Clinical trial NCT03118232
Conditions: Hospitalization Infection
Keywords: Nursing Homes Health Facilities Infections Readmissions Multi-Drug resistance Antibiotic Use
Sponsor: University of California, Irvine
Start date: 2017-04-03; Completion date: 2023-10-10
Enrollment: 13952 participants; Allocation: RANDOMIZED; Intervention model: PARALLEL

Brief summary:
This is a cluster-randomized trial of nursing homes to assess whether decolonization with routine chlorhexidine bathing and periodic use of nasal antiseptics can reduce hospitalizations associated with infections, antibiotic utilization, and multi-drug resistant organism (MDRO) prevalence. The comparator arm will be routine bathing care.

Detailed description:

Arms: arm 1: Nursing homes assigned to this arm will perform decolonization using topical antiseptic products. arm 2: Routine bathing per facility protocol.
Interventions: intervention 1: Chlorhexidine gluconate (CHG) intervention 2: Iodophor (10% povidone-iodine)
Intervention descriptions: intervention 1: 2% no-rinse chlorhexidine gluconate (CHG) for bed bathing and 4% rinse-off CHG showering. Bathing frequency will be per routine plus admission bathing. intervention 2: Nasal decolonization using topical 10% povidone-iodine nasal swabs will be applied to all residents on admission for 5 days twice daily plus every other week Monday-Friday using a twice daily regimen.

Protocol documents:
Prot_SAP_000.pdf: 
Protocol
Protocol for: Miller LG, McKinnell JA, Singh RD, et al. Decolonization in nursing homes to prevent infection and 
hospitalization. N Engl J Med. DOI: 10.1056/NEJMoa2215254
This trial protocol has been provided by the authors to give readers additional information about the work.


The Protect Nursing Home Trial 
Protocol 
Table of Contents 
Protocol.........................................................................................................................Page 2
a.
Original protocol – Group 1 Routine Care Protocol and Toolkit
b.
Original protocol – Group 2 Decolonization Protocol and Toolkit
c.
Final protocol – N/A, same as original protocol
d.
Summary of changes – N/A, no changes
Statistical Analysis Plan...............................................................................................Page 80
a.
Original statistical plan (2/4/2015)
b.
Final statistical plan (8/10/2019)
c.
Summary of changes
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. ._ . _ ::lir ·· 
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PROTECTING NURSING HOME RESIDENTS FROM INFECTIONS AND READMISSIONS 
Data Collection 
The goal of Project PROTECT is to evaluate whether a decolonization strategy can 
better protect residents from multi-drug resistant organisms (MDROs) and their 
associated infections and hospitalizations when compared to routine bathing and 
showering. To understand the impact of decolonization, we will assess several 
outcomes: 
Primary Outcome 
•
Transfers due to infection (% of discharges to a hospital due to infection)
Secondary Outcome 
•
All cause admissions(% of discharges to a hospital)
Other outcomes of interest include MDRO prevalence (MRSA, VRE, ESBL, CRE), 
antibiotic usage, and emergence of resistance (strain collection). Project PROTECT 
coordinators will work closely with the on-site study champions to collect data 
on measured outcomes. Additionally, data will be obtained from state and 
national datasets. 
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Project PROTECT Introduction 
Your nursing home has agreed to participate in Project PROTECT, a 
federally funded randomized trial of nursing homes in Southern 
California. Project PROTECT is being conducted by infectious diseases 
physicians at the University of California Irvine, and Harbor-UCLA. 
 
From this training you will learn: 
Why Project PROTECT is important 
Which group your nursing home belongs to 
The importance of routine care 
How to contact study staff for additional information 
 
This training module will take approximately 10 minutes to complete 
 
2 
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This trial is evaluating whether bathing with special antiseptic soap plus 
using nasal products to remove nose bacteria (universal decolonization) 
is better than routine bathing in nursing homes in reducing infection 
and antibiotic-resistant bacteria 
The goal is to: 
Reduce transfers to hospitals due to infections 
Reduce antibiotic use 
Reduce multi-drug resistant organisms (MDROs) 
The 28 Southern California nursing homes participating in this trial will 
be randomized to one of the following groups: 
Group 1: Routine Care 
Group 2: Universal Decolonization 
Intervention begins April 2017 
 
 
What is Project PROTECT? 
3 
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Your nursing home has been randomized to Group 1: Routine Care 
This means you will continue routine bathing and showering of all 
residents with your usual products as you are already doing  
 
Group 1: Routine Care 
Maintain Current Bathing/Showering 
4 
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Every new strategy needs to be compared to an existing strategy to 
know if it is better or not 
Strategies that are not better than current care, should not be adopted 
Nursing homes in the routine care group serve as the current gold 
standard to assess whether decolonization is helpful or not 
Remember, decolonization is labor-intensive and incurs cost, so we only 
want to implement if we are absolutely certain it is effective 
Critical to maintain current practices 
Do NOT adopt decolonization 
Importance of the  
Routine Care Group 
5 
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Here is a scenario on the importance of maintaining current practices: 
Let’s assume decolonization has a real effect. Group 1 (Routine Care) 
nursing homes add in some additional interventions that improve 
infection rates while Group 2 adopts Universal Decolonization. The trial 
shows no difference between Group 1 and Group 2 nursing homes and 
decolonization is deemed to be not-effective. No nursing homes adopt 
decolonization and an important and effective solution is lost.  
By working together, all nursing homes in the PROTECT Trial will 
collectively learn the effect of the decolonization strategy 
It is Critical to Maintain Current 
Practices 
6 
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Competing Interventions 
7 
IMPORTANT  
Participation in the routine care group includes an agreement to NOT 
implement new quality improvement initiatives that involve CHG or 
iodophor  
 
Your key role is to maintain current processes for bathing and 
showering. Do not change current basic practices.  
 
 
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Special Circumstances 
8 
If a physician places an order to decolonize a resident with iodophor, 
chlorhexidine, or both, you should do the following: 
 
1. Follow the physician orders 
AND 
2. Remind the physician that this nursing home is part of Project 
PROTECT. This nursing home has been randomized to the routine care 
arm which does not involve routine decolonization of residents.  
 
 
21


 
No. The IRB has granted a waiver of consent. 
 
 
This protocol has approved by the UC Irvine IRB and is in compliance 
with the Code of Federal Regulations (45 CFR part 46), to be 
implemented as a minimal risk project. Specifically, this project compares 
two Quality Improvement strategies. This improvement strategy has met 
all criteria for waiver of consent. A handout will be provided for all 
residents upon admission to the nursing home. 
 
 
Is Resident Consent Required? 
9 
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This is a landmark trial to identify the best strategies for 
reducing infection, antibiotic use, hospitalization, and multi-
drug resistant organisms (MDROs) in nursing homes.  
 
Your role in the Routine Care group of this trial is critical to 
understand whether decolonization is or is not beneficial 
over current best practice.  
 
Thank You 
10 
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1/2 
 
 
 
 
 
 
Group 1: Routine Care 
Protocol Training Post-Test  
 
Date: _________________ 
   Title (circle one):  CNA     LVN     RN     OTHER: ____________________ 
 
Name: _________________________________  
 Signature: ______________________________ 
 
 
1. Your nursing home is participating in the PROTECT Project. Which group has your 
nursing home been randomized to? 
A. Group 1: Routine Care 
B. Group 2: Universal Decolonization 
 
2. What is the purpose of the PROTECT Project? 
A. To reduce multi-drug resistant organisms like MRSA and ESBL 
 
B. To protect residents from infection  
C. To reduce antibiotic usage 
D. All of the above 
 
3. As part of the routine care group, your nursing home is agreeing to all of the following 
except? 
A. Continue current bathing and showering practices 
B. Maintain an antibiotic and transfer log 
C. Bathe all residents with chlorhexidine soap 
D. None of the above 
 
4. Participation in the routine care group includes an agreement to NOT implement new 
quality improvement initiatives that involve CHG or iodophor 
A. True 
B. False 
 
5. When will the PROTECT Project intervention launch? 
 
A. April 2017  
B. May 2017 
C. June 2017 
 
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PROTECTING NURSING HOME RESIDENTS FROM INFECTIONS AND READMISSIONS 
Universal Decolonization Toolkit Binder 
Table of Contents 
Document 
Tab 
Welcome & Summary of Goals................................................................ Inside Front Cover 
Study Investigators.................................................................................... 
Inside Front Cover 
What to Expect.................................................................................................................... 
1 
Nursing Protocol.................................................................................................................. 
2 
Frequently Asked Questions............................................................................................... 
3 
Resident Talking Points....................................................................................................... 
4 
Staff Handouts..................................................................................................................... 
5 
Resident Handouts............................................................................................................... 
6 
Skills Assessment.................................................................................................................. 
7 
Data Collection..................................................................................................................... 
8 
Just in Time Training............................................................................................................ 
9 
10 
Safety and Side Effects........................................................................................................ 
11 
Study Related Events .......................................................................................................... 
Contact List................................................................................................ 
Outside Back Cover 
Wall and Shower Cling Laminates........................................................... Inside Back Cover 
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Prevent infections during nursing home stay 
HOW TO APPLY Nasal 10% Povidone-Iodine (Iodophor) 
Instructions 
 Apply iodophor swabs twice daily for 5 days every 
other week 
 Clears bacteria that live in the nose that can cause 
infection, like S. aureus (including MRSA) 
 80% of S. aureus infections are caused by bacteria in 
the resident’s own nose  
 
Povidone-Iodine use in specific circumstances 
 If nasal device in place (NG tube), swab around  
tubing if possible 
 If nasal packing in place, do not apply to that nostril 
 
STAFF 
Do NOT blow nose if solution drips.  
Gently wipe with a tissue. 
Clean nostrils (including tip) with 
tissues prior to  application 
Repeat with 2nd 
swab in other nostril 
Apply nasal povidone-iodine antiseptic swabs to all  
residents twice daily for 5 days every other week 
to help remove germs and prevent infection 
 
How to Use:  
Insert swab into one nostril and 
rotate for 30 seconds, or at least 3 
times around slowly, covering all 
surfaces. Discard swab. 
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PROTECTING NURSING HOME RESIDENTS FROM INFECTIONS AND READMISSIONS 
Data Collection 
The goal of Project PROTECT is to evaluate whether a decolonization strategy can 
better protect residents from multi-drug resistant organisms (MDROs) and their 
associated infections and hospitalizations when compared to routine bathing and 
showering. To understand the impact of decolonization, we will assess several 
outcomes: 
Primary Outcome 
•
Transfers due to infection (% of discharges to a hospital due to infection)
Secondary Outcome 
•
All cause admissions(% of discharges to a hospital)
Other outcomes of interest include MDRO prevalence (MRSA, VRE, ESBL, CRE), 
antibiotic usage, and emergence of resistance (strain collection). Project 
PROTECT coordinators will work closely with the on-site study champions to 
collect data on measured outcomes. Additionally, data will be obtained from 
state and national datasets. 
1 
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Protect Trial: 
Protecting Nursing Homes from Infections and Hospitalization 
Statistical Analysis Plan 
Ken Kleinman, ScD 
February 4, 2015 
PROTECT Trial Design Characteristics and Outcomes 
Study Design 
Cluster-randomized controlled clinical trial 
Study Population 
Residents of 28 Southern California nursing homes serving adults (>18 years old) 
Exclusions 
Facilities routinely using decolonization; dedicated dementia or psychiatric units. 
Age <18y.  
Unit of Randomization 
  Arm 1 (N=14) 
  Arm 2 (N=14) 
Nursing homes 
Routine bathing practice per established protocols 
Use of chlorhexidine for all showering/bathing, nasal mupirocin x 5 days for 
MRSA carriers 
Baseline 
Routine bathing with soap and water 3x/week; no nasal decolonization 
Intervention 
Body decolonization: 3 daily CHG baths on admission, then 3x/week CHG 
bathing  
Nasal decolonization: Nasal iodophor twice daily for 5 days every other week 
Study Period 
18-Month Baseline Period (Retrospective Data): Sept 2015-February 2017 
Training and Phase In Period: March-June 2017 (not included in analysis)
18-Month Intervention Period: July 2017-December 2018 
Primary Outcome 
Infectious admissions (% of transfers to a hospital that are due to infection) 
Secondary Outcomes 
All-cause admissions (% of discharged residents transferred to a hospital for any 
reason)  
Antibiotic usage (average antibiotic days per resident) 
Prevalence of MDRO colonization (MRSA, VRE, ESBL, CRE) * 
* Outcome of an aim distinct from the trial aim in original grant. Has distinct design, 
methods, collection, analysis. 
Randomization 
The unit of randomization will be the nursing home, with all residents within a 
nursing home assigned to the same arm. Cluster randomization is the only feasible 
randomization method when an intervention must be applied (or is typically applied) to an 
entire group, such as a quality improvement initiative. It is also the only method for 
evaluating interventions for which individuals are linked, e.g., by shared exposure to 
contagious illness, as occurs in nursing homes. Cluster randomization also has the 
advantage of studying interventions under conditions of actual use, of minimizing 
disruption to normal practice, and of allowing the use of health system resources for 
ensuring compliance with the intended treatment. 
In cluster-randomized trials, the relatively smaller number of randomized units 
means that simple randomization may not ensure balance of key variables by chance alone. 
The existence of a priori data allows us to improve balance through stratification. One way 
is to establish pairs with similar covariate profiles in which one member per pair is 
assigned to each arm. There is no “best” method of making pairs. One approach we used in 
86


a prior trial145 is to calculate the Mahalanobis distance between facilities across all 
available key variables and choose the pairings with the minimum average within-pair 
distance. In this approach, we standardize the variables and then multiply by values 
calibrated to reflect any difference in their importance with respect to balance.  
To select the best pairing scheme, we will compare this distance method to other 
pair construction schemes, using graphical methods to compare the balance between the 
arms under each scheme. For example, if two variables had to be balanced, we could 
tentatively divide the sample into two groups under a pair 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 distribution 
of balance under a scheme. Comparing the resulting scatterplots from each scheme can 
reveal the relative risks of imbalance and benefits for balance accruing to each scheme. One 
scheme may result in close balance on one key characteristic and very variable balance on 
the other, while a competing scheme may have good median balance on both 
characteristics, but a long tail implying a few bad-luck assignments with poor balance.  
We aim to balance facility case mix based upon population age, wounds, 
comorbidities, and functional status scores, as well as infection prevention activities 
assessed by survey. To assess balance, we will use a parallel coordinates plot, a 
multivariate plot method. A simulation (Figure) shows a potential result of a single pair 
construction method using baseline values of volume, outcome, device  
use, and bathing frequency. Each blue, red, green, or black line  
shows the mean difference between arms for all four variables 
for one realized randomization under the scheme. The results 
show that a few randomizations, in blue, are relatively 
imbalanced on volume and outcome but are balanced on device 
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 
not be an ideal scheme, as medium or poor balance is common. 
 
Statistical Analysis 
All outcomes will be assessed similarly. Here, we use the example of the primary 
outcome: the probability a transfer to a hospital is for an infectious diagnosis. Main trial 
results will be based on as-randomized, unadjusted analyses using logistics regression 
models. Clustering within nursing home will be accounted for using random effects, which 
are added model terms that allow unique baseline rates for each nursing home, and are 
necessary to account for clustered randomization. Model terms will include individual-level 
data on arm, nursing home, outcome events, trial period (baseline vs. intervention) and an 
interaction term between trial period and arm. Trial success will be determined by the 
significance of the interaction term, which assesses whether the difference in log odds 
between the baseline and intervention period differs significantly between the two arms. 
We can write a simple version of the model symbolically as:  
Volume        Outcome 
 Devices          Bathing 
Figure. Parallel Coordinates Plot Showing  
Simulated Balance Across Multiple Variables 
87


𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙൫Prൣ𝑅𝑅𝑖𝑖𝑖𝑖൧൯= log ቆ
Prൣ𝑅𝑅𝑖𝑖𝑖𝑖൧
1 −Prൣ𝑅𝑅𝑖𝑖𝑖𝑖൧ቇ= 𝛽𝛽0 + 𝛽𝛽1𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖+𝛽𝛽2𝑃𝑃𝑃𝑃𝐴𝐴𝑙𝑙𝑙𝑙𝑃𝑃𝑖𝑖𝑖𝑖+ 𝛽𝛽3𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖∗𝑃𝑃𝑃𝑃𝐴𝐴𝑙𝑙𝑙𝑙𝑃𝑃𝑖𝑖𝑖𝑖+ 𝑏𝑏𝑖𝑖 
where 𝑙𝑙 is a nursing home, 𝑗𝑗 is a resident within the nursing home, and 𝑅𝑅𝑖𝑖𝑖𝑖=1 if the transfer 
has an infection diagnosis and 0 if not. 𝐴𝐴𝐴𝐴𝐴𝐴 and 𝑃𝑃𝑃𝑃𝐴𝐴𝑙𝑙𝑙𝑙𝑃𝑃 are indicator variables and are = 0 
for patients in a nursing home in the control arm or baseline period and 1 if in the 
intervention arm or period, respectively. The random effect,𝑏𝑏𝑖𝑖, allows for different 
readmission rates at each nursing home, and accounts for the clustering within a given 
nursing home. The ultimate effect of the intervention is assessed by 𝛽𝛽3. If it is negative with 
p-value < .05 (or 95% CI excluding 0), then we can conclude that the intervention reduces 
the risk of infectious transfer. 
 
Subsequent analyses will include as-treated and covariate-adjusted models. 
Adjusted models will account for individual age, gender, comorbidities (by diagnostic 
codes), and functional status scores. All analyses will use current versions of SAS (9.4), 
SAS/STAT (13.2, SAS Institute, Cary NC) and/or R (3.1.2). 
 
Power 
We assessed power via resampling methods. We used preliminary data on outcomes 
adjusted to an 18-month baseline and intervention period. First, each nursing home was 
randomly assigned to a study arm. Then, individuals were sampled with replacement from 
within each nursing home to simulate “baseline” period data. Individuals were sampled 
again to simulate the “follow-up” period data. For nursing homes assigned to intervention 
arm, observed readmissions among the sampled follow-up individuals were removed at a 
given probability to reflect the effect of the intervention. Finally, the analysis planned for 
the trial was conducted on the resampled “baseline” and “follow-up” data. In this case, the 
planned analysis is a generalized linear mixed model logistic regression with main effects 
for period and arm, with the study effect assessed via their interaction, as described above. 
To estimate power, we repeated this process 200 times. The proportion of times that the 
null hypothesis of no interaction effect was rejected, with alpha = .05, provided the 
estimated power. In contrast to analytic approaches to power, we also needed to find 
confidence limits on the estimate, which we did using exact methods. For cluster-
randomized trials, this approach has the advantage that it does not require guessing the 
value of the intra-class correlation coefficient, or, importantly, its variability. In addition, 
this approach does not require an assumption of constant cluster size.  
For our primary outcome, preliminary data showed that 21.3% of hospital transfers 
from nursing homes were due to infection. For the probability of transfer due to infection, 
we assessed the power for a relative reduction of 15%, from 21.3% to 18.1%. Power was 
estimated at 89% (95% CI [83%, 93%]). For our key secondary outcome of the probability 
of transfer to a hospital among discharges, our preliminary data showed that 37.8% of 
discharges were associated with admission to hospitals across the nursing homes. For this 
outcome, we estimated the power for a relative reduction of 8%, from 37.8% to 34.8%, to 
be 99.5% (95% CI [97%, 99.9%]). Even at a relative reduction of 5%, we have 74% power 
(95% CI [67%, 80%]). These effect sizes are meaningful because there is tremendous 
88


national interest in reducing preventable readmissions, of which infections are a major 
cause. Reductions in overall readmissions of 1-3% are lauded as sizeable in the CMS 
Hospital Readmissions Program which fined 2,225 hospitals in its first year, with each 
fraction of a percent resulting in substantial lost revenue. The second year of this program 
saw a reduction of 0.6% in all cause readmissions. In summary, our power assessment 
demonstrates adequate power to detect plausible and meaningful effects of the proposed 
trial. 
 
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Protect Trial:  
Protecting Nursing Homes from Infections and Hospitalization 
 
Statistical Analysis Plan 
Ken Kleinman, ScD 
August 10, 2019 
 
Study Conditions and Period 
 
Participating nursing homes (28) were randomized to  
• Arm 1 – Routine Care 
Nursing homes in this group arm did not change their bathing materials, timing, 
schedule, or other aspects of bathing regimen.  
• Arm 2 – Universal Decolonization 
Nursing homes in this group used chlorhexidine soap or wipes for admission 
bathing and then for all resident bathing.  They did not otherwise change their 
bathing materials, timing, schedule, or other aspects of bathing regimen.  In 
addition, nasal iodophor was administered twice daily Monday through Friday, 
every other week. 
Study Periods  
• 18-month baseline period: September 2015-February 2017 
• Phase-in period: March-June 2017 (not included in analysis) 
• 18-month intervention period: July 2017-December 2018 
 
Note that the baseline period occurred before the beginning of trial activities, including 
randomization.  Data collection for primary and secondary trial outcomes used electronic 
sources and could not have been affected by randomization status. 
 
Trial Outcomes 
 
Trial Outcomes 
Explanation 
Primary outcome:  
Hospital transfer due to infection, 
among hospital transfers  
If the resident was transferred from the nursing 
home to a hospital, was the transfer made because 
the patient had an infection? 
Secondary outcome:  
Hospital transfer, among transfers 
If the resident was transferred from the nursing 
home, were they transferred to a hospital? 
 
90


Other outcomes are found in the below table.  At the time of the study conception, these 
were planned for secondary manuscript submissions. 
Other Outcomes 
Explanation 
Multidrug-resistant organism 
(MDRO) colonization 
Was a resident colonized with a multidrug-resistant 
organisms (MDROs): (MRSA, VRE, ESBL, CRE) 
Hospital transfer due to infection, 
among hospital transfers in Long 
Stay and Short Stay Subsets 
As in above table, but with analyses stratified by 
length of stay. 
Hospital transfer, among transfers 
in Long Stay and Short Stay Subsets 
As in above table, but with analyses stratified by 
length of stay. 
Emergency Department Transfer 
due to Infection; overall and in 
Long Stay and Short Stay Subsets 
Probability that a resident is sent to the emergency 
department for an infection, overall and stratified by 
length of stay. 
 
Statistical Analysis 
Main trial results will be based on as-randomized, unadjusted analyses using 
generalized linear mixed models logistic regression. All outcomes will be assessed 
similarly. Here, we use the example of the primary outcome: transfer to a hospital for an 
infectious diagnosis. Clustering within nursing home will be accounted for using random 
effects, which are added model terms that allow unique baseline rates for each nursing 
home, and are used to account for clustered randomization.207-208 Model terms will include 
individual-level data on arm, nursing home, outcome events, trial period (baseline vs. 
intervention) and an interaction term between trial period and arm. Trial results will be 
assessed through the interaction term, which measures the difference in log odds between 
the baseline and intervention period between the two arms. We can write a simple version 
of the model symbolically as:  
𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙൫Prൣ𝑅𝑅𝑖𝑖𝑖𝑖൧൯= log ቆ
Prൣ𝑅𝑅𝑖𝑖𝑖𝑖൧
1 −Prൣ𝑅𝑅𝑖𝑖𝑖𝑖൧ቇ= 𝛽𝛽0 + 𝛽𝛽1𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖+𝛽𝛽2𝑃𝑃𝑃𝑃𝐴𝐴𝑙𝑙𝑙𝑙𝑃𝑃𝑖𝑖𝑖𝑖+ 𝛽𝛽3𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖∗𝑃𝑃𝑃𝑃𝐴𝐴𝑙𝑙𝑙𝑙𝑃𝑃𝑖𝑖𝑖𝑖+ 𝑏𝑏𝑖𝑖 
where 𝑙𝑙 is a nursing home, 𝑗𝑗 is a resident within the nursing home, and 𝑅𝑅𝑖𝑖𝑖𝑖=1 if the transfer 
has an infection diagnosis and 0 if not. 𝐴𝐴𝐴𝐴𝐴𝐴 and 𝑃𝑃𝑃𝑃𝐴𝐴𝑙𝑙𝑙𝑙𝑃𝑃 are indicator variables and are = 0 
for patients in a nursing home in the control arm or baseline period and 1 if in the 
intervention arm or period, respectively. The random effect, 𝑏𝑏𝑖𝑖, allows for different 
readmission rates at each nursing home, and accounts for the clustering within a given 
nursing home. The ultimate effect of the intervention is assessed by 𝛽𝛽3. If it is negative with 
p-value < .05 (or 95% CI excluding 0), then we can conclude that the intervention reduces 
the risk of infectious transfer. 
 
Subsequent analyses will include as-treated and covariate-adjusted models. 
Adjusted models will account for individual age, gender, comorbidities (by diagnostic 
codes), and functional status scores. All analyses will use current versions of SAS (9.4), 
SAS/STAT (13.2, SAS Institute, Cary NC) and/or R (3.1.2). 
91


Power 
We assessed power via resampling methods. We used preliminary data on outcomes 
adjusted to an 18-month baseline and intervention period. First, each nursing home was 
randomly assigned to a study arm. Then, individuals were sampled with replacement from 
within each nursing home to simulate “baseline” period data. Individuals were sampled 
again to simulate the “follow-up” period data. For nursing homes assigned to intervention 
arm, observed readmissions among the sampled follow-up individuals were removed at a 
given probability to reflect the effect of the intervention. Finally, the analysis planned for 
the trial was conducted on the resampled “baseline” and “follow-up” data. In this case, the 
planned analysis is a generalized linear mixed model logistic regression with main effects 
for period and arm, with the study effect assessed via their interaction, as described above. 
To estimate power, we repeated this process 200 times. The proportion of times that the 
null hypothesis of no interaction effect was rejected, with alpha = .05, provided the 
estimated power. In contrast to analytic approaches to power, we also needed to find 
confidence limits on the estimate, which we did using exact methods. For cluster-
randomized trials, this approach has the advantage that it does not require guessing the 
value of the intra-class correlation coefficient, or, importantly, its variability. In addition, 
this approach does not require an assumption of constant cluster size.  
For our primary outcome, preliminary data showed that 21.3% of hospital transfers 
from nursing homes were due to infection. For the probability of transfer due to infection, 
we assessed the power for a relative reduction of 15%, from 21.3% to 18.1%. Power was 
estimated at 89% (95% CI [83%, 93%]). For our key secondary outcome of the probability 
of transfer to a hospital among discharges, our preliminary data showed that 37.8% of 
discharges were associated with admission to hospitals across the nursing homes. For this 
outcome, we estimated the power for a relative reduction of 8%, from 37.8% to 34.8%, to 
be 99.5% (95% CI [97%, 99.9%]). Even at a relative reduction of 5%, we have 74% power 
(95% CI [67%, 80%]). These effect sizes are meaningful because there is tremendous 
national interest in reducing preventable readmissions, of which infections are a major 
cause. Reductions in overall readmissions of 1-3% are lauded as sizeable in the CMS 
Hospital Readmissions Program which fined 2,225 hospitals in its first year, with each 
fraction of a percent resulting in substantial lost revenue. The second year of this program 
saw a reduction of 0.6% in all cause readmissions. In summary, our power assessment 
demonstrates adequate power to detect plausible and meaningful effects of the proposed 
trial. 
92


The Protect Nursing Home Trial 
Statistical Plan Amendments  
 
Date 
Statistical Plan Amendments 
2/4/2015 
Original  
8/20/2018 • 
Defined ‘other’ outcomes for secondary publications. Previously, clinicaltrials.gov 
listed both secondary outcomes for the primary manuscript and outcomes for 
secondary manuscripts under the same heading. After the ability to separate the two 
under “other outcomes,” we clarified additional outcomes that were not trial 
outcomes. 
o Multidrug-resistant organism (MDRO) colonization (separate aim from the 
trial in original grant; distinct design, methods, collection, analysis) 
o Hospital transfer due to infection, in Long Stay and Short Stay Subsets 
o Hospital transfer for any reason, in Long Stay and Short Stay Subsets 
o Emergency Department visits due to Infection; overall and in Long Stay and 
Short Stay Subsets 
6/19/2019 • 
Removed secondary outcome (antibiotic usage) 
o We had proposed to use the CMS Minimum Data Set (MDS) to assess this and 
other trial outcomes. At the time of the grant proposal, antibiotic usage was a 
newly-added variable. We assumed that this variable would increase in usage 
and accuracy with time. However, even five years after the variable was 
added, expert opinion from the creators of the MDS dataset, was that 
antibiotic data capture in MDS was poor and insufficient for analysis. Thus, 
the steering committee removed this as an outcome and filed a change with 
clinicaltrials.gov at that time. We note that MDS data is released 18 months 
following completion of a calendar year. This decision was made prior to 
receipt of any intervention data (2017-2018 MDS data) for the trial and prior 
to any data cleaning or analysis. 
8/10/2019 • 
Updated layout, no change in content or meaning 
• 
Removed randomization details from statistical plan 
 
 
93