DECIDE - ICD: A Multicenter Trial of a Shared DECision Support Intervention for Patients Offered Implantable Cardioverter-DEfibrillators
Phase: NA
Conditions: Heart Failure, Systolic, Heart Failure
Sponsor: University of Colorado, Denver

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w 
COMIRB Protocol 
COLORADO MULTIPLE INSTITUTIONAL REVIEW BOARD 
CAMPUS BOX F-490    TELEPHONE:  303-724-1055  Fax:  303-724-
0990 
 
 
 
Protocol #: 
17-1697 
Project Title: 
A Multicenter Trial of a Shared DECision Support Intervention for Patients offered 
implantable Cardioverter-DEfibrillators: DECIDE - ICD Trial 
 
 
 
 
Principal Investigator:  Dan Matlock, MD, MPH 
Version Date:  
 
 
 
 
 
  
 
I. Hypotheses and Specific Aims:   
The research team proposes to assess the real-world effectiveness and implementation of patient 
decision aids (PtDA) for high-risk decisions using the implantable cardioverter-defibrillator (ICD) 
as a model.  
 
Hypotheses 
   1a)  Decision aids will reach over 50% of eligible patients. 
   1b)  ICD PtDAs will be effective in improving decision quality in real-world practice. 
   1c)  Better informed patients will have lower anxiety, higher rates of planning for the possibility 
of deactivation, and increased identification of a surrogate decision maker. 
 
Specific Aims 
1) Assess real-world effectiveness of PtDA for high-risk decisions using the ICD as a model. 
 
II. Background and Significance:  
Patient-centered care is growing in both practical and political importance. As the primary process 
of involving patients directly in their care, SDM is explicitly supported both by the IOM1,2 and in the 
Affordable Care Act.3 The Food and Drug Administration has recently begun the Patient Preference 
Initiative and is considering SDM as part of the approval process.4 Medicare recently included 
requirements for SDM as a condition of reimbursement for lung cancer screening5 and the left atrial 
appendage closure (WATCHMAN) device placement.6 Our group takes a broad view of SDM as a 
“meeting between experts.”7 This definition from 1985 acknowledges the clinical expertise of the 
clinicians and the lived expertise of the patients. We reject incorrect assumptions that SDM is just 
presenting information to patients and asking them to choose.8 Rather, SDM is an intimate process 
between patients and clinicians whereby treatment recommendations are tailored to both the best 
evidence and well-informed patient preferences. 
 
Implantable cardioverter-defibrillators (ICD) are an ideal model to implement SDM. Over 200,000 
ICDs are implanted annually (both new devices and replacements)9 in an effort to prevent sudden 
cardiac death (SCD), a leading cause of death in the US. Once surgically implanted, the ICD 
monitors the patient’s heart for dangerous rhythms, quickly delivering a defibrillation shock to 
restore a healthy rhythm. Despite their clinical utility in improving mortality from SCD, 10-12,13 a 
number of clinical and quality of life (QOL) threats exist. Some studies suggest that patients with 
ICDs have more heart failure admissions,14 a lower quality of life— particularly if shocked15,16—and 
an increased incidence of anxiety, depression, and post-traumatic stress disorder.17 Further, ICDs 
can fail,18 shock inappropriately,19 and, if not properly deactivated cause unnecessary suffering at 
the end of life.20-23 Patients have described an ICD shock as “getting kicked in the chest by a 
mule”24 leading some to have their ICDs removed for fear of repeated shocks.25 
 

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Patients with ICDs may face multiple preference-sensitive decisions. Patients recommended for 
primary prevention ICD therapy face the ICD decision in several contexts: 1) initial implantation 
(130,000/year); 2) re-implantation when an existing battery dies after 5-7 years (70,000/year); and 
3) when considering whether to include defibrillation in cardiac resynchronization therapy (CRT) 
(40% of ICDs).9 Each decision hinges on whether the patient’s goals align with accepting a device 
in the hope of extending life, while foregoing the possibility of sudden death. Patients facing this 
decision may intuitively seize the opportunity to prevent sudden death, but many of the same 
patients also intuitively indicate that they would prefer to die peacefully in their sleep, which the ICD 
prevents.26 
 
The current state of decision making in ICD care is poor. Even beyond drastic regional differences 
in rates of ICD implantation highlighted in our earlier work (see preliminary data C2.1),27 reports 
frequently highlight suboptimal practice with respect to patient education and inclusion in decision 
making.24,28 Patients with ICDs frequently report never having had a conversation about 
periprocedural risks, expected benefits, or potential QOL problems.24 Studies of clinicians’ 
perspectives identify guideline-based, rather than patient-preference-based decision making. Dr. 
Matlock led an integrative review of patient perspectives that highlighted a paternalistic approach to 
decision making.24,29 It is not surprising that patients overestimate the benefits of ICDs, 
underestimate the risks, and are uninformed about device deactivation.29  
 
PtDAs improve SDM and decision outcomes. A Cochrane review of 115 randomized trials 
demonstrated that PtDAs improve knowledge, satisfaction, patient/provider communication, 
increase patient involvement in decision making, and reduce patient decisional conflict and 
regret.30 PtDAs come in many forms including paper, video,31 interactive web sites,32 and even 
telenovelas.33 Our group has developed and piloted ICD PtDAs in multiple media formats (see 
preliminary data section). We have learned that most patients prefer the combination of a paper 
version with a video.34 Patients did not utilize the website, and they found the shorter paper Option 
Grid (essentially a 1-page FAQ) to be less helpful.35 In response, we have developed and tested 
paper and video PtDAs for use during: 1) initial ICD implantation; 2) ICD reimplantation (after the 
battery dies); and 3) ICDs in the context of cardiac resynchronization (CRT). 
 
Despite their established efficacy, PtDAs are not implemented outside the research setting.36 Much 
of the prior work on PtDA implementation has occurred in primary care settings with lower-risk 
interventions.30,36,37 A recent systematic review of PtDA implementation (co-author Dr. Lewis – Co-
I) identified a host of logistical barriers, including clinicians’ perception of time necessary to use 
PtDAs, lack of reimbursement, and perceived bias inherent in the PtDAs themselves. As described 
above, ICDs are a higher risk treatment with significant trade-offs. Professional guidelines and 
proposed quality measures recommend SDM.38,39 However, we have shown that clinician opinions 
vary40 and many clinicians endorse paternalistic views which de-emphasize patient preferences out 
of fear that patients may “make an unwise decision and not proceed with therapy.”29  
 
This proposal builds on prior lessons learned. We have identified many problems in the field of 
SDM and PtDAs. With this innovative type II effectiveness/implementation hybrid design,41 we will 
study solutions to these problems (Table 1). We assert that the combination of the high-risk nature 
of the decision, the engaged study team of clinical champions, a user-centered approach to PtDA 
development, a reproducible implementation strategy, and recent changes in professional 
guidelines will lead to implementation success. 
 
 
 
 
   
Table 1: Problems and Solutions Addressed  
Conceptual Area 
Problem 
Our Solution 
Aim 1  1a: Reach 
Unclear how to best reach eligible 
A suite of 3 decisions aids and clinical 

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patients 
champions will assist in normalizing the 
new process. 
1b. Effectiveness 
Lack of data regarding educational 
outcomes in real-world practice 
Exploring effectiveness in a real-world trial 
rather than a tightly controlled research 
setting. 
1c: Downstream 
effects 
Lack of data regarding QOL 
impact of SDM in real-world 
practice 
Test the hypotheses that better informed 
patients have improved psychosocial 
outcomes. 
 
 
III. Preliminary Studies/Progress Report:   
Section 1: Strength of our infrastructure and investigators 
Our transdisciplinary research team has an established record of success in a broad range of 
topics related to SDM development, measurement, ICDs, and implementation (Table 2). 
 
Table 2: DECIDE-ICD Study Team Expertise 
Core 
team 
Expertise 
in SDM 
Expertise in 
implementation 
Expertise 
in ICDs  
Analytic 
team 
Dan Matlock, MD, MPH, Project Lead 
X 
X 
X 
 
X 
Larry Allen, MD, MHS, Co-I 
X 
X 
X 
X 
 
Carmen Lewis, MD, MPH, Co-I 
X 
X 
X 
 
 
Chris Knoepke, PhD, MSW, Co-I 
X 
X 
 
 
X 
William Sauer, MD, Site-PI – U. of CO 
 
 
 
X 
 
Dan Kramer, MD, MPH, Site-PI – Beth Israel 
X 
 
 
X 
 
Sanjaya Gupta, MD, Site-PI, Mid America Heart Institute 
 
 
 
X 
 
John Mandrola, MD, Site-PI – Baptist Health 
 
 
 
X 
 
Paul Varosy, MD, MS, Site-PI – DVAMC 
X 
 
 
X 
 
Lucas Marzec, MD, Site-PI – Denver Health 
??? 
 
 
 
 
Pam Peterson, MD, Site-PI – Denver Health 
 
 
 
 
 
Scott Brancato, MD, Site-PI – Providence Health 
 
 
 
X 
 
Diane Fairclough, DrPH, Lead Biostatistician 
X 
 
 
 
X 
 
Core team experience with SDM and implementation: Drs. Allen, Matlock, and Lewis have 
significant collective experience in research and implementation of PtDAs. They are currently 
conducting the DECIDE-LVAD trial, a Patient-Centered Outcomes Research Institute (PCORI)-
funded 3-year, type II effectiveness/implementation hybrid under the guidance of Russ Glasgow 
and the RE-AIM framework.42 Our proposed trial is similar to the DECIDE-LVAD use of a stepped-
wedge design to implement an Left Ventricular Assist Device (LVAD) PtDA at 6 sites across the 
United States. This trial is halfway through, and is currently overenrolling and early estimates of 
reach are near 100% at some sites. Dr. Matlock is leading the implementation evaluation of this 
project.  
 
Dr. Lewis has extensive experience leading or participating in PtDAs and SDM implementation in 
primary care.43,44,37,45-48 The challenges presented by the low-risk nature of primary care decisions 
and the resultant lack of clinician receptivity to SDM have informed a new scope of work with Dr. 
Matlock in Colorado to design PtDAs with a user-centered design approach specifically for 
enhanced implementation. She and Dr. Matlock are funded by the Dean to develop a program in 
SDM and they now co-lead the SDM Core on campus. 
 
Electrophysiologists as site investigators: The team of site-PIs are all highly engaged clinical 
electrophysiologists poised to be champions of SDM implementation. Dr. Matlock has previously 
published with many of these site-PIs.49,29,50 This high level of engagement will facilitate 
implementation within this project and dissemination after this project.  
 
Advisors: We also have two expert advisors. John Spertus, MD (cardiologist with experience in 
multisite DA implementation) and Russ Glasgow, PhD (expert in implementation science).   

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Section 2: Prior research on ICD decision making 
Through our extensive work on ICD decision making—including patterns of ICD use, qualitative 
interviews of patients and clinicians, and clinician and patient surveys—we have identified 
problems in current ICD decision-making practice, including wide practice variation, clinician 
paternalism, and low patient knowledge. 
 
Wide practice variation: Using data from the 
Medicare-mandated ICD registry, we found 
significant variation (4.5 fold between the lowest 
and highest quintile) in the utilization of ICDs among 
fee-for-service Medicare beneficiaries (Figure 1).51 
Surprisingly, this variation was completely unrelated 
to the regional supply of electrophysiologists or 
cardiologists. We followed this study with another 
analysis examining the decision to receive a single 
or dual lead ICD at the hospital level and found that 
use of this therapy ranged from 0% at some hospitals 
to 100% at other hospitals.52 Physician, hospital, and regional effects 
far outweighed patient effects, further suggesting that patients’ 
considerations were not included in current ICD decision making 
practices. 
 
National clinician survey demonstrating paternalism: Following this study, we conducted a national 
survey of over 1200 members of the American College of Cardiology to determine if attitudes 
towards ICDs varied across regions of utilization. Notably, over half of the cardiologists rated “ICD 
mortality data” as more important than “patient preferences” in their recommendations for ICDs.40 
Clinicians in high use regions from Figure 1 were more likely to recommend an ICD to a “frail” 
patient or a patient with a life expectancy of less than one year, which is discordant with guideline 
recommendations.53  
 
Patient survey demonstrating poorly informed patients: As part of the development of a decision 
quality measure, we surveyed all patients with ICDs within the Kaiser Permanente Colorado 
(KPCO) system (n=417). We asked a host of knowledge, values, and participation questions 
surrounding ICD decision making. Responses raised concerns about patients’ understanding 
surrounding their ICDs: 58% incorrectly thought that the ICD should never be turned off and 60% 
overestimated the lifesaving benefit of ICD therapy by 400%. Most interestingly, 61% noted that 
they would rather “die quickly and not live as long” than “live as long as possible and die slowly of a 
progressive illness,” which is in diametric opposition to ICD function. Dan Kramer (site-PI, Beth 
Israel) found similar knowledge deficits in a recent survey where only about 15% of patients 
reported ever hearing from their doctors about the possibility of ICD deactivation.54 
 
Qualitative studies about ICD decision making: To understand decision needs surrounding ICD 
decision making (for theoretical framework, see Section 3), we conducted a qualitative study of 
patients with ICDs, patients who had declined ICDs, and cardiologists regarding ICD decision 
making.29  
• 
Cardiologists varied markedly in approach to ICD decision making. Most were heavily 
influenced by “the guidelines.” Indeed, the guidelines seem to promote a 
beneficence/paternalism that impedes a frank discussion with patients: “I think my biggest 
concern is if I convey the risks to them too strongly, that they will choose or make an unwise 
decision and not proceed with therapy.” 
• 
Patients with ICDs indicated not being offered a choice: “I was…told by two heart doctors I had 
to have it, so there you go. There ain’t much thinking about it. Just get it done.” These same 
Figure 1. Rate ratio of receiving an ICD 
compared to the national average 

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patients also noted learning of the risks after implantation: “…I don’t think anything I read 
touched on how depressed I was going to be about it.”  
• 
Patients declining ICDs expressed concerns that the numbers didn’t apply to them and/or the 
benefits were not enough: “It’s like walking around with a motorcycle helmet on when you’re 
driving your car.” One patient addressed the trade-off between dying sooner from sudden 
death and living longer and dying of progressive heart failure: “Going into my decision is also 
the fact that I can’t work because of my medical condition. I can’t work at the hospital; I can’t 
work at the church. My life is sitting here and watching TV… to take a defibrillator would just 
lengthen the process that I don’t really want lengthened.” 
• 
Behavioral economics were applied via a framework analysis of two sets of interviews. We 
found evidence of cognitive bias in decision making, including patients attributing a halo effect 
to a clinician, who then presents a status quo bias to perform an ICD in the context of 
influential framing techniques. Taken together, the biases appeared to encourage ICD 
treatment.(under review) 
 
Development of a measure of decision quality: Decision quality is an essential element of the 
Ottawa Decision Support Framework (Figure 2) defined as “the extent to which the implemented 
decision reflects the considered preferences of a well-informed patient.”55 Accordingly, a decision is 
“a quality decision” if the treatment chosen is concordant with a knowledgeable patient’s 
values.55,56 Some have proposed that measures of decision quality be included in the pay for 
performance agenda57 and the Affordable Care Act calls for “the development of quality measures 
that allow for the assessment of the experience, quality, and use of information provided to and 
used by patients...”3 We developed a measure of ICD decision quality using a robust process of 
patient and clinician assessment to determine the key knowledge items (content validity). We then 
tested this measure on a large population of patients and determined test-retest reliability (r=0.56, 
p>.01) and internally-reliable assessment of patient knowledge (α=.61). We demonstrated 
discriminant known-groups validity of the knowledge measure by showing that clinicians scored 
better than patients with ICDs who scored better than patients without ICDs (98% vs. 60% vs. 40% 
mean scores, respectively).  
For values items, we found that patients would rate competing values (such as dying quickly or 
living as long as possible) very highly when asked as single items, leaving little variability in 
responses. When forced to choose on a visual analogue scale, however, we found that groups of 
patients would discriminate their preferences. In total, the values items included herein account for 
53% of the variance in relevant patient values. This item will be used to assess value-concordance 
going forward. 
 
Chris Knoepke’s dissertation: In his doctoral dissertation,58 Dr. Knoepke surveyed current 
defibrillator patients at the University of Colorado (with the guidance of Dr. Matlock - PI) to 
conceptualize relationships between how patients learned about their ICDs, how well they 
understand benefits and risks associated with ICD therapy, and QOL. In addition to creating new 
ICD knowledge and informational media history measures, the data gathered in this project 
highlighted group-based differences in treatment knowledge and information-seeking behavior. 
When compared to their younger counterparts, the oldest patients in the sample (age >71.5) both 
used fewer forms of media to learn about their ICD (being particularly less likely to use online 
information) and scored lower on a treatment knowledge measure, as did participants who did not 
complete high school when compared to those with at least a college degree. Significant 
depression and anxiety were also highly prevalent in this sample of patients (58% and 39.5%, 
respectively), and depression illustrated high statistical collinearity in models predicting patients’ 
QOL. Dr. Knoepke continues to analyze whether this association indicates that depression drives 
QOL among ICD patients to a greater extent than previously thought, and/or the possibility that 
depression mediates other factors’ influence on QOL. 
 

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Hartford-sponsored symposium on older adults: In 2014, Drs. Matlock and Kramer received funding 
from the Hartford Foundation to host a multi-disciplinary symposium of researchers to discuss the 
practice and research agenda for older adults considering ICDs. The proceedings from this 
symposium highlighted many aspects of ICD research and practice, including the importance of 
SDM for ICDs.49  
 
In sum, not only do our preliminary data demonstrate our team’s strong history of research on 
decision making and patient engagement, but the above studies reveal substantial gaps in decision 
making, including wide practice variation, a tendency towards paternalism among clinicians, and 
low patient understanding regarding important aspects of their ICD, clearly demonstrating the need 
for PtDA implementation in the real world.  
 
Section 3: ICD PtDA theory, development, pilot, and implementation:  
Theoretic foundation behind the ICD PtDAs: Normative theories of decision making, such as 
Expected Utility Theory, are based on an ideal—that all patients can approach decisions rationally 
and are able to weigh the risks and benefits of various interventions.59 Descriptive theories of 
decision making, such as Prospect Theory, demonstrate that humans are subject to cognitive 
biases that cause decision making to deviate from the normative/rational.59,60 The Dual-Process 
Theory and more recent theories such as Fuzzy Trace Theory argue that people make decisions 
both intuitively, drawing on past experiences and emotion, and rationally, using a reasoned analytic 
process.59,61 Prescriptive frameworks attempt to define ways in which decision making can be 
improved.62 The Ottawa Decision Support Framework (Figure 2) is the prescriptive framework we 
used to guide development and evaluation of the ICD PtDAs. This framework asserts that 
participants’ decisional needs (e.g., 
knowledge, values, support) will 
affect decision quality, which then 
impacts subsequent outcomes such 
as emotions (e.g., regret, blame), 
behavior, and appropriate use of 
health services.63 The framework 
asserts that decision support can 
improve decision quality by addressing unresolved decisional 
needs.29  
 
ICD PtDA development process: We have developed combined paper and video PtDAs for patients 
considering several aspects of implanted defibrillation: 1) Initial implantation; 2) Reimplantation; 
and 3) Defibrillation in the setting of CRT. Following the Ottawa Decision Support Framework and 
International Patient Decision Aid Standards (IPDAS), and utilizing the extensive needs 
assessment described above, we developed drafts of the pamphlet PtDAs. We included all 
components suggested in the IPDAS, including information about options, unbiased probabilities, 
values clarification exercises, and structured guidance on deliberation and communication.64 We 
iteratively reviewed the PtDAs with multiple stakeholders, including 29 patients, the 
electrophysiology faculty at the University of Colorado, the Colorado Cardiovascular Outcomes 
Research Consortium, the Palliative Care research group, all the site-PIs, and an expert review 
panel. We performed 18 formal iterations until all stakeholders felt that the PtDAs met the criteria of 
accuracy, readability, and lack of bias.65 Following completion of the paper PtDA, we developed the 
video PtDA and began another process of iteration. In addition to meeting all IPDAS criteria, our 
PtDAs incorporate a host of innovations that were deemed necessary based on the preliminary 
work. 
 
1) Addressing intuitive and emotional aspects of decision making: The PtDA design was 
informed by the Dual-Process Theory,59,60 recognizing that patients use both intuitive 
“automatic” and reasoned “reflective” processes in their approach to decisions. In response, 
Figure 2. Ottawa Decision Support Framework 

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our PtDAs utilize palliative care communication techniques such as naming/addressing 
emotion, normalizing language, and avoiding euphemisms.66 
2) Framing around death: Our needs assessment caused us to rethink our approach to SDM 
around the topic of death. Prospect Theory advises that people will be risk-seeking when 
faced with losses.60 When choosing to forgo an ICD is framed as a certain loss (i.e., death), 
people will naturally “do anything” to avoid it. Our PtDAs frame both options as reasonable. In 
the videos, this is achieved with narration by Paul Varosy (Site-PI, DVAMC) and with patients 
describing their competing choices. This “imagined futures” exercise is a strategy move into a 
more cognitive space where they can make a reasoned decision while considering their 
values.67 
3) Balanced and standardized patient and caregiver testimonials: We made an explicit decision 
to include patient narratives in our PtDAs. First, in our needs assessment, many patients 
stated that they wanted to hear from other patients. Second, biased narratives from industry 
and other sources are abundant on the internet - by including tested and controlled 
narratives, we were able to ensure honesty in the messages patients receive.68 We were 
aware of concerns that narratives may be overly biasing.69 To avoid this, we chose to include 
only process narratives (e.g., “Here is how I made my decision”) and avoid outcome 
narratives (e.g., “This is the best/worst decision I’ve ever made”).70 
4) Combined pamphlet and video PtDAs: The combined format of a pamphlet and video was 
chosen based on our prior work and designed to fit diverse clinical contexts. Further, some 
data suggests that videos are better for patients with lower health literacy.71  
5) Design for implementation: While we followed IPDAS guidelines, our iterative development 
process utilized a user-centered design perspective with both patients and clinicians. 
Consistent with user-centered design principles, feedback from users took precedence over 
IPDAS guidelines specifically for the purpose of facilitating future implementation.72,73 
 
Pilot trial of the ICD PtDAs: 21 patients enrolled; 15 were randomized to the intervention. 67% 
found the PtDAs to be unbiased, 22% found them biased toward ICDs, and 11% found them 
biased toward not getting an ICD. Furthermore, 89% found the PtDAs helpful, and 100% would 
recommend them to others. The pilot was feasible at all sites; however, using clinic staff to identify 
eligible patients was more efficient than research assistants conducting chart review. Intervention 
patients had trends towards improvements in both domains of decision quality: greater knowledge 
about ICDs (M=14.0/20 vs. 11.6/20, ns) and increased concordance between their decision and 
values (71% concordant vs. 29%, p=0.06).34 
 
High Value Health Collaborative (HVHC) implementation: Dr. Matlock’s initial iteration of ICD 
PtDAs were adopted by the HVHC as part of a national SDM implementation project funded by the 
Centers for Medicare and Medicaid (CMMI). Implementation occurred across 17 health systems 
using a paid health coach as the agent of PtDA delivery. At the end of the grant, none of the health 
systems continued funding the health coaches independently and the practice came to a halt with 
no maintenance. As part of an additional PCORI grant, we qualitatively studied this natural 
implementation using an organizational systems theory approach. We interviewed health coaches, 
site leaders, and program leadership. A major reason the implementation was not successful was 
the timeline and change in scope from patient engagement to SDM imposed by CMMI halfway into 
the project. Limited resources hindered buy-in; for example, at many sites, health coaches were 
expected to do all the relationship building. Plus, health coaches were working across multiple 
conditions in multiple clinics, and they were not able to connect with clinic staff to normalize the 
process for sustainability in their absence. The most successful site achieved clinician engagement 
at multiple levels, and health coaches shared positive reactions to the PtDAs. One health coach 
told a story about how the Spanish version of the PtDA helped a suspicious patient visiting from 
Panama decide that the ICD would be a good choice for him. Lessons learned from observing this 
failed implementation heavily inform the strategy herein. Namely, use of normalization process 

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theory and clinical champions and staff to develop a low-cost, sustainable strategy of PtDA 
delivery. 
 
IV. Research Methods 
 
A. Outcome Measure(s):   
Reach: Reach is defined as the proportion of the target population who participate in the 
intervention. While this is a straightforward measure, defining the denominator of eligible patients is 
surprisingly difficult. We will assess the percentage of patients that both receive and remember 
reviewing the PtDAs. Representativeness will be assessed by comparing participants to those who 
opt out on a range of available demographics and clinical indicators (e.g., age, gender, 
comorbidities) from chart review. As we are only enrolling a subset of patients into the trial at each 
site, we will ask the staff delivering the PtDAs to maintain a log of intervention recipients. We will 
obtain a waiver of consent to calculate aggregate reach at each site among all patients receiving 
ICDs.  
 
Effectiveness:  
• 
Primary outcome: Decision quality is an essential element of the Ottawa Decision Support 
Framework (Figure 2), defined as “the extent to which the implemented decision reflects 
the considered preferences of a well-informed patient.”55 Decision quality measures consist 
of two domains: knowledge and values.  
o 
Knowledge: Consistent with methods developed by Sepucha et al.,55,56 we have 
developed a knowledge measure. We will use this measure to test knowledge at 
baseline and at 1 months and 6 months.  
o 
Value-treatment concordance: We will calculate concordance between patients’ values 
and the treatment they choose according to the validated methods of Sepucha et 
al.55,56 We will measure this in two ways: 1) we will measure the values-clarity sub-
scale of the decision conflict measure (test-retest reliability and Cronbach’s alpha > 
0.78, correlated with knowledge, regret, and treatment discontinuance) and 2) we will 
explore the prevailing value trade-off between “living longer even if it means getting an 
invasive therapy” versus “not living as long and avoiding an invasive therapy” as this 
item was able to discriminate between groups.  
 
• 
Secondary outcomes: Additionally, we will collect the following secondary outcomes: 
o 
IPDAS process measures: We will use seven questions based on key domains of 
decision process as outlined in the IPDAS background document. Dr. Matlock used 
these questions in his prior survey and they had significant reliability (Cronbach’s alpha 
of 0.78).74 
o 
Decision choice: We will use single item measures of decision predisposition, choice, 
and enactment.  These questions have test-retest reliability of 0.9 and correlates with 
values.75,76  
o 
Self-efficacy: The ‘Decision Self-Efficacy Scale’ measures self-confidence or belief in 
one’s ability to make decisions, including participate in shared decision making.77 
o 
Control Preferences: This is a 2 question measure of a patient’s opinion of their 
preferred and actual role in decision-making.78 
o 
The Decision Conflict Scale (DCS):  DCS is a 10-item instrument that measures 
decision quality and determines decisional uncertainty.79 The DCS quantifies personal 
perception of: a) uncertainty in choosing options; b) modifiable factors contributing to 
uncertainty such as feeling uninformed, unclear about personal values, and 
unsupported in decision making, and c) effective decision making such as feeling the 
choice is informed and values-based, and expressing satisfaction with the choice. DCS 
reliability measures include test-retest correlation and Cronbach’s alpha coefficients 
exceeding 0.78-0.9030,74. The DCS discriminates between groups who make and delay 
decisions.30 

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o 
The Decision Regret Scale: Decision regret is a 5-item scale which measures regret 
with the decision making. It is a commonly used measure in decision aid trials and has 
good reliability (Cronbach’s alpha of 0.82-0.91).30,74 It correlates with satisfaction, 
decision conflict, and QOL.80 
o 
The Patient Health Questionnaire-2 (PHQ-2): This comprises the first 2 items of the 
PHQ-9, inquires about the degree to which an individual has experienced depressed 
mood and anhedonia over the past two weeks. 81 
o 
The Generalized Anxiety Disorder Scale (GAD-2): In primary care patients, the 
Generalized Anxiety Disorder scale (GAD-7) had high sensitivity for detecting 
generalized anxiety disorder and panic disorder. The GAD-2 had high sensitivity and 
specificity for GAD and high specificity for panic disorder, social anxiety disorder, and 
post-traumatic stress disorder.82 
o ICD experiences: These will be questions developed and used previously to describe 
ICD-specific issues such as whether the participant has experienced a shock or 
complication and whether they have considered discussing their ICD in an advance 
directive or with a surrogate decision maker.  
 
B. Description of Population to be Enrolled:   
Inclusion Criteria: 
1. The participants in the trial will be adult (age ≥18) patients who have been offered a 
primary prevention ICD for initial implant, reimplantation, or CRT with defibrillation. Primary 
prevention must be for heart failure.  
Exclusion Criteria: 
1. Under 18 years of age 
2. Non-English speaking (decision aids and study assessments are in English only currently) 
3. Unable to consent 
4. Currently has a left ventricular assist device (LVAD) 
5. Prisoners 
Given that the PtDAs and study assessments are in English, we must limit recruitment to English 
speaking patient participants. However, the paper tool will also be available in Spanish and we will 
measure whether this facilitated implementation among this group using aggregate measures. As 
we do not have the resources to enroll every patient at every site, we will enroll a subset of eligible 
patients. Specifically, we will ask each site to enroll the first ~6 patients each month, or until at least 
6 patients are enrolled per month. While not completely random, there is also no systematic reason 
why demography would differ. This approach allows each site-PI to meet their aim of 50 enrolled 
patients per year without going over and maintaining awareness if they are under-target. A lesson 
learned from the DECIDE-LVAD trial is that without this constriction, over-enrollment is possible. If 
the site researcher is not able to enroll up to 6 patients in the first week of the month, then s/he 
may continue to approach patients until 6 have been enrolled that month.  
Setting: We propose a 7-hospital trial (Table 3). Each site investigator is a practicing  
electrophysiologist within the setting. In addition to access to patients, each has research expertise 
in the area of ICDs in his/her own right. This proposal has already benefitted from strong 
engagement by all sites in crafting the PtDA and the proposal. A multi-site design affords many 
advantages:  
• 
Aim 1: Conducting the trial at multiple sites avoids biases that could confuse the results. If 
conducted at a single center, it would be difficult to determine which changes in the 
decision process were due to the PtDAs versus those that were happening regardless 
(secular trends). Multiple sites allow for sequential transition of hospitals to the PtDAs 
(stepped-wedge design, see Table 4), thereby allowing for changes to the PtDA to be 
teased out from changes occurring in the background.  
• 
Aim 2: Individual programs have 
diverse policies, providers, and 
institutional cultures that reflect the 
Table 3: Randomized sites and annual ICD volumes 
Site 
ICD Volume 
University of Colorado Hospital 
200 
Denver Health Medical Center 
70 
The Denver VA Medical Center 
100 
Providence Health, Oregon 
300 
Baptist Health 
87 
Beth Israel 
250 
Mid America Heart Institute 
350 

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hospital and surrounding community. Testing the PtDAs in multiple programs across the 
U.S. can help determine how well the PtDA would disseminate to other hospitals. 
 
C. Study Design and Research Methods   
Overview: We propose 
a type II effectiveness 
implementation hybrid 
trial41 of our suite of ICD 
PtDAs. Our pragmatic 
design will allow us to 
simultaneously assess 
real-world effectiveness 
(Aim 1), and 
implementation (Aim 2). 
We will use a 6-site stepped-wedge design, with implementation guided by normalization process 
theory and evaluation guided by the RE-AIM framework (reach, effectiveness, adoption, 
implementation, and maintenance).83-85 For the purpose of this study, and as discussed with our 
grant proposal team, we will include 2 hospitals as one site during randomization in order to meet 
demographic requirements. As such, the University of Colorado Hospital and Denver Health 
Medical Center will be considered 1 site. We have 7 diverse sites (3 academic, 2 private, 1 county 
hospital, and 1 Veteran’s Affairs hospital) to allow us to explore broad contextual issues related to 
implementation variation. Sites all begin in the control phase, where usual care consists of the 
current education, decision making, and informed consent process. When sites reach their 
randomly assigned time to transition to the intervention, we will travel to the site to conduct the 
implementation. The pamphlet and video PtDAs will be formally integrated into the existing 
process. In both pre- and post-phases, we will enroll patients and survey them prior to their ICD 
decision and then again at 1 month and 6 months post-decision to determine the intervention’s 
effect on decision quality and a host of secondary outcomes. These patient-centered outcomes will 
be compared before and after implementation to determine intervention effectiveness. We will also 
survey clinicians and staff before, during, and after implementation of the intervention. In the broad 
view, this study will inform the science of decision support implementation for many interventional 
therapies in cardiovascular disease.  
 
Stepped-wedge multicenter trial (Table 3): We considered three potential designs for this trial. A 
classic patient-level randomization is not possible since programs (rather than patients) are 
effectively the primary targets of intervention implementation. The traditional cluster randomized 
trial would be disadvantaged by the small number of sites. Therefore, we propose a multicenter trial 
that is randomized at the clinic-level for a phased rollout of the shared decision support 
intervention.42,84 A variant of the cluster trial design, the stepped-wedge trial design is a one-way 
crossover cluster trial where all the groups will receive the intervention, but the time of intervention 
is randomly ordered. It is a compelling choice of design when 1) one of the goals is to study 
intervention implementation, 2) the intervention is thought to be efficacious, 3) all sites wish to 
receive the intervention and continue its implementation after the trial if it is effective, and 4) 
contamination/diffusion of the intervention effects to other subjects is likely. The stepped-wedge 
design is more efficient in terms of statistical power when compared to the classical cluster 
randomized design because the impact of the intervention is estimated using both within and 
between cluster differences. 
 
 
 
 
 
 
Table 5: Measures 
Baseline 
Pre Decision 
1 Month 
Post Decision 
6 Months 
Post Decision 
Table 4: Overview of the Stepped-Wedge Design 
Site 
Baseline 
(1 year) 
Intervention rollout sequentially 
across all sites (3 years) 
Follow-up 
(1 year) 
Random Site 1 
 
 
 
 
 
 
 
 
 
Random Site 2 
 
 
 
 
 
 
 
 
 
Random Site 3 
 
 
 
 
 
 
 
 
 
Random Site 4 
 
 
 
 
 
 
 
 
 
Random Site 5 
 
 
 
 
 
 
 
 
 
Random Site 6 
 
 
 
 
 
 
 
 
 

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COVARIATES 
 
 
 
- Demographics (combined survey and short chart review) 
X 
 
 
- Medications, Comorbidities, Heart Failure Type/Severity 
X 
 
X 
- Literacy (REALM-R 7 item)85 and Subjective Numeracy84  
X 
 
 
EFFECTIVENESS OUTCOMES (see descriptions below) 
 
 
 
- Decision Quality – Knowledge 
X 
X 
X 
- Decision Quality – Value-concordance 
X 
X 
X 
- Decision Process  
 
X 
X 
- Decision choice 
 
X 
X 
  - Self-Efficacy Scale 
X 
X 
X 
- Control Preferences 
X 
X 
X 
- Decision conflict 
X 
X 
X 
- Decision regret 
 
X 
X 
- PHQ-2 
X 
 
X 
  - GAD-2 
X 
 
X 
  - ICD Experiences (+/- shock; +/- complications etc.) 
 
X 
X 
 
 
Data collection and storage: Baseline questionnaire and case report forms can be completed as 
soon as is medically reasonable, ideally within 48 hours of enrollment. Patient characteristics shall 
include basic demographics (age, gender, educational level, health insurance status, employment), 
additional social support, medical history and medications, as well as health literacy86 and 
numeracy.87 Questionnaires will also be completed at 1 month and 6 months after enrollment (see 
Table 5).  
In addition, there will be a 6-item survey asked of clinicians per patient they see. We have 
developed and piloted six questions related to: 1) the clinician’s assessment of appropriateness of 
SDM for a particular patient; and 2) the clinician’s assessment of how well they think this particular 
patient will do with an ICD. While ICD is arguably a preference-sensitive decision, clinicians tend to 
think about preference sensitivity while integrating other aspects of patient clinical characteristics. 
We expect that this has been an important barrier to adoption of SDM in clinical practice. For 
example, a younger, patient with a higher risk of sudden death will find a clinician strongly 
encouraging an ICD while an older patient with multi-morbidity will find a clinician more comfortable 
engaging with SDM. This is why we organized this part of the evaluation under “adoption.” By 
exploring this preference on a per-patient basis, we will begin to understand how patient 
characteristics influence adoption of SDM and PtDAs. 
 
Data will be collected electronically using RedCAP, the University of Colorado IRB-preferred 
system. Data forms will be programmed by our analyst at the start of the project with supervision 
from the PI and the Lead Statistician. Data quality checks will be performed every 6 months during 
periods of active data collection. 
  
D. Description, Risks and Justification of Procedures and Data Collection Tools: 
Participants: We believe that this study is minimal risk to all subjects involved.  Participants are only 
answering surveys and interview questions and allowing for medical record review, thus there is no 
physical risk to them.  While there is the possibility that the participants may be upset by some of 
the survey/interview questions because it discusses the possible futures that patients with heart 
failure have,  studies of patients with serious illness show that patients actually are not upset by 
such studies and can find them helpful. Further, patients may terminate participation at any point. 
 
The survey and decision aids talk about patients’ understanding of the ICD. This includes 
discussions of sudden cardiac death and progressive heart failure. This may be upsetting to 
patients and/or they may become tearful.  This is however, a normal reaction to information and not 
an adverse event.  If a patient becomes so upset that they cannot complete the interview, this 
would be noted in the database.  Only in cases where subjects are so distraught that they need to 

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be referred for emergency counseling or supportive services will this be considered an adverse 
event.  
 
Given that these are all patients have heart failure, it is unlikely that any of them will be pregnant. 
This would be extremely rare but it is not specifically an exclusion criteria. Again the researchers 
may not know this, and regardless the study poses no risk to the health of a pregnant 
embryo/fetus. 
 
Data Safety: Data collection and storage has been planned where it is improbable that participant 
confidentiality will be breached. All patients will be given a unique identification number, and study 
data and identifiable information will always be kept separate. REDCap, the COMIRB-preferred 
system, will be used to store all data, and access will be limited to study personnel only. All paper 
documents will be stored in a secure and locked file cabinet in a secure and locked office building – 
again, all study data paperwork will be stored separate from paperwork with identifiable information 
(i.e. signed consent forms). 
 
E. Potential Scientific Problems:  
Stepped-Wedge Trials are difficult logistically: For all of their associated advantages, stepped-
wedge designs are not without challenges including logistical management of multiple sites in 
varying stages of implementation over time, the possibility of treatment diffusion (to patients initially 
seen during the control period but receiving follow-up care during the intervention), and the 
requirement that all sites begin and end evaluation at the same times. Fortunately, Drs. Matlock & 
Allen both have experience with this design in a recent trial assessing implementation and 
effectiveness of PtDAs for ventricular assistance device therapy.42 Also, the baseline survey 
combined with the importance of getting all sites to start at the same time is why we’ve included a 
year of start-up time. 
 
Enrolling 900 patients is overly optimistic: Each site will be expected to enroll 50 patients per year. 
Because we are implementing a suite of PtDAs, we expect no trouble enrolling the appropriate 
amount. In our pilot trial at the site with the lowest volume (the Denver VA) we enrolled 9 patients 
in 6 months for only the initial implant decision aid. We learned that there were many patients 
getting reimplantation and CRT devices whom were missing. With the suite of PtDAs available in 
this trial, we think we’ll have no trouble enrolling the eligible amount of patients. Also, with our 
experience with the DECIDE-LVAD trial, we are having more trouble with over-enrollment which is 
only a problem in rebudgeting to reimburse sites per patient.  
 
What if the study is not successful? If the study does not meet its primary endpoint, our formative 
evaluation will provide information about potential reasons that it was not successful and lead to 
better studies of alternative dissemination and implementation strategies for advanced progressive 
illness. In a pragmatic design such as this, most of the threats (e.g. low reach, low 
implementation) bias to the null. Indeed, this is precisely why this is a more interesting study 
than doing a research staff controlled efficacy trial.  
 
How will the PtDAs stay Up-To-Date and Responsive to Perceived Needs? Given the rapid 
changes in ICD technology, the PtDAs must be kept current to maintain relevance. This will be 
accomplished via: 
• 
Annual reviews of literature: With incorporation of important changes into the PtDAs.  
Modular design: The sectional build of the PtDAs—clearly defined pages for the pamphlet and 
segmented video—allows for simple revision as problems are identified or information changes 
over time.  
 
F.   Data Analysis Plan:   
Quantitative analysis (Hypotheses 1a, 1b, 1c): The analyses of the effectiveness will use a 
repeated measures mixed model. This strategy allows for partially incomplete data (e.g. missing 
follow-up or one of the collection periods) and relaxes the missing data assumptions to missing at 

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random conditional on observed data. Prior to these analyses, we will contrast the participants in 
the 2 phases of the study, identifying any patient/site characteristics that are unbalanced. If more 
than 3-5 variables are identified, we will develop a propensity score for the likelihood of being in the 
intervention phase. Each analysis model will include an indicator variable for the intervention 
phase, indicators for each of the sites and the variables identified above. 
 
Sample Size and Power Calculation: This trial was designed to have diversity in implementation 
settings.  As such, to effectively be powered to explore implementation across diverse settings, we 
are overpowered to test our primary quantitative outcomes (the knowledge domain of decision 
quality). With a sample size of 900 patients and a standard deviation of 18%, achieved power 
would approach 1.00 to detect a 10% improvement in knowledge (in the 2011 Cochrane Review, 
knowledge improved an average of 13.8%, in our pilot trial, knowledge improved 12%).6 Even with 
variance inflation for correlation within sites (assuming intraclass correlation 0.01), the power 
remains good at 0.98. This level of power will afford the ability to assess some heterogeneity of 
effect across sites and vulnerable patient groups, as well as adequate assessment of secondary 
effectiveness measures and implementation evaluation. To test the hypothesis that better informed 
patients will have less anxiety (hypothesis 1c), we will correlate the knowledge score with the 
HADS- A. Using a using a Pearson’s correlation coefficient, for a sample size of 300 (based on 1/3 
illustrating significant anxiety) then the person’s correlation is 0.18 for a power of 90% and an alpha 
of 0.05.   
 
G.  Summarize Knowledge to be Gained:   
We have designed the implementation to use and leverage existing resources to implement the 
intervention. Further, we have designed the study as an effectiveness/implementation study with 
emphasis on formative evaluation. Together, it is our hope that if the intervention is successful, we 
will develop an implementation guide and online distribution of materials for other ICD programs 
and other practitioners interested in testing and disseminating effective therapies.  
 
The lessons learned herein will apply broadly. As discussed in the Significance section, policies are 
changing. Patients are increasingly demanding involvement and seeking additional sources of 
information. The PtDA development group in Colorado has been contracted by the American 
College of Cardiology to develop a PtDA for patients with severe aortic stenosis considering 
surgical versus transcutaneous repair. The American College of Cardiology has also inquired with 
Colorado about developing a tool for the recently approved left atrial appendage occlusion device. 
Science on real-world effectiveness and implementation is essential in informing the field of PtDA 
delivery for high risk medical conditions. 
 
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