Implementation and Effectiveness of Engagement and Collaborative Management to Proactively Advance Sepsis Survivorship: A Hybrid Effectiveness-Implementation Randomized Controlled Trial
Phase: NA
Conditions: Sepsis
Sponsor: Wake Forest University Health Sciences

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 Introduction  
Sepsis survivors represent a vulnerable population with high morbidity, mortality, and 
hospital readmissions, and strategies to improve transition and recovery are urgently needed. 
Post-sepsis care recommendations target specific deficits experienced by sepsis survivors; 
however, these recommendations are infrequently applied. Our STAR program implements 
evidence-based post-sepsis care recommendations, directed to high-risk sepsis survivors, and 
delivered using a proactive, nurse navigation process demonstrated to reduce readmissions and 
costs. This project will evaluate if implementation of the STAR program within a large healthcare 
system will improve outcomes for high-risk sepsis patients and will generate knowledge of best 
practices for implementation and dissemination of post-sepsis transitions of care. 
The proposed research consists of 1) a pragmatic, stepped-wedge, cluster randomized 
controlled trial, 2) prospective economic evaluation, and 3) concurrent analyses of the 
implementation process. 
1) We will conduct a two-arm, pragmatic, stepped-wedge, cluster randomized controlled trial to 
test the effects of two post-sepsis care implementation strategies on clinical outcomes. At each 
four-month interval, one of eight hospitals will be randomly assigned to transition from ARM 1) 
usual care (UC) consisting of current, standard peri- and post-discharge activities, to ARM 2) 
STAR program beginning during index hospitalization for sepsis and extending 90 days after 
hospital discharge. 
2) Cost analysis will occur through a prospective economic evaluation that uses empirical trial 
data on healthcare costs, health utilization, and clinical outcomes from the hospital system's 
data warehouse as inputs to a Markov state transition model for formal cost-effectiveness 
analysis. 

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3) The study's implementation will be guided by the Consolidated Framework for 
Implementation Research. A mixed methods evaluation framework will be used to inform future 
implementation and dissemination activities. 
Primary Objective 
The primary objective is to evaluate composite 90-day hospital readmissions and mortality 
between usual care and the STAR program. 
Hypothesis 
Patients allocated to receive the STAR program intervention will have lower combined hospital 
readmission and mortality rates at 90 days post hospital discharge, compared to patients 
allocated to receive usual care. 
Primary Outcome Variable 
Composite, dichotomous endpoint of all-cause mortality or unplanned hospital readmission 
assessed 90 days post index hospital discharge. Both inpatient and observation status 
hospitalizations will count towards the readmission outcome because either status represents 
an adverse event important to patients and healthcare systems. For the composite primary 
outcome, we will capture all patients with either date of death or eligible hospital readmission 
prior to 90 days post discharge as event-positive. 
Secondary Outcome Variables 
1. 90-day all-cause mortality 
2. 90-day all-cause hospital readmission 
3. 90-day all-cause emergency department visits 
4. 90-day outpatient provider visits 

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5. 90-days cause-specific hospital readmissions for a) infection, b) chronic lung disease, c) 
heart failure, and d) acute kidney injury 
6. 90-day acute care-free days alive, defined as the sum of days alive without inpatient, 
observation, and emergency department encounters during the 90 days after discharge 
7. Percentage of patients with documented inpatient functional assessment or physical therapy 
consult 
8. Percentage of patients with documented inpatient mental health assessment 
9. Percentage of patients with documented referrals to a) rehabilitation services, b) speech 
therapy, and c) behavioral health 
10. Percentage with outpatient follow-up within 7 days of hospital discharge 
11. Percentage with designated medication reconciliation form in the electronic health record 
during the 90 days post hospital discharge 
12. Percentage with palliative care consult 
13. Percentage with completed care preferences documentation 
14. Percentage discharged to hospice care from initial sepsis hospitalization 
15. Place of death 
Subject Selection 
Consistent with our pragmatic study design concept, eligibility criteria are broad, the sample size 
is large, and study procedures are embedded into the context of routine care. Patients are 
eligible if they present to the emergency department and are subsequently admitted under 
inpatient or observation status to a participating hospital. Specific inclusion and exclusion 
criteria are described. 

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Inclusion Criteria: 
1. At least 18 years of age upon admission 
2. Oral/parenteral antibiotic or bacterial culture order within 24 hours of emergency department 
presentation 
a) culture drawn first, antibiotics ordered within 48 hours or 
b) antibiotics ordered first, culture ordered within 48 hours (adapted from criteria applied in 
development of the Third International Consensus Definitions for Sepsis and Septic Shock) 
3. Deemed as high-risk for death or 90-day readmission using risk-scoring models 
4. Not discharged at the time of patient list generation 
Exclusion Criteria: 
1. Antibiotic doses administered in the operating room will be excluded from eligibility 
determination as these administrations are likely to represent pre-operative infection prophylaxis 
and not presumed infection 
2. Patients transferred from other acute care hospitals due to lack of data on disease course 
prior to transfer receipt 
3. Patients with a change in code status (i.e., do not resuscitate, do not intubate) within 24 hours 
after infection onset due to presumed limitation of aggressive treatment. 
4. Patient has not been randomized previously. 
Risk models include clinical and administrative data to produce a near-real time risk score that 
identifies cohorts at high risk for death and 90-day readmission, delivering the information at the 
point of care. Risk models will run each morning and automatically generate lists of eligible, 
high-risk patients admitted over the prior 72 hours. During the intervention phase at each facility 

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(i.e., after transition from UC to STAR), the navigator will receive the list of admitted, high risk 
patients via secure email. Patients will only be identified as eligible for randomization once 
during the index hospital stay and will not be eligible for study inclusion during subsequent 
admissions. Patients assigned to the intervention will be contacted and introduced to the 
navigation process before hospital discharge. 
Randomization 
Eight participating hospitals will be randomized to a staggered sequence (i.e., steps 1 to 8) of 
transitioning from UC to STAR group assignments using SAS Enterprise Guide v7.1 (Cary, NC). 
All eight hospitals will start with a control period of varying length, dependent on timing of 
transition to the intervention arm. Every four months, one randomly assigned hospital will cross 
over to initiate the STAR program and remain in the intervention arm through study completion. 
All patients identified as eligible using the risk model will be allocated to the study ARM that 
matches the admitting hospital's assignment (STAR or UC). During the intervention phase at 
each facility (i.e., after transition from UC to STAR), the navigator will receive the list of 
admitted, high risk patients via secure email. 
Study Procedures 
Patient identification: Each morning, the list of eligible, high-risk patients is automatically 
securely transmitted to the study database within REDCap and the study PIs are notified of 
completed transmission. The list of eligible, high risk patients at hospitals allocated to receive 
the STAR program (based on randomly assigned months) will also be automatically emailed via 
secure transmission to the STAR navigators. 
Initial contact with the STAR program: After the patient list is generated, the STAR navigator will 
contact the care team (e.g., bedside nurse, hospitalist) to obtain permission to discuss the 
STAR program with patients assigned to STAR. At any time, the hospitalist, bedside nurse, or 

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patient may decline for the patient to participate in the STAR program or any components of 
usual care. 
Summary of STAR services duration: The STAR navigator will provide telephone- and EHR-
based support within the hospitalization and to patients across all discharge settings with 
remote monitoring at specified intervals following hospital discharge. Within 48 hours of 
discharge, patients allocated to STAR will have their first post-discharge follow-up. They will 
continue to receive STAR directed services for 90 days following their discharge. Subsequently, 
they will be transitioned back to the next appropriate care location. 
Detailed description of STAR services: At the initial contact with the patient or caregiver during 
hospitalization, the STAR navigator will introduce the STAR program and conduct health literacy 
screening and mental health screening using the Patient Health Questionnaire (PHQ)-2, with 
reflex administration of PHQ-9 for positive PHQ-2 (i.e., > or = 3).130 The STAR navigator will 
convey results to the care team for appropriate behavioral health referrals. The STAR navigator 
will also confirm consultations with physical therapy (recommendations delivered to care team), 
antibiotic stewardship (i.e., a coordinated program that promotes appropriate antibiotic use) with 
additional infectious disease consult if ongoing Systemic Inflammatory Response Syndrome 
criteria more than 48 hours after infection onset (i.e., abnormal body temperature, heart rate, 
respiratory rate, white blood cell count), and palliative care team. For discharge, the STAR 
navigator will provide disease-specific education to the patient and caregiver and discharge 
education including what to expect during transition and a "playbook" with information on 
planned follow-ups. The navigator will also record all in-person and phone follow-up and all 
disciplines with post-acute touchpoints in the provider discharge plan. Because communication 
of care gaps and recommendations can be difficult in the complex acute care environment, our 
group has conducted a pilot with 15 patients to define communication strategies and contact 
points. Important elements include connecting to: patient and caregiver personal phones while 

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admitted; inpatient case managers for estimated discharge date, primary care provider 
information, and regular 24-48-hour contact until discharge; EHR-based messaging to 
providers; and post-acute care team within 24 hours of discharge (e.g., home health clinical 
supervisor) to initiate two-way communication. 
Irrespective of discharge location (e.g., home versus skilled nursing facility), the STAR 
navigator will provide monitoring through phone contact with patient, caregiver, or provider (as 
appropriate for post-acute care location) at <48 hours, 72-96 hours, and 7-10 days post-
discharge. These touchpoints will include medication reconciliation, infection and respiratory 
symptom monitoring, vitals and weight checks, and confirmation that the patient can make 
scheduled outpatient appointments. Concerns identified through proactive monitoring will 
prompt a primary care provider contact for follow-up. If the primary care provider cannot be 
reached after one attempt, the navigator will contact the hospitalist back-up provider. Following 
the immediate post-acute interval, the STAR navigator will maintain weekly telehealth 
touchpoints with patients who remain at high-risk for poor outcome (i.e., any previous positive 
screen, high-risk comorbid condition [e.g., chronic lung disease, heart failure], or low health 
literacy) and every third week touchpoints with patients considered low-risk after the immediate 
post-acute interval. These 90-day post-acute touchpoints will include infection and respiratory 
symptom check, vitals and weight monitoring, and consideration for repeat outpatient provider 
visit. Identified concerns will prompt attempts to contact the primary care provider followed by 
hospitalist back-up provider. The STAR support service will complete 90 days post hospital 
discharge. 
Statistical Analysis 
Sample Size Determination: This study is designed to detect a 20% relative reduction in 
composite 90-day mortality and hospital readmission. The usual care group is indicated to have 

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roughly 40% combined 90-day mortality and readmission rate. With a total sample size of 4392 
patients enrolled over 36 unit-months, we will have 90% power (α=0.05) to detect a 20% relative 
reduction in composite 90-day mortality and hospital readmission. 
Statistical Methods 
Aim 1: Evaluate the effectiveness of an innovative, virtual nurse navigator 
implementation strategy to improve post-sepsis care.  
All primary and secondary analyses will follow intention to treat approach, such that all 
patients meeting identical criteria and randomized will be analyzed, regardless of adherence to 
intervention assignment. This real-world approach will assess intervention effectiveness while 
limiting selection biases associated with adherence. We will compare the two groups (STAR 
versus usual care) at baseline for age, sex, race, and comorbidities to assess for balance 
inferred randomization. The primary outcome is the composite of hospital readmission and 
mortality at 90 days post hospital discharge. Because hospitals are randomized to STAR in a 
staggered sequence and the outcome varies at the patient level, we will use a generalized linear 
mixed-effects model to compare the composite 90-day mortality and readmission primary 
outcome measure between the intervention conditions, adjusting for clustering effects across 
multiple levels (e.g., admitting hospital, implementation timing) to include hierarchical data 
structures. The intervention fixed-effect coefficient will compare STAR versus UC conditions 
(i.e., UC as reference). We will evaluate covariates for patient (e.g., sex, race, comorbidities, 
length of stay, discharge disposition [skilled nursing facility, rehabilitation, home]) and 
organizational factors (e.g., admitting hospital, guideline adherence, urban versus rural setting, 
implementation timing) to identify any potential differences between study arms. We will include 
covariates in model if unbalanced between STAR and UC groups. We will also evaluate effect 

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modification by conducting analyses stratified by these characteristics (e.g., discharge 
disposition) and by process measures with potential to effect outcomes (e.g., referrals). 
 
Secondary outcomes are described below and will be assessed using the same 
approach. That is, we will construct generalized linear mixed-effects models, like for the primary 
outcome analysis, for individual assessments of secondary outcomes and process measures. 
We will test different distribution parameters to determine the optimal distribution family for each 
model and outcome variable (e.g., binomial, gamma distributions). 
1. All-cause mortality. We will assess the individual mortality component of the primary 
composite endpoint separately. We will construct generalized linear mixed-effects models with 
all-cause mortality as the dependent variable, assessed as a binary outcome at 90 days post 
discharge. The intervention fixed-effect coefficient will compare STAR versus UC conditions, 
using an intent-to-treat approach. 
2. All-cause hospital readmission. We will assess the individual hospital readmission 
component of the primary composite endpoint separately. We will construct generalized linear 
mixed-effects models with all-cause hospital readmission, including inpatient and observation 
status, as the dependent variable, assessed as a binary outcome at 90 days post discharge. 
The intervention fixed-effect coefficient will compare STAR versus UC conditions, using an 
intent-to-treat approach. 
3. All-cause emergency department visits. We will assess the number of emergency 
department visits. We will construct generalized linear mixed-effects models with the number of 
emergency departments visits as the dependent variable assessed at 90 days post discharge. 
We will use the Poisson distribution to model the outcome due to the expected distribution of the 

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counts of emergency department visits. The intervention fixed-effect coefficient will compare 
STAR versus UC conditions, using an intent-to-treat approach. 
4. Outpatient provider visits. We will assess the number of outpatient provider office 
visits. We will construct generalized linear mixed-effects models with the number of outpatient 
provider office visits as the dependent variable assessed at 90 days post discharge. We will use 
the Poisson distribution to model the outcome due to the expected distribution of the counts of 
outpatient provider office visits. The intervention fixed-effect coefficient will compare STAR 
versus UC conditions, using an intent-to-treat approach. 
5. Cause-specific hospital readmission. We will assess the cause-specific hospital 
readmissions related to a) infection, b) chronic lung disease, c) heart failure, and d) acute 
kidney injury. We will construct separate generalized linear mixed-effects models for each of the 
four cause-specific hospital readmission conditions. The dependent variable for each model will 
be infection-, chronic lung disease-, heart failure-, and acute kidney injury-related hospital 
readmissions, respectively, assessed as a binary outcome at 90 days post discharge. The 
intervention fixed-effect coefficient will compare STAR versus UC conditions, using an intentto-
treat approach. 
6. Acute care-free days alive. We will assess the total sum of days alive without 
inpatient, observation, and emergency department care utilization. The number of acute-care 
free days will be calculated from the date of index hospital admission through 90 days after 
discharge or date of death if prior to 90 days. We will construct generalized linear mixed-effects 
models testing different distribution patterns to determine the optimal distribution family for the 
count of acute care-free days. The intervention fixed-effect coefficient will compare STAR 
versus UC conditions, using an intent-to-treat approach. 

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7. Inpatient functional assessment or physical therapy consult. We will compare the 
receipt of inpatient functional assessment or physical therapy consult, captured from the 
electronic health record. We will construct generalized linear mixed-effects models with 
documented functional assessment or physical therapy consult as the dependent variable, 
assessed as a binary outcome at the time of hospital discharge. The intervention fixed-effect 
coefficient will compare STAR versus UC conditions, using an intent-to-treat approach. 
8. Inpatient mental health assessment. We will compare the receipt of inpatient mental 
health assessment, captured from the electronic health record. We will construct generalized 
linear mixed-effects models with documented mental health assessment as the dependent 
variable, assessed as a binary outcome at the time of hospital discharge. The intervention fixed-
effect coefficient will compare STAR versus UC conditions, using an intent-to-treat approach.  
9. Support service referrals. We will compare the receipt of support service referrals. 
Documented referrals to a) rehabilitation services, b) speech therapy, and c) behavioral health 
will each be captured separately from the electronic health record. We will construct separate 
generalized linear mixed-effects models with documented referral to rehabilitation services, 
speech therapy, and behavioral health each as the dependent variable of one model. Each 
variable will be assessed as a binary outcome at 90 days after hospital discharge. The 
intervention fixed-effect coefficient will compare STAR versus UC conditions, using an intentto-
treat approach. 
10. Early outpatient provider follow-up. We will compare the receipt of early outpatient 
provider followup, defined as an outpatient provider encounter within 7 days of hospital 
discharge and captured from the electronic health record. We will construct generalized linear 
mixed-effects models with documented early outpatient provider follow-up as the dependent 
variable, assessed as a binary outcome at 7 days after hospital discharge. The intervention 

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fixed-effect coefficient will compare STAR versus UC conditions, using an intent-totreat 
approach. 
11. Outpatient medication reconciliation. We will compare the receipt of outpatient 
medication reconciliation, captured from the electronic health record. We will construct 
generalized linear mixed-effects models with designated outpatient medication reconciliation 
form completion as the dependent variable, assessed as a binary outcome at 90 days after 
hospital discharge. The intervention fixed-effect coefficient will compare STAR versus UC 
conditions, using an intent-to-treat approach. 
12. Palliative care consult. We will compare the receipt of palliative care consult, 
captured from the electronic health record. We will construct generalized linear mixed-effects 
models with documented palliative care consult completion as the dependent variable, 
assessed as a binary outcome at index hospital discharge. The intervention fixed-effect 
coefficient will compare STAR versus UC conditions, using an intent-to-treat approach. 
13. Completed care preferences. We will compare the completion of care preferences 
documents, captured from the electronic health record. We will construct generalized linear 
mixed-effects models with care preferences document completion as the dependent variable, 
assessed as a binary outcome at index hospital discharge. The intervention fixed-effect 
coefficient will compare STAR versus UC conditions, using an intent-to-treat approach. 
14. Discharged to hospice care. We will compare the discharged to hospice care from 
the index hospitalization for sepsis, captured from the electronic health record and hospital 
discharge records in the enterprise data warehouse. We will construct generalized linear mixed-
effects models with discharged to hospice care as the dependent variable, assessed as a binary 
outcome at index hospital discharge. The intervention fixed-effect coefficient will compare STAR 
versus UC conditions, using an intent-to-treat approach. 

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15. Place of death. We will compare the place of death for individuals who die over the 
90-day trial interval, captured from the electronic health record and hospital discharge records. 
We will construct generalized linear mixed-effects models with place of death as the dependent 
variable, assessed as a categorical outcome at 90 days after hospital discharge. The 
intervention fixed-effect coefficient will compare STAR versus UC conditions, using an intent-to-
treat approach.  In addition to primary intent-to-treat analyses, we will conduct modified intent-
to-treat analyses excluding sepsis patients 1) who do not survive index hospitalization since 
STAR is designed to support patients during their transition out of the hospital; and 2) 
discharged against medical advice since providers do not have the opportunity to deliver full 
care and prepare the patient for discharge. Based on published data and internal estimates, we 
expect less than 10% of study patients will die during index hospitalization and fewer than 2% 
will be discharged against medical advice. We will present results from group comparisons as 
odds ratios and 95% confidence intervals. 
We do not anticipate substantial missing data because all outcomes are routinely 
collected variables and utilization is broadly captured within the large integrated system. While 
utilization may occur outside of CHS, this is not expected to be a major limitation because of 
CHS market share and accessibility. Specifically, CHS operates three large hospitals in 
Mecklenburg County, the only acute care hospitals in Anson, Burke, Cleveland, Lincoln, Stanly, 
and Union counties, where most of this study will be conducted, and more than 40 hospitals in 
the region overall. Additionally, any utilization that occurs outside the system is anticipated to be 
non-differentially distributed between groups and thus impact treatment groups equally. Further, 
internal data indicates nearly 75% of high-risk patients are Medicare-insured and have complete 
healthcare claims within and outside CHS facilities captured through participation in the ACO 
managed by CHS. We will leverage this data for subgroup analyses and to explore missing data 
patterns that can be adjusted using pattern-mixture methods in sensitivity analyses.  

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All hypothesis tests will be two sided and data will be analyzed using SAS Enterprise 
Guide v7.1 (Cary, NC) or R v3.5 (Vienna, Austria). 
Interim Analysis 
Interim analyses will be conducted when 50% of enrollment targets are met and 90-day 
patient outcomes are obtained. All interim analyses will be presented to the Data and Safety 
Monitoring Board (a Steering Committee representative, an independent clinician investigator, 
and an independent biostatistician), who will make a recommendation on continuing the trial as 
planned or modified. The following analyses will be conducted by the study statistician and 
provided to the Data and Safety Monitoring Board: 
1. Sample size re-estimation 
a. Drop-out rate for STAR: We define the drop-out rate for the STAR program to 
be the rate of nonadherence to the intervention. Non-adherence is defined by those 
patients who declined, were discharged before contact by STAR staff, and were not able 
to be contacted after hospital discharge. The population is defined as all patients who 
were deemed eligible and identified as high risk for readmission or mortality. We 
propose to assess the drop-out rate in the STAR intervention arm when half of patients 
have 90-day follow up information available (n=2196). At that time, we will re-estimate 
the sample size needed for the study using drop-out rates. 
b. Event rate: We propose to re-estimate the sample size based on the combined 
90-day hospital readmission and mortality rates for both patients randomly assigned to 
STAR and usual care when half of the information is available for the trial (n=2196). We 
will base the re-estimation on the original 20% relative reduction to determine if more 
patients are needed to maintain 90% power. We will measure the composite outcome as 
defined for the primary effectiveness analysis, i.e., we will capture all patients with either 

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date of death or eligible hospital readmission prior to 90 days post discharge as event-
positive.  
2. Interim efficacy analysis with 50% information (i.e., 50% of target accrual with 90-day follow 
up data). 
We propose to conduct an interim effectiveness analysis of the primary outcome of 
composite 90-day hospital readmission and mortality rates once 50% of patients have been 
accrued and have 90-day outcomes. We will use the Haybittle-Peto procedure with boundary of 
less than 0.001. No additional interim analyses will be planned until the sample size re-
estimation and futility analyses are reviewed and a recommendation is made to continue, stop, 
or modify the study. 
3. Futility analysis/conditional power at 50% complete data information (50% of target 
accrual with 90-day follow-up data. We propose to conduct a futility analysis at 50% information 
accrual for the trial. The first futility analysis will be conducted at the same time as the sample 
size re-estimation based on the drop-out rate. Conditional power is the probability of detecting a 
difference at the end of the study (p<0.05) given the current data trend. We will estimate 
conditional power under the null hypothesis of no difference, the designed alternative of 20% 
relative reduction, and the current observed trend. A recommendation would be made to stop 
the study for futility if the conditional power is less than 0.10 for the designed alternative. 
Aim 2: To assess the cost-effectiveness of virtual nurse navigator-driven STAR program 
Within-trial Incremental Cost Effectiveness Ratio.  
We will estimate Incremental Cost Effectiveness Ratio (ICER) of STAR versus UC using 
data derived during the study interval as a measure of benefit to the hospital system. We will 
estimate ICER as incremental costs divided by incremental QALYs. The intervention group beta 
weight from cost model will be divided by the intervention group beta weight from QALYs model. 

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We will perform a one-way probabilistic sensitivity analysis to address uncertainty by varying 
inputs within 10-15%. 
Subgroup Analyses: Reliance on CHS data warehouse for measuring healthcare use may miss 
costs incurred in other health systems. We expect cross-system healthcare utilization to be 
similar between groups and low overall due to the size and accessibility of CHS in the study 
region. However, we will repeat analyses within the subgroup of patients enrolled in the ACO 
managed by CHS (more than 140,000 lives covered in 2018). Within this subset, we will have 
complete visibility of all healthcare claims within and outside of CHS during the study interval. 
Data from this group will be informative to health policy given the introduction of the CMS BPCI, 
a value-based alternative payment model to improve post-acute care for specific hospital 
diagnoses, including sepsis. Subgroup analyses will also be performed by hospital and patient 
factors (e.g., hospital length of stay, discharge disposition, comorbidities). 
Extrapolated Incremental Cost Effectiveness Ratio. We will build a Markov-type state-
transition simulation model using TreeAge (TreeAge Software Inc.) to estimate ICER of 
receiving STAR. We will use data derived during the study interval and other evidence-based 
sources to extrapolate cost-effectiveness analyses to 12 months after discharge. This analysis 
will be conducted to assess benefit from a societal perspective and determine if the intervention 
costs associated with STAR are offset by any subsequent reduction in morbidity costs (e.g., 
reduced hospital readmissions) and improvements in mortality. We will estimate the ICER as 
the incremental costs divided by the incremental QALYs, including a 3% standard discount for 
costs and outcomes. The intervention group beta weight from the cost model will be divided by 
the intervention group beta weight from the QALYs model. Sensitivity Analyses and Uncertainty: 
Model parameters will be sampled from their appropriate distributions over 1000 runs. We will 
vary key costs and effectiveness estimates across the range of plausible values to determine 
whether cost-effectiveness of STAR is sensitive to changes in specific assumptions made to 

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inform the baseline analysis. We will also estimate 95% confidence intervals around ICER point 
estimates using probabilistic analyses and create acceptability curves comparing STAR 
favorability at various willingness to pay thresholds. 
The data analyses for Aim 2 will be supported by a health economist consultant. The health 
economist consultant will support the cost and cost-effectiveness analysis of the intervention 
and will securely maintain the data files on a secure server with encryption. Data will be shared 
using a secure file transfer protocol, and will be facilitated through the Atrium Health Information 
and Analytics Services division. A consulting agreement is in place which specifies the sharing 
of data under a limited data set.  
Aim 3: To determine the key enablers, barriers, and contextual factors that contribute to 
the successful implementation of the STAR program 
The goal of this mixed methods assessment is to identify perceived barriers to, and 
enablers of, implementing the STAR program into the peri-discharge setting and feedback 
results to improve process. The Consolidated Framework for Implementation Research will 
guide discovery and evaluation of implementation barriers and enablers. Other outcomes will be 
assessed based on elements from the Reach, Effectiveness, Adoption, Implementation, and 
Maintenance (RE-AIM) model to quantify the reach and effectiveness of the STAR program and 
its potential for dissemination and scalability. 
Our mixed-methods approach includes: a) semi-structured interviews conducted with 
administrative leaders and providers during Pre-Implementation to assess organizational 
support, culture, and recommendations for STAR implementation and then Post-implementation 
to plan future dissemination; b) navigator/provider interviews and surveys during Implementation 
to assess inner setting, intervention, and individual factors; c) patient/caregiver interviews and 
surveys during implementation phase (recruited from the intervention arms) to learn about their 

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experiences with the STAR program and perceived barriers and facilitators to act on STAR 
recommendations; and d) focused ethnography of the navigator role in the program conducted 
on four work days (two days for each navigator) at the three 8-month intervals during 
implementation. Focused ethnography is an innovative component of our study, selected to 
obtain a holistic and nuanced understanding of the navigator’s role in the intervention. Interview 
recordings and ethnographic observation fieldnotes will be transcribed for coding and qualitative 
analysis using ATLAS.ti v8.0. We will perform content analysis using the constant comparison 
method, an inductive grounded theory approach for developing code structure through iterative 
comparison of newly coded text with previously coded text of the same theme until final 
thematic refinement is achieved. We will finalize the code list and repeat data review using the 
finalized code structure for coding reliability. Participant recruitment will be primarily facilitated 
through email survey invitation in REDCap. Subject identifiers, including email address, will also 
be tracked and captured in REDCap. 
RECRUITMENT AND RETENTION PLAN 
Recruitment Plan 
Pragmatic Clinical Trial 
Eligible patients at any of the (8) selected hospitals will be included in the study. Risk 
models will run each morning and automatically generate lists of eligible patients admitted over 
the prior 72 hours identified to be high-risk for readmission or mortality. During months allocated 
to the intervention, the navigator will receive the list of actively admitted, high risk patients via 
secure email. Assignment to a study arm will be based on the date the eligible patient is 
identified by the risk model. Patients will only be identified as eligible for randomization once 
during the index hospital stay, regardless of hospital length of stay. Patients with repeat 
hospitalization will be eligible for repeat study inclusion during admissions that begin more than 

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90 days after discharge from index hospitalization. Patients assigned to the intervention will be 
contacted by telephone via the bedside nurse (or other member of the care team) and 
introduced to the Sepsis Transition and Recovery program and navigation process prior to 
hospital discharge. 
Mixed-methods process analyses 
A subset of patients randomized to receive the STAR program and providers and 
administrative leaders will be recruited to provide interviews for analyses of STAR program 
implementation processes. For pre- and postimplementation data collection, we will recruit and 
interview 8 administrative leaders and 8 providers regarding organizational support and 
recommendations for STAR implementation. During the implementation phase, the study team 
will gather data from providers, navigators, and patients at 12-, 20-, and 28-month 
implementation time points to inform and adapt the implementation process. We will recruit 
administrative leaders and providers via email with three attempts made to contact and enroll 
each person. For patient recruitment, we will identify patients or their caregivers prior to hospital 
discharge. The STAR navigator will ask about interest in participating in the research interview. 
Research staff will contact interested individuals for written informed consent, and Dr. Eaton or 
other qualitatively trained research staff will conduct the interviews. 
Duration of Human Subjects Involvement 
It is expected that participant enrollment will begin by July 2020 and that human 
subjects’ activity will be completed by December 2023. 
Length of Active Enrollment 
It is expected that participant enrollment and randomization for the stepped-wedge 
randomized trial will take place from July 2020 through June 2023. 

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