Pathways to Cardiovascular Disease Prevention and Impact of Specialty Referral in Underrepresented Racial/Ethnic Minorities With HIV (Coordinating Center)
Conditions: HIV/AIDS, Cardiovascular Diseases
Sponsor: Duke University

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Pathways to cardiovascular disease prevention and impact of specialty 
referral in under-represented racial/ethnic minorities with HIV.  
ClinicalTrials.gov ID NCT04025125 
November 27, 2024 

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Protocol Title 
 Pathways to cardiovascular disease prevention 
and impact of specialty referral in under-
represented racial/ethnic minorities with HIV. 
(DCRI Central and Statistical Coordinating 
Center)  
 
Protocol Short Title 
Pathways to CVD Prevention (DCRI Central and 
Statistical Coordinating Center)  
 
Protocol identifying number 
 Pro00101104 
Principal Investigator 
Gerald Bloomfield, MD, MPH 
Duke University Health System 
Duke Clinical Research Institute 
Funded by 
NIH  
Protocol version identifier 
v.4.0 
Date of last version of protocol 
27 November 2024 
Research objectives 
1. To identify factors associated with cardiology 
referral in under-represented racial and 
ethnic minority (URM) populations with HIV 
and elevated cardiovascular risk  
2. To evaluate the association between 
cardiology referral and CVD outcomes in 
under-represented racial and ethnic 
populations with HIV and elevated 
cardiovascular risk 
a. To evaluate the association between 
cardiology referral and guideline-directed 
CVD prevention measures in URM 
populations with HIV and elevated CVD risk 
3. To identify facilitators and barriers to 
optimal CVD prevention using semi-
structured interviews 
 
 
 
 
 

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TABLE OF CONTENTS 
1. LIST OF ABBREVIATIONS ............................................................................................... 4 
2 LIST OF RESPONSIBLE PARTIES ..................................................................................... 5 
3 AMENDMENTS AND UPDATES ...................................................................................... 6 
4 PURPOSE OF THE STUDY.................................................................................................. 6 
5 BACKGROUND AND SIGNIFICANCE .............................................................................. 6 
6 STUDY AIMS FOR AIMS 1, 2, AND 3................................................................................ 7 
7 RESEARCH DESIGN AND METHODS .............................................................................. 7 
7.1 Study Design .............................................................................................................. 7 
7.1.1 Study design for Aims 1 and 2 ...................................................................... 7 
7.1.2 Study design for Aim 3 .................................................................................. 7 
7.2 Study sites .................................................................................................................. 8 
7.2.1 Study Sites for Aims 1 and 2 ......................................................................... 8 
7.2.2 Study Sites for Aim 3 .................................................................................... 8 
7.3 Inclusion and exclusion for Aims 1, 2 and 3 .............................................................. 8 
7.3.1 Patient EHR data for Inclusion criteria for Aims 1 and 2 ............................. 8 
7.3.2 Patient EHR data for Exclusion criteria for Aims 1 and 2: ........................... 8 
7.4 Inclusion and exclusion criteria for Aim 3 ................................................................. 9 
7.4.1 Inclusion criteria for Aim 3 for Patient Participants .................................. 9 
7.4.2 Exclusion criteria for Aim 3 for Patient Participants ................................. 9 
7.4.3 Inclusion criteria for Aim 3 for Provider Participants ............................... 9 
7.4.4 Exclusion Criteria for Aim 3 for Provider Participants ............................. 9 
8 STUDY PROCEDURES ........................................................................................................ 9 
8.1 Aims 1 and 2 Study Procedures ................................................................................. 9 
8.1.1 Feasibility Surveys and Queries ............................................................................ 10 
We plan to distribute the following to DataMart sites as needed to meet study aims: .. 10 
8.2 Overall Study Schematic for Aims 1 and 2 .............................................................. 11 
8.3 Aims 1 and 2 Data Flow........................................................................................... 11 
8.4 Study Procedures for Aim 3 ..................................................................................... 11 
8.4.1 Interviews .............................................................................................................. 11 
8.4.2 Patient record linkage ............................................................................................ 12 

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8.4.3 Provider record linkage ......................................................................................... 12 
8.5 Aim 3 Data Flow ...................................................................................................... 13 
9 PARTICIPANT RECRUITMENT AND COMPENSATION ............................................. 13 
9.1 Participant recruitment and compensation for Aims 1 and 2 ................................... 13 
9.2 Participant compensation for Aim 3 ........................................................................ 13 
9.3 Patient Participant recruitment for Aim 3 ................................................................ 13 
9.4 PROVIDER PARTICIPANT RECRUITMENT FOR AIM 3 .......................................... 14 
10 CONSENT PROCESS........................................................................................................ 15 
10.1 Consent Process for Aims 1 and 2 --N/A. .............................................................. 15 
10.2 Consent process for Patient Participants for Aim 3 ............................................... 15 
10.3 Consent process for Provider Participants for Aim 3............................................. 16 
10.3 Subject’s capacity to give legally effective consent for Aim 3 .............................. 16 
11 RISK/BENEFIT ASSESSMENT ....................................................................................... 16 
11.1 Risk/benefit assessment for Aims 1 and 2 ............................................................. 16 
11. 2 Risk assessment for Aim 3 .................................................................................... 17 
11.3 Benefit assessment for Aim 3 ................................................................................ 17 
12 DATA ANALYSIS & STATISTICAL CONSIDERATIONS .......................................... 17 
12.1 Analysis Plan for Aim 1 ......................................................................................... 17 
12.1.1 Expected power and sample size for Aim 1 .............................................. 18 
12. 2 Analysis Plan for Aim 2 ........................................................................................ 18 
12.2.1 Expected power and sample size for Aim 2 .............................................. 19 
12. 3 Analysis Plan for Aim 3 ........................................................................................ 19 
12.3.1 Number of participants for Aim 3 ............................................................. 21 
13 DATA AND SAFETY MONITORING ............................................................................. 21 
14 DATA STORAGE & CONFIDENTIALITY ..................................................................... 21 
14.1 Data Storage and Confidentiality for Aims 1 and 2 ............................................... 21 
14.2 Data Storage and Confidentiality for Aim 3 .......................................................... 21 
15. REGUALTORY CONSIDERATIONS............................................................................. 22 
15.1 Single IRB for Aims 1, 2, and 3 submission and review of protocol. ................... 22 
15. 2 Regulatory Considerations for type of data set Aims 1 and 2............................... 23 
15. 3 Clinical Trials Registration ................................................................................... 23 
16 LITERATURE CITED ....................................................................................................... 23 

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1. List of Abbreviations 
 
ART 
Antiretroviral therapy 
CDM 
Common Data Model 
CRN 
Clinical Research Network 
CVD 
Cardiovascular Disease 
CWRU 
Case Western Reserve University 
DCRI 
Duke Clinical Research Institute 
DUHS 
Duke University Health System 
DRN OC 
Distributed Research Network Operations Center 
EHR 
Electronic Health Record 
HIV 
Human Immunodeficiency Virus  
MUSC 
Medical University of South Carolina 
NDI 
National Death Index 
NPI 
National Provider Identifier 
PCORnet 
The National Patient Centered Clinical Research Network 
PLWH 
People Living with HIV 
PHI 
Protected Health Information 
RA 
Research Assistant 
URM 
Under-Represented Racial and Ethnic Minority 
VUMC 
Vanderbilt University Medical Center 
WFUHS 
Wake Forest University Health Sciences 
  
 
 

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2 List of Responsible Parties  
 
Name, degree(s) 
Title 
Affiliation 
E-mail  
Gerald Bloomfield, MD, MPH 
PI 
DCRI 
gerald.bloomfield@duke.edu 
Eric Meissner, MD 
Co-Investigator 
Medical 
University of 
South Carolina 
meissner@musc.edu 
 
April Pettit, MD 
Co-Investigator 
Vanderbilt 
University 
Medical 
Center 
april.pettit@vumc.org 
Chris Longenecker, MD 
Co-Investigator  
University of 
Washington, 
Seattle, WA 
ctlongen@uw.edu>;  
Caryn Gee Morse, MD, MPH 
Co-Investigator 
Wake Forest 
University 
Health 
Sciences 
cmorse@wakehealth.edu 
Keith Marsolo, PhD 
 
Co-Investigator 
Duke  
 keith.marsolo@duke.edu 
 
Kevin Thomas, MD 
Co-Investigator 
Duke 
kevin.thomas@duke.edu 
 
Charles Muiruri, PhD 
Co-Investigator 
Duke   
charles.muiruri@duke.edu 
 
Nwora “Lance” Okeke, MD 
Co-Investigator 
Duke   
nwora.okeke@duke.edu 
 
Karen Chiswell, PhD 
Statistical 
Investigator 
 
DCRI 
karen.chiswell@duke.edu 
Larry Hill, PhD 
Lead Statistician 
DCRI 
claude.hill@duke.edu 
Robert Clare, MS 
Lead Statistician 
DCRI 
bob.clare@duke.edu 
Gretchen Sanders, MSN 
Project Leader 
DCRI 
gretchen.sanders@duke.edu 

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3 Amendments and Updates 
a. Update cover page on November 27, 2024 for clinicaltrials.gov information. 
4 Purpose of the study 
1. To identify factors associated with cardiology referral in under-represented racial and ethnic minority 
(URM) populations with HIV and elevated cardiovascular risk  
2. To evaluate the association between cardiology referral and CVD outcomes in under-represented 
racial and ethnic populations with HIV and elevated cardiovascular risk 
a. To evaluate the association between cardiology referral and guideline-based CVD prevention 
measures in URM populations with HIV and elevated CVD risk 
3. To identify facilitators and barriers to optimal CVD prevention  
 
Hypotheses to be tested: 
1. We hypothesize that patient- (e.g., health insurance) and provider-level (e.g., patient-provider race 
discordance) factors are associated with cardiology referral in URM people living with HIV (PLWH) at 
elevated CVD risk. The primary outcome measure is visit with a cardiologist after meeting criteria for 
elevated CVD risk.  
2. We hypothesize that URM PLWH who are seen by a cardiologist will have better CVD outcomes 
compared to those without referral. Primary outcomes are blood pressure and lipid control, and 
secondary outcomes are myocardial infarction, stroke, death and cardiovascular death both 
individually and as a composite, as a function of whether an individual at elevated CVD risk was seen 
by a cardiologist prior to first event. 
a. 
We hypothesize that guideline-directed care including tobacco cessation 
counseling, cardiac testing, blood pressure and cholesterol medication management, glycemic 
control and appropriate use of statins mediate the relationship between cardiology referral 
and CVD outcomes. 
3. Aim 3 is not driven by a pre-specified hypothesis.   
 
5 Background and Significance 
Cardiovascular disease among PLWH disproportionately affects URM populations in the U.S., and is an NIH HIV 
High Priority Topic of research. Multiple cohort studies have found that HIV infection is associated with 
increased risk of myocardial infarction, ischemic stroke, and heart failure.1-3 The literature shows a 27-75% 
increased risk for CVD attributable to HIV infection.1,4 In addition, PLWH are less likely than uninfected persons 
to receive treatment and procedures related to CVD.5 Under-represented racial and ethnic populations in the 
Southeast “HIV-belt” and “Heart Attack belt” face the highest comorbidity from HIV and cardiovascular disease 
(CVD) in the nation, accounting for 43% of all new HIV infections and CVD death rates 4x greater than other 
regions.6,7 Despite the benefit of cardiology referral for primary prevention of CVD8, URM are the least likely to 
be referred to cardiologists when overt CVD exists,9-11 or when patients are at high risk for CVD.12,13. Due to 
historical stigma, PLWH have been reluctant to engage in medical care outside of their HIV provider. Referral 

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to cardiology care may benefit PLWH at elevated CVD risk, however, we lack knowledge on the determinants 
of specialty referral and whether referrals improve cardiovascular outcomes for URM PLWH. The field needs 
knowledge on the effectiveness of cardiology referrals for URM PLWH. Such evidence will ultimately inform 
clinical care guidelines and health system interventions to improve health for URM populations with HIV.  
Thus, the long-term goal of this proposal is to generate evidence-based recommendations for the 
management of CVD risk in PLWH. The overall objectives of this application are to demonstrate the effect of 
cardiology referral on CVD outcomes in a racially/ethnically diverse cohort of PLWH, and to generate 
qualitative data with which to develop of a future intervention. Our central hypothesis is that cardiology 
referral reduces incident CVD events in URM populations with HIV compared to non-referral. Our hypothesis 
has been formulated based on our own work identifying that race and provider specialty impact cardiovascular 
risk management. The rationale for our research is that, once it is known how health disparity populations with 
HIV access cardiology referrals, and the impact on CVD outcomes, an intervention can be appropriately 
designed resulting in new and innovative approaches to the management of URM PLWH at elevated CVD risk. 
6 Study Aims for Aims 1, 2, and 3 
Aim 1: To identify factors associated with cardiology referral in under-represented racial and ethnic minority 
(URM) populations with HIV and elevated cardiovascular risk  
Aim 2: To evaluate the association between cardiology referral and CVD outcomes in under-represented racial 
and ethnic populations with HIV and elevated cardiovascular risk 
a. To evaluate the association between cardiology referral and guideline-directed CVD 
prevention measures in URM populations with HIV and elevated CVD risk 
 Aim 3: To identify facilitators and barriers to optimal CVD prevention  
7 Research Design and Methods 
7.1 Study Design 
This study has two overall arms with different study designs and distinct patient populations.   
7.1.1 Study design for Aims 1 and 2 
This study will perform a retrospective electronic health record-driven study of three institutions in the STAR 
Clinical Research Network.      
Aims 1 and 2 will be addressed by a retrospective cohort study design that does not involve enrolling 
patients. We will analyze patient-level and provider-level data from electronic health records (EHRs) from 
STAR Clinical Research Network (CRN) institutions that have been mapped to the Common Data Model (CDM). 
These institutions include Duke University Health System, Vanderbilt University Medical Center, Medical 
University of South Carolina, and Wake Forest University Health Sciences.  
7.1.2 Study design for Aim 3  
Aim 3 will be addressed by a prospective, qualitative descriptive study enrolling consented participants—
patients and health care providers—across four sites to identify the contextual, patient and healthcare 
provider-reported barriers and facilitators for achieving optimal primary CVD prevention. This aim provides the 
qualitative framework to develop a contextually appropriate model to prevent CVD among URM PLWH.  
 

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7.2 Study sites 
7.2.1 Study Sites for Aims 1 and 2 
Aims 1 and 2: Coordinating Center will collect health system data from Duke University Health System 
and Vanderbilt University Medical Center, and Medical University of South Carolina, and Wake Forest 
University Health Sciences via the STAR CRN using CDM.   
7.2.2 Study Sites for Aim 3 
Aim 3: Duke University Health System, Vanderbilt University Medical Center, Medical University of 
South Carolina will identify patients and health care providers for interviews; Case Western Reserve 
University will identify only health care providers for interviews. 
Participants will be identified/consented for the qualitative interviews from the following  sites: 
• 
Duke University Health System (DUHS):   
Patients & Health Care Providers 
• 
Vanderbilt University Medical Center (VUMC): Patients & Health Care Providers 
• 
Medical University of South Carolina (MUSC):   Patients & Health Care Providers 
• 
Case Western Reserve University (CWRU):   
Health Care Providers 
7.3 Inclusion and exclusion for Aims 1, 2 and 3 
7.3.1 Patient EHR data for Inclusion criteria for Aims 1 and 2 
(1) Race equals Black/African-American, American Indian/Alaska Native, Asian, Native Hawaiian/Pacific 
Islander, or More than one race, and/or Ethnicity equals Hispanic or Latino; 
(2) Documented evidence of HIV positive status (HIV positive diagnosis (ICD10 codes B20-B24, or ICD9 
codes 042, V08) and prescription of antiretroviral therapy (ART)); 
(3) Documented evidence of elevated AtheroSclerotic CardioVascular Disease risk (ACC/AHA ASCVD 10 
year risk ≥5%24, or Framingham Cardiovascular Disease 10 year risk ≥5%25) after HIV diagnosis. The 
date when the patient first meets either of these CVD risk thresholds and with 1 prior encounter not 
having CVD risk score defines the index time-point for Aim 1 of this study. These risk calculations 
depend on sex, age, body mass index, diabetes, current smoking, total cholesterol, HDL cholesterol, 
systolic blood pressure, and treatment for hypertension (defined from diagnosis codes). If cholesterol 
measures are not available, then body mass index may be used in place of lipids in the Framingham 
risk calculation; NOTE:  must have at least one prior encounter within 365 days within health system 
prior to index  
(4) Presence of a modifiable risk factor: hypertension, diabetes, elevated total cholesterol, elevated LDL 
cholesterol and/or tobacco use. 
7.3.2 Patient EHR data for Exclusion criteria for Aims 1 and 2: 
 
(1) Age <18 years of age or >99 years of age at index event;  
(2) Pre-existing ASCVD prior to index event, including a previous diagnosis of any acute myocardial 
infarction, heart failure, acute coronary syndromes, stable or unstable angina, arterial 

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revascularization (includes coronary arterial or peripheral), stroke, transient ischemic attack or 
peripheral arterial disease presumed to be of atherosclerotic origin determined by ICD codes;  
(3) Encounter with cardiology specialist within 1 year prior to index 
(4) Evidence of ART for pre-exposure prophylaxis (i.e., Truvada [emtricitabine/tenofovir disoproxil 
fumarate] or post-exposure prophylaxis (e.g., Truvada plus raltegravir) without HIV diagnosis. 
7.4 Inclusion and exclusion criteria for Aim 3 
7.4.1 Inclusion criteria for Aim 3 for Patient Participants 
(1) URM PLWH > 40 years of age, with 
(2)  a modifiable risk factor for CVD (such as hypertension, diabetes, elevated total cholesterol, high 
LDL cholesterol, or currently use tobacco), and/or known CVD 
7.4.2 Exclusion criteria for Aim 3 for Patient Participants 
(1) Unwilling or unable to provide oral informed consent; 
(2) Unable to perform an interview in English; 
(3) Diminished capacity to give oral consent; 
(4) Unwilling to be interviewed. 
 
7.4.3 Inclusion criteria for Aim 3 for Provider Participants 
(1) HIV providers will include infectious disease physicians, Internists or advance practice practitioners 
who report having seen > 1 PLWH under their care in the last 6 months; 
AND 
(2) Cardiology providers (physicians or advance practice providers) will be required to have taken care 
of at least 1 HIV-positive patient in the past 3 years; 
7.4.4 Exclusion Criteria for Aim 3 for Provider Participants 
(1) Unable to perform an interview in English; 
(2) Unwilling to be interviewed. 
8 Study Procedures 
8.1 Aims 1 and 2 Study Procedures 
We will analyze patient-level and provider-level data from electronic health records (EHRs) from STAR 
Clinical Research Network (CRN) institutions using CDM. These institutions include Duke University 
Health System, Vanderbilt Universities, and Medical University of South Carolina. 
We  (DCRI CC) will retrospectively ask the DataMarts to query the EHR for eligible patients based on 
age,  race, ethnicity, and HIV diagnosis to identify Black/African-American and other URM PLWH. 
Additional data that are already collected as part of routine medical care will be extracted by sites 

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from patient EHRs starting at the first clinical contact after January 1, 2010, to create a contemporary 
cohort with at least 5 years of longitudinal follow-up data.  Longitudinal follow- up data will be 
collected through December 31, 2020.   Data will be reviewed and or collected from patient EHR from 
January 1, 2010 through December 31, 2020. Patients will be included for analysis in this retrospective 
study at the time of the first clinical encounter during an (“eligibility period”) when they meet the 
inclusion/exclusion criteria (listed below), with a “look back period” of 1 years prior to “enrollment” 
(i.e., back as far as 2010) to establish medical history and other baseline factors. These dates may be 
adjusted during the course of the study depending on availability of data in the various DataMarts. The 
overall scientific premise is depicted in Section 8.2 which shows our approach without specific dates. 
DataMarts, Site PIs and Site study teams will link local EHR records with death events and cause of 
death prior to sending the final dataset to the DCRI CC. Sites will query and link records using National 
Death Index Plus, State Death records, Social Security Death Mastefile, Hospital death record and/or 
similar death record for causes of death including cardiovascular death, retaining protected health 
information needed to query these databases at the site level. 
Then, DCRI will take the query data that the sites provide to identify and further stratify data for 
potential cohort of patients, based on additional clinical characteristics.  At the earliest clinical contact 
within look back period, we will identify HIV status according to ICD9/10 codes and prescription of 
ART. We will include individuals >18 years of age and < 99 years of age, plus will ascertain time-
updated data for the following potential characteristics:  
hemoglobin, creatinine (or glomerular filtration rate), age, sex, race, ethnicity, insurance status, total 
cholesterol, HDL cholesterol, LDL cholesterol, systolic blood pressure, prescription of anti-hypertensive 
medications, family history of coronary artery disease, presence of diabetes, previous diagnosis of any 
acute myocardial infarction, hospitalization for heart failure, acute coronary syndromes, stable or 
unstable angina, arterial revascularization, stroke, transient ischemic attack and peripheral arterial 
disease presumed to be of atherosclerotic origin determined by ICD9/10 codes. Behavioral factors are 
smoking status, number of clinic visits per year, length of time between clinic visits and missed 
appointments with specialist after referral. Data will also be ascertained on HIV viral load, CD4 count, 
prescription of any ART and length of time on all medications. This list captures the majority of the 
data related to our scientific aims and will be modified as the availability of these data are discovered. 
8.1.1 Feasibility Surveys and Queries 
We plan to distribute the following to DataMart sites as needed to meet study aims: 
1. Feasibility survey(s):   
DCRI Coordinating Center (CC) will distribute questionnaire to understand the feasibility of 
capturing and populating key data elements in the Common Data Model (CDM) for study. 
2. Prep to Research (PTR) Query(s): 
DCRI CC will distribute PTR query to assess potential sample size at DataMart sites. 
3. Study Specific Data Characterization Query Activity(s):  
DCRI CC will program a study‐specific data characterization package(s) to ensure needed data 
elements are well‐populated within the Common Data Model at participating DataMart sites and 
pass quality checks. The study will also be a preliminary test of the cohort criteria for the study.  
The Data Marts will execute the queries, review output and then work with the CC address any 
data remediation necessary to successfully participate in the study and return essential data 
elements.   
4. Analytic Query(s) Activity:  

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DCRI CC will develop analytic queries to be distributed to DataMart sites to execute the program 
and provide output with data specifications as required for analysis from the indicated Common 
Data Model tables.  
  
8.2 Overall Study Schematic for Aims 1 and 2 
 
 
Sites extract data from their EHR and other ancillary systems (e.g., NDI plus, NPI, laboratory information 
systems) in order to populate the CDM.  The quality of the data within the CDM is assessed using a set of data 
curation routines, which are executed every time the data are refreshed (once per quarter).  The DCRI team 
will develop a study-specific query to identify patients using the inclusion/exclusion criteria defined below and 
then retrieve the required patient-and provider-level variables from the CDM at each site.  Datasets will be 
combined into a single study database for analysis by the statisticians. 
 
8.3 Aims 1 and 2 Data Flow    
 
8.4 Study Procedures for Aim 3 
  
8.4.1 Interviews 
Based on the assumed homogeneity of the study population, we expect to interview around 30 patients, 
around 16 HIV providers, and around 16 cardiologist providers. 
• 
We will conduct qualitative, semi structured interviews with each study participant. Patient 
participants will have interviews done by trained interviewers from their local site (e.g. Duke, 
Vanderbilt, or MUSC) and provider participants will have interviews done by trained interviewer from 
DUHS.  Participants will be asked to provide demographic information including but not limited to,  
age, sex, race, ethnicity, marital status, highest level of education, employment status, insurance 
status, if they see a primary care provider along with HIV providers, length of time seen at HIV/ID 
clinic, most recent HIV-related lab results,  and number of years on ART (see Demographics Form for 
PLWH).  
In the semi-structured interview, participants will be asked to first describe their general thoughts and 
perceptions of CVD prevention. Next, they will be asked a series of questions on the following topics: 
importance of heart health, concerns about heart health,  self-management of CVD risk factors, 
providers’ role in managing their CVD and CVD risk factors, barriers to CVD prevention structural, social 
support, affordability, access among others. The interviews will last approximately 1 to 1.5 hours and 
will be audio-recorded with the participant’s permission. Detailed notes will be taken for interviews 
conducted with individuals who decline audio-recording. Provider interviews will be conducted on the 
telephone while patient interviews will be in person, or by telephone if preferred by the patient (see 
Interview Guide Template for PLWH).  

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As a standard practice used in qualitative interview research, changes may be made to the qualitative 
question guide during study preparation, training, pre-testing, and data collection to improve phrasing, 
probes, or the flow of questions or to add related questions.    
 
8.4.2 Patient record linkage 
Each site will maintain a record to link patient participant name (that matches patient oral consent) to a 
unique ID number. The ID number will be used in place of their name in all data collection forms 
(demographics) and for the file name of the audio files/detailed notes and all patient participant information 
uploaded to Duke Box. For example, the name of the audio file/detailed notes uploaded to Duke Box should 
include the participant’s unique ID, the date of the interview, patient clinical site ID/name, and the initials of 
the interviewer.  Site identification is indicated as part of the unique PINs for each site. 
 
 
Unique PINs for patients by site are: 
 
• 
DUHS = D101 to D120 
• 
MUSC = SC101 to SC120 
• 
VUMC = V101 to V120 
 
For example, for a patient interview conducted at DUHS:    
• 
Participant Unique ID= D101 
• 
Interview conducted on December 5, 2018  
• 
Interviewer Brian Perry 
 
The audio file name: “D101_2018-12-05_BP”.   
 
8.4.3 Provider record linkage 
Each clinical site will provide provider participant contact information (first and last name, telephone 
number, and email address and clinical site) to Duke Box for DUHS personnel to contact eligible 
providers for interviews. DUHS will then maintain a record to link provider participant name (that 
matches provider information to unique ID number). The ID number will be used in place of their 
name in all data collection forms (demographics) and for the file name of the audio files/detailed notes 
and all provider participant information uploaded to Duke Box.  For example, the name of the audio 
file/detailed notes uploaded to Duke Box should include the provider participant’s unique ID, the date 
of the interview, and the initials of the interviewer.  Site identification is indicated as part of the 
unique PINs for each site. 
 
 
 
 
 
 
Unique PINs for infectious disease provider participants by site are: 
• 
DUHS = D201 to D210 
• 
CWRU =CW201 to CW210  

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• 
MUSC = SC201 to SC210 
• 
VUMC = V201 to V210 
 
Unique PINs for cardiologist provider participants by site are: 
 
• 
DUHS = D301 to D310 
• 
CWRU =CW301 to D310 
• 
MUSC = SC301 to SC310 
• 
VUMC = V301 to V310 
 
 
For example, for an infectious disease provider participant interview conducted at DUHS:    
• 
Participant Unique ID=D202 
• 
Interview conducted on December 4, 2018  
• 
Interviewer Amy Smith   
 
The memo file name: “D202_2018-12-04_AS”.   
 
 
  
8.5 Aim 3 Data Flow 
 
      
9 Participant recruitment and compensation 
9.1 Participant recruitment and compensation for Aims 1 and 2 
Aims 1 and 2 do not involve individual subject recruitment or compensation. 
9.2 Participant compensation for Aim 3 
Participants will be compensated with a one-time payment of $50 for participation in the semi-structured 
interview.  There are no costs to research participants.  
9.3 Patient Participant recruitment for Aim 3 
We will use purposive sampling on and will not be using a probability sample, based on the patient eligibility 
criteria, to recruit patient participants. Each site (Duke University Health System, Vanderbilt University Medical 
Center, and Medical University of South Carolina) will recruit, identify, and consent their own potential patient 
participant’s for Aim 3.  Eligible PLWH will be identified from the electronic medical record (EHR queries for all 
sites; Maestro Care report platform, DEDUCE queries if necessary for DUHS) based on the study criteria. To 
increase participation, patients will also be informed of the study by their provider during routine clinic visits 
and then approached and give oral consent to the study coordinators/research assistants if they choose to 
participate. After a brief screening, potential participants not meeting the inclusion criteria will be thanked for 
their time and excluded from enrollment. Those meeting inclusion criteria and exclusion criteria will be 
scheduled for an interview with the study coordinators/research assistant located at each site.  

14 
 
 
 
 
1. Study coordinator/Research Assistants will sensitize clinic staff and clinicians about the study using 
face-to-face communication and/or IRB-approved flyers, 
2. Each site will identify their own potential patient participant from a HIV or cardiology clinic, 
3. A study coordinator/research assistant will review patient participant charts for eligibility criteria,    
4. Study team (i.e., provider, research assistant, or coordinator) may also send an email recruitment 
letter to potential patient participants who meet eligibility criteria (see Patient Recruitment Letter) 
5. An informed consent form will be mailed electronically to the participant or a hard copy will be 
provided for participant to review by the local site key study personnel, and 
6.  Oral informed consent will be obtained by local site Key Personnel prior to enrollment, either in 
person or over the phone. 
 
 9.4 Provider Participant recruitment for Aim 3 
We will use purposive sampling on and will not be using a probability sample based on the provider eligibility 
criteria, to recruit provider participants.  Each site PI (Duke University Health System, Vanderbilt University 
Medical Center, and Medical University of South Carolina) and Case Western Reserve University will assist in 
the identification of HIV providers and cardiologist providers at their sites.   
NOTE:  For the provider interviews, we are requesting a waiver of informed 
consent.  Given the study design (interviews) and the study topic of factors 
related to specialty referral, we believe that part of this study could have 
qualified for Exempt Research status under 45 CFR 46.101(b)(2) if presented as a 
stand-alone protocol  (and not as part of a protocol that includes other data 
collection activities—i.e., the interviews with patients—that would not be 
considered exempt). This is because the interviews involve only the use of 
standard interview procedures and no identifiable personal health information 
will be collected from providers. All providers will receive an informational sheet 
that describes the purpose of the study, data collection procedures, risk, 
benefits, confidentiality procedures, and who to contact if they have 
questions/concerns. We will distribute the informational sheet to all potential 
providers during recruitment and before any data collection activities begin 
  
1. Investigators at each site will sensitize HIV providers and cardiologists about the study using face-to-
face communication, faculty meetings, email (see Provider Email template and Information Sheet for 
Provider Participants) 
2. Each site will identify their own potential provider participants from a HIV or cardiology clinic  
3. We will distribute the informational sheet to all potential providers during recruitment and before any 
data collection activities begin. After provider agrees to be interviewed, then 

15 
 
 
 
4. Provider name and contact information (i.e., phone number and email and clinical site) of the 
identified provider participant will be shared with DUHS study coordinators/research assistants via a 
secure database stored on Duke Box 
5. DUHS study coordinators/research assistants will then contact the identified providers for enrollment 
in the study via telephone or email by DUHS personnel 
6. Provider interviews will be conducted by telephone by DUHS (see Interview Guide Templates for 
Infectious Disease Providers and Cardiology Providers).  We will collect self-reported data on eligible 
providers on their basic demographics including, sex, age, race, ethnicity, specialty, number of years of 
practice and practice site (see Demographic forms for Infectious Disease Providers and Cardiology 
Providers). 
 
 
10 Consent process 
10.1 Consent Process for Aims 1 and 2 --N/A. 
Aims 1 and 2 do not involve informed consent.  
 
10.2 Consent process for Patient Participants for Aim 3 
Patient participants for Aim 3 will provide oral informed consent to participate in the project.  
Note:  For the interviews with patients, we are requesting a waiver of 
written informed consent (i.e., a signed consent form) under 45 CFR 
46.117(c) (1), which states the following: 
An IRB may waive the requirement for the investigator to obtain a signed 
consent form for some or all subjects if it finds…that the only record linking the 
subject and the research would be the consent document and the principal risk 
would be potential harm resulting from a breach of confidentiality... 
We are requesting this because patients will only take part in one research 
activity and we are not linking individually-identifiable information (e.g. 
names) to the data; therefore, having written informed consent would be 
the only paper record with participant names linking the participant to the 
research and the principal risk if a breach of confidentiality occurred.  
Obtaining oral consent provides an additional layer of protection if there 
a breach in confidentiality of our study records.  We will collect 
participant names but only for scheduling purposes. The oral consent 
form contains all the same information as a written consent form. We will 
provide the oral consent form to potential participants during recruitment. 
Study staff will obtain oral consent from each patient before any data 
collection activities begin (i.e., when individuals first arrive for the 
interview). Study staff sign the oral consent form to verify that the 
participant gave their informed consent. Additionally, each participant’s 

16 
 
 
 
ID number is written on the oral consent form and we document in a 
study log that informed consent is obtained from each participant as per 
local/institutional policies.  
 
10.3 Consent process for Provider Participants for Aim 3 
(We are requesting a waiver of consent for provider participants.)   
NOTE:  We are requesting for the provider interviews, a waiver of informed 
consent.  Given the study design (qualitative interviews) and the study topic, to 
identify facilitators and barriers to optimal CVD prevention), we believe that 
part of this study could have qualified for Exempt Research status under 45 CFR 
46.101(b) (2) if presented as a stand-alone protocol (and not as part of a 
protocol that includes other data collection activities—i.e., the interviews with 
patients—that would not be considered exempt). This is because the interviews 
involve only the use of standard interview procedures and no identifiable 
personal health information will be collected from providers. All providers will 
receive an informational sheet that describes the purpose of the study, data 
collection procedures, risk, benefits, confidentiality procedures, and who to 
contact if they have questions/concerns. We will distribute the informational 
sheet to all potential providers during recruitment and before any data 
collection activities begin.  
 
  
10.3 Subject’s capacity to give legally effective consent for Aim 3 
No subjects with diminished capacity will be included in this protocol for Aim 3.   
11 Risk/benefit assessment 
11.1 Risk/benefit assessment for Aims 1 and 2 
Because this study involves an analysis of EHRs without any protocol-specified intervention, it will involve very 
minimal risks or benefits to the individuals whose records are involved in Aims 1 and 2. There is the slight risk 
of breach of confidentiality. All reasonable effort will be taken to keep data confidential.  
While there is no direct benefit expected for the individual patient whose medical records are reviewed, the 
overall benefit of this study will be the knowledge gained regarding risk of CVD in PLWH, the impact of 
specialty care on CVD outcomes and an analysis of other factors related to this risk. This knowledge will 
potentially benefit many other PLWH as it relates to broader dissemination of our findings. Ultimately, this 
may result in benefits to the community of science as a whole and have direct impacts on health care for 
PLWH throughout the U.S. and internationally. Other potential benefits of our study relate to the constituency 
of our investigative team representing physician, patient and patient advocate investigators familiar with the 
challenges of managing CVD in PLWH. Patient partners will be encouraged to participate in all aspects of the 
study including IRB approval, statistical analysis, manuscript writing and dissemination. Similarly, academic and 
community-based researchers will discover the rich experience of understanding research from the patient’s 
perspective in clinical research, patient reported outcomes and patient centered dissemination.  

17 
 
 
 
11. 2 Risk assessment for Aim 3 
We do not anticipate that the study participants (patients and providers) will experience any social, emotional, 
or physical risks from participating in the IDIs. Although as with most social science research, there is a 
potential risk of loss of confidentiality. Nevertheless, all study participants will be told that they can decline to 
answer any question during the semi-structured interviews and demographic questionnaire, and that they can 
terminate their participation at any time. In addition, every effort will be made to protect study participants’ 
privacy and confidentiality; however, as with all research, there is no guarantee that privacy and confidentiality 
can be fully maintained.  Given the nature of this research, we do not expect any negative consequences to 
occur if there is a breach of confidentiality.  
 
11.3 Benefit assessment for Aim 3 
There are no direct benefits to participants who take part in this research, although participants may have a 
sense of satisfaction by helping to facilitate recommendations that reduce barriers to CVD prevention in 
research.  
12 Data analysis & statistical considerations 
12.1 Analysis Plan for Aim 1  
For this aim, the primary outcome variable is referral to a cardiology specialist within 6 months of baseline. 
This will be measured as a binary variable, with ‘yes’ defined if there is documented evidence that a referral is 
made within 6 months of becoming eligible by CVD risk score and kept within 3 months of referral, and ‘no’ 
otherwise. We will also determine the time from baseline to cardiology referral and the time until the first visit 
with the cardiology specialist.  
We will consider a number of patient and provider factors evaluated at baseline that are potentially associated 
with referral to a cardiology specialist. Patient factors will include demographic factors (age, sex, race, 
ethnicity), level of ASCVD risk, Charlson comorbidity index26, SES (e.g.;  5 digit zip code, health insurance 
([yes/no]), type of health insurance (Medicare, Medicaid, private, Ryan White, etc.), and history of keeping 
appointments during 2 years prior to baseline. Provider factors will include primary management strategy (co-
primary management defined as having both an HIV doctor and Primary Care Practitioner vs. HIV provider as 
primary care), and years of practice since training.  Cardiologist availability will be a site-determined variable. 
Based on prior literature, we are specifically considering sex as a biological variable that may be related to 
specialty referral. 27 
Baseline characteristics of the cohort will be summarized overall and by referral status. We will include the 
patient and provider factors described above, as well as vital signs (including weight, body mass index and 
blood pressure), laboratory measures (particularly total and HDL cholesterol, CD4 cell count, HIV viral load), 
and relevant medications (ART and cardiovascular medications). We will document the patterns of HIV related 
CVD comorbidity in the study population, as the funding opportunity announcement specified. 
Univariable and multivariable logistic regression analysis will be used to identify factors that are most strongly 
associated with cardiology referral. The patient and provider factors listed above are of primary interest and 
will be included as covariates in the regression models. Subgroups of primary interest will be defined by 
race/ethnicity (e.g., Non-Hispanic Black, Hispanic Black, Other) and by Insurance Status (None, Medicare, 
Medicaid, private, Ryan White, etc.). Univariable and multivariable associations will be reported stratified by 
sub-groups, and reported with interaction tests between the subgroup and other factors. Statistical 
significance will be determined at the two-sided 0.05 level.  

18 
 
 
 
We will also consider a number of sensitivity analyses to evaluate robustness of our study results to various 
assumptions. For example, to evaluate the impact of drop-outs we will re-run analyses only including patients 
with routine follow-up within the health care system (e.g., as evidenced by a certain number of visits during 
the study period). In another sensitivity analysis, we will extend the period for counting a cardiologist referral 
from 6months to 12 months. If there are subgroups of patients where referral is extremely common (e.g., very 
high ASCVD risk) or extremely uncommon (e.g., uninsured patients) then we will evaluate the effect of 
excluding these subgroups in additional sensitivity analyses.  
 12.1.1 Expected power and sample size for Aim 1  
Aim 1 expected power and sample size: Preliminary counts collected from sites suggest that we will 
identify at least 6,000 adult PLWH of Black/African-American and other URM race/ethnicity. We 
anticipate that 20-25% of these patients will meet the ASCVD risk threshold25,28-30 and be eligible for 
inclusion in the population for Aim 1, for an anticipated sample size of 1,200-1,500 patients. 
Preliminary data from our EHR and clinical experience suggests that 20-25% of those at elevated risk 
will have been referred to a cardiologist. This would provide at least 80% power to detect an 
association between cardiology referral, and a binary risk factor with 0.33 incidence, when the odds 
ratio for the association is 1.6 or greater. With 1,500 eligible patients the power to detect an 
association with an odds ratio of 1.5 would be >85%. 
12. 2 Analysis Plan for Aim 2  
Starting with the population selected for Aim 1, we will create a sub-population by matching patients referred 
to a cardiology specialist (the primary outcome variable for Aim 1) to similar non-referred patients. The goal of 
this matching is to balance factors that may be confounders of the relationship between referral and 
cardiovascular outcomes. We will identify factors found to be associated with cardiology referral from Aim 1, 
as well as factors that are expected to be associated with outcomes such as, age, race, insurance status/type, 
ASCVD risk score, and comorbidity index. We will not seek to match on factors that may be associated with 
referral but unlikely to be directly associated with outcomes. For example, we may not want to match on 
cardiologist availability by site if this is only related to referral and not independently to outcomes. Variables 
that potentially modify the effect of referral (e.g., sex, race/ethnicity, insurance status) will be included among 
the matching factors. Patients will be matched within sites and year of baseline encounter.  
For each patient referred to a cardiologist we will identify one non-referred patient (1:1 matching) within each 
site. We will match using values of the individual confounders, with an exact match on sex, race/ethnicity, 
insurance status/type, and to within a small caliper for continuous variables (e.g., ±5 years for age). Matched 
pairs will be identified using optimal matching. If this approach fails to identify an adequate number of 
matched pairs, then 1:1 matching will be implemented using a propensity score.  
Once the matching has been implemented, the same baseline characteristics reported for Aim 1 will be 
summarized in the matched cohort by referral status. Metrics (e.g., standardized differences) will be reported 
to evaluate how well-balanced the referred and non-referred groups. 
 For this aim, the primary outcome variable will be blood pressure control based on prevailing guidelines 
during the study period (blood pressure <140/90 mmHg).32,33 The co-primary outcome is lipid control, defined 
by prevailing guidelines during the study period.24,34 These outcomes will be evaluated longitudinally during 5-
years of follow-up. Secondary outcome variables include time to the first major adverse cardiovascular event 
(MACE = cardiovascular death, myocardial infarction (MI)), stroke, heart failure hospitalization, peripheral 
artery disease procedure, coronary artery disease (based on diagnosis codes or coronary artery 
revascularization procedure), and all-cause death. Events ascertained from relevant diagnosis and procedure 
codes, and from a query of the National Death Index Plus, State Death records, Social Security Death 
Masterfile, Hospital death record and/or similar death record for causes of death including cardiovascular 

19 
 
 
 
death. We will also evaluate time to BP control and time to lipid control among patients who were not 
controlled at study start. For this Aim, “time zero” for the matched patient pair will be defined as the time 
from baseline (see Aim 1) when the referred patient kept their cardiology appointment. Events will be 
ascertained starting from time zero and until the end of follow-up. For each patient, end of follow-up will be 
defined as the date of the last outcome event or health system encounter without an event, occurring ≥3 
months prior to the retrospective study review period of December 31, 2020 of the EHR.  
 
To evaluate the association between cardiology referral and subsequent blood pressure and lipid control (co-
primary outcomes), we will first evaluate these measures between referred and non-referred patients at 
baseline, and then compare prevalence and time trends between referred and non-referred patients using 
methods appropriate for longitudinal data analysis. For example, we will explore whether the proportion of 
patients with adequately controlled BP at each year is higher in referred compared to non-referred patients. 
Because these analyses are conducted in the matched population, the results are adjusted for the potential 
confounders included in the identification of the matched pairs.  
To evaluate the association between cardiology referral and CVD events (secondary outcomes), the cumulative 
incidence of primary and secondary outcomes will be summarized in the referred and matched non-referred 
patients. Follow-up will be censored at the earliest of 8 years or end of follow-up. For each outcome variable a 
Cox regression model will be fit, and the hazard ratio (referral vs. not referred) and 95% CI, and p-value will be 
reported. The Cox regression analysis will account for the matched pairs design using the empirical sandwich 
variance estimate. Statistical significance will be determined at the two-sided 0.05 level. 
The relationship between referral status and outcomes will also be evaluated within subgroups of primary 
interest: sex, race, ethnicity and insurance as defined in Aim 1. Evidence for a differential association between 
referral status and CVD outcomes for different subgroups will be quantified using an interaction test in the Cox 
regression analysis. The considerations and approaches outlined in Aim 1 regarding missing data and sensitivity 
analyses will also be applicable for Aim 2. 
12.2.1 Expected power and sample size for Aim 2 
Aim 2 expected power and sample size: With an anticipated cardiology referral rate of 20-25% among 
1,200-1,500 patients included in Aim 1, the sample size in the matched cohort study is expected to be 
250-375 per group. These patients will be accrued into the study over 5 years (2012-2016) and then 
followed to evaluate BP and lipid control, and for cardiovascular events for up to 8 years through 
December 31, 2020. For the primary endpoints, the rates in the non-referred group are anticipated to 
be 30% and 45% for BP and lipid control, respectively. With 300 per group, the study is predicted to 
have >70% power to detect a 10% increase in rate of BP control, and >95% power to detect a 15% 
increase in rate of lipid control due to cardiology referral. For the secondary endpoints, the rate of 
MACE events is anticipated to be fairly low (i.e., a cumulative incidence ~5% at 5 years and ~10% at 10 
years) and we anticipate modest power to detect even large reductions in risk of CVD events due to 
cardiology referral.  However, this aim will provide useful information about clinical event rates and 
estimates of the potential to effect event rates that could be attributed to cardiology referral. 
 12. 3 Analysis Plan for Aim 3  
All audio files from interviews will be professionally transcribed by a Duke approved transcription 
company (GMR Transcription, Inc). Immediately after the interviews, the study coordinator/research 
assistant will provide a memo using a semi-structured debriefing form. The forms will outline the 
major domains explored during the interview and prompt the interviewer to summarize the discussion 
for each domain. Debriefing forms will be analyzed using a rapid qualitative analysis approach to 

20 
 
 
 
efficiently categorize information contained in the expanded notes and identify emergent themes 
using qualitative analysis software (QSR International, NVivo 12) and standard Microsoft Excel 
software.35 Debriefing form information will be transferred to an Excel matrix where, by domain, a 
team of analysts (two study coordinators/research assistants) and Dr. Muiruri, Co-I)  will read through 
the summaries and quickly identify key themes as they emerge across respondents.36 Study 
Coordinators/research assistants, along with the Co-I, will determine the degree of thematic saturation 
within each domain, which will be used to inform whether or not to continue data collection. 
 
Participant responses will be coded and summarized using qualitative analysis software (QSR 
International, NVivo 12) to identify themes and catalog relevant quotes. Two trained qualitative study 
coordinators/research assistants in DUHS will have access to the data collected in Aim 3, including the 
audio files, debriefing forms, and transcripts from the various sites. They will work closely with Dr. 
Muiruri to develop the codebooks, as well as code and interpret the study findings. During the data 
collection phase, we will evaluate whether we will have achieved thematic saturation with the 
proposed sample size by reviewing information captured in the interviewers’ debriefing forms. 
Template analysis will be used to analyze qualitative data.18 This analytical method has previously been 
applied to health services research.19-23 Template analysis is a qualitative analysis technique which 
involves developing a coding ‘template’ that includes hierarchical coding to summarize themes, which 
are identified a priori and modified throughout data analysis. This method incorporates both deductive 
and inductive approaches, as it recognizes that research design often employs a conceptual 
framework. Template analysis uses a priori codes (from the conceptual framework), however these 
codes may be modified, dispensed or added to in order to create a template to serve as the basis for 
data interpretation.18 If needed, inductive approach may be used and emergent codes will be added to 
the analytical template (i.e., code book) as data analysis progresses. Furthermore, a priori codes may 
be modified and dispensed as necessary.  
Implementation of template analysis will involve several steps. First, the initial a priori coding template 
will be developed based on a subset of interviews and debriefing forms as well as the template 
outlined in the interview questionnaire and conceptual framework. Transcripts will be reviewed by two 
coders (two qualitative study coordinators/research assistants at DUHS) and the coding template will 
be independently applied to a subset of interviews (n~5). Coders will check for consistency in applying 
the coding template to the transcribed interviews through discussion and reconciliation and revisions 
to the codebook will be made accordingly. Once the application of the codebook have been refined, 
coding all transcripts will continue and further modification to the template (and coding in previously 
coded text) may be made in an iterative process as new information emerges. Codes will be organized 
in a hierarchal fashion, and a final template will be created to include both inductive and deductive 
codes. Once the initial application of a priori codes is complete, the two study coordinators/research 
assistants will review the segmented text captured in each major code and use an inductive process for 
identifying the emergent information related to each a prior topic. As new codes are identified the 
analysts will arrange this information under explicit and implicit themes that emerge from the data. 
The final codebook template will include the a priori and emergent codes. We will then summarize the 
key findings from each interview topic and these summaries will include exemplar quotes to add 
richness to the findings. We will also reflect back to the conceptual framework to find out if 
modification would be warranted. Finally, we will summarize the findings of the study and propose 
recommendations to improve the quality of CVD prevention in URM PLWH by illuminating the barriers 
and facilitators.  

21 
 
 
 
12.3.1 Number of participants for Aim 3 
In this Aim, we will rely on saturation - the idea that no new information was gleaned from additional 
interviews - to determine the number of participants. Guest et al.16 proposed that approximately 12 
participants are sufficient to achieve saturation when there is sample homogeneity. We assumed a certain 
degree of participant homogeneity because in purposive samples, participants are, by definition, chosen 
according to some common criteria. In our case, one sample only includes HIV specialists and cardiologist. The 
sample size as planned is based on research that has demonstrated when information saturation is likely to 
occur.  Based on the assumed homogeneity of the study population, we expect to interview around 30 
patients, around 16 HIV providers, and around 16 cardiologists. 
 
  
13 Data and safety monitoring 
Because this study will not involve significant risks or benefits to patients, a data and safety monitoring board 
is not required. Data safety and monitoring will ultimately be the responsibility of the Principal Investigator. 
14 Data storage & confidentiality 
14.1 Data Storage and Confidentiality for Aims 1 and 2  
 
Once the data from the DataMarts is transferred to Duke Box, database will then be transferred to DCRI Linux 
Servers to the DCRI statistical team, it will be stored on the PLP-SOMANLYT01 server which is maintained by 
DHTS and behind the Duke Medicine firewall. Data will be stored in a study specific folder 
/dcri/sigmadata/hiv_cvd is restricted to personnel assigned to the project. 
14.2 Data Storage and Confidentiality for Aim 3 
Several procedures will be put in place to protect participant confidentiality. Participants’ names will not be 
recorded on any data collection instrument. Study staff will only collect PHI when absolutely necessary to 
conduct the study. All information obtained that identifies a subject will be kept confidential as required by 
law. Federal Privacy Regulations provide safeguards for privacy, security, and authorized access. No individual 
participant will be identified in any report or publication. 
Each participant will be assigned a unique participant identification number for use on all data collection 
materials. Participant contact information (i.e., name, phone number, and email) will be kept in a separate, 
secure file; will only be used to contact participants for scheduling purposes; and will not be linked to study 
data. Limited individuals will have access to the list.  
All participant data generated during the study (e.g., handwritten notes, typed transcripts) will be stored and 
protected per local/institutional data security and confidentiality requirements.  Access to participant data will 
be limited to key research staff, who will pull materials only as needed to complete transcription, analysis, and 
or management tasks.  
All participant interviews will be recorded using an encrypted Olympus DS-7000 Digital Voice Recorder. The 
Olympus device is password protected and has DSS Pro real-time 256-bit file encryption.  The recordings will 

22 
 
 
 
be immediately uploaded to the study specific folder on Duke Box a secure cloud platform. After uploading the 
audio files into Duke Box, the interviewers will delete the files from the recording devices.  
The audio files will then be uploaded to the Duke-approved transcription company (GMR Transcription, Inc) for 
professional transcription.  Once the interview transcripts are made available to the study team, the audio 
recording will be deleted from the GMR Transcription Inc databases in accordance with their stated practices 
viewable here https://www.gmrtranscription.com/privacypolicy.aspx. Electronic copies of the transcript will be 
kept on a password-encrypted folder on the secure Duke Box folder as will electronic copies of demographic 
forms and summary notes from interviews. Only members of the study team will have access to this folder on 
Duke Box. Electronic data files (e.g.; typed transcripts, copies of transcripts, digital voice recordings, memos, 
demographic data)  will be stored on limited-access (i.e., study team only) secure network drives in the Duke 
University Population Health file folder as needed to complete transcription, analysis and or management 
tasks.   The digital voice recordings at Duke will be destroyed after publication of the study’s main findings. All 
hard copies of data and electronic files, except for voice recordings, will be stored securely at Duke University 
for up to six years after completion of the study.    
Any disseminated or publicly presented data will be de-identified and devoid of any way to link back to 
individual participants. Data analysis will be conducted using de-identified data on encrypted and password 
protected computers.   
For paper and non-digital media, PHI and social security numbers (for reimbursement purposes) will be stored 
temporarily and protected per local/institutional data security and confidentiality requirements. Research PHI 
for subjects are kept in the Clinical Research office of the study coordinator/research assistant. Only study key 
research personnel will have access to paper or non-digital media. 
 Subject reimbursement will occur locally from each site (e.g. Duke, Vanderbilt, MUSC, and CWRU) with 
reimbursement to each site’s subject participants   
Note:  no social security numbers will be transferred from Vanderbilt, MUSC, or CWRU to Duke for participant 
compensation.    
 
Software environment for research data collection and storage include: NVivo 12(Qualitative Analysis 
Software); SAS, Version 9.4; standard Microsoft Excel software; GMR Transcription Inc; Duke Box. Duke private 
storage on \\duhsnas-pri\dusom_dphs\private\, Olympus DS-7000 Digital Voice Recorder.   
The study documents will be retained in our research record for at least six years after the study is completed.  
15. REGUALTORY CONSIDERATIONS 
15.1 Single IRB for Aims 1, 2, and 3 submission and review of protocol. 
To fulfil the NIH’s January 25, 2018 Single IRB mandate, we will use single IRB review for this protocol for Aims 
1, 2, and 3. The Duke Health System IRB has agreed to serve as the Reviewing IRB for this research, using the 
SMART IRB master reliance agreement.  Each engaged institution has joined SMART IRB by signing a Joinder 

23 
 
 
 
Agreement to the master SMART IRB Agreement. Vanderbilt, Case Western Reserve and Medical University of 
South Carolina, and Wake Forest University Health Sciences have agreed to rely (cede review) to the Duke IRB.   
15. 2 Regulatory Considerations for type of data set Aims 1 and 2  
Limited data set only 
15. 3 Clinical Trials Registration  
This study will be registered on clinicaltrials.gov as a Observational Study. The study does not meet the 
definition of Applicable Clinical Trial, per the Food and Drug Administration Amendments Act (FDAAA 801). 
However, as publication is an end goal, we will register the research for the purpose of meeting ICMJE 
(International Committee of Journal Editors) requirements.   
16 Literature Cited 
 
1. 
Freiberg MS, Chang C-CH, Kuller LH, et al. HIV infection and the risk of acute myocardial infarction. 
JAMA Intern Med 2013;173(8):614–22.  
2. 
Butt AA, Chang C-C, Kuller L, et al. Risk of heart failure with human immunodeficiency virus in the 
absence of prior diagnosis of coronary heart disease. Arch Intern Med 2011;171(8):737–43.  
3. 
Currier JS, Lundgren JD, Carr A, et al. Epidemiological Evidence for Cardiovascular Disease in HIV-
Infected Patients and Relationship to Highly Active Antiretroviral Therapy. Circulation 2008;118(2):e29–
e35.  
4. 
Triant VA. HIV infection and coronary heart disease: an intersection of epidemics. J INFECT DIS 
2012;205 Suppl 3:S355–61.  
5. 
Pearce D, Ani C, Espinosa-Silva Y, et al. Comparison of in-hospital mortality from acute myocardial 
infarction in HIV sero-positive versus sero-negative individuals. The American Journal of Cardiology 
2012;110(8):1078–84.  
6. 
Casper M, Kramer MR, Quick H, Schieb LJ, Vaughan AS, Greer S. Changes in the Geographic Patterns of 
Heart Disease Mortality in the United States: 1973 to 2010. Circulation 2016;133(12):1171–80.  
7. 
Ritchey MD, Wall HK, Gillespie C, George MG, Jamal A, Division for Heart Disease and Stroke 
Prevention, CDC. Million hearts: prevalence of leading cardiovascular disease risk factors--United 
States, 2005-2012. MMWR Morb Mortal Wkly Rep 2014;63(21):462–7.  
8. 
Gilstrap LG, Malhotra R, Peltier-Saxe D, et al. Community-Based Primary Prevention Programs Decrease 
the Rate of Metabolic Syndrome Among Socioeconomically Disadvantaged Women. Journal of 
Women's Health 2013;22(4):322–9.  

24 
 
 
 
9. 
Sleder A, Tackett S, Cerasale M, et al. Socioeconomic and Racial Disparities: a Case-Control Study of 
Patients Receiving Transcatheter Aortic Valve Replacement for Severe Aortic Stenosis. J Racial Ethn 
Health Disparities 2017;4(6):1189–94.  
10. 
Thomas KL, Al-Khatib SM, Kelsey RC, et al. Racial disparity in the utilization of implantable-cardioverter 
defibrillators among patients with prior myocardial infarction and an ejection fraction of <or=35%. AJC 
2007;100(6):924–9.  
11. 
Cook NL, Ayanian JZ, Orav EJ, Hicks LS. Differences in specialist consultations for cardiovascular disease 
by race, ethnicity, gender, insurance status, and site of primary care. Circulation 2009;119(18):2463–70.  
12. 
Gregory PC, LaVeist TA, Simpson C. Racial disparities in access to cardiac rehabilitation. Am J Phys Med 
Rehabil 2006;85(9):705–10.  
13. 
LaVeist TA, Arthur M, Morgan A, Plantholt S, Rubinstein M. Explaining racial differences in receipt of 
coronary angiography: the role of physician referral and physician specialty. Med Care Res Rev 
2003;60(4):453–67–discussion496–508.  
14. 
Cook NL, Hicks LS, O’Malley AJ, Keegan T, Guadagnoli E, Landon BE. Access to specialty care and medical 
services in community health centers. Health Aff (Millwood) 2007;26(5):1459–68.  
15. 
Richardson KK, Bokhour B, McInnes DK, et al. Racial Disparities in HIV Care Extend to Common 
Comorbidities: Implications for Implementation of Interventions to Reduce Disparities in HIV Care. 
Journal of the National Medical Association 2016;108(4):201–3.  
16. 
Guest G, Bunce A, Johnson L. How many interviews are enough? An experiment with data saturation 
and variability. Field Methods 2006;18(1):59–82.  
17. 
Kelly SE. Qualitative Interviewing Techniques and Styles. In: Bourgeault I, Dingwall R, de Vries R, editors. 
The SAGE Handbook of Qualitative Methods in Health Research. London:  
18. 
King N. Template analysis. In: Symon G, Cassell C, editors. Qualitative methods and analysis in 
organizational research A practical guide. Thousand Oaks: 1998. p. 118–34. 
19. 
King N, Bell D, Thomas K. Family carers' experiences of out-of-hours community palliative care: a 
qualitative study. Int J Palliat Nurs 2004;10(2):76–83.  
20. 
McCluskey S, Brooks J, King N, Burton K. The influence of “significant others” on persistent back pain 
and work participation: a qualitative exploration of illness perceptions. BMC Musculoskelet Disord 
2011;12(1):236.  
21. 
King N, Carroll C, Newton P, Dornan T. “You can't cure it so you have to endure it”: the experience of 
adaptation to diabetic renal disease. Qual Health Res 2002;12(3):329–46.  
22. 
Gollop R, Whitby E, Buchanan D, Ketley D. Influencing sceptical staff to become supporters of service 
improvement: a qualitative study of doctors“ and managers” views. Qual Saf Health Care 
2004;13(2):108–14.  

25 
 
 
 
23. 
Goldschmidt D, Schmidt L, Krasnik A, Christensen U, Groenvold M. Expectations to and evaluation of a 
palliative home-care team as seen by patients and carers. Support Care Cancer 2006;14(12):1232–40.  
24. 
Goff DC, Lloyd-Jones DM, Bennett G, et al. 2013 ACC/AHA guideline on the assessment of 
cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task 
Force on Practice Guidelines. Journal of the American College of Cardiology 2014;63(25 Pt B):2935–59.  
25. 
Thompson-Paul AM, Lichtenstein KA, Armon C, et al. Cardiovascular Disease Risk Prediction in the HIV 
Outpatient Study. Clin Infect Dis 2016;63(11):1508–16.  
26. 
Charlson M, Szatrowski TP, Peterson J, Gold J. Validation of a combined comorbidity index. J Clin 
Epidemiol 1994;47(11):1245–51.  
27. 
Ayanian JZ, Landrum MB, Guadagnoli E, Gaccione P. Specialty of ambulatory care physicians and 
mortality among elderly patients after myocardial infarction. N Engl J Med 2002;347(21):1678–86.  
28. 
Losina E, Hyle EP, Borre ED, et al. Projecting 10-yr, 20-yr and Lifetime Risks of Cardiovascular Disease in 
Persons Living with HIV in the US. Clin Infect Dis 2017; 
29. 
De Socio GV, Pucci G, Baldelli F, Schillaci G. Observed versus predicted cardiovascular events and all-
cause death in HIV infection: a longitudinal cohort study. BMC Infect Dis 2017;17(1):414.  
30. 
Monroe AK, Haberlen SA, Post WS, et al. Cardiovascular disease risk scores' relationship to subclinical 
cardiovascular disease among HIV-infected and HIV-uninfected men. AIDS 2016;30(13):2075–84.  
31. 
Bergstralh EJ, Kosanke JL, Jacobsen SJ. Software for optimal matching in observational studies. 
Epidemiology 1996;7(3):331–2.  
32. 
Chobanian AV, Bakris GL, Black HR, et al. The Seventh Report of the Joint National Committee on 
Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA. 
2003;289(19):2560–72.  
33. 
James PA, Oparil S, Carter BL, et al. 2014 evidence-based guideline for the management of high blood 
pressure in adults: report from the panel members appointed to the Eighth Joint National Committee 
(JNC 8). JAMA 2014;311(5):507–20.  
34. 
Greenland P, Alpert JS, Beller GA, et al. 2010 ACCF/AHA guideline for assessment of cardiovascular risk 
in asymptomatic adults: executive summary: a report of the American College of Cardiology 
Foundation/American Heart Association Task Force on Practice Guidelines. Circulation. 
2010;122(25):2748–64.  
35.  
Meyer DZ and Avery LM. Excel as a Qualitative Data Analysis Tool. Field Methods. 2009; 21: 91-112. 
36. 
Averill JB. Matrix analysis as a complementary analytic strategy in qualitative inquiry. Qual 
Health Res. 2002; 12: 855-66.