Trial Intervention Phase are as follows: 
 
1) Conduct a 50+-hospital cluster-randomized controlled trial of routine chlorhexidine bathing and 
selective MRSA decolonization versus standard-of-care practices for all hospitalized patients in non-
critical care adult medical, surgical, and oncology units. The primary outcome will be clinical burden of 
gram-positive multi-drug resistant organisms. Secondary outcomes will include clinical burden of gram-
negative multi-drug resistant organisms, bloodstream infections, and readmissions caused by infection. 
 
2) Assess whether universal chlorhexidine bathing and selective MRSA decolonization result in increased 
resistance to chlorhexidine or mupirocin among bacterial strains collected during the trial. 
 
3) Estimate the costs associated with the intervention (chlorhexidine + selective MRSA decolonization) 
and the attributable medical costs of healthcare associated infections in adult general inpatient units 
and infectious readmissions, in order to evaluate the potential for cost savings associated with the 
strategy of reducing bioburden to prevent infection 
 
 
 


4 
 
1.0 Significance - Overall 
 
1.1 The Problem of Healthcare-Associated Infections: Burden and Trends 
 
Healthcare-associated infections (HAI) are one of the top 10 causes of death in the United States. Each 
year in the U.S., over 1.7 million HAIs occur, resulting in 100,000 annual deaths at a cost of over 6.5 billion 
dollars.1 In Europe, over 4.5 million HAIs occur each year and are responsible for 16 million additional hospital 
days and 37,000 attributable deaths at a cost of over 7 billion Euros.2 Among developed nations, the World 
Health Organization (WHO) has reported that 1.4 million people have an HAI at any given time. 3 Thus, 5-10% 
of hospitalized patients experience an HAI, a risk that is estimated to be 20-fold higher in developing nations.  
 
The recognition of HAIs as a leading cause of death has led to numerous state and national responses. 
In 1999, the Institute of Medicine (IOM) report “To err is human: building a safer health system” galvanized 
efforts to prevent healthcare associated adverse events, including HAIs.4 In 2003, the IOM identified HAI 
prevention as a top 20 priority area for national action.5 In 2008, the U.S. Government Accountability Office 
issued a report on HAIs in Hospitals calling for national efforts by the Department of Health and Human 
Services to prioritize prevention practices and standardize HAI surveillance.6 In the meantime, The Joint 
Commission continued to increase its requirements for routine HAI surveillance for hospital accreditation,7 and 
the Centers for Medicare & Medicaid Services (CMS) outlined and implemented a multi-year plan requiring 
hospitals to publicly report HAIs and to ultimately perform well on HAI rankings or face reductions in 
reimbursement.8 Furthermore, state legislative mandates have been passed in 29 states which require 
hospitals to report HAI events, generally through the Centers for Disease Control and Prevention’s (CDC) 
National Healthcare Safety Network (NHSN) system.9 In addition to providing gold-standard criteria for 
identifying HAIs, NHSN has become the national repository for hospitals to report HAI surveillance data; nearly 
5,000 hospitals report detailed data on HAI events through this system. Through use of NHSN data, numerous 
state health departments are generating public reports of hospital-specific HAI performance. 
 
1.2 Major Types of Healthcare-Associated Infections and Their Consequences 
 
The most frequent HAIs are urinary tract infections, pneumonia, bloodstream infections, and surgical 
site infections. Each carries significant morbidity and mortality both in and out of ICUs. CDC estimates 530,000 
healthcare-associated urinary tract infections, 275,000 surgical site infections, 220,000 bloodstream infections, 
235,000 pneumonias, and 350,000 other infections annually.1 These HAIs account for 100,000 estimated 
deaths. HAIs also prolong hospitalizations and cause readmissions.4-6 Finally, HAIs incur large costs, with 
average direct medical costs of approximately $500-1,000 per urinary tract infection and $10,000-20,000 per 
surgical site infection, central line-associated bloodstream infection, or pneumonia.10  
 
1.3 Pathogenesis and Preventability of Healthcare Associated Infections 
 
The largest fraction of HAIs are caused by bacteria that reside on the skin and in the nose and gain 
access to the bloodstream, lungs, and bladder by way of devices and incisions that breach normal host 
defenses. These bacteria may be the patient’s normal flora, or they may be new, often antimicrobial resistant, 
organisms acquired during hospitalization. Current evidence and expert opinion suggests that 65-70% of 
catheter-related bloodstream and urinary tract infections may be preventable.11 We discuss below a prevention 
strategy, decolonization of the skin and nasal reservoirs, that should positively impact these types of infections. 
 
1.4 Rationale for Testing Strategies for HAI Reduction Beyond Intensive Care Units 
 
For over 30 years, the major focus of HAI prevention has centered on ICUs because the combination of 
high complexity medical care and severity of illness result in ICU patients having the highest risks for HAIs.12 13 
14 15 16 17 18 19 Numerous studies have described the morbidity and mortality attributable to this setting and 
demonstrated gains in reducing catheter-related bloodstream infections,14-17 20 catheter-related urinary tract 
infections,21 22 23 24 25 and pneumonia26 27 28 29 in ICU settings.  
In recent years, the substantial gains in reducing HAIs in ICUs have caused national attention to turn to 
the much larger number of HAIs occurring outside of ICUs. Non-ICU settings most commonly consist of step-
down units which represent an intermediate level of care between the ICU and a routine non-ICU area, as well 
as medical, surgical, mixed medical/surgical, and oncology units. It is estimated that 75% of HAIs occur outside 
of ICU settings.1 Nevertheless, there have been no large scale trials to reduce HAIs in the non-ICU general 
inpatient units. For this reason, we will adapt our successful decolonization strategy for HAI reduction in ICUs 
to target the majority of healthcare-associated infections in non-critical care units. 
 
 
In addition, the effect of the decolonization regimen we will study should persist beyond discharge. 
Chlorhexidine bathing reduces skin bioburden for several days after use, and mupirocin reduces MRSA nasal 


5 
 
reservoirs for several weeks. Thus, inpatient decolonization may prevent HAIs that manifest in the highly 
vulnerable period immediately following discharge. We have shown that a large number of HAIs occur shortly 
after discharge and cause readmissions.30 31 32 33 34 35 36 37 This post-discharge period is a time of high risk for 
subsequent infection due to medical devices such as central venous catheters and surgical drains, surgical 
wounds, decubitus ulcers, fragile pulmonary status, and poor clearance of secretions. For example, we have 
shown that 24% of inpatients who acquire methicillin-resistant Staphylococcus aureus (MRSA) during a 
hospital stay develop post-discharge MRSA infection. Half of these infections occur within one month of 
discharge.30 33 We have similarly shown that over one third of HAIs due to vancomycin-resistant enterococci 
occur shortly after discharge.32 The importance of HAIs as a cause of 30-day readmissions is also been 
highlighted by the Pennsylvania Health Care Cost Containment Council, which reported that patients with HAIs 
were five times more likely to be readmitted within 30 days due to a new HAI or complication of HAI.38  
It is important that we not simply assume that approaches that work in ICUs will also work in non-ICU 
settings or have a beneficial effect on HAIs that manifest after discharge. There are several reasons for this. 
First, the decolonization regimen cannot be delivered in an identical fashion in the non-ICU setting. Patients 
are awake and some may refuse a daily bath. In contrast to the ICU, where bed baths are uniformly performed, 
patients may choose to perform their own bed bath. Others may choose to shower, where rinsing of the 
chlorhexidine leaves less residual effect on the skin compared to patients who receive a no-rinse bed bath. 
Thus, in the current delivery model, it will be more difficult to standardize the intervention or ensure that it is 
applied uniformly and effectively. Nevertheless, this is an important aspect of pragmatic clinical trials in non-
ICU settings. Second, the level and intensity of contact differs between patients and nursing staff, and between 
patients themselves, especially those sharing a room. Since these interactions are important determinants of 
transmission of pathogens to patients, the results of an ICU intervention are not necessarily predictable in the 
non-ICU setting. Third, because the use of invasive devices is lower in non-ICU settings, reducing the density 
of bacterial reservoirs on the skin and in the nose may convey a smaller benefit. Finally, when applied to all 
patients in general hospital units, widespread use can incur meaningful incremental cost, small risks of skin 
rashes, the unpleasantness of intranasal application of ointment, and a risk of eliciting antimicrobial resistance. 
There are also potential opportunity costs of widespread adoption, since hospitals will need to be alert to 
concomitant use of other products that are not compatible with chlorhexidine bathing. Thus, it is important to 
test the effectiveness of a decolonization regimen under conditions of actual use, and to assess both its impact 
on infections, but also to monitor adverse effects on individuals and microbial resistance patterns.  
 
1.5 Importance of Methicillin Resistant Staphylococcus aureus (MRSA) Subset of HAIs 
 
 
Any strategy for preventing a large fraction of all HAIs must be effective against MRSA, which is 
arguably the most important single pathogen in healthcare-associated infection when accounting for virulence, 
prevalence, diversity of disease spectrum, and propensity for widespread transmission.30  33 39 40 41 MRSA is 
highly antibiotic resistant and causes a wide spectrum of disease, including skin infection, deep organ 
abscesses, pneumonia, surgical site infections, bone and joint infections, blood and urine infections, and 
sepsis.39 40 MRSA causes or complicates 278,000 U.S. hospitalizations annually, resulting in 56,000 septic 
events and 19,000 deaths. 39 Prevention efforts have focused on healthcare facilities since critically and 
chronically ill patients experience the majority of MRSA morbidity and mortality.  
 
 
Among HAIs in 2009-10, S. aureus (two-thirds of which is MRSA) has become the most common 
pathogen. It is the most common cause of ventilator associated pneumonia and surgical site infection, and has 
recently become the most common cause of central-line associated bloodstream infections.42 43 It is also the 8th 
most common cause of catheter-associated urinary tract infections.39 Increasing MRSA morbidity and mortality 
have fueled a national call for action. The IOM ranked prevention of MRSA infection in the quartile of highest 
importance when asked to provide a “top 100” list of health issues for comparative effectiveness research and 
rank them into quartiles.44 Over 15 states have mandated reporting of MRSA infections through CDC’s NHSN 
surveillance system, and 10 states require screening of high risk patients in hospitals to ensure detection, 
promote contact precautions (i.e. single room, use of gowns and gloves), and prevent transmission.9 
Furthermore, CMS will reduce reimbursement to hospitals that fail to report MRSA bloodstream infections by 
2013. This widespread concerted and legislated response toward MRSA is unprecedented in US healthcare. 
 
1.6 MRSA Strategies for Disease Prevention – Screening and Isolation 
 
 
We first describe MRSA prevention strategies as a prelude to describing strategies that target all 
pathogens. Two main strategies have emerged as successful in reducing MRSA infection in hospitals. The first 
involves “active screening” to find MRSA carriers and isolate them to prevent the spread of MRSA to other 


6 
 
patients. Active screening involves culturing patient’s nostrils (nares), the major reservoir for MRSA in humans. 
The goal of this strategy is to implement contact precautions for all MRSA carriers to prevent transmission of 
MRSA to patients who are not colonized.45 46 Preventing transmission prevents many infections since 33% of 
patients who become colonized in the hospital develop infection within 1 year.30 33 We and others have shown 
that active screening can reduce hospital-associated transmission and MRSA infection by 35-70%.47 48 49 50 51 
     
Although active screening for high risk patients is mandated by 10 states,9 screening and isolation do 
not benefit the large and growing population of 1.8 million inpatients who already harbor MRSA.41 Between 5-
12% of hospitalized patients are MRSA carriers at discharge,41 48 52 and we have shown that many develop 
invasive disease within one month of discharge.30 33 Thus, interventions to reduce post-acquisition morbidity 
could have the highest benefit and meet the greatest need by targeting existing carriers of MRSA.  
 
1.7 MRSA Strategies for Disease Prevention –Decolonization 
 
 
The second major strategy for preventing MRSA infection involves the use of various topical,17 53 54 and 
sometimes systemic,55 antimicrobial agents to “decolonize” MRSA carriers and prevent subsequent MRSA 
infection. Systemic regimens are not favored in the US due to side effects and the theoretical risk of 
accelerated emergence of antibiotic resistance. Most commonly, a combination of chlorhexidine body washes 
and mupirocin ointment applied to the nares is used for a period of 5-7 days. This strategy can be applied to 
carriers identified through routine clinical cultures, or coupled to active screening programs. We will test the 
combination of decolonizing the skin with chlorhexidine, which targets MRSA and other bacteria, plus 
decolonizing the nose with mupirocin, which targets the main reservoir for MRSA carriers. 
 
1.8 Universal Strategies Affecting All Pathogens 
 
 
In contrast to the above strategies that specifically target MRSA, it is possible that universal strategies 
that impact all pathogens may be more effective than strategies that target a specific pathogen like MRSA. 
This is particularly relevant due to rising numbers of other highly antibiotic resistant pathogens such as 
vancomycin-resistant enterococcus and multi-drug resistant gram negative bacilli, including extended spectrum 
beta-lactamase producers and carbapenemase-resistant enterobacteriaceae, for which therapeutic options are 
limited. Such strategies may also require fewer resources, and thus be “dominant” in terms of coupling greater 
benefits with lower costs. For example, a successful universal strategy could obviate the need for routine 
screening for MRSA and applying targeted isolation or decolonization. We describe here the evidence in 
support of universal chlorhexidine bathing and universal application of mupirocin to high risk patients to prevent 
infections due both to S. aureus and also to other pathogens. 
 
 
One increasingly well evaluated universal strategy for pathogen control in ICUs is the routine use of 
chlorhexidine bathing in place of usual soap and water baths for all patients. This strategy stems from the 
strong evidence that chlorhexidine is the most effective skin preparation for central line placement when 
outcomes of central line infections are measured, and from other studies showing marked reductions in skin 
bacterial colony counts for several days following serial bathing.54-58 In fact, national recommendations for 
prevention of surgical site infections (SSI) from the CDC’s Hospital Infection Control Practices Advisory 
Committee (HICPAC) call for bathing or showering with chlorhexidine at least twice before the operation. The 
CDC ranks this recommendation as a category IB: “Strongly recommended for implementation and supported 
by some experimental, clinical or epidemiologic studies and strong theoretical rationale.”56 
 
 
In addition, the Society of Thoracic Surgeons has recommended universal mupirocin for all cardiac 
surgery patients unless screening nares cultures show lack of S. aureus carriage.57 This strategy is supported 
by evidence of reduction in S. aureus surgical site infections in excess of 20%.58 59 
 
1.9 Effectiveness of Decolonization with Mupirocin and Chlorhexidine 
 
 
The use of decolonization to prevent HAI has biological plausibility. It reduces bacterial carriage, which 
commonly precedes infection. This reduction in bioburden reduces the likelihood of infection from a patient’s 
own flora and also reduces the spread of pathogens from one patient to another. Nevertheless, definitive trials 
are needed to develop best practice guidance on when decolonization should be used, and, as with any 
antimicrobial agent, use must be balanced by the risk of engendering antibiotic resistance.  
 
 
Mupirocin is a prescription drug that was FDA approved in 2002 for topical treatment of mild wounds 
due to S. aureus and Streptococcus pyogenes. A nasal formulation is also approved for eradicating nasal 
carriage of S. aureus. Mupirocin is highly effective in eradicating S. aureus in the short term. Several studies 
have shown 90% efficacy within two weeks of a 5-day regimen.60 61 62 63 64 It also significantly reduces short-
term hospital-associated MRSA transmission and infections by over 50% in observational and cross-over 


7 
 
intervention studies. 16 17 53 54 65 66  Long term efficacy over weeks to months drops substantially after a single 
treatment regimen to approximately 60% after 6-8 weeks, largely due to re-colonization with the patient’s 
original strain.17 57-59 61-64 67 Increased failure to decolonize was noted if additional cultures were taken from 
body sites other than the anterior nares, suggesting that both serial treatment as well as additional 
decolonization of sites other than the nose are necessary for prolonged eradication of MRSA.17 65 68 69  
 
To improve body surface decolonization, chlorhexidine body washes are now routinely given with 
mupirocin for MRSA decolonization. Chlorhexidine gluconate (CHG) has been safely used for bathing, 
showering and dental hygiene for over 50 years. It is an over-the-counter product that is 4% solution intended 
for direct application to skin as an antimicrobial skin cleanser. Numerous studies have shown marked 
reductions in skin bacteria following serial CHG bathing or showering,70-76 and it is widely used as a pre-
operative showering agent based upon CDC guidelines that recommend its use.56 It is also the gold standard 
in periodontal hygiene, including oral care in ventilated patients.70 71 72 Evidence exists to support the need for 
repeated application for sufficient skin decontamination, 74 73 74 75 as well as for low bacterial counts to persist for 
one to two weeks following use.76 77 In addition, the concept of a universal strategy for decolonization has 
gained favor since evidence is mounting that CHG can reduce colonization and infection from a variety of 
healthcare associated pathogens.15 16 84 Studies by members our investigative group and others have 
demonstrated a 52-87% reduction in bloodstream infection in ICU patients.15-17 78 There is also growing 
evidence that CHG skin bathing may reduce MRSA acquisition and infection by 50% in high risk settings such 
as ICUs.16 17 55 66 This approach and others led to our recent definitive cluster-randomized trial of 43 hospitals 
demonstrating the success of routine chlorhexidine bathing and mupirocin decolonization in ICUs to 
significantly reduce HAIs (see Preliminary Data Section 6.10.2, (confidential trial results). 
 
 
The definitive success of decolonization with chlorhexidine and mupirocin in reducing HAIs in ICUs 
provides the foundation for a trial evaluating whether these successes can be translated to non-critical care 
units. Since most HAIs occur outside the ICU and after hospital discharge, targeting the larger general patient 
population has the potential to make a much greater impact on HAIs by reducing the skin and nasal carriage of 
important pathogens during a time when patients are highly vulnerable to infection. 
 
1.10 Safety of Mupirocin and Chlorhexidine 
 
Both mupirocin and CHG have excellent safety profiles. Systemic absorption of both drugs is minimal.79 
80 81 82 83 Of the minimal amount of mupirocin that is absorbed, nearly all is rapidly converted to monic acid, an 
inactive metabolite.79 80 Furthermore, systemic absorption remains negligible following single or repeated 
intranasal applications over consecutive days in adults.84 Multiple observational studies and randomized 
controlled trials have also shown no systemic absorption of mupirocin following intranasal application.82 83 85 86 
87 Safety data for mupirocin from the manufacturer states that <1% of patients in clinical trials withdrew due to 
adverse events. The most frequently reported adverse events were as follows: rhinitis (1.0%), taste perversion 
(0.8%), and pharyngitis (0.5%). Post-marketing surveillance has not resulted in any additional concerns.  
 
As an over-the-counter skin cleanser used in healthcare for over 50 years, CHG has an even more 
extensive safety record.15 17 53 54 88 89 90 91 92 93 94 Several groups have confirmed the absence of systemic 
absorption following topical use or oral rinsing with CHG.95 96 97 98 Moreover, even if ingested, CHG is known to 
have negligible absorption with undetectable blood levels.99 100 101 Side effects are largely limited to skin 
irritation, which is rare, and anaphylaxis has only been reported as case reports.102 103 Estimates for these 
effects are expected to be very small given the large numbers of people using an unregulated over-the-counter 
product. No deleterious effects have been reported with daily use in either long-term ICU patients16 78 85 (Sage 
Inc, personal communication regarding routine use in >400 ICUs) or outpatient daily bathing for 9 months.74 
The major manufacturer of over-the-counter CHG states that CHG “can be used many times a day without 
causing irritation, dryness, or discomfort.” 74 It is also safe on superficial wounds.104 
 
1.11 Emergence of Resistance to Mupirocin and Chlorhexidine 
 
 
The use of mupirocin and chlorhexidine for decolonization appropriately raises the issue of whether 
these agents will engender antimicrobial resistance. Small proportions of mupirocin resistance have been 
reported in some,48 105 but not all studies.17 106 107 As an example, Robicsek et al. reported that mupirocin 
resistance remained relatively low during a 3-year period of widespread use, but did increase from 4.1% to 
7.2% among MRSA isolates.48 Other studies have reported declines in mupirocin resistance under continued 
use for high risk patients.108 109 110 Finally, one ICU study has reported evidence of clinically meaningful levels 
of mupirocin resistance at 8.6% despite lack of use,111 suggesting variability in regional susceptibility may exist. 
 
 
Chlorhexidine resistance in MRSA has not been reported in the United States. A chlorhexidine 


8 
 
resistance gene in MRSA has been newly reported in Europe, but the clinical significance is not fully known.112 
Regardless of whether or not significant resistance has emerged, any trial evaluating the use of these products 
for decolonization purposes needs to perform careful monitoring for the development of antibiotic resistance.  
 
1.12 Need for a Cluster-Randomized Controlled Trial in General Inpatient Units to Achieve Significant 
Reduction in Healthcare-Associated Infections 
 
Cluster randomization is the preferred experimental method for evaluating decolonization approaches 
to prevent all-cause HAIs. There are several reasons for this. Foremost is the fact that there is considerable 
transfer of pathogens from one patient to another, for instance on the hands of healthcare workers and 
sometimes via inanimate objects (medical equipment, furniture, etc). Thus, interventions that affect all patients 
in a location are more likely to succeed, and also much more likely to predict the performance in actual 
practice. Cluster randomization also has advantage of studying interventions under conditions of actual use, of 
minimizing the disruption to normal practice, and of allowing the use of health system resources for ensuring 
compliance with the intended treatment. We discuss these issues in more detail in Sections 2.1-2.3 below. 
 
1.13 Summary of Overall Significance 
 
 
The well publicized focus on HAI prevention by all major agencies and stakeholder groups, including 
the CDC, CMS the Joint Commission, the Institute of Healthcare Improvement, the IOM, the National Quality 
Forum, the newly formed national Partnership for Patients of the Department of Health and Human Services 
(dedicated to patient safety and HAI prevention across the continuum of healthcare), and numerous national 
societies including the American Medical Association, the Infectious Diseases Society of America, and the 
Society for Healthcare Epidemiology of America, provide the impetus to perform a definitive high impact trial on 
HAI prevention in non-ICU inpatient populations. 
Large cluster randomized trials for HAI prevention (our own) in ICUs have demonstrated that it is 
possible to prevent many HAIs. There is a great need to focus on broader patient populations in whom the 
majority of HAIs occur and in whom medical care, risk of infection, patient-to-patient interactions, pathogen 
transmission, and bathing practices differ considerably from ICU settings. A randomized trial sufficiently 
generalizable to assess HAI reduction in broad patient populations has not previously been undertaken 
because the scope of such a study is not possible under usual funding mechanisms. A head-to-head trial of 
decolonization versus standard of care bathing will enable us to determine whether this potentially important 
strategy has a role in all patient populations.  
 
2.0 Significance – UH2 Planning Year 
 
 
The topic for this proposal is thus a critically needed area of comparative effectiveness study. It 
addresses the following two top quartile (the highest rating) priority topics from the Institute of Medicine’s top 
100 national priority areas across all domains of medicine released in 2009:44 
 
 
Compare the effectiveness of strategies for reducing healthcare-associated infections (HAI), including 
catheter-associated bloodstream infection, ventilator associated pneumonia, and surgical site infections  
 
 
Compare the effectiveness of various screening, prophylaxis, and treatment interventions in eradicating 
methicillin resistant Staphylococcus aureus (MRSA) in communities, institutions, and hospitals. 
 
The ability to conduct a well designed pragmatic trial requires careful thought as to the best study design to fit 
the question to be answered – in this case, how best to ascertain whether routine decolonization can reduce 
HAIs, including MRSA infection, in the general hospitalized patient population.  
 
2.1 Design Elements of Pragmatic Design in Comparative Effectiveness Trials 
 
In designing this proposal, we will provide a practical roadmap of a well-designed trial that showcases 
the six defining characteristics of comparative effectiveness research (CER) highlighted by the Institute of 
Medicine in their 2009 report “Initial National Priorities for Comparative Effectiveness Research.”44 In so doing, 
it will enable other trials to adopt and mirror strategies for effective research. We are well suited to provide this 
roadmap as an investigative team that has previously worked together to conduct a related cluster-randomized 
trial on decolonization in ICUs (see Section 5.0.1-5.0.2 Investigative Team and Sections 5.30.1, 6.10.1-6.10.2, 
Preliminary Data). In this proposal, we will highlight the ability of the ABATE Infection Trial to address the six 
defining characteristics of comparative effectiveness research (CER) (see Section 4.3). These six 
characteristics are:  
 #1: CER has the objective of directly informing a specific clinical decision from the patient perspective or a 
health policy decision from the population perspective. 


9 
 
 #2: CER compares at least two alternative interventions, each with the potential to be “best practice.” 
 #3: CER describes results at the population and subgroup levels. 
 #4: CER measures outcomes—both benefits and harms—that are important to patients. 
 #5: CER employs methods and data sources appropriate for the decision of interest. 
 #6: CER is conducted in settings that are similar to those in which the intervention will be used in practice. 
 
2.2 Usual Processes for Quality Improvement in Hospitals 
 
Most quality improvement activities in hospitals focus on process measures rather than outcomes. To  
the extent they do address outcomes, they are often unable to determine with confidence that an intervention 
improves (or harms) patients. We highlight two important reasons for the inability to learn as much as possible 
from quality improvement initiatives. First, the improvements are implemented without a robust comparator. It 
is very common, for instance, to perform a before-after comparison. These comparisons provide the least 
persuasive evidence,113 and are well documented to lead to incorrect conclusions.114 Second, evaluations of 
interventions in routine care settings often include too few patients to provide a robust estimate of their effect.  
 
Through this trial, we will create new tools and approaches to conducting robust, reliable, generalizable 
research that incorporates the best quality improvement methods. This will be a key step in the creation of a 
learning health system – one that generates evidence in the course of delivering best quality clinical care. We 
will share methods and materials for implementation as well as programs for trial processes and outcomes. 
 
2.3 Cluster-Randomized Trial Design for Quality Improvement Research for HAI 
Despite their strengths, conventional randomized clinical trials (RCTs) have important limitations. 
Although they are excellent tools for judging efficacy (performance under ideal conditions), they often fail to 
judge effectiveness (performance under conditions of actual use). This is because most RCTs require more 
standardization and a higher level of medical care than occurs in practice. In addition, generalizability may be 
lost because participants in RCTs are often not representative of the eventual target group. Furthermore, RCTs 
are often very costly and time-consuming to implement. For example, the ALLHAT study of initial treatment of 
hypertension, often cited as a pragmatic clinical trial,115 cost over $80 million and took 8 years to complete.116 
Cluster-randomized trials are RCTs which randomize groups (clusters) rather than individuals. Cluster 
randomization is the only feasible method for randomization when an intervention must be applied or is usually 
applied to an entire group, such as a community-based health promotion campaign or a hospital quality 
improvement initiative applied to hospital units or clinical services. They are also the only method for evaluating 
interventions for which the status of individuals is linked, for instance when shared exposure to contagious 
illness is an important consideration, as commonly occurs in hospital settings.  
Cluster-randomized trials have several advantages in comparative effectiveness studies. First, by 
applying interventions at the hospital, practice, or health plan level, cluster-randomized trials can more readily 
study interventions under conditions of actual use. For instance, a cluster-randomized trial that uses existing 
clinical and administrative mechanisms incorporates the impact of group dynamics (advocacy, peer pressure, 
reminders) among healthcare providers. Second, cluster-randomized trials are often intended to be applied to 
an entire hospital, Intensive Care Unit (ICU) or clinic population without exclusion, which enhances 
generalizability. Third, cluster-randomized trials are able to harness the healthcare delivery system’s existing 
administrative capacities, including quality improvement programs and data collection systems, simplifying the 
logistics of implementation and reducing study costs.19 
The increasing availability of electronic health information facilitates the implementation of cluster-
randomized trials, as routinely collected electronic health information can be used to assess baseline status, 
monitor implementation, and measure outcomes. This proposal will provide a seminal trial that demonstrates 
several of the design strengths of cluster-randomized trials. It will generate comparative effectiveness evidence 
in an efficient and timely manner, thus enabling swift policy action and impacting best practice guidance. 
 
2.4 Critical IRB Considerations for Cluster-Randomized Trials 
 
For the US healthcare system to adopt cluster randomization as a common method for studying 
comparative effectiveness, several conditions will need to be satisfied.19 First, the concept of group 
randomization, often without individual consent, raises important ethical issues about individual choice and 
participation in research.117 There will need to be agreement that it is ethical to perform cluster randomization. 
Importantly, these concerns must be balanced by the fact that quality improvement decisions are routinely 
made on behalf of patients every day in U.S. hospitals, often without sufficient evidence to guide the choice of 
protocol or product. In addition, without cluster-randomized trials, it would not otherwise be possible to study  


10 
 
quality improvement initiatives in the manner in which they are actually applied in healthcare.  
In general, cluster-randomized trials require waiver of individual informed consent to compare group-
level effects in response to interventions such as a formulary change, a new hospital policy or campaign, or an 
insurer change in policy. Requirement of individual consent would preclude the ability to test population 
response because the intervention would not be uniformly adopted across all patients. The IRB governing such 
trials must be familiar with the national guidance related to waiver of documented individual informed consent 
and also familiar with the intersection of research inquiry and routine healthcare improvement processes.117 118 
Additional experience and familiarity is needed related to minimal risk criteria, the issues related to 
randomizing groups (hospitals, clinics, communities) rather than individual patients, whether the activity is 
being performed under standard quality improvement methods, and whether the activity under study is already 
commonly used in healthcare despite lack of a definitive trial. Finally, the IRB needs to understand the full 
range of options, including delegation of the consent decision to a designated representative of the cluster. 
 
In this proposal, we will share and publish our ethical considerations and approach to IRB issues that 
are highly relevant to this and other cluster-randomized trials. This shared experience will help the medical 
community use rigorous research methods to develop evidence for best practice as part of usual medical care.    
 
3.0 Significance – UH3 Trial Implementation Years 
 
As mentioned above, our topic addresses two of the top 100 national priority areas –HAI elimination 
and MRSA eradication - identified by the Institute of Medicine for medical comparative effectiveness 
research.44 If successful, routine decolonization through daily bathing with chlorhexidine would become best 
practice for preventing infection among 40 million patients hospitalized each year in the United States alone.  
 
3.1 Context and Conduct of Hospital-based Cluster-Randomized Trials for Disease Prevention  
 
Although the cluster-randomized trial design is well described in public health settings, there are 
relatively few successful examples in modern clinical care environments that demonstrate the power of this 
design, its advantages for conducting research embedded in usual medical care, and the relative practicality of 
implementation. This is particularly important for the fields of infectious diseases and infection prevention since 
assumptions of independence are violated among individual participants due to contagiousness. In these 
situations, identifying large numbers of randomizable groups for study is critically important to determining 
realistic intervention effects. Since the usual method of improving care in medical facilities and clinics is to 
adopt or test improvement strategies for all patients or for a selected subset of hospital units or clinics, the 
study of interventions in this setting should apply to all patients in a medical area and be highly generalizable.  
 
The methodology and learning experiences needed to successfully conduct large cluster-randomized 
trials that randomize entire hospitals is not well described due to lack of experience. This proposal will provide 
guidance as to the conduct of such trials, including approach, materials, and use of technology to expedite and 
standardize trial activities, to assess compliance during usual care, and to efficiently obtain results. This trial 
will expand our understanding of simplified large scale research among numerous hospitals in many states.   
 
3.2 Secular Trends and Best Practice Foundation  
 
Secular trends in medical care are well described phenomena that can negatively impact proper 
assessment of clinical trials. Without control groups, trialists may conclude that an intervention has had an 
effect when they are actually only observing concurrent changes occurring in the population-at-large. This is 
partly because hospitals frequently introduce new practices and launch multiple initiatives that overlap in time 
and place. Thus, cluster-randomized trials should employ a control arm. In addition, control arms should reflect 
current best practice in order to understand whether the intervention advances science by producing benefit 
over the current gold standard. Thus, it is vital to ensure that best practice is achieved in participating groups 
before the start of the trial. Finally, it is imperative that medical approaches and processes be monitored during 
the course of the trial to prevent additional quality improvement initiatives from confounding results. 
 
3.3 Implementation of Cluster-Randomized Trials in Hospitals 
Effectiveness research should maximize use of usual care and care improvement processes. Trials 
targeting infectious disease prevention should therefore engage existing hospital infection prevention programs 
and use their methods to effect change in clinical practice and ensure compliance for patient safety. It is well 
known in the field of hospital epidemiology that practice change succeeds best when there is a unit champion 
or leader to serve to promulgate new initiatives and influence peers.119 120 121 Thus, engaging existing unit 
champions such as nurse managers or medical directors for trial initiatives is a usual and customary approach 


11 
 
to effect change. In addition, it is uncommon for hospitals to implement divergent processes across similar 
units. To mirror actual practice, prevent confusion and avoid trial contamination due to shared patients and 
staff across units, cluster-randomized trials of inpatient processes should ideally randomize hospitals, not units. 
In addition, pragmatic clinical trials should utilize routine and recognizable hospital processes such as 
standardized nursing protocols, computer-based training, and routinely scheduled events (e.g. nursing acuity 
assessments, bathing, central line dressing changes) and be cognizant of the impact of the trial on necessary 
and expected activities such as nursing shift change, and cleaning and product restocking schedules. 
 
3.4 Maximizing the Use of Electronic Health Records to Support Research 
In order to effectively and efficiently conduct trials in care settings, all segments of the healthcare and 
lay communities need to understand the importance of acquiring information during the routine delivery of 
care.122 123 The advent of robust electronic health record (EHR) systems and the impetus provided by the 
meaningful use incentive programs by CMS allow trials to be conducted in highly efficient and technologically 
advanced ways. In this trial, we will showcase how robust EHR systems across large health systems can be 
leveraged to implement standardized protocols, easily query compliance, and obtain key data elements such 
as the presence of devices during customary nursing documentation routines, employ usual hospital 
surveillance and infection reports, and ensure rapid data capture and cleaning for trial analyses. In this way, 
robust EHR systems can close the gap between effectiveness and efficacy across hospital populations.  
 
3.5 Analysis of Cluster-Randomized Trials 
Finally, cluster-randomized clinical trials need to employ different statistical methods that account for 
the relatedness of outcomes within groups. For example, increasing numbers of patients with infectious 
outcomes will increase the contagiousness level in a group setting. Thus, accounting for intra-cluster 
correlation is imperative to obtaining credible results for trials that use this methodology.  
 
3.6 Trial Significance – Overall Summary 
Healthcare-associated infections are a top 10 cause of death in the U.S. and a focus of intense national 
attention by numerous federal agencies. Although most prevention trials have focused on ICUs, where the 
daily risk for infection is the highest, the majority of healthcare-associated infections occur outside of ICUs. 
This cluster-randomized controlled trial will evaluate whether decreasing the bacterial bioburden of non-ICU 
patients through bathing with chlorhexidine and eradicating MRSA with nasal mupirocin prevents healthcare-
associated infections and the readmissions they cause. Importantly, the conduct of this trial by a highly 
experienced team will provide a critical roadmap to the design, conduct, and analysis of large scale cluster-
randomized trials, and will produce generalizable toolkits and programs for future trials.  
 
4.0 Innovation  
 
 
This proposal to conduct a large scale cluster-randomized trial will be one of the most innovative and 
important trials for prevention of HAIs. It has direct applicability to all hospitalized patients and it will generate 
guidance, materials, resources, and software products that will improve the design and conduct of future 
cluster-randomized trials.  
 
4.1 Innovation – Overall Project 
We will conduct a trial whose result can influence the care of the majority of hospital patients while 
demonstrating all key elements of comparative effectiveness research as follows: 
 
 
Element #1: CER directly informs a specific clinical decision from the patient perspective or a 
health policy decision from the population perspective. 
The ABATE Infection Trial will evaluate whether chlorhexidine bathing should be routinely used for 
essentially all hospitalized patients to prevent hospital and post-discharge infections. This is relevant for 
both patients and policy. 
 
 Element #2: CER compares at least two alternative “best practice” interventions.  
The ABATE Infection Trial is a head-to-head comparison of routine bathing of non-critical care patients 
with soap and water (current best practice) versus daily chlorhexidine bathing (improvement strategy) 
for all patients plus nasal mupirocin for decolonization of MRSA carriers.  
 
 
Element #3: CER describes results at the population and subgroup levels. 
The ABATE Infection Trial will analyze its results across the overall hospital population and also among 
critical subgroups of patients who may be at higher risk for outcomes, such as those harboring multi-


12 
 
drug resistant pathogens, and those with significant comorbidities (e.g. diabetes, renal failure, cancer) 
This is important since it is well documented that these patient subsets are at higher risk13 30-35 40 45-46 of 
healthcare-associated infections and may benefit disproportionately from decolonization. 
 
 
Element #4: CER measures outcomes—both benefits and harms—that are important to patients. 
The ABATE Infection Trial will evaluate not only beneficial outcomes of reducing HAI and the 
readmissions they cause, but also potential harms including whether resistance to the decolonization 
agents (chlorhexidine and mupirocin) is differentially engendered in the two arms of the trial. 
 
 
Element #5: CER employs methods and data sources appropriate for the decision of interest. 
The ABATE Infection Trial uses a cluster-randomized trial and a corporate data warehouse to evaluate 
this question across a large number of hospitals. In addition, it uses routinely available data sources 
that are commonly used to assess HAIs by infection prevention programs in the U.S. and applies them 
in such a way so as to maximize and showcase the use of electronic medical records. 
 
 
Element #6: CER is conducted in settings that are similar to those used in practice. 
The ABATE Infection Trial uses a cluster-randomized trial to mimic the way bathing care is usually 
delivered in hospitals, and the method by which quality improvement is performed in hospitals. In 
addition, this trial will be implemented in a large number of community hospitals, the most common type 
of hospital providing care in the U.S.. 
 
The focus on prevention of HAIs in non-ICU settings is an important area of innovation. Trials to reduce 
infection in non-critical care areas are largely lacking because of the large number of hospitals and patients 
that are needed to address events that occur with low frequency, but affect very large numbers of patients in 
the aggregate. Collectively, patients in non-ICU settings are responsible for the majority of healthcare-
associated infections and any hope for elimination must invest in these critically needed trials to determine the 
best strategy for adoption. This trial will be a first-in-class effort in this important patient safety realm.  
This trial will also make an important contribution to the active debate about when to focus HAI 
prevention strategies on those that target specific pathogens, like MRSA, vs strategies intended to impact 
many pathogens.124 This is an important issue that has both scientific and practical ramifications. The scientific 
issues concern the impact of decolonization on patients’ normal bacterial flora and on the ecology of the 
healthcare systems that employ it. The practical consequences have much to do with the organization and cost 
of medical care.  Our recent trial, the REDUCE MRSA trial125 (see Preliminary Data Sections 5.30.1, 6.10.1-
6.10.2) has definitively opened the door to testing all pathogen approaches in the general patient population. 
 
4.2 Innovation – UH2 Planning Year 
 
In contrast to most trials, the ABATE Infection Trial will recruit community hospitals which provide the 
majority of inpatient care in the U.S.. Thus, our trial will take place in generalizable contexts and produce 
generalizable solutions. Our Health Care System (HCS) collaborator, Hospital Corporation of America (HCA), 
consists of over 160 community hospitals nationwide, structured into 3 regional groups across 20 states. 
Another unique feature is that this trial will demonstrate high efficiency in startup time for a large scale 
cluster-randomized trial. Our previous trial recruited 45 hospitals in 6 weeks. We will again utilize an 
infrastructure that enables rapid recruitment and encourages hospitals to cede ethical review to a lead IRB for 
oversight. This will streamline the approval process and serve as an example for future trials.  
An additional significant innovation includes the development of statistical graphics for cluster-
randomized trials that allow improved strata construction to improve balance among covariates. This is 
important since the number of clusters in such trials is often small enough that imbalance in covariates is more 
likely to occur by chance when compared to large individual randomized trials. Thus, measures to reduce the 
chance of imbalance will be a substantial contribution to the field. 
Finally, our proposal capitalizes on an excellent data warehouse infrastructure to efficiently track patient 
outcomes. We will include not only outcomes of infections occurring during the hospital stay, but also extended 
outcome assessment to the immediate post-discharge period. This design feature is particularly important to 
assess impact across the continuum of healthcare delivery and will be a valuable consideration for other trials.   
 
4.3 Innovation – UH3 Trial Intervention Years 
 
 
This pragmatic clinical trial will advance the nation’s progress toward what the IOM calls a “learning 
healthcare system” - one that both generates and uses evidence to guide clinical decision-making. It will 


13 
 
accomplish this in several ways. First, it will call upon the usual quality improvement infrastructure at each 
intervention hospital to implement our decolonization strategy in a manner familiar to their medical staff. We 
will engage Infection Prevention program managers at each participating hospitals and secure representative 
unit champions. Second, it will create a recognizable electronic module within standard nursing documentation 
that will enable nurses to enter answers to a small number of process and compliance questions. Third, these 
answers will be beneficial not only to the trial, but will also serve to capture essential device utilization data that 
assists with publicly reported metrics. Reports generated from these results will be retrievable by users at the 
participating hospitals. This provides a key example of the use of electronic medical records for research. 
 
Beyond the innovative intervention of using chlorhexidine bathing in non-critical care units to reduce 
HAIs, this trial will be one of the first to ensure that other quality improvement projects adopted by the hospital 
during the baseline and trial period do not interfere with the interpretation of the trial’s effect. The first trial to do 
this was our previous cluster-randomized trial of chlorhexidine bathing in ICU areas. The ABATE Infection Trial 
will assess potential new initiatives on a frequent basis and will proactively request that all participants report 
any new initiatives under consideration so that investigators may determine if a conflict exists. 
We will also generate sharable computer programs related to both trial process and analysis. This 
programming code, employing parallel commercial and open-source code, will be made publicly available. 
5.0 Approach – UH2 Planning Year 
 
5.0.1 Investigative Team – Expertise  
 
We have assembled an impressive multidisciplinary team with expertise in infectious diseases, infection 
prevention and healthcare epidemiology, microbiology, individual and cluster-randomized clinical trials, survey 
design, statistics, and cost analysis. 
 
 
Susan Huang, MD MPH (Co-Principal Investigator) is an Associate Professor of Medicine and the 
Medical Director of Epidemiology & Infection Prevention at the University of California Irvine (UCI) Medical 
Center. She has over a decade of research experience addressing healthcare associated infections (HAIs), 
including multi-drug resistant pathogens, surgical site infections, bloodstream infections, and outbreaks. This is 
coupled with practical experience in leading quality improvement initiatives as a hospital epidemiologist. With 
longstanding expertise in MRSA, Dr. Huang is the lead investigator of two federally-funded randomized clinical 
trials on decolonization strategies. She is the lead investigator for a recently completed 43-hospital cluster-
randomized trial (REDUCE MRSA Trial)125 evaluating ICU decolonization with chlorhexidine and mupirocin. 
She is also the lead investigator for a large individual randomized trial on post-discharge decolonization for 
MRSA carriers (Project CLEAR).126 The ABATE Infection Trial provides the important bridge to study the value 
of decolonization in non-ICU settings. currently co-leads a CDC Prevention Epicenters grant, a research 
consortium for preventing HAI. 
 
Dr. Huang is joined by several experts (Table 1) with combined extensive expertise in HAI research, 
including comparative effectiveness research, clinical trials, and decolonization studies (Table 2). 
 
 
Table 1. Co-Investigators for the ABATE MRSA Infection Trial 
Co-Investigator 
Institution 
Relevant Expertise 
Ed Septimus, MD 
Hospital Corporation of 
America 
Medical Director, Infection Prevention program for national corporation of 
>160 US hospitals; proactive quality improvement programs for MRSA, rapid 
dissemination of best practice 
Mary Hayden, MD  
Rush University Medical 
School 
Associate Professor of Medicine (Infectious Diseases) and 
Pathology/Laboratory Medicine; Expertise in infectious diseases and 
microbiology; use of mupirocin and chlorhexidine for decolonization, 
antimicrobial susceptibility testing 
John Jernigan, MD MS 
Centers for Disease 
Control  
and Prevention (CDC) 
Deputy Director, Division of Healthcare Quality Promotion at CDC, 
Epidemiologist and infectious diseases physician; expertise in MRSA, 
national HAI surveillance, definitions, reporting, outcomes 
Robert Weinstein, MD  
Stroger Hospital of Cook 
County 
Professor and Chair of Medicine, renown expertise in infectious diseases, 
healthcare epidemiology; pioneered chlorhexidine for decolonization 
Ken Kleinman, ScD 
Harvard Medical School/ 
Harvard Pilgrim Health 
Care Inst. 
Associate Professor and biostatistician with longstanding expertise in 
longitudinal and clustered data methods related to infectious diseases, and 
statistical software. Involved with numerous HAI projects, including lead 
statistician for the REDUCE MRSA Trial (ICU decolonization) 
Daniel L. Gillen, PhD 
UC Irvine 
Associate Professor in Statistics; biostatistician and trialist. Lead statistician 
for multiple randomized trials. 


14 
 
Table 2. Investigative Team Expertise in Healthcare Associated Infections 
 
 
 
5.0.2 Investigative Team – Experience in Randomized Controlled Trials 
Our seasoned investigative team are experts in decolonization with relevant experience working 
together in two major clinical trials – the REDUCE MRSA Trial: a 43-hospital cluster-randomized trial of an 
 
RANDOMIZED 
CONTROL TRIAL 
COHORT  
STUDY 
TIME  
SERIES 
MODELING 
SIMULATION 
ECONOMIC 
ANALYSIS 
 MRSA 
 
 
 
 
 
Detection / surveillance 
     Hospitals, 127 128 nursing homes, 129 metrics 35, 130 131 132 
 
X 
 
 
 
Risk factors: adults, 30, 133 134 children 135 
 
X 
 
 
 
Sequelae: pre- and post-discharge 30, 31, 33 
 
X 
 
 
 
Prevention  
      Assessing vaccine targets by antibody response 136    
Active surveillance, isolation on bacteremia, RCT 18, 47, 
137 138 139 140 141 142 
     Environmental cleaning on transmission 143 144 
     ICU Decolonization: 3 arm cluster RCT 19, 125 
     Post-Discharge Decolonization RCT 126 
X 
X 
X 
 
 
Dynamic modeling: inter-facility transmission 145 146 147 148 
 
 
 
X 
 
Cost-effectiveness 149 
 
 
 
 
X 
Guidelines 150 151 
 
 
 
 
 
Resistant Gram Negative Pathogens 
 
 
 
 
 
    Klebsiella pneumoniae carbapenemase 152 153
   Prevention 154 155 
 
X 
 
 
 
 Blood Stream Infection (BSI) 
 
 
 
 
 
Detection / surveillance  
     Adults, 156 157 Neonatal ICU 158 159 
 
X 
 
X 
 
Risk factors: ICU 160 161 
 
X 
 
 
 
Sequelae: ICU 158 
 
X 
 
 
 
Prevention  
     Decolonization 15, 16, 78, 162  
     Transducer cleaning RCT 163 
X 
X 
 
 
 
 Surgical Site Infection (SSI) 
 
 
 
 
 
Develop/validate automated detection algorithms:  
     Inpatient algorithms 164 165 166 167 168 
     Post-discharge algorithms 169 170 171 172 173 174 
 
X 
 
 
 
Risk factors 175 176 177 178 179 
 
X 
 
 
 
Sequelae: 180 181 
 
X 
 
 
 
Prevention 182 183 184 
   Choice of antimicrobial 176 
Intraoperative antibiotic redosing RCT 185 
Antibiotic prophylaxis RCTs 186 187 
X 
X 
 
 
 
Cost-effectiveness 177, 188 
 
 
 
 
X 
 Ventilator Associated Pneumonia (VAP) 
 
 
 
 
 
Detection / surveillance 189 190 191 192 193 
 
X 
 
 
 
Guidelines 194 
 
 
 
 
 
 Catheter Associated UTI (CA-UTI) 
 
 
 
 
 
Detection / surveillance 195 
 
X 
 
 
 
Risk factors 196 
 
X 
 
 
 
Sequelae: Increased mortality 25 
 
X 
 
 
 
Prevention 
Antimicrobial irrigation RCT 197 
Tamper-evident seals RCT 24 
X 
 
 
 
 
Cost-effectiveness 198 
 
 
 
 
X 
Guidelines 199 
 
 
 
 
 
 Hospital Outbreak Detection 
 
 
 
 
 
    Automated algorithms 200 201 
 
X 
 
X 
 


15 
 
critical care MRSA decolonization quality improvement strategy,19 125 and Project CLEAR: a post-discharge 
randomized clinical trial on education vs decolonization for MRSA carriers.126  
The recently completed REDUCE MRSA Trial was a three-way cluster-randomized trial of 43 hospitals 
(74 ICUs) in the Hospital Corporation of America healthsystem - the same healthcare system partner in this 
proposal. The three arms included: 1) screening and isolation (standard of care): nasal screening for MRSA 
followed by isolation if positive, 2) targeted decolonization: screening, followed, if positive, by isolation and 
decolonization with chlorhexidine baths and nasal mupirocin for 5 days, and 3) universal decolonization: 
cessation of screening, universal application of mupirocin for 5 days, and daily chlorhexidine baths. Its primary 
outcome was MRSA clinical cultures with a secondary outcome of all pathogen ICU-associated infection (see 
Section 6.10.2). 
The REDUCE MRSA recruitment, design, implementation tools, data capture methods, and analytic 
processes are highly relevant for this trial of decolonization in non-critical care settings. In addition, the genesis 
of the REDUCE-MRSA trial was based upon extensive, long-term collaboration between the CDC public health 
experts, academic investigators, and HCA clinical leaders.  
Project CLEAR is an ongoing chlorhexidine and mupirocin decolonization trial that enrolls MRSA+ 
patients upon hospital discharge to either education or serial decolonization. Its primary outcomes are MRSA 
infection and readmission and its implementation tools will also be translatable to the ABATE Infection Trial. 
 
5.0.3 Health Care System (HCS) Partners 
Our healthcare system partner is Hospital Corporation of America (HCA) and its three regional U.S. 
Groups. As one of the largest providers of healthcare in the U.S., HCA provides 5% of all major acute care 
services in the U.S. though its 160+ hospitals. HCA has conducted several successful infection prevention 
campaigns at the Group and Corporate level, including MRSA screening of high risk patients, antibiotic 
stewardship, and prevention bundles for central line associated infections. HCA has also actively engaged in 
several research studies, especially those in the arena of HAI. They have been the healthcare system partner 
for multi-center studies on reducing central line-associated bloodstream infections and surgical site infections, 
in addition to the REDUCE MRSA Trial. Key highlights of the HCA system include: 
 
 
Corporate Structure for Infection Prevention/Quality 
Our HCA co-investigators are the corporate leaders of Quality, Performance Improvement, and Infection 
Prevention who lead all regional and system-wide initiatives in this arena. HCA will contribute the highly 
experienced services of Dr. Edward Septimus (HCA site principal investigator, Medical Director of Infection 
Prevention and Epidemiology), Jason Hickok, MBA RN (Associate Vice President, Patient Safety and 
Infection Prevention), and Julia Moody, MS (Clinical Director of Infection Prevention and Epidemiology) to 
pursue this second major cluster-randomized trial with study investigators. This group has previously 
provided key collaborative coordination and implementation support for the REDUCE MRSA Trial. 
 
 
Corporate/Group/Divisional Structure for Recruitment  
HCA hospitals are divided into the National Group, Central Group, and the Southwest Group. The Groups 
consist of 50+ hospitals and are governed by a Group President and Chief Financial Officer. New quality 
improvement initiatives are determined and implemented at the Group level. In the ABATE Infection Trial, 
we will leverage the leadership from all three Groups to ensure adequate and rapid recruitment.  
 
 
Corporate Data Warehouse  
HCA has established a corporate data warehouse that includes highly standardized hospital and unit 
census information, patient demographic data, admission and discharge information, billable charges, 
including coded diagnostic and procedure codes from each admission, and supply chain data. In addition, 
laboratory and microbiology data are also stored in the HCA data warehouse. Data flow continuously from 
each hospital to the central database where HCA Information Technology teams can access, retrieve, 
standardize, and analyze clinical data as needed for this proposal and other needs.  
 
 
Corporate Information Technology and Services (IT&S) 
HCA’s IT&S sector consists of 3,605 employees nationwide. Corporate IT&S is located in Nashville, TN 
where decision support programmer and analytic teams build and maintain HCA’s clinical documentation 
system and data warehouse. HCA’s IT&S sector has previously provided key elements for research trial 
needs, including computer based training modules, pharmacy logic to target products to participating units, 
nursing queries for daily documentation of trial protocols, and patient-level data for analysis. 
 
 


16 
 
 
Corporate Supply Chain  
HCA Supply Chain Services (SCS) coordinates products and supplies to its facilities. For trial purposes, 
HCA’s SCS is able to channel products and divert products that may be incompatible with trial products. 
They also assess product volume needs in real time and ensure proper garnering of necessary supplies.  
 
 
Corporate Regulatory/Compliance for Trial Assistance  
HCA’s corporate regulatory affairs and risk management office (David Vulcano MBA, Associate Vice  
President for Clinical Research) will work closely with a lead investigator IRB to provide coordinated central 
IRB approval for participating hospitals. This office is familiar with the regulatory process of clinical trials 
and has facilitated FWA, IRB, and required research training necessary for hospitals to either submit their 
own IRB or cede to an investigator IRB in order to participate in cluster-randomized trials.  
 
 
Corporate Financial Services Group 
HCA’s Financial Services Group handles conducts value analyses and cost-effectiveness analyses on the 
Group and corporate level and will be a valuable resource for this proposal.   
 
HCA provides an ideal test bed for a cluster-randomized trial of hospitals to reduce HAI. Its community hospital 
base provides generalizability to U.S. hospitals and its centralized infrastructure allows efficient implementation 
and data collection across participating hospitals. Importantly, HCA has pursued initiatives to ensure best 
practice standards so that this trial would evaluate improvement strategies over current gold standard care.  
 
5.10.0 Aim 1: Recruit hospitals for a cluster-randomized trial of 1) routine daily bathing with 
chlorhexidine for all patients plus 2) routine nasal decolonization with mupirocin for patients harboring 
methicillin-resistant Staphylococcus aureus (MRSA) in general medical and surgical units to reduce 
infection and readmissions. 
 
It is important to improve the speed and efficiency of clinical trials to address urgent needs for 
evidence-based practice. The ABATE Infection trial will use a novel, efficient process to recruit hospitals to 
participate in this cluster-randomized trial. We will focus on recruiting community hospitals, to increase the 
relevance of our research to the U.S. healthcare system.  
 
5.10.1 Preliminary Data 
 
As mentioned above, we recently completed a 43-hospital 16-state cluster-randomized trial of 
decolonization in ICUs. Recruitment for that study was accomplished within 6 weeks. Recruitment activities 
leveraged HCA’s managerial leadership and communications systems to inform hospital decision makers 
(chief executive officers, chief medical officers, chief nursing officers, ICU directors) about the trial and to 
advocate for participation. The trial also used corporate survey systems to assess eligibility.  
Specifically, HCA’s 3 regional Groups supported recruitment. Each Group governs quality improvement 
initiatives for its 50+ hospitals. Requests for participation were endorsed by the President of each Group. Each 
respondent was given a survey to confirm inclusion criteria were met, including minimal use of decolonization 
products. Participation was confirmed by a signed letter of participation from hospital administration.  
 
5.10.2 Recruitment Infrastructure and Process 
We will leverage similar systems to recruit 54 hospitals for the ABATE Infection Trial. First, we will use 
email and provide webinars through HCA’s webhosting system to introduce the trial and the process for 
participation. Second, solicitations for hospital participation will be extended to all 160 HCA hospitals through 
the three regional Group Presidents with corporate HCA support (see letter of support). Third, our HCA co-
investigators, who are the system-wide leaders of quality, performance improvement, and infection prevention, 
will provide direct-to-hospital endorsement for the trial. Finally, we will directly reach out to hospital participants 
of our previous trial. Recruitment announcements will utilize usual corporate and regional HCA communication 
channels and will be directed at hospital leadership (Chief Executive Officer, Chief Medical Officer, Chief 
Nursing Officer) and infection prevention program directors.  
 
5.10.3 Criteria and Survey for Eligibility 
 
Enrollment criteria for hospitals include a) being a licensed acute care U.S. hospital belonging to 
Hospital Corporation of America, and b) willingness to be randomized at the hospital-level to either usual 
bathing care or decolonization. Within hospitals, units that were eligible for participation included a) 
participating adult units of the following types: adult step-down (transitional care after leaving an ICU), medical, 
surgical, medical/surgical, and oncology non-critical care units, b) minimal current use of routine daily 
chlorhexidine bathing in these units (<30% of patients), c) less than 30% of unit patients undergoing cardiac or 


17 
 
arthroplastic surgery, and d) average length of stay of at least 2 days. Among eligible units, we will allow for 
national standards for pre-operative bathing with chlorhexidine and pre-operative S. aureus decolonization for 
cardiac and arthroplastic surgery.  In addition, hospitals must confirm that they will avoid other discretionary 
infection prevention and quality initiatives for the duration of the trial, both the 1-year baseline and 18-month 
intervention period, (NOTE: intervention was extended to a 21-month trial in June 2015). The baseline period 
begins in the planning year and extends into the first half of the UH3 trial phase. Further details of the trial 
design, including rationale for the baseline observation period and timeline are found in UH3 Aim 3, Section 
6.10.4. 
We will exclude pediatric and specialty units, including psychiatry, obstetrics/post-partum, bone marrow 
transplant, and rehabilitation/skilled nursing units. Adult units that occasionally have children <12 are 
permissible, but children <12 admitted to participating units will not be included in the mupirocin protocol. 
 
For each potential hospital participant, enrollment criteria will be assessed by administering an 
electronic survey to each eligible unit’s champion as well as through the evaluation of baseline administrative 
data from the HCA data warehouse. The survey will additionally request contact information and details about 
infection prevention practices, bathing practices, and use of chlorhexidine and mupirocin. As done previously, 
surveys will be administered through HCA’s usual survey channels and results will be compiled and returned to 
investigators in a database format. An example survey from our previous REDUCE MRSA trial is provided in 
Appendix A. 
 
5.10.4 Limitations and Planned Solutions 
 
The main limitation of this recruitment aim is the potential failure to recruit 50 hospitals. While we 
believe this is unlikely due to our previous trial of 43 HCA hospitals, should enrollment be less than 50, we will 
pursue an elongation of the 18-month trial, (NOTE: intervention was extended to a 21-month trial in June 
2015).  
 
5.10.5 Milestones and Timeline 
Recruitment will occur in the first quarter of the UH2 planning year. A full description of UH2 milestones 
and timeline is found below in Section 5.60.0. 
 
5.20.0 Aim 2: Obtain Institutional Review Board (IRB) approval for the cluster-randomized trial and 
maximize efficiency by having most hospitals cede to a central IRB.  
 
The current standard of engaging institutional review board (IRB) processes at each participating 
hospital is a substantial hurdle for multi-center studies, particularly those involving dozens of sites, as this 
proposed study does.  In fact, even minor modifications required by different IRBs may substantially delay or 
even prevent standardization across a trial, without measurable improvements to patient safety.202 203 In 
addition, requiring institution-specific IRB review for multi-center trials has been associated with lower 
participation rates and highly redundant effort.204  Additionally, smaller and community-based hospitals often 
lack the resources, expertise and infrastructure to maintain local IRB committees. Outsourcing to commercial 
IRBs is a costly option that may deter facilities from participating in worthwhile research opportunities. These 
factors contribute to slower, smaller, less representative trials.  
 
5.20.1 Preliminary Data – Experience with Centralized IRB Coordination 
 
We have relevant experience of IRB coordination from our REDUCE MRSA cluster-randomized trial. 
The Harvard Pilgrim Health Care (HPHC) IRB, in conjunction with HCA’s corporate regulatory affairs liaison, 
coordinated the IRB approval of 43 hospitals. Efficient approval was realized by having 38 of 43 IRBs cede to 
the HPHC IRB, a process that also included helping hospitals with no Federal Wide Assurance number obtain 
one. Human protection training requirements were set by HPHC for all ceded sites. Changes to the study 
protocol received streamlined review by the HPHC IRB and the other 4 IRBs that governed the remaining 5 
hospitals. In addition, HCA corporate and the CDC, which co-funded the trial with the Agency for Healthcare 
Research and Quality, both delegated IRB oversight to the HPHC IRB through IRB Authorization Agreements.   
 
The HPHC IRB has 16-years of experience governing a breadth of research studies, including cluster-
randomized trials, multi-site intervention and observational cohort studies, genetic typing studies, studies 
involving the collection of bacterial strains, and studies involving large administrative databases. Many of its 
research studies have included quality improvement innovations, a critical expertise for this trial. As mentioned 
above in Section 2.4, IRBs that govern trials of quality improvement strategies must be familiar with the 


18 
 
intersection of research inquiry and routine healthcare improvement processes, and with the national guidance 
related to a waiver of documented informed consent.117 118  
 
For the REDUCE MRSA trial, the HPHC IRB determined that a decolonization initiative for ICU patients 
was minimal risk. It further waived documentation of informed consent based upon regulatory criteria under 45 
CFR 46.116(d), 117(c) (2) and 56.109(c) (1).(38). Posted notices describing the trial and arm-specific details 
were required in all patient rooms (see Appendix B). The HPHC IRB also required hospital infection 
prevention programs to attest to performing surveillance for healthcare associated infections during the trial 
and to inform the study team of unusual rates or types of ICU infections.  
 
5.20.2 Centralized IRB Coordination for the ABATE Infection Trial 
 
We will employ a similar centralized IRB process for the ABATE Infection Trial. The HPHC IRB, in 
conjunction with HCA’s corporate regulatory affairs and risk management office (David Vulcano MBA, 
Associate Vice President for Clinical Research), will provide central oversight for this trial and will facilitate 
delegation of IRB governance from participating hospitals and assurance of human subjects training through a 
custom-tailored “Human Subjects Protection Training for ABATE Site Research Liaisons” that was co-
developed by HCA and HPHC.  This training will be offered via the centralized HCA HealthStream training 
module, which allows for real-time facility/individual completion tracking. Since the ABATE Infection Trial 
involves a similar decolonization intervention to the REDUCE MRSA Trial, except in a lower risk non-critical 
care population, we anticipate that this trial will also have documentation of informed consent waived based 
upon regulatory criteria under 45 CFR 46.116(d), 117(c) (2) and 56.109(c) (1).(38) since 1) trial activities meet 
minimal risk criteria, 2) the trial randomizes hospitals, not patients, 3) all assigned activities will be performed 
under standard hospital quality improvement procedures, and 4) some US hospitals have implemented similar 
quality improvement protocols despite lack of definitive evidence on best practice. We will collect attestations 
from hospital infection prevention programs stating that they will continue to conduct routine surveillance for 
HAIs in all participating units and will inform the trial team of any unusual trends or occurrences.  
 
While we have optimized ceding and delegation procedures for this study, we believe the foundational 
work that we and our IRB have done has general utility.117 We would be very willing to share our experience 
and materials with other UH2/UH3 programs and with Collaboratory leadership. 
 
5.20.3 Limitations and Planned Solutions 
 
Despite our structured approach and HCA corporate support of ceding IRB oversight to the HPHC IRB, 
it will be the prerogative of participating hospitals to use their own IRBs. For hospitals that choose to do so, we 
will coordinate standardized IRB protocols and all subsequent modifications during the trial.  
 
5.20.4 Milestones and Timeline 
IRB approval for all participating hospitals will occur in the second quarter of the UH2 planning year. A 
full description of UH2 milestones and timeline is found below in Section 5.60.0. 
 
5.30 Aim 3: Develop trial educational materials and routine electronic nursing queries for daily bathing 
and decolonization in general medical and surgical inpatient areas. 
 
In the planning year, we will develop all trial materials for hospital staff and patients, as well as 
compliance assessment tools and processes for the phase-in and intervention periods.  
 
5.30.1 Preliminary Data 
We have previously developed materials for two chlorhexidine and mupirocin decolonization trials (see 
description in Section 5.0.2). The REDUCE MRSA Trial produced materials for ICU staff and Project CLEAR 
produced materials for patients following hospital discharge. These materials contribute to the proposed 
protocol since our ICU materials involved educating staff for decolonizing sedated patients, and our post-
discharge materials involved direct-to-patient education for decolonization at home. The ABATE Infection Trial 
will need materials for staff whose patients are awake, and often ambulatory.  
Relevant materials from the REDUCE MRSA Trial are found in Appendix C, and include a 1) 
PowerPoint presentation introducing the trial to participating hospital leadership and unit champions, 2) 
computer based training modules for frontline staff, 3) a nursing decolonization protocol, 4) “Just in Time” 
training materials for registry (temporary/float) nurses, 5) frequently asked questions by staff, 6) talking points 
for staff to patients, 7) adverse event forms and training documents, and 8) a brief daily electronic nursing 
documentation module for assessing chlorhexidine and mupirocin compliance in ICU patients, as well as 
documenting reasons for non-use (Appendix C); this was incorporated into HCA’s centralized system for daily 


19 
 
documentation of nursing care. Relevant materials from the Project CLEAR Trial include 1) bathing and 
showering instructions for patients, 2) frequently asked questions by patients, 3) adverse events information, 
and 4) a quick reference guide (Appendix D). 
In addition, during the REDUCE MRSA trial, we collected MRSA isolates from all participating hospitals. 
To do this, we created laboratory toolkits (Appendix E), which included 1) detailed instructions, 2) a Frequently 
Asked Questions (FAQ) sheet, 3) collection log sheets, 4) deidentified study ID labels, and 5) a quick reference 
wall chart (Appendix F) providing a streamlined diagram of the key elements for collection. Details were 
reviewed in laboratory-specific coaching calls (Appendix G). 
 
5.30.2 Development of Hospital Trial Materials for Participating Units 
The ABATE Infection Trial materials will be modeled after those developed for the REDUCE MRSA and 
Project CLEAR Trials. Here, we describe the materials that will be created in the planning year. The manner in 
which these materials will be used is described in detail in UH3 Aim 1 (see Section 6.10.8 and 6.10.9).  
Study arm-specific binders will be developed for distribution after randomization (see Section 6.10.5). 
These will be distributed to each hospital’s leadership, infection prevention program liaisons, and the nurse 
educator, nurse manager, and medical director of participating units. We anticipate 50 hospitals with an 
average of 4 participating units each, for a total of 14 binders per hospital, or 700 total binders (350 per arm). 
Binder contents are detailed in Table 3. In addition to distributing paper copies of this binder by mail, we will 
also use HCA’s intranet (Atlas) to post these trial-specific materials. Similar to our prior trial, study arm-specific 
intranet sites will be created; each site will have access only to material about its assigned treatment regimen.  
 
Table 3. Contents of ABATE Infection Trial Toolkit Binders 
Educational Material
Description
1. 
Welcome and Summary of Goals 
Introductory information on the trial 
2. 
Study Investigators 
Lists all investigators and collaborators involved in the trial  
3. 
Table of Contents 
Summary of documents included 
4. 
Phone Matrix 
Contact information for lead investigators, HCA co-investigators, and study staff 
5. 
Frequently Asked Questions* 
Answers to common staff questions about the trial or protocol 
6. 
Patient Talking Points* 
Talking points for common patient questions about the trial or protocol 
7. 
Dos and Don’ts*  
Quick reference guide on protocol 
8. 
Kick-Off Protocol PowerPoint* 
PowerPoint slides used in the kick-off webinar 
9. 
Computer Based Training* 
Printout of protocol training module 
10. Just in Time Training* 
On-the-spot training and reference guide for temporary/float nurses 
11. Patient Instructions 
Patient instruction sheets for self-bathing/showering with chlorhexidine 
12. Nursing Protocol** 
Nursing protocol for use of chlorhexidine and mupirocin 
13. Study Related Events Form** 
Forms for reporting study related adverse events  
14. Technical Document* 
Instructions on setting up nursing protocol and pharmacy orders 
*Indicates arm specific information (control vs. intervention arm) 
** Decolonization arm only 
 
HCA routinely uses computer based training (CBT) modules to ensure completion of required health 
learning activities. The system (HealthStream) allows selective assignment to specific hospitals, units, and job 
types throughout HCA hospitals. We will develop arm-specific CBT modules to be completed by all frontline 
staff who work in participating units. The CBTs will focus on the rationale behind the trial, review protocol 
details related to the assigned treatment arm, and make clear that the assigned treatment is routine practice 
for the hospital for the duration of the trial. Compliance will be tracked locally and centrally by HCA’s Director of 
Clinical Education Development, who will provide detailed reports to HCA co-investigators.  
 
5.30.3 Development of Electronic Nursing Documentation Modules 
 
HCA uses a centralized system (MEDITECH) for documentation of nursing care. We will create arm-
specific electronic MEDITECH documentation screens that will require nurses to answer questions each 
daytime and evening shift about whether each of their patients was bathed with chlorhexidine and whether 
nasal mupirocin was administered. In the Usual Care Arm, if a chlorhexidine bath or shower was provided 
(non-compliance with protocol), it will require one of the following reasons (e.g. pre-operative bathing, 
physician order, or free-text response). In contrast, in the Decolonization Arm, if chlorhexidine is not given 
(non-compliance with protocol), it will similarly require one of a different set of reasons (e.g. patient allergic, 
patient refused, physician refused, or free-text response). An example from our prior trial is found in Appendix 
C. These responses will be tracked for monthly compliance assessments in both arms.  
 
 


20 
 
5.30.4 Development of Laboratory Materials for Collecting Bacterial Isolates and Launch of Baseline 
Strain Collection 
 
In the planning year, we will also develop materials for the microbiology laboratories of participating 
hospitals to begin to collect bacterial strains throughout the trial. The reason for collection is to assess whether 
universal decolonization engenders resistance to decolonizing agents. Requested strains will include MRSA as 
well as select gram negative bacteria in which resistance to chlorhexidine has been reported. Laboratories will 
collect, batch, and send bacterial isolates on a monthly basis using the same protocol regardless of arm. Full 
details are provided in UH3 Aim 2, but the development of materials is described here. 
We will create a PowerPoint presentation for an introductory webinar to participating laboratories at the 
beginning of strain collection. This presentation will introduce laboratory staff to study staff and detail the entire 
process for de-identified strain collection. This will be followed by mailed laboratory toolkits (see example from 
prior trial, Appendix E), which will include 1) a Step-by-Step instruction sheet, 2) a Frequently Asked 
Questions (FAQ) sheet, 3) collection log sheets with a clipboard, 4) de-identified study ID labels, 5) FedEx 
mailing instruction sheets, 6) a shipment packing list, 7) a shipping schedule, and 8) a quick reference wall 
chart with a streamlined diagram of key elements for collection (see example from prior trial, Appendix F).  
In order to ensure that the volume of MRSA+ and gram-negative rod (GNR) isolates that are collected 
during the baseline period are sufficient to support analysis around whether or not study agents engendered 
resistance, it will be crucial to obtain a full 12-months worth of isolates.  It is anticipated that participating 
ABATE Infection Study hospital laboratories will begin collecting and banking isolates that fit study criteria 
beginning 3/01/2013 and continue collection through 2/28/2014 in order to support this activity.  Collected 
isolates will include MRSA+ and the following eight select GNR organisms.  Isolates will have already been 
collected as part of a patient’s routine medical care. 
E. coli  
P. aeruginosa  
K. pneumoniae, K. oxytoca 
A. baumannii  
P. mirabilis  
S. maltophilia  
S. marcescens  
Burkholderia spp.  
 
Once full IRB approval is obtained (either via ceding to the HPHC IRB or pursuing individual review) at 
each participating hospital during the baseline year (see Section 5.20.0), isolates may then be shipped to the 
central study laboratory at Rush University, Chicago for analysis and storage.   
 
5.30.5 Limitations and Planned Solutions 
 
The main limitation to this aim would be insufficient development and preparation time. Since similar 
materials exist from our prior trials, we anticipate rapid development and assembly.  
 
5.30.6 Milestones and Timeline 
All laboratory trial materials will be developed during the first half of the planning year, prior to initiating 
baseline collection of isolates. Hospital trial materials will be developed in the latter half of the planning year.   
A full description of UH2 milestones and timeline is found below in Section 5.60.0. 
 
5.40.0 Aim 4: Obtain baseline data on HAI infection rates for participating hospitals to lay the 
foundation for trial outcomes. These will be obtained using corporate data warehouse capabilities of 
Hospital Corporation of America (HCA).  
 
During the planning year, we will work with the NIH Collaboratory to develop and validate our electronic 
methods and tools for data sampling. We will extract data from the centralized data warehouses of HCA and 
develop adequate control measures to ensure high quality data extraction and cleaning methods. 
 
5.40.1 Preliminary Data 
We have had extensive experience with data from HCA’s centralized data warehouse through our 
previous trial. The REDUCE MRSA Trial evaluated similar outcomes (clinical cultures and all-cause 
bloodstream events) in ICU locations. Data streams familiar to this investigative group include hospital census, 
microbiology, pharmacy, supply chain, MEDITECH documentation, and administrative data.  
 
 
 


21 
 
5.40.2 Hospital Corporation of America Centralized Data Warehouse 
The following data elements (Table 4) will be requested from HCA’s Information Technology team to 
sample patient descriptors and baseline primary outcomes and ensure adequate processes for high quality 
data pulls. For details about trial design, outcomes, and analysis, see UH3 Aim 1 (Section 6.10). 
 
Table 4. Data Elements Requested from HCA’s Centralized Data Warehouse 
Source Data 
Desired Results 
Elements 
Primary Outcome
Census Data 
Adult patient days by unit 
Readmissions within 30 days 
Patient identifier (coded), hospital, unit location, hospital and unit 
admission date, hospital and unit discharge date, age in years, gender 
Microbiology 
Finalized Results 
Sterile site cultures, all 
pathogens 
Pathogen name (if culture is positive), patient identifier (coded), date 
of collection, body site of collection, antimicrobial susceptibility, 
quantification of urine culture count (e.g. >50,000 colonies) 
Claims data  
Identification of infectious 
readmissions 
Diagnosis codes (ICD9) 
Procedure codes (ICD9/CPT codes)  
Descriptors
Claims data  
Identification of comorbidities 
Use as case mix adjustors 
Diagnosis codes (ICD9) 
Pharmacy 
Mupirocin dispensing 
Patient identifier (coded), date range dispensed, unit location 
Supply Chain 
Chlorhexidine use 
Patient identifier (coded), date used, unit location 
 
5.40.3 Data Access – Virtual Machine 
To maximally protect the large volume of protected health information required for the ABATE Infection 
Trial, HCA will establish a mechanism similar to our previous trial where our programmer analysts gain access 
to HCA data behind the HCA firewall. Programmer analysts will receive remote access to a virtual machine 
where requested data will be placed after extraction by HCA’s information technologists. This extraction 
process will involve replacement of names and medical record numbers with coded identifiers. Programmers 
will access this virtual machine to clean and analyze data, and to generate summary level output for review 
with our statistician. All data cleaning and analysis will be performed using SAS 9.3 (SAS, Cary, NC). 
 
5.40.4 Data Validation, Cleaning and Calculation of Baseline Outcome Data 
We will use data from March 1, 2013-Feb 28, 2014 as the baseline data for our study cohort. This 
requires exclusion of the first two days in a hospital unit (CDC criteria).206 We will describe their age, gender, 
and comorbidity characteristics based upon ICD-9 diagnostic codes. We will compare these characteristics 
across units and across hospitals and identify any outliers in the data by looking for unexpected month to 
month variation or substantial missing data across hospitals and units. Data discrepancies will result in 
discussions with HCA co-investigators and information technologists for repeat data pulls and examination of 
data for errors in coding or variations in data storage. 
We will evaluate primary outcomes in the baseline period by identifying isolation of a gram-positive 
multi-drug resistant organism (MRSA or VRE) from a clinical culture among the patients who meet eligibility 
criteria. Secondary outcomes derived from the HCA data warehouse will include isolation of a gram-negative 
multi-drug resistant organism from a clinical culture, all-cause bloodstream infection, blood culture 
contaminants, urinary tract infection (with an a priori expectation of greater impact in men than women due to 
endogenous infection), C. difficile infection, 30-day infectious readmissions and all-cause readmissions, and 
emergence of chlorhexidine and mupirocin resistance in bacteria isolated from participating units. 
Using CDC criteria, blood cultures due to skin commensals will require at least two cultures within 2 
calendar days to quality as a sterile site event.206 Urine cultures will be required to have at last 50,000 colony 
counts. Outcomes will be calculated as events per 10,000 patient days, and similar evaluations for data 
discrepancies or missing data will be performed. 
5.40.41 Data Standardization 
 
Currently, microbiology data from HCA facilities is found in a single system (Meditech). However, 
Meditech allows for four methods of reporting microbiology results, including a form of free text. In response to 
excessive data cleaning requirements to standardize across facilities, the investigative team and the HCA 
corporate laboratory services leadership instituted the standardization of reported microbiology results across 
the ABATE facilities. This decision was driven in large part by the perceived value of this direction for corporate 
HCA. ABATE Trial facilities are considered a pilot roll out of what will ultimately be a corporate-wide 
microbiology laboratory standardization campaign. 


22 
 
 
Microbiology laboratories serving facilities in the ABATE Infection Trial were notified of the requirement 
to report microbiology results via a single acceptable reporting mechanism in March 2013, one month prior to 
the start of baseline data collection. The selected mechanism allows for maximal electronic parsing of data into 
required elements for determining microbiologic outcomes. As of March 2013, only 30% of participating 
hospitals were using this desired mechanism for reporting culture results. By June 2013, half of participating 
hospitals had fully converted to this mechanism, and by early September 2013, 91% (50/54) of facilities had 
converted. Previously used methods of robust data cleaning and standardization will be required from the start 
of the baseline period until complete adoption for all hospitals and we are well-versed in this process. 
5.40.42 Data Creation 
Since tracking of patient bathing is not routinely done in U.S. hospitals, a single nursing query was 
created to be completed once daily asking if a bath/shower was completed in the past 24 hours and whether 
the bath/shower involved chlorhexidine. If a bath/shower was not provided (regardless of use of chlorhexidine), 
the prompt requires a reason by providing common drop down options or free text entry. This bathing query 
was activated in March 2013 and is in use at all participating hospitals. 
 
5.40.5 Limitations and Planned Solutions 
Should data barriers fail to be resolved for specific hospitals, we will evaluate the possibility of obtaining 
needed data variables directly from the facility rather than the data warehouse. If missing or insufficient data 
cannot be resolved, we will note the time period and exclude the hospital from analysis for that period of time. 
We note that this has not been the case for previous trial evaluations using HCA data. 
 
5.40.6 Milestones and Timeline 
During the planning year, HCA data will be extracted for 4-months of the baseline period.  A full 
description of UH2 milestones and timeline is found below in Section 5.60.0. 
 
5.50.0 Collaboratory Work Groups  
 
 
Throughout the planning and intervention phases, we will work closely with the NIH Collaboratory 
Coordinating Center to align study design, implementation processes, data extraction and quality control plans, 
and confirmation of study outcomes. Our lead investigators will actively participate in the HCS Research 
Collaboratory Steering Committee, and members of our investigative team will actively participate in HCS 
Research Collaboratory Work Groups established by the Collaboratory Coordinating Center as follows: 
 
Table 5. Collaboratory Work Group Members 
Collaboratory Work Group
Investigative Team Member(s)
Electronic Health Records (EHR)
Adrijana Gombosev (Project Coordinator)  
Regulatory/Ethics 
Julie Lankiewicz, MPH (Project Coordinator), Sheila Fireman, JD (IRB Administrator, 
HPHC), and David Vulcano MBA, (Associate Vice President for Clinical Research, HCA) 
Biostatistics/Study Design 
Ken Kleinman, ScD (Lead biostatistician), Daniel Gillen, PhD (biostatistician). Extensive 
experience in clinical trials, including HAI and cluster-randomized trial experience. 
Provider-Health Systems 
Interactions 
Adrijana Gombosev (Project Coordinator), Susan Huang, MD MPH (PI) 
Stakeholder Engagement
Edward Septimus, MD (Site PI, HCA) 
Collaboratory Work Group
Investigative Team Member(s)
 
5.60.0 UH2 Planning Phase Milestones and Timeline  
 
 
We summarize our milestones for this 1-year planning phase (Table 6). Each is described with 
concrete deliverables and its contribution to the UH3 intervention phase.  
 
Table 6. Planning Year Milestones and Timeline 
UH2 Milestone 
Details / Purpose 
Timeline
Q1
Q2
Q3
Q4
Aim 1: Recruitment
 
 
 
 
 
1. Call for Participation 
Identify interested hospitals for ABATE Infection Trial 
x 
 
 
 
2. Deliver Survey for Unit Eligibility 
Tool to assess 1) stable infection control practices and  
2) sufficiently low use of trial intervention products 
x 
 
 
 
3. Administer Survey for Eligibility 
Exclusion of ineligible units within hospitals  
x 
 
 
 
4. Finalize Letters of Participation 
Final set of hospital and unit participants identified for trial 
x 
 
 
 
Aim 2: IRB  
 
 
 
 
 
1. Lead Institution IRB Approval 
Central IRB application and approval obtained 
x 
 
 
 
2. Active IRB Ceding of Participants 
Streamlined process for transferring primary IRB responsibility  
 
x 
x 
 
 
 
 
 
 
 


23 
 
3. Deliver Adverse Events Forms 
Reporting forms for monitoring study related events 
 
 
 
x 
Aim 3: Develop Study Materials 
 
 
 
 
 
1. Finalize Trial Timeline w CCC 
Confirm plans for baseline and intervention periods, launch dates 
 
 
 
 
2. Host Kick-Off Webinar 
Develop and host PowerPoint to introduce process and protocol  
 
x 
 
 
3. Launch Bathing e-Documentation 
Develop (Q2) and activate (Q3 start) MEDITECH e-
documentation nursing for all participating units to assess bathing 
frequency and products 
 
 
x 
x 
4. Deliver Complete Trial Toolkit 
Develop nursing protocol, FAQs, patient talking points, computer 
based training module, bathing/showering instruction sheets 
 
 
 
x 
5. Host Lab Introductory Webinar 
Develop and host PowerPoint to introduce collection protocol 
 
x 
 
 
6. Deliver Complete Microbiology 
Collection Toolkit 
Develop strain collection instruction sheet, inventory sheet, FAQs, 
packing instructions, log sheets  
 
x 
 
 
7. Launch Strain Collection 
Begin baseline strain collection to assess resistance 
 
 
x 
 
Aim 4: Data Sampling  
 
 
 
 
x 
1. Establish remote access to HCA 
virtual machine 
Set up structure for analytic access to HCA data behind their 
firewall for all trial analysis 
 
 
 
x 
2. Extract sample census and 
outcome variables 
Demonstration of data extraction and cleaning process to CCC 
related to accessibility, process, quality control, programming 
 
 
 
x 
3. Sample baseline outcomes 
Calculate baseline outcomes using a sample of data from 
baseline time period. This will enable confirmation of projected 
baseline outcomes and confirmation of adequate power for trial 
 
 
 
x 
4. Finalize trial outcomes with CCC 
Based upon baseline sample, finalize all trial outcomes 
 
 
 
x 
 
 
Together, these milestones will perform five critical functions to enable the launch of the trial in the UH3 
intervention phase. First, we will identify and finalize the 54 participating hospitals and their intervention units. 
Second, we will achieve IRB approval at all sites for trial launch. Third, we will initiate the baseline period, 
which will include collection of baseline bacterial strains from all participating hospitals and will assess baseline 
bathing/showering frequencies in all units. Fourth, we will develop all main trial materials and toolkits in 
preparation for intervention launch. Fifth, we will demonstrate the ability to pull a sample of baseline data and 
outcomes. This will enable us to demonstrate the feasibility and efficiency of our data processes, identify data 
cleaning and quality control routines, and confirm power estimates for the study using actual baseline data. 
The achievement of these milestones will enable a smooth transition into the UC3 trial phase, which includes 
the second part of the baseline period plus the phase-in and intervention periods. 
 
5.70.0 UH2 Planning Phase Summary 
This UH2 proposal provides the foundation for a critically needed trial to reduce the majority of 
healthcare-associated infections which are occurring outside of ICU settings. Despite large successes in the 
ICU arena in reducing healthcare associated infections (HAIs), these gains have not translated to reductions in 
the greater number of non-critical care units in hospitals. This trial will enable the testing of a decolonization 
protocol that can be applied during routine bathing and showering processes to assess its ability to reduce 
HAIs for the vast majority of hospitalized patients. 
 
6.0 Approach – UH3 Trial Intervention Phase 
 
 
6.0.1 Estimated US Impact 
HAIs remain a top 10 cause of death in the U.S. despite marked reductions in ICU infections. The 
ABATE Infection Trial (Addressing Bioburden while Admitted To Eliminate Infection) seeks to further national 
gains by targeting the majority of HAIs which occur in non-ICU medical, surgical, and oncology units each year, 
including readmissions due to HAI occurring shortly after discharge.1 Our goal is to reduce a large part of the 
1.7 million HAIs that occur each year by testing decolonization strategies that reduce the risk for all patients. If 
successful, routine decolonization through daily bathing with chlorhexidine would become best practice for 
preventing infection among 40 million patients hospitalized each year, and would substantially reduce the $6.5 
billion dollars of direct healthcare costs associated with HAIs in the U.S. alone.  
 
6.0.2 Hypothesis 
We hypothesize that daily bathing and showering of non-critical care hospitalized patients with 
chlorhexidine will meaningfully reduce healthcare-associated gram positive multi-drug resistant bacteria 
(MRSA and VRE). We further hypothesize that it will also reduce other healthcare associated events such as 
gram negative multi-drug resistant pathogens, all cause bloodstream infection, urinary tract infection, C difficile 
infection, blood culture contamination, and readmissions. This intervention strategy will be compared to routine 
bathing and showering care in 50 community hospitals.   


24 
 
 
6.10 Aim 1: Conduct a 50+-hospital cluster-randomized controlled trial of routine chlorhexidine bathing 
and selective MRSA decolonization versus standard-of-care practices for all hospitalized patients in 
non-critical care adult medical, surgical, and oncology units. The primary outcome will be clinical 
burden of gram-positive multi-drug resistant organisms. Secondary outcomes will include clinical 
burden of gram-negative multi-drug resistant organisms, bloodstream infections, and readmissions 
caused by infection. 
 
This aim describes the study design, implementation, and analysis of a large cluster-randomized trial to 
assess whether universal chlorhexidine bathing plus selective MRSA decolonization will significantly reduce 
sterile site infections and infectious readmissions in patients on non-critical care units. This study will illustrate 
the efficiency of cluster randomization in a network environment for evaluating the effectiveness of decreasing 
bioburden for HAI reduction. Salient features include minimizing the burden of implementation and evaluation, 
ensuring advocacy by system leaders, streamlining multi-site policy implementation, using existing quality 
improvement personnel and methods, using information about processes of care and outcomes obtained as 
part of routine medical care, and leveraging informatics capabilities that allow centralized access to these data. 
The use of an existing infrastructure is maximized by randomizing entire hospitals, so that all participating units 
in each hospital use the same intervention protocol, supply chain, and compliance and reporting procedures. 
 
6.10.1 Preliminary Data – A Multi-center Cluster-Randomized Trial of Decolonization in ICU Patients 
 
We highlight additional features of the REDUCE (Randomized Evaluation of Decolonization vs 
Universal Clearance to Eliminate) MRSA Trial (see initial description in Section 5.0.2),19 125 a recently 
completed 43-hospital cluster-randomized trial by this investigative group. Use of similar features will enable 
efficient implementation of the ABATE Infection Trial. Features include: 
 
Robust health system infrastructure for corporate communications, decision-making and 
coordination of activities at the regional and corporate levels, and information technology support.   
 
Efficient recruitment and IRB clearance have already been described in UH2 Aims 1 and 2. 
 
Easy accessibility to trial staff in multiple sites through a real-time collaborative e-mail platform – 
enabling multi-user simultaneous editing for categorizing and prioritizing email inquiries, assigning them 
to specific responders, and streamlining workflow processing – and toll-free number enabling excellent 
coverage across time zones. Over 5,000 distinct email inquiries were fielded, as well as over 200 
phone calls originating from unit champions or infection prevention programs at participating hospitals.  
 
Division of labor by trial staff enabled collection of 5,000+ specimens by 44 microbiology laboratories 
which were coordinated by east coast staff due to the fact that laboratories generally work early shifts 
from 6am-2pm. Conversely, since bathing of ICU patients was generally performed on the evening/ 
night shift, the west coast trial staff fielded all coordination questions related to protocol implementation. 
 
Readily available training and trial materials were provided to unit champions and infection 
prevention programs, including study binders (see Preliminary Data section 5.30.1), computer-based 
training modules posted to participating unit staff, 55 site visits for chlorhexidine cloth bathing 
instruction and training as well as ad hoc requests for additional site visits.  
 
Centralized product distribution by HCA’s corporate central supply and pharmacy systems enabled 
tracking of usage to ensure adequate supply. It also allowed diversion of product when demands 
exceeded planned supply. Importantly, central supply chain prevented distribution of incompatible 
products and facilitated substitutions. Specialized pharmacy routines were also used to ensure trial 
product delivery to participating units. 
 
Routine mechanisms for compliance were leveraged, including electronic nursing queries inserted in 
the usual framework of nursing documentation, and associated electronic reports of these queries 
which were retrievable by unit champions. In addition, periodic observed bathing events (3 per quarter 
per unit) were performed by the unit champion and reported to study staff.  
 
Arm-specific coaching calls were used to build camaraderie and engagement. They occurred weekly 
in intervention arms and every other week for standard-of-care arms for the first several months of the 
initial trial period before converting to monthly calls. A total of 48 coaching calls were provided during 
the REDUCE MRSA Trial. They were used to allow same-arm hospitals to discuss issues and share 
solutions about protocol implementation. In addition, coaching calls enabled investigators to review 
compliance performance across participating hospitals, and inquire about the presence of new 
initiatives and adverse events. Roll call was taken at all calls. Special topic coaching calls and podcasts 


25 
 
were also provided to give expert review of the literature and allow expanded question and answer 
sessions for common questions from frontline staff (e.g. safe use of chlorhexidine on wounds).  
 
Trial-specific coordinating calls were held regularly, including weekly investigator group calls and 
monthly steering committee calls during the intervention period, followed by weekly data analyst 
meetings and bimonthly statistical meetings during the end-intervention and post-intervention period. 
The investigator and steering committees played the important role of adjudicating whether new or 
upcoming hospital initiatives were in conflict with the trial. In the REDUCE MRSA Trial, a total of 70 new 
initiatives were brought before the investigative team with 35 of them not initiated because of a real or 
perceived conflict with the trial. One hospital dropped out of the trial in favor of a conflicting initiative. 
 
Detailed logs were maintained related to contact information on unit champions and infection 
prevention liaisons, adverse event logs, new initiative requests, and the determination of whether they 
conflicted with the trial. All calls and emails were cataloged into major groups and documented with 
various study staff in charge of specific types of inquiries. 
 
6.10.2 Preliminary Data – Results of REDUCE MRSA Trial of Decolonization in ICUs  
The REDUCE MRSA Trial was an 18-month 3-arm cluster-randomized trial involving 74 adult ICUs in 
43 community hospitals,205 with the goals of reducing clinical isolates of MRSA and bloodstream infections due 
to all pathogens. All ICUs in a hospital were assigned to the same arm. Study arms were: 1) screening and 
isolation (pre-trial standard of care): nasal screening for MRSA followed by isolation if positive, 2) targeted 
decolonization: screening, followed, if positive, by isolation and decolonization with chlorhexidine baths and 
nasal mupirocin for 5 days, and 3) universal decolonization: cessation of screening, universal application of 
mupirocin for 5 days, and daily chlorhexidine baths. Proportional hazards models with shared frailty were used 
to assess differences in infection reductions across the arms while accounting for clustering by hospital.  
We randomized 43 hospitals in 16 states. There were 74 adult ICUs with 48,390 admissions in the 
baseline period and 74,256 in the intervention period. There were significant differences between arms in the 
relative hazards for intervention vs. baseline for both clinical isolates of MRSA and bloodstream infections 
caused by all pathogens (Table). In each case, universal decolonization produced a significantly greater 
reduction than screening and isolation. Targeted decolonization was not significantly different from screening 
and isolation alone. Adjusted analyses yielded similar results (Table). 
 
Universal decolonization with chlorhexidine and mupirocin in adult ICUs resulted in a 37% reduction in 
MRSA clinical cultures and a 44% reduction in bloodstream infections due to all pathogens. The targeted 
decolonization arm was not significantly different from screening and isolation alone. Universal decolonization 
also eliminates the need to obtain surveillance cultures and reduces need for isolation.  
 
6.10.3 Preliminary Data - HAI Rates in Non-Critical Care Units in Health Care System Partner Hospitals   
 
Using non-ICU data from hospitals participating in the REDUCE MRSA Trial, we estimated the baseline 
rates of bacteremia due to all pathogens as well as due to MRSA alone (Table 7). These data reflect non-ICU 
data from only the 13 hospitals (104,852 admissions) in the universal decolonization arm of the REDUCE 
MRSA Trial. We use this arm to provide conservative estimates since this arm had non-significant reductions in 
post-ICU sterile site infections due to all pathogens.  
 
 
Table 7. Estimates of HAI Rates in Non-ICU Areas Targeted by the ABATE Infection Trial 
Non-ICU Estimate
Events/10,000 Patients
MRSA Bloodstream Infection
62/104,852 
5.9 
All Pathogen Bloodstream Infection 
894/104,852 
85.3 
 


26 
 
These data include all patients in non-ICU areas excepting areas that would be excluded in this trial 
(pediatrics, psychiatry, obstetrics, rehabilitation/skilled nursing, bone marrow transplant units).  
 
Readmission Rates due to Infections in Health Care System Partner Hospitals   
 
Using the same cohort above, but restricting to those discharged during the study period (N=103,874), 
we calculated a 0.038 rate of readmission due to infectious diagnoses within 30 days of discharge. Admissions 
were deemed to be due to infections if ICD-9 infectious diagnoses were found in the primary or secondary 
diagnosis position. Infectious diagnoses were limited to those likely to be hospital associated (mainly bacterial). 
These data will be used for power calculations for the ABATE Infection Trial (Section 6.10.12). 
 
 
6.10.4 Trial Design and Study Population 
The ABATE Infection Trial will be a cluster-randomized controlled trial of 54 HCA hospitals, evaluating 2 
regimens to reduce HAIs in general hospital adult units (Figure 1).  
Our intent is to move forward with 54 hospitals to allow for unanticipated withdrawal where a hospital might 
drop from the trial. For example, early withdrawal from the trial may be required due to a conflicting 
intervention. Hospitals generally have multiple simultaneous campaigns ongoing to improve patient care. For 
the ABATE Trial, we require monthly confirmation of either no interventions/ new campaigns at their hospital or 
a reporting to the Steering Committee of what campaigns or interventions are in consideration or being 
launched. Should the Steering Committee identify a conflict, the hospital must decide to either withdraw from 
the trial or not pursue the proposed campaign or intervention. Given that conflicts are likely to arise and 
hospitals have the option to drop out of the trial despite CEO commitment to the trial, we will proceed with the 
54 hospitals with the intent that at least 50 will remain in the trial for the full duration. 
The ABATE Infection Trial will employ a 12-month baseline period from March 1, 2013 to February 28, 
2014, a 2-month phase in period from April 1 to May 31, 2014and an 18-month intervention period from June 
1, 2014 to November 30, 2015, (NOTE: intervention was extended to a 21-month trial in June 2015, ending 
February 29, 2016). The unit of randomization will be the hospital, with all participating units within a hospital 
assigned to the same arm. This preserves the usual structure for quality improvement and avoids confusion of 
having similar units, often with shared patients and staff, assigned to different protocols. 
Since 2007, current practice across all HCA hospitals involves high compliance nasal MRSA screening 
for the following high risk patients: patients transferred from other facilities (including other acute care 
hospitals, rehabilitation centers, skilled nursing facilities, assisted living centers), ICU patients, hemodialysis 
patients, and patients undergoing cardiac, orthopedic, and open spine surgery. Screening compliance has 
been >90% on serial HCA internal surveys. MRSA+ patients are placed into isolation precautions, which 
involve assignment to a single room plus gown and glove use by medical staff. 
 
 
Figure 1: Two study arms of the 
ABATE Infection Trial 
 
 
 
 
 
 
 
 
 
HCA hospitals will be randomized into two study arms. Both arms will continue to conduct routine 
MRSA screening for high risk patients and will place MRSA+ patients in isolation precautions. The first arm will 
continue the routine policy of offering assisted or unassisted daily showers/bed baths with regular soap, and 
will agree not to institute components of the other arm for the duration of the study. In the second arm, all 
patients will be encouraged to shower or have a bed bath daily with chlorhexidine products. In addition, 
patients known to be MRSA+ will receive nasal mupirocin ointment twice a day for 5 days. We anticipate an 
average of 4 adult non-critical care units per hospital, for a total of 200+ units across the 54 hospitals. 
For full recruitment details, see UH2 Aim 1. Participating hospitals will commit to having all eligible 
general adult non-critical care units participate in the trial. These are defined as step-down, medical, surgical, 
medical/surgical, and oncology units. Children <12 admitted to participating units will not receive the mupirocin 
protocol. 
Arm 1: Usual Care (N = 25+ hospitals)
 
 
Routine bathing practice per established protocols  
Current HCA Policy
Routine MRSA screening  
for high risk patients 
Arm 2: Decolonization (N = 25+ hospitals)
 
 
Use of chlorhexidine for all showering/bathing  
 
Active encouragement of daily showering/bed bath 

Nasal mupirocin x 5 days for MRSA+ patients


27 
 
Hospital leadership will identify a trial champion for each unit as well as a liaison from their Infection 
Prevention Program and their Microbiology Laboratory. Participants agree to report all new initiatives slated to 
occur during the trial and to defer them if study investigators determine that they conflict with the trial. In 
addition, participating hospitals agree to collect bacterial isolates as specified in UH3 Aim 2 (Section 6.20). 
A summary of the overall study design is found in Table 8. Despite the large-scale nature of this 
cluster-randomized trial, The ABATE Infection Trial will take precautions to adjust for imbalance in confounders 
or outcomes across the 54 participating hospitals. As described further below, imbalance in the numbers of 
patients and baseline outcomes will be minimized by a randomization strategy that stratifies by admission 
volume and baseline multi-drug resistant organisms from a clinical or screening culture (primary outcome) 
across participating populations in trial hospitals. Residual imbalance in baseline outcomes will be further 
addressed by including a 1-year baseline period and comparing the changes between intervention and 
baseline periods between the two arms of the trial (see Analysis Section 6.10.11). As mentioned in UH2 Aim 4, 
complete data for the baseline period will be obtained from electronic health records and can be obtained after 
recruitment and after the trial is completed.  
 
    Table 8. Key Design Elements of the ABATE Infection Trial 
Study Design 
Cluster-randomized trial 
Unit of Randomization 
Hospitals 
Study Population 
Adults (>12 years old) in one of the following non-critical care units: step-down, medical, 
surgical, medical/surgical, oncology units 
Exclusions 
Children (<12 years old), specialty units, including pediatrics, psychiatry, obstetric/post-
partum, rehabilitation/skilled nursing, bone marrow transplant. Units with high utilization 
of chlorhexidine and mupirocin, including units with >30% cardiac/orthopedic surgery 
patients. Units with mean length-of-stay < 2 days. 
Study Period 
Baseline Year: March 1, 2013-February 28, 2014 
2-Month Phase In Period: April 1, 2014-May 30, 2014 (not included in analysis) 
21-Month Intervention: June 1, 2014-February 29, 2016 (NOTE: intervention was 
extended from an 18-month to 21-month trial, in June 2015) 
 
 
6.10.5 Operational Plan for Implementation 
We describe several elements of our operational plan and infrastructure for conducting the trial, including 
protocols for governance and communication. In addition, we provide details regarding randomization and 
intervention implementation. 
 
6.10.51 Governance 
The ABATE Infection Trial is governed by a Steering Committee of trial investigators that convenes weekly to 
discuss plans, track progress, ensure timelines, address problems and concerns, and oversee actions of trial 
subcommittees. The Steering Committee reviews all trial protocols, surveys, training and educational materials, 
and communications to trial participants. A critical function of the Steering Committee includes soliciting and 
reviewing hospital-based interventions and campaigns to assess if a trial conflict exists. Site visits are also 
conducted by Steering Committee members.  
 
Subcommittees that report to the Steering Committee include project coordination, data cleaning and 
analysis, information technology, and products. Each of these teams meets weekly to ensure trial progress. 
Each team includes at least one member of the steering committee. All troubleshooting issues are raised with 
the Steering Committee. Project coordination teams include east coast (Harvard) and west coast (UCI) teams 
which cover trial communications across all time zones. East coast project coordination includes oversight of 
IRB activities, laboratory strain collection, study-related events, and product compatibility issues. West coast 
project coordination includes all help line (phone and email) communication with trial hospitals about 
implementation and conduct, creation and deployment of all trial materials including surveys, and toolkits (e.g. 
protocols, computer-based training, staff and patient education materials, frequently asked questions, coaching 
call presentations and notifications, and on-site training).  
 
The data cleaning and analysis subcommittee meets weekly and consists of programmer analysts, the 
lead statistician, and lead investigator. This team defines required data pulls, reports on monthly quality control 
checks and preparing for stratified randomization of participating hospitals. The information technology team 
involves trial programmer analysts interfacing with HCA data warehouse experts and HCA project managers to 


28 
 
identify and pull multiple data streams corresponding to needed variables. Finally, the products team includes 
HCA supply chain and ABATE project coordinators to plan the roll out of trial product, including cloth warmers, 
and decolonization products. 
 
Lastly, the ABATE Infection Team attends weekly Collaboratory Grand Rounds and interfaces closely 
with several NIH Collaboratory Working Groups.  
 
6.10.52 Communication Infrastructure to Hospitals and Laboratories 
 
We have a robust communication infrastructure to participating hospitals and laboratories (Table 9). 
 
Table 9. ABATE Infection Trial Communication Infrastructure 
Communication Component
Purpose
Toll-Free Number  
ABATE Help Line for participating hospitals and units 
Email Account  
ABATE email for participating hospitals and units 
Electronic Surveys 
Method for obtaining standardized information from participants. To date, 4 surveys have been 
conducted (enrollment, facility details, unit details, unit engagement) 
Coaching Calls 
Webinar-based power point presentations detailing progress, upcoming activities, and timeline. 
Also used to feedback compliance to participants. Occasionally used for relevant special expert 
presentations (cutting edge science). System logs user and time on/off call. During intervention 
phase, coaching calls will be arm-specific and occur once monthly for the routine care arm and 
twice monthly for the intervention arm.  
Polling Questions 
Electronic webinar-based polling questions that detail respondent and answer for rapid, real-
time inquiries during coaching calls 
Toolkits 
Binders distributed to each unit and hospital trial leaders with introduction, trial phone 
numbers/contacts, protocols, staff training materials, patient education materials, frequently 
asked questions, wall posters/clings.  
Podcasts 
Brief audio files (~3 min) recorded by trial investigators covering topics raised by participating 
hospitals. Intended for use by hospital educators for front line staff to address concerns, 
misconceptions, and important processes. 
Video 
Brief video for bathing training intended for front line staff by educators, managers 
Observation Forms 
Standardized assessment forms for direct observation of adequacy of bathing process; used as 
one method for determining compliance which is feedback to participants 
Study-Related Event Form 
Form to report potential study-related events 
 
On-Site Training 
In-person train-the-trainer sessions on application of bathing cloths  
 
6.10.53 Randomization 
Randomization will occur at approximately the 8th month of the baseline period. Each participating 
hospital will be notified of their placement at that time. This will be done because of the requisite 1-3 month 
period to submit and schedule intervention protocols for approval by relevant hospital committees which often 
meet monthly or quarterly. As per routine policy in all hospitals, no training or implementation activities may 
occur prior to obtaining requisite hospital committee approvals. This will allow approval to occur and 
appropriate training of staff to occur prior to the phase in period which will involve acquisition and introduction 
of intervention product.  
While this study is one of the largest cluster-randomized trials of hospitals, simple randomization of 54 
hospitals will not ensure balance of key variables by chance alone, and without blocking could even result in 
unequal numbers of hospitals in each arm. For example, with a naïve randomization, there would be a 9% 
chance of a 22-33 split, or worse. Thus, randomization will be stratified, with strata constructed to maximize the 
chance of balance for both baseline admission volume and the primary outcome, MRSA and VRE clinical 
cultures attributable to participating units. In addition, we will evaluate the possibility of constructing strata that 
balance additional variables, such as the mean comorbidity index (Romano score) of all patients in a hospital’s 
participating units, the percent of patients bathing daily, and the baseline use of chlorhexidine and mupirocin.  
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. 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 


29 
 
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 enhance methods to inform the choice of tuplet-construction scheme which we developed in the 
REDUCE MRSA trial,125 205 and share them (see software sharing plan). One example method is to 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 must be 
balanced, we could 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 presented an early version of this work at the April 2013 coordinating center meeting. 
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. A simulated example is shown to the 
right in Figure 2. There we show a potential result of a single tuplet construction method. The variables shown 
are volume, baseline rate of an outcome, the baseline rate of chlorhexidine use, and baseline rate of bathing. 
Each blue, red, green, or black line shows the mean difference between arms for all four variables for one 
potential realized randomization. The results show that a few randomizations, in blue, are relatively imbalanced 
on volume and outcome but balanced on chlorhexidine use and 
bathing, while a few others, in black, have the reverse pattern. 
The green and red realizations are approximately equally 
balanced across these variables. If we considered it more 
important to balance on volume and outcome, this would 
probably not be an ideal scheme. 
As a final note, the statistical core advised us to consider 
the relative costs and benefits of strata of four, rather than 
tuplets, which are strata of two. There are sound statistical 
reasons to expect power to be slightly better with strata of four, 
although there is some debate on this point.206 207 However, the 
balance between the arms may be worse. The balance is of central  
importance, since balance ensures that the observed effect is not 
confounded—confounding requires that the confounder be out of balance between the arms.  We will examine 
whether the gain in power is strong enough, and the loss of balance slight enough, to pursue the strata of four 
in place of tuplets. 
6.10.6 Finalized Outcomes  
Study outcomes were finalized following deliberation of the Steering Committee during the UH2 
planning year. Deliberations included response to recent published literature, including the REDUCE MRSA 
Trial which was conducted by our investigative team.125 205 The primary outcome will be the presence of at 
least one clinical culture with gram-positive multi-drug resistant bacteria (MRSA and VRE) attributable to a 
participating unit. This outcome was solidified following recent clinical trial evidence of the success of 
chlorhexidine (with and without mupirocin) in reducing these pathogens in ICUs.125 205 An a priori secondary 
outcome intended for the primary manuscript is all-cause bloodstream infection attributable to a participating 
unit. Additional a priori secondary study outcomes intended for secondary manuscripts are provided in Table 
10. 
 
Table 10. Study Outcomes 
Primary Outcome 
        MRSA and VRE clinical cultures a 
Secondary Outcomes (Primary Manuscript)
volume
outcome
chlor.mu
bathing
Figure 2. Parallel Coordinates Plot Showing 
Simulated Balance Across Multiple Variables 


30 
 
        All-cause bloodstream infection a b 
Secondary Outcomes (Secondary Manuscripts) 
Gram-negative multi-drug resistant organisms a 
Urinary tract infections a 
C difficile clinical tests a 
Blood culture contamination 
30-day infectious readmissions 
Emergence of resistance to chlorhexidine or mupirocin a 
Cost effectiveness 
a Attributable to participating units. Defined as occurring >2 days into 
  a participating unit stay through 2 days following unit discharge 
b Includes subsets of GP and GN MDROs as well as key pathogens such as S. aureus 
 
These outcomes are designed to maximize the evaluation of the impact of decolonization in non-critical 
care settings. They will address major concerns in healthcare related to reduction of antibiotic-resistant 
pathogens, and impact on a range of hospital-associated infections. They will also assess the likelihood that 
bacterial strains will develop resistance to chlorhexidine and mupirocin following broad use among inpatients. 
6.10.7 Baseline Period Activities 
 
The 1 year trial baseline period begins in the UH2 Planning Phase and ends in the UH3 Trial 
Intervention Phase. It serves two major purposes. Its primary function is to provide baseline outcome data for 
both arms (see Analysis Section 6.10.11). Second, it enables the collection of baseline bacterial strains which 
will be used to assess if antibiotic resistance is differentially engendered between the study arms (see strain 
collection details in UH3 Aim 2, Section 6.20. These activities will occur during the Planning Phase, when we 
will perform recruitment, develop educational materials and computer based training modules, and program 
electronic nursing prompts and compliance reports for the intervention protocol (UH2 Aim 3, Section 5.30).  
 
6.10.8 Phase-In Period Activities  
There will be a 2-month phase-in period after the baseline period. During this period, which will not 
contribute to either the baseline or intervention periods, we will disseminate campaign products and materials 
to the Decolonization Arm. Since our intervention utilizes usual care processes, including standardized nursing 
protocols (for chlorhexidine, a non-prescription product) and standardized order sets (for mupirocin, a 
prescription drug FDA approved for this use), these processes need to undergo usual hospital approval by 
nursing standards committees, and medical executive or quality standards boards. All hospitals will schedule 
protocol approval for the committee meetings immediately following the randomization date. After 
randomization, hospital champions will submit trial decolonization protocols to requisite committees to ensure 
timely approval for implementation in the phase-in period. This was achieved in <30 days in our prior trial.   
Following randomization, frontline staff will be required to complete trial-specific and arm-specific 
computer based training modules (see Trial Materials, Section 5.30.2). CBTs will be assigned to specific units 
and frontline staff and, for the decolonization arm, will include a bathing video on bathing technique. CBT 
training can and will begin prior to hospital committee approval and staff will be given 21-days to complete it. 
In addition, hospitals will participate in arm-specific coaching calls. Calls will occur every other week 
during the phase-in period. All coaching calls will be webinars and led by trial investigators, including active 
attendance and support by HCA leadership. A PowerPoint slide set will be reviewed with a question and 
answer session, and a set of key questions will be posed to each hospital via automated webinar poll. All 
coaching calls will be recorded and placed on arm-specific ABATE Infection Trial sites on the HCA intranet for 
continued access by designated participants in each arm. Common questions posed to Decolonization Arm 
participants during this phase will include hospital committee approval status, CBT training status, product 
stocking, removal/replacement of products incompatible with chlorhexidine and posting of wall clings providing 
bathing instruction, (see description of trial materials in Section 5.30.2, and wall cling examples from a prior 
trial in Appendix B, H). Participants will be highly encouraged to share concerns and solutions with one 
another.  
In both Decolonization and Usual Care Arm coaching calls, hospital participants will be required to 
confirm that no new hospital initiatives have been planned (or report the initiative to trial investigators for 
determination of trial conflict). Hospitals that are not represented on coaching calls will receive an email from 
core staff and a phone call from HCA trial liaisons. In our previous trial, we found that sharing hospital-specific 
status reports in PowerPoint presentations, and rapid follow-up for non-response resulted in very high rates of 


31 
 
participation from all hospitals in all arms. Between calls, core staff will follow up on any ongoing issues and 
document resolution. 
 
As described in UH2 Aim 2, IRB approval will be obtained for each hospital during the Planning Year; 
we most hospitals to cede authority to a central IRB for approval. In the phase-in period, once trial protocols 
are further approved by required hospital committees, intervention activities will be put into place (Table 11). 
Decolonization Arm hospitals will replace usual soap with 4% liquid chlorhexidine solution in all showers in 
participating units. Mesh sponges will be provided for shower use since they substantially enhance product 
lathering. In addition, 2% no-rinse chlorhexidine cloths for bed baths (contributed by Sage, Inc – see letter of 
support) will be stocked for routine bed baths by patients and nursing assistants. Unit nurse educators will be 
engaged and all nurses and bathing staff will be trained to encourage daily bathing or showering. In addition, 
the phase-in period will include compliance assessments that routinely accompany HCA quality improvement 
initiatives. A summary of activities is found in Table 11 and is detailed in UH2 Aim 3 (Section 5.30.2). 
 
Table 11. Phase-In Activities for Decolonization Arm 
Phase-In Protocol Activities 
Details 
Activated 
1. 
Arm-Specific Binder and 
Talking Points Materials 
Disseminated 
Sent to all hospital liaisons, including hospital Nurse Educators for 
dissemination via usual infrastructure. Reviewed on coaching calls for 
train-the-trainer assistance. 
Just prior to Phase 
In 
 
2. 
Arm-Specific Coaching Calls 
Weekly for Decolonization Arm during Phase-In period and early part 
of Intervention period, then transitions to twice per month when 
processes stably underway. (Of note, twice  monthly for Usual Care 
Arm) 
Just prior to Phase 
In 
 
3. 
Computer Based Training and 
Bathing Video 
Investigator prepared modules will be posted  to participating unit staff 
and tracked through usual corporate assignment infrastructure 
Just prior to Phase 
In; 
21-day required 
completion time. 
4. 
Just-In Time Training and 
Buddy Process 
Materials created for registry nurses readily available with assigned 
buddy process for new nurses. Common registry services notified. 
Just prior to Phase 
In 
 
5. 
Chlorhexidine Product and 
Warmer Stocked 
Stocking and par levels initiated. Warmers delivered and plugged in. 
Immediately prior 
to Phase In 
 
6. 
Compatibility Verification 
Bathing and prophylactic (non-treatment) wound products 
incompatible with chlorhexidine removed from stocks and replaced 
with compatible products. Alternative products available for those with 
allergies. 
Prior to Phase In 
 
7. 
Pharmacy Module for 
Mupirocin Ordering 
Electronic standardized order sets will be developed to enable 
automatic prescribing of mupirocin for MRSA+ patients. 
Testing at Start of 
Phase In, Activated 
at Go Live Date 
8. 
Bathing Training Site Visit 
Site visits jointly by trial investigators and Sage Inc Medical Science 
Liaisons (MSLs) for usual training on 2% chlorhexidine cloths and 
proper warmer use. MSLs will ensure training is consistent with trial 
scripts and talking points. 
Month Prior to 
Phase In 
9. 
Daily Nursing Queries  
Programmed nursing queries (standard for HCA hospitals) will be 
posted to units for daily e-documentation of whether 
bathing/showering occurred and with what product. 
Already activated in 
Baseline period, in 
both Arms 
10. Wall Clings Posted  
Decolonization bathing instructions will be posted in all rooms in 
participating units 
Go Live date during 
Phase In 
11. Compliance Reports 
Compliance e-reports added to routine e-reports run by unit 
managers/champions and assessed daily until reasonable 
compliance achieved (determined by investigator team). Once 
achieved, compliance assessment changed to weekly.  
Go Live date during 
Phase In 
12. Bathing Observations 
Unit managers will observe 3 assisted bed baths per quarter per unit 
and to query nursing assistant about comfort level with bathing 
wounds and other common occurrences (see prior trial example, 
Appendix I and explanation of skills assessment forms, Appendix J ) 
Go Live date during 
Phase In 
 
During the phase-in period, core staff will work with participating units in Decolonization Arm hospitals 
to ensure all bathing and prophylactic (non-treatment) wound products in participating units are compatible with 
chlorhexidine. Study staff will use previously established spreadsheets from our recent ICU decolonization trial 
(see REDUCE MRSA Trial in preliminary data Section 5.30.1) to collect data on products not previously 


32 
 
assessed. Detailed information for the active and inactive ingredients of all additional products, including 
soaps, lotions, and skin barrier products will be collected. Manufacturers of these products will be contacted to 
obtain any known information regarding chlorhexidine compatibility. In the absence of data, a PhD Ingredient 
Scientist (Dr. Frederick Siegel) will be consulted to adjudicate additional ingredients.  
 
Hospitals will set a Go Live date no later than the start of the second month of the phase-in period. All 
interventions will be discontinued upon discharge from a participating unit. Patients readmitted to a 
participating unit will have the protocol applied anew. Timeline for Phase In is provided in Table 12. 
 
    Table 12. Timeline for Phase In Processes 
Randomization 
Randomization will occur in November 2013 
Notification 
Hospitals will be notified of their randomization status in Nov-Dec 2013 to schedule the 
decolonization protocol into their relevant hospital committees for approval  
Committee Approvals 
Jan-Feb 2014 
Supply Chain 
Assessing facility requirements for cloth warmers (space, voltage) will occur Feb-Mar 2014; 
Stocking of warmers will occur Mar 2014; Products to hospital units the week before launch.  
Training 
Coaching calls, training materials distributed Feb-Mar 2014. Computer-based training 
requirement completed by all relevant staff Feb-Mar 2014. On site bathing training Mar-Apr 
2014 by ABATE trial investigators/study staff in conjunction with Medical Science Liaisons from 
Sage Inc (supplier of 2% chlorhexidine cloths) 
Launch – Phase In 
April 1, 2014 for 2 months, rapid feedback for compliance using daily bathing queries, direct 
observation by nurse managers using standardized assessment tools 
Intervention – Go Live 
June 1, 2014 for 18 months (NOTE: intervention was extended to a 21-month trial in June 
2015) 
 
 
6.10.9 Intervention Period Activities  
 
Intervention period activities will be identical to phase-in period activities. As mentioned in Table 11, 
compliance feedback will be shared monthly during coaching calls based upon nursing queries embedded in 
usual e-documentation (see UH2 Aim 3 and Table 11 above) that will require a response each shift about 
whether a bath or shower had occurred and, if so, with what product. For the Decolonization Arm, a negative 
response will require a reason from a drop down menu. E-reports will be used for chlorhexidine since it is not a 
prescription drug. Pharmacy records will be used to track dispensing of mupirocin. 
In addition to on-site bathing training provided to all intervention hospitals in the phase-in period, we will 
perform site visits to participating hospitals in either arm based upon requests for assistance, concerns about 
intervention protocols, or evidence of low compliance. For the usual care arm, evidence of decolonization 
would constitute “low compliance.” Site visits would be comparable to those used by HCA when implementing 
other Quality Improvement protocols. In addition, HCA leadership (specifically, the quality performance and 
infection prevention team that are co-investigators on this proposal) performs routine site visits to all hospitals 
on a rotating basis and will incorporate encouragement, inquiries, and process assessments related to trial 
activities and microbiology laboratory submission of isolates (see UH3 Aim 2) during visits to trial hospitals. 
Special coaching calls and podcasts on trial rationale will be provided to both arms. As in our previous 
trial, wound care nurses from HCA hospitals will provide expertise to the Decolonization Arm hospitals during 
coaching calls and will be reachable for questions throughout the phase in and intervention period.   
 
Investigators will continue to have weekly calls during the intervention phase, as well as active 
participation in the NIH HCS Research Collaboratory Steering Committee and Collaboratory Work Groups. All 
activities will be conducted in concert with the HCS Research Collaboratory Coordinating Center (CCC).  
 
6.10.10 Data Acquisition  
 
The Data and Analytics Team will include lead investigators, HCA co-investigators, trial programmer 
analysts, statisticians, and HCA information technologists. Data pulls will be based upon detailed specifications 
and will be accompanied by data dictionaries. Data will be pulled from census, microbiology, pharmacy, supply 
chain, nursing query reports, and administrative data from HCA corporate data warehouses as described in 
UH2 Aim 4 (Section 5.40). Numerators for most outcome data will be derived from microbiology final result files 
limited to sterile site specimens from participating hospitals. Denominators will be derived from unit-specific 
census data. We will also provide descriptive data including demographic and comorbidity data from 
administrative claims data, compliance data from nursing queries, and product use from pharmacy and supply 
chain data, and data on use of hand hygiene products, glove, and gown use across arms.  


33 
 
 
 
 
 
6.10.11 Statistical Analysis 
 
All outcomes will be assessed similarly. Here, we use the example of the primary outcome: clinical 
cultures with MRSA or VRE (first per patient). MRSA and VRE clinical cultures will be attributed to participating 
units if the collection date occurred >2 days after admission to that unit through two days after discharge from 
that unit. This attribution is consistent with CDC guidance for surveillance of nosocomial infections.208 
 
Main trial results will be based upon as-randomized, unadjusted analyses using proportional hazards 
models to account for patients’ variable tenure in the unit. This is necessary for the usual reasons: 
dichotomizing patients into those with vs. without infections would require us to define a fixed time-frame 
(within first x days of eligibility, ignoring some infections and omitting patients with shorter stays) or to ignore 
exposed time (counting infection during unit stay regardless of different length of stay). In addition, power is 
greater for proportional hazards models than for logistic regression models.209 210 211 212 
As discussed with the Statistical Core Working Group of the Collaboratory, clustering within hospital will 
be accounted for using shared frailties. The frailties are added model terms that allow unique hazards ratios for 
each hospital, and are necessary to account for clustered randomization.213 Model terms will include individual-
level data on arm, hospital, outcome events, trial period (baseline vs. intervention) and an interaction term 
between trial period and arm. The assessment of trial success will be determined by the significance of the 
interaction term, which assesses whether the difference in hazard between the baseline and intervention 
period differs significantly between the two arms. We can write a simple version of the model symbolically as:  
𝜆௜௝(𝑡) = 𝜆଴(𝑡)𝑒ఉభ஺௥௠೔ೕାఉమ௉௘௥௜௢ௗ೔ೕାఉయ஺௥௠೔ೕ∗௉௘௥௜௢ௗ೔ೕାఊ೔ 
where 𝑖 is a hospital, 𝑗 is a person within the hospital, 𝐴𝑟𝑚 and 𝑃𝑒𝑟𝑖𝑜𝑑 are indicator variables and are = 0 for 
patients in a hospital in the control arm or baseline period and 1 if in the intervention arm or period. The overall 
hazard rate over time for person 𝑖𝑗 is defined as 𝜆௜௝(𝑡), a function of the baseline hazard 𝜆଴(𝑡), which is similar 
to the intercept in a linear model, times the proportionality for that subject, which is defined by the covariates 
𝐴𝑟𝑚௜௝ and 𝑃𝑒𝑟𝑖𝑜𝑑௜௝ and the associated parameters, as well as the frailty, γi. The frailties are closely analogous 
to the random effect in generalized linear mixed models, and account for the clustering (similarity of hazard) 
within a given hospital. The ultimate effect of the intervention is assessed through 𝛽ଷ: as parameterized, if it is 
negative and has p-value < .05 (or 95% CI excluding 0) then the intervention reduces the risk of infection. We 
plan to assess the need for different frailties by hospital by period (instead of just by hospital), as well as 
additional clustering by unit within hospital. In addition, we will investigate the need to adjust for the stratified 
randomization scheme described above in Section 6.10.53 
 
Subsequent analyses will include as-treated and covariate-adjusted models. Adjusted models will 
account for individual characteristics such as age, gender, comorbidities based upon ICD-9 codes, and receipt 
of intervention products. We will also account for unit type (step down, medical, surgical, etc.) and baseline 
bathing frequency if this is not balanced after randomization. All analyses will be performed using current 
versions of SAS (9.4, as of writing, SAS Institute, Cary NC) and/or R (3.0.2, as of writing).214 
In addressing considerations of interim analyses to determine whether early stopping might be 
possible, the decision has been made not to pursue an interim analysis for several reasons. First, this trial 
meets the requirements of a minimal risk study. The study of topical bathing/decolonization therapy 
necessitates neither interim analyses nor stopping rules since reasonably anticipated adverse events are 
considered minor. Second, the collection time plus the lag in obtaining data and the relatively sparse power 
suggest that it is highly unlikely that an early look would result in a stoppage of the trial for either futility or 
success. Third, the addition of an interim analysis would affect power estimates in such a way to create an 
elongation of the trial beyond the time period that is acceptable to our health system partner. As described in 
detail above, participation in the trial requires a continuous assertion that other hospital interventions and 
campaigns that may conflict with the trial will not be pursued. This restriction to the usual tendency of hospitals 
to pursue multiple simultaneous interventions for prolonged periods of time was a critical consideration in 
designing the length and size of this trial. With regard to assessment of adverse events, we have built in a 
reporting system for both mild and severe side effects, defining study-related events that show no signs of 
improvement within 7 days of stopping product as serious events. The study-related events monitoring and 
reporting system is described in detail in our Data Safety Monitoring Plan and in our Decolonization 
Educational Materials.  


34 
 
6.10.12 Power (Updated June 2015, following reassessment of power) 
In many settings, an analytic approach to power is possible: given the assumptions of the model (e.g., 
logistic regression) are met, a relatively simple closed-form solution exists. However, generating the expected 
values to plug in may be difficult. In addition, some settings are complex enough that closed-form solutions 
may be difficult to generate. Many cluster-randomized designs fall into this class. In cluster-randomized 
problems, it is also difficult to obtain reliable estimates of the additional parameters that are required, most 
notably the between-cluster variance or, equivalently, the intra-class correlation coefficient. Further additional 
complications are introduced for time-to-event outcomes such as those needed in the ABATE Infection Trial. 
In a previous trial, we used the logistic regression analogue to proportional hazards regression models 
and simulation to estimate power.210 Now, however, we propose an interesting and, to our knowledge, novel 
approach to power calculation, which we dub “bootstrap power calculation.” This method is described below 
and in an article published since the trial was planned.215 Briefly, the bootstrap is a powerful technique that 
uses the observed sample to approximate the underlying population, rather than a convenient analytic 
distribution.216 The bootstrap power approach relies on the fact that we possess a large quantity of baseline 
data already. Loosely put, we (1) bootstrap a sample of observations from our observed baseline data to serve 
as the baseline sample in the power calculations. Then we (2) bootstrap another sample from the baseline 
data to serve as the intervention period data. Next, (3) we implement the randomization scheme in the 
bootstrapped sample. Then (4), for a randomly selected subset of the outcomes (e.g., bloodstream infections) 
observed in the bootstrapped intervention period sample in the hospitals randomized to intervention, we 
artificially change the outcome from infection to no infection. This represents the effect of the intervention, 
which we control by changing the size of the subset selected for this change. Simultaneously (5), for this 
subset we change the date of the event from the date of infection to the date of discharge, transfer to a non-
eligible unit, or death—i.e., the date of censoring, had no infection been observed. Finally (6), we fit the 
planned frailty model described in Section 6.10.11 above and record whether the null hypothesis of no 
association was rejected or not. This process is repeated many times, and the proportion of rejections is an 
estimate of the power under the given effect size. A confidence limit on this estimated power can be generated. 
In the table below, we show the power for removing the outcome from 0, 10, 20, and 30% of the subjects in the 
intervention arm in the intervention period. Removing 0% of the outcomes is a test of the technique: since we 
are not reducing the infection rate, the null is true, and rejections should occur only about 5% of the time.  
In the initial planning of the trial, we used the available 4-month baseline sample of patients from our 
participating trial hospitals and used three bootstrap samples to represent each 12-month baseline and 4.5 
bootstrap samples to represent the 18-month intervention period. This resulted in the power shown in Table 
13. 
 
Table 13: Power and Exact 95% CI for Primary and Select Secondary Outcomes*   
Intervention Effect
Primary Outcome
MRSA, VRE Clinical Cultures 
Gram Negative MDRO**
Clinical Cultures 
All Pathogen Bacteremia
*0% 
5.6% (4.3-7.2%) 
4.1% (3.0 – 5.5%) 
5.2% (3.9 – 6.8%) 
10% 
33% (28-37%) 
15% (12 – 18%) 
23% (19 - 27%) 
20% 
92% (90-94%) 
44% (40 – 49%) 
68% (63 - 72%) 
30% 
100% (99-100%) 
82% (79 – 86%) 
98% (96 – 99%) 
*Based on 500 bootstrap samples for each effect size, except for the 0% estimate, for which we did 1000 simulations to increase 
confidence that the alpha level is maintained when the null is true. 
** Gram-negative multi-drug resistant organism clinical cultures 
 
The above power estimates (Table 13) show that the technique has the desirable characteristic of 
rejecting the null only 5% of the time when it is true. We also see that power for MRSA or VRE clinical culture 
is ample, even if we prevent only 20% of the infections. For both selected secondary outcomes, the power is 
less, but still quite acceptable if the intervention prevents 30% of the infections. The primary strengths of the 
bootstrap power approach are that it allows us to avoid using literature estimates for the parameters when 
such estimates may not apply to the trial population, and it also avoids unrealistic assumptions about regularity 
(equal cluster sizes) or distribution (logistic instead of frailty models). The main weakness in this case is that 
correlation within hospital is generated to be the same in the baseline and intervention periods. In addition, we 
have not received all desired randomization stratification data by the time of this submission. Thus, 
stratification in the bootstrap process is limited to hospital size and the baseline rate of the outcomes. We 
believe that these are relatively benign issues: the correlation structure is unlikely to change importantly from 


35 
 
period to period during the actual study, and the stratification is mainly to promote balance. It may affect the 
power, but mainly by reducing the variability in the outcome. 
After the baseline period, we were able to re-estimate the power, using the data collected in the full 
baseline period.  We used the bootstrap process outlined above, except that we used the observed 12-month 
data for the baseline. We assessed the power for the original proposal of an 18-month intervention and for a 
slightly extended 21-month intervention period, oversampling the observed 12-month baseline data as needed 
in each case (see Table 14). This updated power assessment was considered to be more accurate in that it 
used approximately three times as much real data—12 months vs. four months—compared to the original 
estimate. Furthermore, additional cleaning steps were applied to the 12 months of baseline data. In these new 
assessments we focused on an intervention effect of 20%. The power for the primary outcome was 99.9% 
(95% CI: 99.4-99.99%) with 18 or 21 months of follow-up.  For all pathogen bacteremia, the power was 85% 
(95% CI: 83-87%) with 18 months follow-up and 89% (87-90%) with 21 months follow-up.  The steering 
committee decided to use 21 months of follow up to ensure greater power with less common secondary 
outcomes and account for lessened effects at sites that may drop out of the trial.  
 
Table 14: Revised Power and Exact 95% CI for Primary and Select Secondary Outcomes*    
Analysis
Effect
Primary Outcome
MRSA, VRE Clinical Cultures 
All Pathogen Bacteremia
As-randomized, 18-mo 
20% 
99.9% (99.4% – 99.99%) 
85% (83% – 87%) 
As-randomized, 21-mo 
20% 
99.9% (99.4% – 99.99%) 
89% (87% – 90%) 
 
 
6.10.13 Limitations and Planned Solutions 
 
This study has limitations. First, we require the enrollment of at least 50 HCA hospitals. While we 
believe this is possible due to our previous trial of 43 HCA hospitals, should enrollment drop unexpectedly to 
less than 50, we will pursue an elongation of the 18-month trial, (NOTE: intervention was extended to a 21-
month trial in June 2015). Secondly, if hospital committee approvals for trial protocols are delayed, we will 
expand the phase-in period to a total of 3 months. We note that our prior trial completed phase-in within 30 
days. Third, if decolonization is significantly better at preventing infection, we appreciate that we are unlikely to 
be able to distinguish between effects of chlorhexidine and the effects of active encouragement to bathe or 
shower. Nevertheless, the results of this study would reflect a real world intervention which would likely entail 
both processes. 
Cluster randomization does not allow every question of interest to be addressed. It typically results in 
more misclassification of exposure than individual randomized controlled trials do, either because individual 
providers choose to use a non-recommended regimen for some patients, or they fail to adhere fully to the 
assigned regimen. To the extent that the frequency of failure is consistent with the level that occurs in usual 
clinical practice, these failures are part of the overall effectiveness measure. Thus, cluster randomization in 
inpatient systems may offer a relatively low-cost and broadly generalizable means to examine many of the 
priority topics for comparative effectiveness research. In addition, this study design does not allow for blinded 
assignment or implementation. Nevertheless, this reflects the pragmatic nature of our intervention and directly 
mirrors the design and implementation of hospital quality improvement initiatives. 
 
6.10.14  Dissemination Plan 
 
 
We will disseminate trial results through presentations at national meetings, and publication in peer-
reviewed journals, and press releases. We will also use our contacts at the CDC, membership on the national 
advisory committee that recommends infection prevention strategies (HICPAC – the Healthcare Infection 
Control Practices Advisory Committee), membership on the Institute of Medicine Roundtable on Value & 
Science-Driven Health’s Innovation Collaborative on Clinical Effectiveness, participation in national societies 
such as the Infectious Diseases Society of America (IDSA) and the Society for Healthcare Epidemiology of 
America (SHEA), and contacts with state departments of public health to disseminate trial results.  
 
 
Through the Collaboratory’s website and dissemination mechanisms, we will also make our 
decolonization toolkit publicly available. This toolkit includes information about healthcare-associated 
infections, mupirocin protocols, chlorhexidine bathing protocols (for showering and bed bathing), extensive 
product compatibility guidance, frequently asked questions guides, wall charts for patient bathing instruction, 
audio podcasts and special coaching calls.  
 


36 
 
 
6.20 Aim 2: Assess whether universal chlorhexidine bathing and selective MRSA decolonization result 
in increased resistance to chlorhexidine or mupirocin among bacterial strains collected during the 
trial. 
 
In order to assess whether routine use of chlorhexidine for bathing and selective use of mupirocin for 
MRSA decolonization engenders increased resistance to these agents over the course of the trial, we will 
collect bacterial strains from participating units during the baseline and intervention periods in each study arm.  
 
6.20.1 Preliminary Data (Confidential, do not cite or distribute) 
 
We collected 4,321 MRSA isolates during the REDUCE MRSA trial, a trial of decolonization with 
chlorhexidine and mupirocin in ICUs to reduce MRSA carriage and infection. 993 strains in the baseline period 
and 3,328 in the 18-month intervention period were collected from microbiology laboratories in 43 HCA 
hospitals across 3 arms. Strains were assessed for the presence of chlorhexidine resistance and high-level 
mupirocin resistance, which has been associated with decolonization failure.55 68 217  
Chlorhexidine resistance was essentially non-existent: there was a single chlorhexidine resistant isolate  
which occurred in the usual care arm during the intervention period. No formal analyses were pursued. 
At baseline, high-level mupirocin resistance was present at 6.7% (67/993). There were no significantly 
different increases across the arms between baseline and intervention periods. In the targeted decolonization 
arm of the REDUCE MRSA Trial, which most resembles the proposed intervention in the ABATE Infection 
Trial, high-level mupirocin resistance was 5.9% in the baseline period and 5.4% in the intervention period.  
 
Processes and materials used in the collection of bacterial isolates during the REDUCE MRSA trial will 
be replicated by trial investigators for the ABATE Infection Trial as described below. 
 
6.20.2 Strain Collection  
Strain collection will include MRSA and other pathogens since chlorhexidine targets a broad range of 
microbes and rare cases of resistance have been reported in select gram-negative bacteria, namely 
acinetobacter, burkholderia, E coli, klebsiella, proteus, pseudomonas, serratia, and stenotrophomonas.218 219 
220 Study staff will coordinate with each clinical microbiology laboratory director for the collection of MRSA and 
these gram negative bacteria for a 9-month period toward the end of both the baseline and intervention periods 
for each arm. A total of 2,400 MRSA and 2,800 gram negative bacteria will be collected. 
Trial staff will provide each microbiology laboratory with a toolkit with detailed instructions about strain 
collection, including the list of pathogens, participating units, and time window for collection. Microbiology 
technologists will be instructed to target strains with a collection date at least 2 days after admission based 
upon unit location and admission date, which are available during usual specimen processing. Similar to our 
prior trial, the toolkit will include a Step-by-Step instruction guide, a Frequently Asked Questions (FAQ) sheet, 
FedEx mailing instruction sheet, deidentified study ID labels, collection log sheets with clipboard, shipment 
packing list, and shipping schedule (see example from prior trial, Appendix E, G). In addition, a quick 
reference wall cling will be sent that provides a streamlined diagram of the key elements for collection (see 
example from prior trial, Appendix F). Details will be reviewed in laboratory-specific coaching calls.  
Chocolate agar slants and shipping kits (STP-250MD, Saf-T-Pak Inc.) will be sent to each laboratory on 
a regular basis for monthly batch shipment of strains. Initially, trial staff will contact laboratory liaisons once 
weekly until processes are in place. Trial staff will contact each laboratory prior to each anticipated shipment. 
Trial staff will receive faxed log sheets that include the hospital, unit, collection date, specimen source, 
and coded ID. A separate fully-identified log sheet will be sent internally to HCA co-investigators who will use 
identifiers to subsequently link coded specimen study IDs to coded individual-level trial data during the analysis 
phase. In the event that resistance is found to be engendered by decolonization, this linkage will allow further 
exploration regarding patient exposure to study products prior to specimen collection. 
 
6.20.3 Strain Processing  
For the collection of MRSA and gram negative bacterial isolates (performed by HCA staff) at 
participating HCA hospitals from their microbiology laboratories, log sheets that identify the patients from whom 
the isolates arise will be provided to HCA corporate study investigators and retained for the duration of the 
study period. Direct patient identifiers will not be shared external to HCA. All information transferred to Rush 
University, the isolate colletion core and the research analytic core at the Department of Population Medicine 
(DPM) at Harvard Pilgrim Health Care, will be stripped of direct identifiers and replaced by a coded identifier. 
Isolates will be shipped to Dr. Mary Hayden in the Division of Clinical Microbiology, our Co-Investigator at the 


37 
 
Rush University Medical Center in Chicago, IL. Bacterial identification will be confirmed by standard 
microbiologic methods. For MRSA isolates, resistance to methicillin will be verified by cefoxitin disk testing221 
and mupirocin susceptibility will be assessed by the E-test method (bioMérieux, Durham, NC). Mupirocin 
resistance will be determined according to Eltringham.222  
All bacterial isolates will have susceptibility to chlorhexidine determined by a microtiter method using 
20% aqueous chlorhexidine digluconate (Sigma-Aldrich LLC, St. Louis, MO) diluted in broth medium 
appropriate for the microbe tested.218 Since national standards do not yet exist for chlorhexidine resistance, we 
will define resistance as a minimum inhibitory concentration (MIC) of chlorhexidine that is outside of the 
reported wildtype distribution for each microbial species. Based upon recent literature, we will use a resistance 
breakpoint of >8mcg/ml for MRSA,17 >32 mcg/ml for klebsiella, pseudomonas, and serratia,216-217 and >64 
mcg/ml for burkholderia, proteus, and stenotrophomonas.215-217 These species were chosen because of their 
relatively high chlorhexidine MICs compared to other bacterial species and/or their association with outbreaks 
linked to contaminated commercial chlorhexidine products (burkholderia). In addition to assessing MIC 
breakpoints, we will evaluate the proportion of strains with a high MIC within the susceptible range between 
baseline and intervention strains. Decreased susceptibility to chlorhexidine has been associated with the 
presence of various multidrug efflux pumps.223 224 225 If chlorhexidine resistant (or nearly resistant) isolates are 
identified, we will use polymerase chain reaction methods to evaluate isolates for the presence of efflux pump 
genes.  If efflux pump genes are found, additional characterization of resistance may be done including assays 
to quantify expression of efflux pumps and to measure the effect of inhibition of efflux on chlorhexidine MICs.   
6.20.4 Analysis  
For mupirocin and chlorhexidine separately, we will report the proportion of resistant isolates in each 
arm in the baseline and intervention periods. We will test differences between arms in resistance by fitting a 
generalized linear mixed logistic regression model with arm, baseline vs. intervention period, and their  
interaction as predictors. The interaction term assesses whether the difference in the probability of resistance 
between the baseline and intervention periods is significantly different between arms.  
 
6.20.5 Power and Sample Size 
 
Based upon our recent trial data, we assume high level mupirocin resistance in 0.07 of MRSA isolates 
at baseline. We estimate collection of 2,400 isolates (600 per arm in the each of the intervention and baseline 
periods).  As in UH3 Aim 1, power is based on a post-only comparison of 1200 subjects, 24 per hospital.  
Assuming intracluster correlation of 0.001, we will have 80% power to detect 0.12 high level mupirocin 
resistance in the decolonization arm, assuming that resistance remains at 0.07 in the usual care arm.  
For both MRSA and gram negative bacteria, we will calculate the proportion of isolates resistant to 
chlorhexidine. If the proportion of MRSA that is resistant is truly as large as 0.1%, we will have at least a 90% 
probability of detecting >1 resistant strain among 2400 isolates, 70% among the 1200 post-period isolates, and 
45% among 600 isolates in the post period in the intervention arm. If the resistance rate increases to 0.5%, we 
have a 95% chance of observing at least one case in the 600 intervention isolates from the follow-up period. 
 
6.20.6 Limitations and Planned Solutions 
 
If decolonization is successful, it is conceivable that isolation of MRSA and other bacterial pathogens 
may be substantially reduced, resulting in inadequate collection. In the event that this occurs, we will broaden 
the time window for isolate collection. Since our baseline period is 12-months and our intervention period is 18-
months, (NOTE: intervention was extended to a 21-month trial in June 2015), the current plans to collect 
isolates for 9-months in each period still allows for additional collection time should it be necessary. We will 
monitor collection progress continually so that the collection period can be extended seamlessly, if this is 
needed. 
 
6.30 Aim 3: Estimate the costs associated with the intervention (chlorhexidine + selective MRSA 
decolonization) and the attributable medical costs of healthcare associated infections in adult general 
inpatient units and infectious readmissions, in order to evaluate the potential for cost savings 
associated with the strategy of reducing bioburden to prevent infection. 
 
Costs of healthcare associated infections (HAIs) have been studied mainly in academic medical 
centers.10 226 227 228 229 230 Estimates of HAI costs in community hospitals are needed since community hospitals 
represent the majority of inpatient care in the U.S., and costs may differ from academic hospitals.231 232 233 In 
addition, the cluster-randomized design affords an important and unique opportunity to assess the economic 
value of decolonization across a large number of hospitals. In particular, case mix differences between 


38 
 
hospitals can be closely adjusted for due to combination of several factors: 1) control group of hospitals with 
stratified randomization, 2) baseline data from each hospital, and 3) availability of individual patient adjustors 
from electronic health records and administrative data. In this aim, we will estimate the incremental hospital 
costs associated with the intervention and evaluate differences in total medical costs among patients with and 
without HAIs in the intervention and control arms.  We will then estimate the net cost of medical care for the 
intervention and the control groups, adjusting for key health factors. 
 
6.30.1 Cost Analysis Study Design 
 
A cost analysis will be pursued with HCA health economist, Kate Nolte, Ph.D., if the ABATE Infection 
Trial demonstrates a significant beneficial effect in its primary outcome. If this occurs, we hypothesize that 
decolonization will be associated with reduced costs associated with hospitalization and readmission. We will 
take advantage of the cluster randomized trial design to test the hypothesis that the Decolonization Arm is 
associated with a significantly greater reduction in total medical costs between the intervention vs. baseline 
period when compared to the Usual Care Arm. Our study population will include adult patients in the trial who 
have spent time on a participating unit. In this population, we will assess the costs of the initial hospitalization 
and any subsequent infectious admission within 30 days following discharge.  
 
6.30.2 Data Sources 
For overall hospitalization costs, we will obtain complete hospitalization charges from HCA financial 
data, which do not include intervention costs. HCA has highly standardized financial records where charges for 
hospital beds, procedures, and various drugs and supplies are aggregated to produce a total hospitalization 
charge. Since hospital charges do not reflect true costs that hospitals incur, we will apply hospital cost-to-
charge ratios obtained from HCA to obtain total hospitalization costs per admission. 
 
Intervention costs, which will not be included in HCA charge data, will be determined as follows. We will 
measure the volume of supplies used multiplied by a standard unit cost. For all participating hospitals, we will 
collect unit-specific dispensed daily doses of mupirocin and chlorhexidine product. Mupirocin dispensing will be 
obtained from pharmacy data and chlorhexidine usage will be obtained from daily bathing compliance data 
obtained during the trial. Standard unit costs for each of these will be obtained from the manufacturer, publicly 
available data, or published sources. Chlorhexidine costs will be calculated as the incremental costs over the 
unit costs of common non-chlorhexidine bathing supplies. This will be done in two ways to account for the two 
most common chlorhexidine products on the market. First, we will calculate the incremental cost of disposable 
chlorhexidine-impregnated bathing cloths (as used in this trial) compared to disposable bathing cloths. Second, 
we will assume all bed baths are performed using basin baths and will calculate the incremental costs of 
adding generic liquid chlorhexidine to basins compared to routine soap and water basin baths. Finally, we will 
assess differences in the amount of unit-specific gown and glove use based upon supply chain data between 
arms and between baseline and intervention periods. This will account for potential cost-savings due to 
decreased transmission from the intervention. 
 
6.30.3 Analysis 
 
 
We will use an analytic approach similar to our primary outcome of MDRO organism from a clinical or 
screening culture attributed to a participating non-ICU unit, with the exception that we will model will the 
outcome of hospitalization cost. Rather than proportional hazard models with shared frailties, we will use 
generalized linear mixed models to evaluate whether the Decolonization Arm is associated with significant 
cost-savings when accounting for clustering by hospital. Since cost data are typically quite skewed, data 
transformation may be required to assure adequate fit of the outcome distribution. Models will be chosen to fit 
the distribution: negative binomial models, or possibly, normal distributions after log transformation, for 
example. Either approach reduces the impact of large outliers typical in cost data. Model terms will include 
arm, trial period (baseline vs intervention), their interaction, unit type (step down, medical, surgical, etc.), and 
demographic and comorbidity variables. The assessment of whether outcomes in one arm are significantly 
different from the other will be determined by the significance of this interaction term, which assesses whether 
the mean cost difference between the baseline and intervention period differs significantly between the two 
arms. If significant, we will calculate the mean cost savings per case averted. 
 
We will additionally perform a sensitivity analysis to account for potential mortality effects on total 
hospitalization costs. To do this, we will assign a large fixed hospitalization cost to all deaths and assess if the 
model still predicts cost savings. 
 
6.30.5 Limitations and Planned Solutions 


39 
 
 
 
This analysis is limited by the inability to account for all potential confounders that may put one 
individual at higher risk for hospitalization cost. However, cluster randomization is a major asset for distributing 
both known and unknown confounders. The availability of data from the baseline period is an additional 
strength, since each hospital’s patient population additionally serves as its own control.  
 
6.40 Overall ABATE Infection Trial Summary 
 
 
Healthcare-associated infections (HAI) continue to produce high morbidity, mortality, and over $6 billion 
dollars of annual healthcare costs in the U.S.. Most infections arise from bacterial flora, which overcome host 
defenses during vulnerable times such as hospitalization. Gains in HAI reduction due to decolonization 
regimens have been demonstrated for ICU settings, but evidence is lacking about the effectiveness of such 
strategies in non-ICU settings, where the majority of HAIs occur, and where medical care, risk of infection, 
patient-to-patient interactions, pathogen transmission, and bathing practices differ considerably from ICU 
settings. This 50-hospital cluster-randomized trial will critically assess whether daily bathing with chlorhexidine 
and intranasal mupirocin for MRSA carriers should become standard practice for 40 million patients 
hospitalized each year in the United States alone. Alternatively, it will suggest that tailored strategies distinct 
from those effective in ICU settings are needed to reduce the >1 million HAIs that occur each year in non-
critical care settings. 
 
This trial will also illustrate the strengths of an underused model of clinical effectiveness research that 
quickly and efficiently allows robust randomized clinical trials to be embedded into the normal delivery of health 
care, using the organizational and informatics strengths of a large hospital system.  
 
6.50 UH3 Trial Intervention Phase Milestones and Timeline 
 
 
We summarize our milestones and timeline for the trial intervention phase in Table 15. We will work 
closely with the NIH Collaboratory Coordinating Center during all aspects of the trial, including study design, 
implementation, data extraction, quality control processes, and confirmation of study outcomes. Our lead 
investigators will actively participate in the HCS Research Collaboratory Steering Committee, and members of 
our investigative team will actively participate in HCS Research Collaboratory Work Groups to align goals, 
activities, and deliverables to improve the conduct of pragmatic clinical trials during usual medical care. 
 
 
Table 15. UH 3 Trial Intervention Phase - Annual Milestones and Timeline 
UH3 Milestone
Details / Purpose
Trial Phase Timeline
 
 
Y1
Y2
Y3
Y4
Aim 1: Decolonization Trial
 
 
 
 
 
1. Clinical Trial Registration 
Registration with Clinicaltrials.gov 
x 
 
 
 
2. Randomization of Hospitals 
Cluster randomization using hospitals as the cluster 
x 
 
 
 
3. Standardized Nursing Protocol  
Approval of assigned protocol through requisite hospital 
committees 
x 
 
 
 
4. Computer Based Training 
All frontline staff with confirmed training on trial protocol, by arm 
x 
 
 
 
5. Product Compatibility 
Substitution of skin products incompatible with chlorhexidine 
x 
 
 
 
6. Stocking Product 
Supply chain infrastructure ensures product availability 
x 
 
 
 
7. On Site Bathing Training 
All hospitals randomized to decolonization receive on site training 
x 
 
 
 
8. Coaching Calls  
Monthly arm-specific coaching calls* 
x 
 
 
 
9. Compliance Reports  
Routine capture and reporting of protocol compliance 
x 
x 
 
 
10. Bathing Observations  
Quarterly bathing observations to confirm process 
x 
x 
 
 
11. Data from Central Warehouse 
Routine data pulls for needed trial data elements 
x 
x 
x 
 
12. Data Cleaning Routines  
Establish cleaning routines for serial data pulls 
x 
x 
x 
 
13. Final Data Pull 
Complete final data pulls from centralized data warehouse 
 
 
x 
 
14. Analytic Data Set (Primary) 
Establish final analytic dataset (Primary Manuscript) 
 
 
x 
 
15. Statistical Analysis (Primary 
Statistical analysis of trial outcomes (Primary Manuscript) 
 
 
x 
 
16. Analytic Data Set (Secondary) 
Establish final analytic dataset (Secondary Manuscripts) 
 
 
 
x 
17. Statistical Analysis (Secondary) 
Statistical analysis of trial outcomes (Secondary Manuscripts) 
 
 
 
x 
18. Dissemination of Results 
Presentations, manuscript preparation and submission 
 
 
x 
x 
Aim 2: Resistance
 
 
 
 
 
1. Strain Collection  
Strain collection occurs during baseline and intervention periods 
x 
x 
 
 
2. Resistance Testing  
Testing strains for mupirocin and chlorhexidine resistance 
 
 
x 
 
3. Analytic Data Set 
Establish final analytic dataset after cleaning, quality checks 
 
 
x 
x 
4. Statistical Analysis 
Statistical analysis of resistance outcomes 
 
 
 
x 
5. Dissemination of Results 
Presentations, manuscript preparation and submission 
 
 
 
x 
Aim 3: Cost Effectiveness
 
 
 
 
 


40 
 
1. Data Pull for Charges 
Total hospitalization charges and intervention costs acquired 
 
 
x 
 
2. Literature Review  
Common cost-to-charge ratios and usual product costs assessed 
 
 
x 
 
3. Analytic Data Set 
Establish final analytic dataset after cleaning, quality checks 
 
 
 
x 
4. Statistical Analysis 
Statistical analysis of cost outcomes 
 
 
 
x 
5. Dissemination of Results 
Presentations, manuscript preparation and submission 
 
 
 
x 
Resource/Software Products
 
 
 
 
 
1. Decolonization Toolkit 
Trial related materials to implement decolonization in hospitals 
x 
 
 
 
2. Computer Training Modules 
Computer-based training module for decolonization 
x 
 
 
 
3. E-Documentation Modules 
MEDITECH programs for nursing bathing documentation 
x 
 
 
 
4. Product Compatibility Charts 
Chlorhexidine compatibility charts for skin care products 
x 
 
 
 
5. Podcasts and Webinar Content 
Podcasts and PowerPoint presentations for decolonization 
x 
 
 
 
6. Strain Collection Toolkit 
Detailed directions for trial strain collection 
x 
 
 
 
7. Randomization Software 
SAS/R software for stratified cluster randomization 
 
x 
 
 
8. Microbiology Mining Programs 
SAS programs for parsing and analyzing microbiology results 
 
x 
x 
 
9. Bacterial Strain Bank 
Large strain bank of MRSA and gram negative bacterial pathogens 
 
 
x 
x 
10. Analytic Programs 
SAS programs for analysis of cluster-randomized trials 
 
 
 
x 
11. Publications 
Publication of all trial, resistance, and cost outcomes 
 
 
 
x 
 
 
 
 


41 
 
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143 Goodman ER, Platt R, Bass R, Onderdonk AB, Yokoe DS, Huang SS. Impact of an environmental cleaning 
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146 Lee BY, McGlone SM, Song Y, Avery TR, Eubank S, Chang CC, Bailey RR, Wagener DK, Burke DS, Platt 
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147 Lee BY, McGlone SM, Wong KF, Yilmaz SL, Avery TR, Song Y, Christie R, Eubank S, Brown ST, Epstein 
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148 Lee BY, Bartsch SM, Wong KF, Yilmaz SL, Avery TR, Aingh A, Song Y, Kim DS, Brown ST, Potter MA, 
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163 Platt R, Lehr JL, Marino S, Munoz A, Nash B, Raemer DB. Safe and cost-effective cleaning of pressure-
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164 Platt R, Yokoe DS, Sands KE. Automated methods for surveillance of surgical site infections. Emerg Infect 
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165 Sands K, Vineyard G, Livingston J, Christiansen C, Platt R. Efficient identification of postdischarge surgical 
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166 Yokoe DS, Shapiro M, Simchen E, Platt R. Use of antibiotic exposure to detect postoperative infections. 
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167 Hirschhorn LR, Currier JS, Platt R. Electronic surveillance of antibiotic exposure and coded discharge 
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168 Yokoe DS, Noskin GA, Cunnigham SM, Zuccotti G, Plaskett T, Fraser VJ, Olsen MA, Tokars JI, Solomon S, 
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169 Sands K, Vineyard G, Platt R. Surgical site infections occurring after hospital discharge. J Infect Dis. 
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170 Miner AL, Sands KE, Yokoe DS, Freedman J, Thompson K, Livingston JM, Platt R. Enhanced identification 
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171 Sands KE, Yokoe DS, Hooper DC, Tully JL, Horan TC, Gaynes RP, Solomon SL, Platt R. Detection of 
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172 Platt R, Kleinman K, Thompson K, Dokholyan RS, Livingston JM, Bergman A, Mason JH, Horan TC, 
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173 Huang SS, Livingston JM, Rawson NSB, Schmaltz S, Platt R. Developing algorithms for healthcare 
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175 Kaye KS, Sands K, Donahue JG, Chan KA, Fishman P, Platt R. Preoperative drug dispensing as predictor 
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176 Currier JS, Tosteson TD, Platt R. Cefazolin compared with cefoxitin for cesarean section prophylaxis: the 
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177 Platt R, Zucker JR, Zaleznik DF, Hopkins CC, Dellinger EP, Karchmer AW, Bryan CS, Burke JF, Wikler MA, 
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178 Platt R, Zucker JR, Zaleznik DF, Hopkins CC, Dellinger EP, Karchmer AW, Bryan CS, Burke JF, Wikler MA, 
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179 Currier JS, Campbell H, Platt R, Kaiser AB. Perioperative antimicrobial prophylaxis in middle Tennessee, 
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180 Perencevich EN, Sands KE, Cosgrove SE, Guadagnoli E, Meara E, Platt R. Health and economic impact of 
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181 Zanetti G, Platt R. Antibiotic prophylaxis for cardiac surgery: does the past predict the future? Clin Infect 
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185 Zanetti G, Flanagan HL Jr., Cohn LH, Giardina R, Platt R. Improvement of intraoperative antibiotic 
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186 Platt R, Munoz A, Stella J, VanDevanter S, Koster JK. Antibiotic prophylaxis for cardiovascular surgery. 
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188 Zanetti G, Goldie SJ, Platt R. Clinical consequences and cost of limiting use of vancomycin for 
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189 Klompas M, Kleinman K, Platt R. Development of an algorithm for surveillance of ventilator-associated 
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191 Klompas M, Kulldorff M, Platt R. Risk of misleading ventilator-associated pneumonia rates with use of 
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195 Platt R. Quantitative definition of bacteriuria. Am J Med. 1983;75:44-52. 
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study. PLoS Med 7, e1000238. 


51 
 
                                                                                                                                                                                                                        
201 Stelling J, Yih WK, Galas M, Kulldorff M, Pichel M, Terragno R, Tuduri E, Espetxe S, Binsztein N, O'Brien 
TF, Platt R; Collaborative Group WHONET-Argentina. Automated use of WHONET and SaTScan to detect 
outbreaks of Shigella spp. using antimicrobial resistance phenotypes. Epidemiol Infect. 2010;138:873-883. 
202 Larson E, Bratts T, Zwanziger J, Stone P. A survey of IRB process in 68 US hospitals. J Nurs Scholarsh. 
2004;36(3):260-264. 
203 Dziak K, Anderson R, Sevick MA, Weisman CS, Levine DW, Scholle SH. Variations among institutional 
review board reviews in a multisite health services research study. Health Serv Res. 2005;40(1):279-290. 
204 Stacia A. Finch; Shari L. Barkin; Richard C. Wasserman; Niramol Dhepyasuwan; Eric J. Slora; Robert D. 
Sege.  Effects of Local Institutional Review Board Review on Participation in National Practice-Based 
Research Network Studies. Arch Pediatr Adolesc Med. 2009;163(12):1130-1134. 
205 Huang SS, Septimus E, Kleinman K, Moody J, Hickok J, Avery T, Lankiewicz J, Gombosev A, Terpstra L, Hartford F, 
Hayden M, Jernigan JA, Weinstein R, Fraser VJ, Haffenreffer K, Cui E, Kaganov RE, Lolans K, Perlin J, Platt R. 
Randomized evaluation of decolonization vs. universal clearance to eliminate methicillin-resistant Staphylococcus aureus 
in ICUs (REDUCE MRSA Trial, Abstract 36049). IDWeek (1st Annual Joint Meeting of IDSA, SHEA, HIVMA, and 
PIDS), October 17-21, 2012 (San Diego, CA) 
 
206 Klar N, Donner A. The merits of matching in community intervention trials: a cautionary tale. Statistics in 
Medicine 1997;16(15):1753-64. 
207 Imai K, King G, Nall C. The essential role of pair matching in cluster-randomized experiments, with 
application to the Mexican universal health insurance evaluation. Statistical Science 2009;24:29–53. 
208 Centers for Disease Control and Prevention. National Healthcare Safety Network. Tracking Infections in 
Acute Care Hospitals/Facilities. http://www.cdc.gov/nhsn/acute-care-hospital/index.html. Last accessed on 
September 28, 2013. 
209 Cuzick J. The efficiency of the proportions test and the logrank test for censored survival data. Biometrics 
1982;38:1033–1039. 
210 Annesi I, Moreau T, Lellouch J. Efficiency of the logistic regression and Cox proportional hazards models in 
longitudinal studies. Stat Med 1989;8:1515–1521. 
211 Green MS, Symons MJ. A comparison of the logistic risk function and the proportional hazards model in 
prospective epidemiologic studies. J Chronic Dis 1983;36:715–723. 
212 van der Net JB, Janssens AC, Eijkemans MJ, Kastelein JJ, Sijbrands EJ, Steyerberg EW. Cox proportional 
hazards models have more statistical power than logistic regression models in cross-sectional genetic 
association studies. Eur J Hum Genet. 2008;16(9):1111-6. Epub 2008 Apr 2. 
213 Hayes RH, Moulton LH.  Cluster Randomized Trials. CRC Press, New York 2009, p 207. 
214 R Development Core Team (2013). R: A language and environment for statistical computing. R Foundation 
for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org/. Last accessed 
September 28, 2013. 
215 Kleinman K, Huang SS. Calculating Power by Bootstrap, with an Application to Cluster-Randomized Trials. 
EGEMS (Wash DC). 2017 Feb 9;4(1):1202. doi: 10.13063/2327-9214.1202. eCollection 2016. 
216 Efron B, Tibshirani R. An Introduction to the Bootstrap. Boca Raton, FL: Chapman & Hall/CRC.1993.  
ISBN 0-412-04231-2 
217 Walker ES, Vasquez JE, Dula R, Bullock H, Sarubbi FA. Mupirocin-resistant methicillin-resistant 
Staphylococcus aureus: does mupirocin remain effective? Infect Control Hosp Epidemiol 2003; 24:342–6. 
218 Rose H, Baldwin A, Dowson CG, Mahenthiralingam E. Biocide susceptibility of the Burkholderia cepacia 
complex. J Antimicrob Chemother. 2009;63(3):502-510. 
219 Abuzaid A, Hamouda A, Amyes SG. Klebsiella pneumoniae susceptibility to biocides and its association 
with cepA, qacDeltaE and qacE efflux pump genes and antibiotic resistance. J Hosp Infect. Apr 10 [Epub 
ahead of print]. 
220 Block SS, ed. Disinfection, Sterilization, and Preservation. 5th ed, Lippincott, Williams & Wilkins, USA, 2001. 
221 Clinical Laboratory Standards Institute. Methods for Dilution Antimicrobial Susceptibility Tests for Bacteria 
that Grow Aerobically; Approved Standard. Eighth Edition. Waynes, PA: Clinical Laboratory Standards 
Institute; 2008. 
222 Eltringham I. Mupirocin resistance and methicillin-resistant Staphylococcus aureus (MRSA). J Hosp Infect. 
1997;35(1):1-8. 


52 
 
                                                                                                                                                                                                                        
223 Batra R, Cooper BS, Whiteley C, Patel AK, Wyncoll D, Edgeworth JD. Efficacy and limitation of a 
chlorhexidine-based decolonization strategy in preventing transmission of methicillin-resistant Staphylococcus 
aureus in an intensive care unit. Clin Infect Dis.15;50(2):210-217. 
224 Maillard JY. Antimicrobial biocides in the healthcare environment: efficacy, usage, policies, and perceived 
problems. Ther Clin Risk Manag. 2005;1(4):307-320. 
225 Naparstek L, Carmeli Y, Chmelnitsky I, Banin E, Navon-Venezia S. Reduced susceptibility to chlorhexidine 
among extremely-drug-resistant strains of Klebsiella pneumoniae. J Hosp Infect. Mar 30. [Epub ahead of print] 
226 Maragakis LL, Perencevich EN, Cosgrove SE. Clinical and economic burden of antimicrobial resistance. 
Expert Rev Anti Infect Ther. 2008;6(5):751-63. 
227 Cosgrove SE. The relationship between antimicrobial resistance and patient outcomes: mortality, length of 
hospital stay, and health care costs. Clin Infect Dis. 2006;42 Suppl 2:S82-9. 
228 Cosgrove SE, Qi Y, Kaye KS, Harbarth S, Karchmer AW, Carmeli Y, The impact of methicillin resistance in 
Staphylococcus aureus bacteremia on patient outcomes: mortality, length of stay, and hospital charges. Infect 
Control Hosp Epidemiol. 2005 Feb;26(2):166-74 
229 Abramson MA, Sexton DJ Nosocomial methicillin-resistant and methicillin-susceptible Staphylococcus 
aureus primary bacteremia: at what costs? Infect Control Hospital Epidemiol 1999; 20(6): 408-11 
230 Cosgrove SE, Carmeli Y. The impact of antimicrobial resistance on health and economic outcomes. Clin 
Infect Dis. 2003;36(11):1433-7. Epub 2003 May 16. 
231 Heyssel RM. Costs and care in academic medical centers. Interview by Jane Stein. Bus Health. 1987 
Jan;4(3):50-2. 
232 Topping S, Hyde J, Barker J, Woodrell FD. Academic health centers in turbulent times: strategies for 
survival. Health Care Manage Rev. 1999;24(2):7-18. 
233 Freburger JK, Hurley RE. Academic health centers and the changing health care market. Med Care Res 
Rev. 1999;56(3):277-306. 
 

SAP_001.pdf: 
Active Bathing to Eliminate Infection (ABATE Infection) Trial 
NCT02063867 
Statistical Analysis Plan 
June 30, 2015 


1 
Pragmatic Clinical Trials Demonstration Project 
Title: Decreasing Bioburden to Reduce Healthcare-Associated Infections and Readmissions 
ABATE (Addressing Bioburden while Admitted To Eliminate) Infection Trial 
1.0 Randomization Plan 
1.1 Randomization 
Randomization will occur at approximately the 8th month of the baseline period. Each participating 
hospital will be notified of their placement at that time. This will be done because of the requisite 1-3 month 
period to submit and schedule intervention protocols for approval by relevant hospital committees which often 
meet monthly or quarterly. As per routine policy in all hospitals, no training or implementation activities may 
occur prior to obtaining requisite hospital committee approvals. This will allow approval to occur and 
appropriate training of staff to occur prior to the phase in period which will involve acquisition and introduction 
of intervention product.  
While this study is one of the largest cluster-randomized trials of hospitals, simple randomization of 54 
hospitals will not ensure balance of key variables by chance alone, and without blocking could even result in 
unequal numbers of hospitals in each arm. For example, with a naïve randomization, there would be a 9% 
chance of a 22-33 split, or worse. Thus, randomization will be stratified, with strata constructed to maximize the 
chance of balance for both baseline admission volume and the primary outcome, MRSA and VRE clinical 
cultures attributable to participating units. In addition, we will evaluate the possibility of constructing strata that 
balance additional variables, such as the mean comorbidity index (Romano score) of all patients in a hospital’s 
participating units, the percent of patients bathing daily, and the baseline use of chlorhexidine and mupirocin.  
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. 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 enhance methods to inform the choice of tuplet-construction scheme which we developed in the 
REDUCE MRSA trial,1 2 and share them (see software sharing plan). One example method is to 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 must be 
balanced, we could 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. A simulated example is shown to the 
right in Figure 1. There we show a potential result of a single tuplet construction method. The variables shown 
are volume, baseline rate of an outcome, the baseline rate of chlorhexidine use, and baseline rate of bathing. 


2 
 
Each blue, red, green, or black line shows the mean difference between arms for all four variables for one 
potential realized randomization. The results show that a few randomizations, in blue, are relatively imbalanced 
on volume and outcome but balanced on chlorhexidine use and bathing, while a few others, in black, have the 
reverse pattern. The green and red realizations are approximately equally balanced across these variables. If 
we considered it more important to balance on volume and outcome, this would probably not be an ideal 
scheme. 
As a final note, the statistical core advised us to consider the relative costs and benefits of strata of 
four, rather than tuplets, which are strata of two. There are 
sound statistical reasons to expect power to be slightly better 
with strata of four, although there is some debate on this point.3 
4 However, the balance between the arms may be worse.  The 
balance is of central importance, since balance ensures that the 
observed effect is not confounded—confounding requires that 
the confounder be out of balance between the arms.  We will 
examine whether the gain in power is strong enough, and the  
 
loss of balance slight enough, to pursue the strata of four in 
place of tuplets. 
 
 
2.0 Statistical Analysis Plan 
 
2.1 Finalized Outcomes  
Study outcomes were finalized following deliberation of the Steering Committee during the UH2 
planning year. Deliberations included response to recent published literature, including the REDUCE MRSA 
Trial which was conducted by our investigative team.1 2 The primary outcome will be the presence of at least 
one clinical culture with gram-positive multi-drug resistant bacteria (MRSA and VRE) attributable to a 
participating unit. This outcome was solidified following recent clinical trial evidence of the success of 
chlorhexidine (with and without mupirocin) in reducing these pathogens in ICUs.1 2 An priori secondary 
outcome intended for the primary manuscript is all-cause bloodstream infection attributable to a participating 
unit. Additional a priori secondary study outcomes intended for secondary manuscripts are provided in Table 1.  
 
Table 1. Study Outcomes 
Primary Outcome 
        MRSA and VRE clinical cultures a
Secondary Outcomes (Primary Manuscript)
 
        All-cause bloodstream infection a b 
Secondary Outcomes (Secondary Manuscripts) 
Gram-negative multi-drug resistant organisms a
Urinary tract infections a 
C difficile clinical tests a 
Blood culture contamination 
30-day infectious readmissions 
Emergence of resistance to chlorhexidine or mupirocin a 
Cost effectiveness 
a Attributable to participating units. Defined as occurring >2 days into a participating unit 
stay through 2 days following unit discharge 
b Includes subsets of GP and GN MDROs as well as key pathogens such as S. aureus 
 
These outcomes are designed to maximize the evaluation of the impact of decolonization in non-critical 
care settings. They will address major concerns in healthcare related to reduction of antibiotic-resistant 
pathogens, and impact on a range of hospital-associated infections. They will also assess the likelihood that 
bacterial strains will develop resistance to chlorhexidine and mupirocin following broad use among inpatients. 
 
 
 
 
2.2 Statistical Analysis 
volume
outcome
chlor.mu
bathing
Figure 1. Parallel Coordinates Plot Showing 
Simulated Balance Across Multiple Variables 


3 
 
 
All outcomes will be assessed similarly. Here, we use the example of the primary outcome: clinical 
cultures with MRSA or VRE (first per patient). MRSA and VRE clinical cultures will be attributed to participating 
units if the collection date occurred >2 days after admission to that unit through two days after discharge from 
that unit. This attribution is consistent with CDC guidance for surveillance of nosocomial infections.5 
 
Main trial results will be based upon as-randomized, unadjusted analyses using proportional hazards 
models to account for patients’ variable tenure in the unit. This is necessary for the usual reasons: 
dichotomizing patients into those with vs. without infections would require us to define a fixed time-frame 
(within first x days of eligibility, ignoring some infections and omitting patients with shorter stays) or to ignore 
exposed time (counting infection during unit stay regardless of different length of stay). In addition, power is 
greater for proportional hazards models than for logistic regression models.6 7 8 9 
Clustering within hospital will be accounted for using shared frailties. The frailties are added model 
terms that allow unique hazards ratios for each hospital, and are necessary to account for clustered 
randomization.10 Model terms will include individual-level data on arm, hospital, outcome events, trial period 
(baseline vs. intervention) and an interaction term between trial period and arm. The assessment of trial 
success will be determined by the significance of the interaction term, which assesses whether the difference 
in hazard between the baseline and intervention period differs significantly between the two arms. We can 
write a simple version of the model symbolically as:  
ߣ௜௝ሺݐሻൌߣ଴ሺݐሻ݁ఉభ஺௥௠೔ೕାఉమ௉௘௥௜௢ௗ೔ೕାఉయ஺௥௠೔ೕ∗௉௘௥௜௢ௗ೔ೕାఊ೔ 
where ݅ is a hospital, ݆ is a person within the hospital, ܣݎ݉ and ܲ݁ݎ݅݋݀ are indicator variables and are = 0 for 
patients in a hospital in the control arm or baseline period and 1 if in the intervention arm or period. The overall 
hazard rate over time for person ݆݅ is defined as ߣ௜௝ሺݐሻ, a function of the baseline hazard ߣ଴ሺݐሻ, which is similar 
to the intercept in a linear model, times the proportionality for that subject, which is defined by the covariates 
ܣݎ݉௜௝ and ܲ݁ݎ݅݋݀௜௝ and the associated parameters, as well as the frailty, γi. The frailties are closely analogous 
to the random effect in generalized linear mixed models, and account for the clustering (similarity of hazard) 
within a given hospital. The ultimate effect of the intervention is assessed through ߚଷ: as parameterized, if it is 
negative and has p-value < .05 (or 95% CI excluding 0) then the intervention reduces the risk of infection. We 
plan to assess the need for different frailties by hospital by period (instead of just by hospital), as well as 
additional clustering by unit within hospital. In addition, we will investigate the need to adjust for the stratified 
randomization scheme described in Section 1.1. 
 
Subsequent analyses will include as-treated and covariate-adjusted models. Adjusted models will 
account for individual characteristics such as age, gender, comorbidities based upon ICD-9 codes, and receipt 
of intervention products. We will also account for unit type (step down, medical, surgical, etc.) and baseline 
bathing frequency if this is not balanced after randomization. All analyses will be performed using current 
versions of SAS (9.4, as of writing, SAS Institute, Cary NC) and/or R (3.0.2, as of writing).11 
In addressing considerations of interim analyses to determine whether early stopping might be 
possible, the decision has been made not to pursue an interim analysis for several reasons. First, this trial 
meets the requirements of a minimal risk study. The study of topical bathing/decolonization therapy 
necessitates neither interim analyses nor stopping rules since reasonably anticipated adverse events are 
considered minor. Second, the collection time plus the lag in obtaining data and the relatively sparse power 
suggest that it is highly unlikely that an early look would result in a stoppage of the trial for either futility or 
success. Third, the addition of an interim analysis would affect power estimates in such a way to create an 
elongation of the trial beyond the time period that is acceptable to our health system partner. As described in 
detail above, participation in the trial requires a continuous assertion that other hospital interventions and 
campaigns that may conflict with the trial will not be pursued. This restriction to the usual tendency of hospitals 
to pursue multiple simultaneous interventions for prolonged periods of time was a critical consideration in 
designing the length and size of this trial. With regard to assessment of adverse events, we have built in a 
reporting system for both mild and severe side effects as described in our Data Safety Monitoring Plan and in 
our Decolonization Educational Materials.  
 
2.3 Power  
In many settings, an analytic approach to power is possible: given the assumptions of the model (e.g., 
logistic regression) are met, a relatively simple closed-form solution exists. However, generating the expected 
values to plug in may be difficult. In addition, some settings are complex enough that closed-form solutions 
may be difficult to generate. Many cluster-randomized designs fall into this class. In cluster-randomized 


4 
 
problems, it is also difficult to obtain reliable estimates of the additional parameters that are required, most 
notably the between-cluster variance or, equivalently, the intra-class correlation coefficient. Further additional 
complications are introduced for time-to-event outcomes such as those needed in the ABATE Infection Trial. 
In a previous trial, we used the logistic regression analogue to proportional hazards regression models 
and simulation to estimate power.7 Now, however, we propose an interesting and, to our knowledge, novel 
approach to power calculation, which we dub “bootstrap power calculation.” This method is described below 
and in an article published since the trial was planned.12 Briefly, the bootstrap is a powerful technique that uses 
the observed sample to approximate the underlying population, rather than a convenient analytic distribution.13 
The bootstrap power approach relies on the fact that we possess a large quantity of baseline data already. 
Loosely put, we (1) bootstrap a sample of observations from our observed baseline data to serve as the 
baseline sample in the power calculations. Then we (2) bootstrap another sample from the baseline data to 
serve as the intervention period data. Next, (3) we implement the randomization scheme in the bootstrapped 
sample. Then (4), for a randomly selected subset of the outcomes (e.g., bloodstream infections) observed in 
the bootstrapped intervention period sample in the hospitals randomized to intervention, we artificially change 
the outcome from infection to no infection. This represents the effect of the intervention, which we control by 
changing the size of the subset selected for this change. Simultaneously (5), for this subset we change the 
date of the event from the date of infection to the date of discharge, transfer to a non-eligible unit, or death—
i.e., the date of censoring, had no infection been observed. Finally (6), we fit the planned frailty model 
described in Section 2.2 above and record whether the null hypothesis of no association was rejected or not. 
This process is repeated many times, and the proportion of rejections is an estimate of the power under the 
given effect size. A confidence limit on this estimated power can be generated. In the table below, we show the 
power for removing the outcome from 0, 10, 20, and 30% of the subjects in the intervention arm in the 
intervention period. Removing 0% of the outcomes is a test of the technique: since we are not reducing the 
infection rate, the null is true, and rejections should occur only about 5% of the time.  
In the initial planning of the trial, we used the available 4-month baseline sample of patients from our 
participating trial hospitals and used three bootstrap samples to represent each 12-month baseline and 4.5 
bootstrap samples to represent the 18-month intervention period. This resulted in the power shown in Table 2. 
 
 
Table 2: Power and Exact 95% CI for Primary and Select Secondary Outcomes*    
Intervention Effect 
Primary Outcome 
MRSA, VRE Clinical Cultures 
Gram Negative 
MDRO** 
Clinical Cultures
All Pathogen Bacteremia 
*0% 
5.6% (4.3-7.2%) 
4.1% (3.0 – 5.5%) 
5.2% (3.9 – 6.8%) 
10% 
33% (28-37%) 
15% (12 – 18%) 
23% (19 - 27%) 
20% 
92% (90-94%) 
44% (40 – 49%)
68% (63 - 72%)
30% 
100% (99-100%)
82% (79 – 86%)
98% (96 – 99%)
*Based on 500 bootstrap samples for each effect size, except for the 0% estimate, for which we did 1000 simulations 
to increase confidence that the alpha level is maintained when the null is true. 
** Gram-negative multi-drug resistant organism clinical cultures 
 
 
The above power estimates (Table 2) show that the technique has the desirable characteristic of 
rejecting the null only 5% of the time when it is true. We also see that power for MRSA or VRE clinical culture 
is ample, even if we prevent only 20% of the infections. For both selected secondary outcomes, the power is 
less, but still quite acceptable if the intervention prevents 30% of the infections. The primary strengths of the 
bootstrap power approach are that it allows us to avoid using literature estimates for the parameters when 
such estimates may not apply to the trial population, and it also avoids unrealistic assumptions about regularity 
(equal cluster sizes) or distribution (logistic instead of frailty models). The main weakness in this case is that 
correlation within hospital is generated to be the same in the baseline and intervention periods. In addition, we 
have not received all desired randomization stratification data by the time of this submission. Thus, 
stratification in the bootstrap process is limited to hospital size and the baseline rate of the outcomes. We 
believe that these are relatively benign issues: the correlation structure is unlikely to change importantly from 
period to period during the actual study, and the stratification is mainly to promote balance. It may affect the 
power, but mainly by reducing the variability in the outcome. 
After the baseline period, we were able to re-estimate the power, using the data collected in the full 
baseline period. We used the bootstrap process outlined above, except that we used the observed 12-month 


5 
 
data for the baseline. We assessed the power for the original proposal of an 18-month intervention and for a 
slightly extended 21-month intervention period, oversampling the observed 12-month baseline data as needed 
in each case (see Table 3). This updated power assessment was considered to be more accurate in that it 
used approximately three times as much real data—12 months vs. four months—compared to the original 
estimate. Furthermore, additional cleaning steps were applied to the 12 months of baseline data. In these new 
assessments, we focused on an intervention effect of 20%. The power for the primary outcome was 99.9% 
(95% CI: 99.4-99.99%) with 18 or 21 months of follow-up.  For all pathogen bacteremia, the power was 85% 
(95% CI: 83-87%) with 18 months follow-up and 89% (87-90%) with 21 months follow-up. The steering 
committee decided to use 21 months of follow up to ensure greater power with less common secondary 
outcomes and account for lessened effects at sites that may drop out of the trial. 
 
Table 3: Revised Power and Exact 95% CI for Primary and Select Secondary Outcomes*    
Analysis 
Effect 
Primary Outcome 
MRSA, VRE Clinical Cultures
All Pathogen Bacteremia 
As-randomized, 18-mo 
20% 
99.9% (99.4% – 99.99%) 
85% (83% – 87%) 
As-randomized, 21-mo 
20% 
99.9% (99.4% – 99.99%) 
89% (87% – 90%) 
 
 
Bibliography and References Cited 
1 Randomized Evaluation of Decolonization versus Universal Clearance to Eliminate MRSA (REDUCE MRSA) 
Cluster Randomized Clinical Trial http://clinicaltrials.gov/ct2/show/NCT00980980. Funded by Agency for 
Healthcare Research and Quality (Task Order PI: Huang; DEcIDE Network PI: Platt). Last accessed on April 
19, 2012. 
2 Huang SS, Septimus E, Kleinman K, Moody J, Hickok J, Avery T, Lankiewicz J, Gombosev A, Terpstra L, 
Hartford F, Hayden M, Jernigan JA, Weinstein R, Fraser VJ, Haffenreffer K, Cui E, Kaganov RE, Lolans K, 
Perlin J, Platt R. Randomized evaluation of decolonization vs. universal clearance to eliminate methicillin-
resistant Staphylococcus aureus in ICUs (REDUCE MRSA Trial, Abstract 36049). IDWeek (1st Annual Joint 
Meeting of IDSA, SHEA, HIVMA, and PIDS), October 17-21, 2012 (San Diego, CA) 
3 Klar N, Donner A. The merits of matching in community intervention trials: a cautionary tale. Statistics in 
Medicine 1997;16(15):1753-64. 
4 Imai K, King G, Nall C. The essential role of pair matching in cluster-randomized experiments, with 
application to the Mexican universal health insurance evaluation. Statistical Science 2009;24:29–53. 
5 Centers for Disease Control and Prevention. National Healthcare Safety Network. Tracking Infections in 
Acute Care Hospitals/Facilities. http://www.cdc.gov/nhsn/acute-care-hospital/index.html. Last accessed on 
September 28, 2013. 
6 Cuzick J. The efficiency of the proportions test and the logrank test for censored survival data. Biometrics 
1982;38:1033–1039. 
7 Annesi I, Moreau T, Lellouch J. Efficiency of the logistic regression and Cox proportional hazards models in 
longitudinal studies. Stat Med 1989;8:1515–1521. 
8 Green MS, Symons MJ. A comparison of the logistic risk function and the proportional hazards model in 
prospective epidemiologic studies. J Chronic Dis 1983;36:715–723. 
9 van der Net JB, Janssens AC, Eijkemans MJ, Kastelein JJ, Sijbrands EJ, Steyerberg EW. Cox proportional 
hazards models have more statistical power than logistic regression models in cross-sectional genetic 
association studies. Eur J Hum Genet. 2008;16(9):1111-6. Epub 2008 Apr 2. 
10 Hayes RH, Moulton LH.  Cluster Randomized Trials. CRC Press, New York 2009, p 207. 
11 R Development Core Team (2013). R: A language and environment for statistical computing. R Foundation 
for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org/. Last accessed 
September 28, 2013. 
12 Kleinman K, Huang SS. Calculating Power by Bootstrap, with an Application to Cluster-Randomized Trials. 
EGEMS (Wash DC). 2017 Feb 9;4(1):1202. doi: 10.13063/2327-9214.1202. eCollection 2016.  
13 Efron B, Tibshirani R. An Introduction to the Bootstrap. Boca Raton, FL: Chapman & Hall/CRC.1993.  
ISBN 0-412-04231-2