PCSK9 Inhibition After Heart Transplantation
Phase: PHASE2
Conditions: Vasculopathy
Sponsor: Stanford University

 
 
 
 
PCSK9 Inhibition After Heart Transplantation 
 
 
Study Protocol and Statistical Analysis Plan 
NCT03537742 
January 14, 2021 

CLINICAL PROTOCOL SYNOPSIS 
 
Brief Title:  
PCSK9 Inhibition after Heart Transplantation 
 
Focus of the Study:  
The focus of this study is to test the safety and efficacy of the PCSK9 inhibitor, alirocumab when administered 
early after heart transplantation (HT). 
 
Objectives:  
The main objective of this project is to test the safety and impact on cardiac allograft vasculopathy (CAV) of 
alirocumab when given early after HT. In this process, we will also characterize the pleiotropic effects of 
alirocumab, in particular focusing on its effects on lipoprotein (a) [Lp(a)], apolipoprotein B (apoB), triglyceride 
rich lipoproteins (TRL), and C-reactive protein (CRP) and their relationship to development of CAV and 
abnormal coronary physiology and endothelial function. A secondary objective is to demonstrate the value of 
invasive coronary physiologic and functional parameters for predicting development of CAV, independent of 
intravascular ultrasound (IVUS) findings. 
 
Study Design:  
This is a prospective, randomized, placebo-controlled, double-blind phase II study. 
 
Intervention to be Tested:  
Alirocumab will be compared to placebo starting within the first 8 weeks after HT and continued for one year. 
Cardiac transplant recipients will undergo baseline laboratory studies, coronary angiography, IVUS, coronary 
physiology (fractional flow reserve (FFR), coronary flow reserve (CFR), and the index of microcirculatory 
resistance(IMR)), and coronary endothelial function assessments within the first 8 weeks after HT. Subjects will 
then be randomized to alirocumab or placebo. Laboratory studies will be performed at 3, 6 and 12 months.  At 
one year coronary angiography, IVUS, coronary physiology, and coronary endothelial function assessments 
will be repeated. 
 
Primary and Important Secondary Endpoints:  
The primary endpoint of this study is the change in plaque volume from baseline to one year as measured by 
IVUS in the alirocumab treated patients compared to the placebo treated patients. The effect of alirocumab on 
LP(a), apoB, TRL and CRP measured at one year and its relationship to CAV and abnormal coronary 
physiology and function will also be assessed. A key secondary endpoint is the ability of invasive coronary 
physiology, namely FFR and IMR, and coronary endothelial dysfunction to predict development of CAV, 
independent of IVUS findings. 
 
Detailed Milestones: 
 
We are currently preparing our IRB submission, including the informed consent document. 
 
R61 Phase: 
 
Month 1-  
● Receive IRB approval if not already received.  
● Register trial at ClinicalTrials.gov website.  
● Begin construction of REDCap database. 
● Begin Manual of Operations.  
● Begin to create electronic case report form.  
 
Month 2-  
● Finalize case report form on REDCap database.  
● Finalize Manual of Operations.  
● Finalize DSMB.   
● Pharmacy and Sanofi/Regeneron to finalize drug/placebo delivery and storage.  

 
Month 3- 
● Steering Committee to begin scheduled meetings to review progress of the study and address 
any delays.  
● Finalize training of the study staff and meet with adult and pediatric heart transplant clinical 
teams to review protocol and site performance plan.  
● Begin screening for enrollment. 
 
Month 4-  
● Enroll first patient. 
 
Month 10- 
● Prepare communication with NIH regarding status of the study. 
 
Month 12- 
● Enroll 20th patient. Goal of enrolling 15% of patients.   
● Present status of the study to NIH. 
 
R33 Phase: 
 
Year 2- 
● Perform 1 year follow-up evaluations on patients enrolled in Year 1.  
● Recruit and enroll 35 patients.  
● Assessment of protocol implementation and performance.  
● Prepare and submit annual review for NIH.  
● Goal of enrolling 50% of patients. 
 
Year 3- 
● Perform 1 year follow-up evaluations on patients enrolled in Year 2.  
● Recruit and enroll 35 patients.  
● Assessment of protocol implementation and performance.  
● Prepare and submit annual review for NIH.  
● Goal of enrolling 80% of patients. 
 
Year 4- 
● Perform 1 year follow-up evaluations on patients enrolled in Year 3.  
● Recruit and enroll 30 patients.   
● Assessment of protocol implementation and performance.  
● Goal to complete enrolment before month 6.  
● Prepare and submit annual review for NIH. 
 
Year 5- 
● During the first 6 months, perform 1 year follow-up evaluations on patients enrolled in Year 4.  
● During months 6-9, complete, clean, and lock the database.  
● During months 9-12, analyze the data for each specific aim. 
● Prepare abstracts to present at national meetings.  
● Prepare manuscripts to publish the primary findings.  
● During month 12, prepare and submit final report to NIH.  
● Submit final results to ClinicalTrials.gov. 
 
Schedule of Clinical and Laboratory Evaluations:  
 
Time post HT 
Event 
0-8 Weeks 
Recruitment and enrollment 
1-8 Weeks 
Angiogram, endothelial function, FFR, IMR, IVUS and laboratory studies 
1-8 Weeks 
Randomization to alirocumab or placebo 
3, 6,12 Months 
Lipid and other laboratory studies 
One Year 
Angiogram, endothelial function, FFR, IMR and IVUS studies 
 
Study Population:  
One hundred and twenty subjects will be recruited soon after HT at Stanford University.  The goal is to recruit 
an equal number of men and women, however more men tend to undergo transplantation. In order to be 

included in this study, subjects must be 12 years or older, be within the first 8 weeks of HT, and provide 
informed, written consent. Younger patients will not be eligible because their coronary arteries are too small for 
IVUS examination. We will exclude patients who remain hospitalized because of complications from their HT. 
The Stanford University transplant program has performed approximately 65 heart transplants per year during 
the past 3 years, with 85% adult recipients and 10-15% recipients who are children over 12 years old. The age, 
gender and ethnic distribution (%) of heart transplant donors and recipients is summarized below. 
 
 
Age 
Gender (F/M) 
White 
Black 
Hispanic 
Asian/Pac. Isl. 
Donors 
2 – 56 
30% / 70% 
80% 
6% 
10% 
4% 
Recipients 
12 - 69 
35% / 65% 
70% 
14% 
8% 
8% 
   
 
Group Assignment:  
A stratified randomization scheme will be applied by age group (≤ 18 years of age vs > 18 years of age) and by 
gender. Within each age/gender stratum, subjects will be randomized using randomized block sizes of 2, 4, or 
6 in an equal and double-blind fashion to either alirocumab (initial dose: 75 mg subcutaneously every 2 weeks) 
or to placebo and begin treatment the day after their baseline angiogram procedure. Both the patient and the 
investigators/treating physicians will be blinded to treatment assignment. Implementation of the randomization 
scheme will be performed by the senior study statistician within the Quantitative Sciences Unit (QSU) at 
Stanford University under the direction of the statistical investigator, Dr. Desai. The QSU has extensive 
experience in designing and implementing randomization schemes and has performed this task for numerous 
clinical trials.  Randomization will occur after enrollment and baseline assessment. The alirocumab and 
placebo will be identical so that the patient and investigators/treating physicians will remain blinded to 
treatment assignment. Regeneron/Sanofi, the maker of alirocumab will provide the study drug and matching 
placebo (see attached letter). After a subject has been on alirocumab/placebo for four weeks, the LDL-C 
cholesterol will be checked. If the LDL-C response is inadequate (LDL-C>70 mg/dL) and there have been no 
adverse effects, the dosage will be increased to the maximum dosage of 150 mg administered every 2 weeks. 
The patient/family will be instructed on how to administer the medication while an inpatient. This will be 
reviewed at the time of clinic visits. The patient/family will administer the medication at home when the patient 
is discharged from the hospital.  Total cholesterol, LDL-C, high density lipoprotein, triglycerides, Lp(a), apoB 
and TRL will be measured at 3, 6, and 12 months post transplantation during clinic visits, but both the treating 
physicians, investigators and patients will be blinded to the results until the patient completes the one year 
invasive study.  Patients will be closely monitored for any adverse effects of alirocumab as detailed below. Dr. 
Josh Knowles is a Co-Investigator on this project who directs Stanford’s Familial Hypercholesterolemia (FH) 
clinic and is the Chief Medical Advisor for the FH Foundation, a patient-founded and patient-led, non-profit 
organization for individuals with FH. He has extensive experience managing patients on PCSK9 inhibitors and 
will oversee this aspect of our proposal. 
 
Statistical Design and Power:  
We will perform an analysis based on intent-to-treat principles to evaluate our primary outcome. This means 
we will analyze all subjects randomized and according to their randomized treatment assignment. The primary 
outcome is plaque volume, and our primary objective is to evaluate change in volume of plaque at 1 year 
post-HT relative to baseline between the two study arms. To address the primary objective, we will employ 
generalized linear mixed effects regression methods. The primary hypothesis will be tested using a two-sided 
Wald test at the 0.05 level of significance. A secondary goal will be to assess the effect of alirocumab versus 
placebo on coronary physiology, FFR and IMR, and on endothelial function. Another important secondary aim 
is to evaluate the mechanisms through which the treatment acts, where potential mechanisms include LDL-C, 
LP(a), apo(B), TRL and CRP. A final secondary aim is to evaluate the ability of FFR, IMR and endothelial 
dysfunction to predict clinically meaningful increases in plaque volume one year post-transplant relative to that 
of plaque volume measured via IVUS at baseline. Secondary analyses will be considered hypothesis 
generating. Dr. Manisha Desai, a Co-Investigator on this proposal and the Director of Stanford’s Quantitative 
Sciences Unit (QSU), is an experienced clinical trialist and biostatistician who will oversee the statistical 
analyses. 
 

Primary and Secondary Outcomes  
The primary objective of this project is to determine whether PCSK9 inhibition with alirocumab early after HT 
impacts the development of CAV. For this purpose, we define the primary endpoint as plaque volume, 
measured by IVUS, in order to compare changes at 1 year post HT relative to baseline between patients in the 
two study arms. Plaque volume as determined by IVUS is used to assess CAV because we and others feel 
that a volumetric evaluation is the most accurate assessment of intimal thickening and changes in plaque have 
been associated with increased long-term mortality after HT.35,36,67 Secondary endpoints include differences in 
LDL-C and other lipid particle values between the two arms at 3, 6 and 12 months, differences in coronary 
physiology (FFR and IMR) and endothelial function between the two groups from baseline to 1 year, and 
adverse events including mortality and retransplantation. 
 
Specific Aim 1. Measure the safety and impact on CAV of the PCSK9 inhibitor, alirocumab after heart 
transplantation.  
 
Descriptive Statistics  
We will present descriptive statistics on the full analysis set (FAS) by study arm such as means, medians, 
standard deviations, and interquartile ranges for continuous variables like plaque volume at each time point 
and plaque change. Frequency distributions for categorical variables such as sex and race will also be 
provided by study arm. Graphical tools such as histograms and boxplots will be used to assess distributional 
properties of continuous variables.  
 
Analytic Tools  
Using intent-to-treat principles, we will address our primary aim by regressing plaque volume on treatment arm, 
days since randomization (time) and an interaction between time and treatment arm on the FAS using 
generalized linear mixed effects regression techniques, where the parameter for the interaction term is of 
interest and represents the differential progression in plaque volume at one year post-HT between study arms. 
Such a model will include a subject-specific random effect to allow for correlation of outcome within a subject 
over time. Using a two-sided Wald test at the 0.05 level of significance, we will assess whether the change in 
plaque volume differs at 1 year by treatment arm. It is important to note that a significant advantage to 
employing this particular modeling strategy is that it enables us to adhere to intent-to-treat principles 
by including the full analysis sample and using robust assumptions about the missing data (described 
in greater detail below). Thus, even those who do not provide change in plaque volume are included in this 
analysis, where the treatment effect on plaque change is estimated for the sample by borrowing strength from 
those who do contribute data on all time points (no imputation is used in the generalized linear mixed effects 
model). Our model is expressed as: 
 
𝑃𝑙𝑎𝑞𝑢𝑒 𝑉𝑜𝑙𝑢𝑚𝑒𝑖𝑗=  𝛽0 + 𝛿𝑖 
+ 𝛽1𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡𝑖+ 𝛽2𝑇𝑖𝑚𝑒 𝑖𝑗+ 𝛽3𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡𝑖 × 𝑇𝑖𝑚𝑒  𝑖𝑗 + 𝜀𝑖𝑗 
where 𝑃𝑙𝑎𝑞𝑢𝑒 𝑉𝑜𝑙𝑢𝑚𝑒𝑖𝑗is the plaque volume measurement for the ith subject at the jth time point; 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡𝑖 is 
an indicator for whether the ith subject is assigned to the experimental treatment arm; and 𝑇𝑖𝑚𝑒 𝑖𝑗 is the 
number of days since randomization for the ith subject at the jth time point. This approach has become standard 
in cardiovascular clinical trials involving longitudinal repeated measures over time such as this one.61-63 
However, as a secondary analysis we will employ a simple t-test to compare the change in plaque volume 
between study arms among those who complete the study and provide outcomes of change using the modified 
intent-to-treat analysis set (MITT).  
 
Hypothesis Tests  
We will test the primary hypothesis using a two-sided Wald test at the 0.05 level of significance.  
 
Power Considerations  
Although there will be some attrition in measures collected over the follow-up period, the initial sample size 
(i.e., those randomized at baseline) will be maintained in the proposed analysis, as we will adhere to intent-to-
treat principles. There may however be an impact of attrition on our power due to the loss in the efficiency (or 
precision) of the estimate as the variation in our estimate of change in plaque may increase. We will therefore 

account for the impact of extra variation in our measure of change by considering a more conservative sample 
size.  
We have excellent power to address 
our primary aim. We will randomize 
120 patients. While all 120 patients 
will be included in the analysis 
according to intent-to-treat principles, 
we anticipate that 108 patients will 
complete the study without 
withdrawal or loss to follow-up. To 
incorporate the impact of attrition 
on the standard error we 
considered a smaller sample size 
of 108 or 54 per treatment arm, 
yielding conservative estimates of 
power. Based on larger studies in 
the literature and from our most 
recent work evaluating 96 patients who had serial IVUS at baseline and at 1 year after HT, we find that the 
common logarithm (base 10) of plaque volume at either time point to be approximately normally distributed.50 
Assuming a standard deviation of change in plaque volume (on the log-scale) of 0.10, based on empirical 
evidence, the effective sample size of 54 per arm gives over 85% power to detect a significant difference of 
change in plaque volume of at least 0.06 on the log scale assuming a type I error rate of 0.05. If the standard 
deviation of change in plaque volume is considerably higher than previously indicated, say 0.25, we have the 
same power to detect a difference in change in plaque progression of at least 0.15 on the log-scale.  
Specific Aim 2: Identify the pleiotropic effects of PCSK9 inhibition after heart transplantation.  
Descriptive Statistics  
As in Aim 1, descriptive statistics will be provided on the FAS by study arm such as means, medians, standard 
deviations, and interquartile ranges for continuous variables including all biomarkers such as LDL-C, 
lipoprotein (a), apolipoprotein B, triglyceride rich lipoproteins and CRP. Frequency distributions of clinically 
meaningful categories of these biomarkers will also be presented.  
 
Analytic Tools  
To evaluate mechanisms of action, we will continue to rely on the framework of generalized linear mixed 
effects regression techniques. Importantly, we will test for mediation through the fitting of multiple regression 
models. Specifically, using the statistical method by MacArthur that modifies the seminal work in this area by 
Baron and Kenney and is described for a broader audience by Kraemer and others, we will evaluate the 
interrelationships among treatment, plaque changes and biomarkers such as LDL-C to evaluate whether 
changes in plaque are mediated by LDL-C, lipoprotein (a), apolipoprotein B, triglyceride rich lipoproteins and 
CRP.64,65 The approach involves fitting a series of models to evaluate associations between the treatment and 
outcome, between treatment and the potential mediator, between the potential mediator and the outcome, and 
finally, between the treatment and outcome in the presence of the mediator. The presence of these various 
associations are necessary criteria in concluding that treatment is mediated through a factor. Essentially, for 
each biomarker or potential mediator of interest, evidence for mediation involves comparing the effect of 
treatment on plaque progression (𝑏3) derived from a full model of plaque volume as a function of treatment 
arm, time, an interaction between treatment arm and time, and the potential mediator of interest (written below 
with LDL-C as the mediator) to the effect of treatment on plaque progression derived from a model that does 
not include the mediator (𝛽3). If the association of treatment arm by time is attenuated in the presence of LDL-
C, this will suggest that PCSK9 affects plaque volume by lowering LDL-C. The full model used to evaluate a 
potential mediator can be described as follows: 
 
𝑃𝑙𝑎𝑞𝑢𝑒 𝑉𝑜𝑙𝑢𝑚𝑒𝑖𝑗=  𝑏0 + 𝛿𝑖 
+ 𝑏1𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡𝑖+ 𝑏2𝑇𝑖𝑚𝑒 𝑖𝑗+ 𝑏3𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡𝑖 × 𝑇𝑖𝑚𝑒  𝑖𝑗 + 𝑏4𝐿𝐷𝐿𝑖𝑗  + 𝜀𝑖𝑗 
Table 4. Power for addressing Aim 1 assuming 54 subjects per arm 
Scenario Average difference in 
log-plaque volume 
progression 
Standard deviation of 
change in log-plaque 
volume progression 
Power 
1 
0.06 
0.10 
0.87 
2 
0.10 
0.10 
> 0.95 
3 
0.15 
0.10 
> 0.95 
4 
0.06 
0.25 
0.24 
5 
0.10 
0.25 
0.54 
6 
0.15 
0.25 
0.87 

Analogous comparisons of such models involving the other three mediators will be drawn using these 
methods. Attenuation in the beta coefficient representing the effect of alirocumab on plaque volume over time 
of 15% or greater in the presence of changes in a biomarker will suggest a potential pathway through which 
alirocumab acts.  We will then perform identical analyses evaluating models of coronary physiology and 
endothelial dysfunction.  
 
Power considerations  
A sample size of 108 achieves 90% power to detect an R-Squared of as low as 0.07 attributed to each 
potential mediator we consider, assuming a type I error of 0.05. The term involving the mediator to be tested is 
adjusted for an additional 4 variables assuming a large R-Squared of 0.20. 
 
Specific Aim 3: Compare the prognostic ability of invasive coronary physiologic and functional parameters to 
IVUS in cardiac transplant recipients.  
 
Descriptive Statistics  
Using the MITT, we will present descriptive statistics on the residuals from our two competing model fits. Using 
graphical tools, such as histograms and boxplots we will assess the distributional properties of the residuals for 
assessment of model fit. Further, scatterplots will be used to evaluate whether residuals are systematically 
related to patient-level characteristics such as age, treatment arm, and other features utilized in the model(s).  
 
Analytic Tools  
Using standard linear regression techniques we will fit and compare two competing models that predict plaque 
volume at 1 year post HT on the MITT (i.e., on the set of subjects who provide an outcome at 1 year). More 
specifically, the first model will evaluate the ability of FFR, IMR, endothelial dysfunction, and IVUS plaque 
volume all measured within 8 weeks of HT as well as treatment arm to predict IVUS plaque volume at 1 year 
post HT. Note that these models differ from the framework above, in that there is only one outcome per 
subject, and thus a simple regression framework can be employed. The model that includes all features will be 
expressed as: 
 
𝑃𝑙𝑎𝑞𝑢𝑒 𝑉𝑜𝑙𝑢𝑚𝑒 𝑎𝑡 1 𝑦𝑒𝑎𝑟𝑖=  𝛾0 + 𝛾1𝐹𝐹𝑅𝑖+ 𝛾2𝐼𝑀𝑅𝑖+ 𝛾3𝐸𝐷𝑖𝑗 + 𝛾4𝑃𝑙𝑎𝑞𝑢𝑒 𝑉𝑜𝑙𝑢𝑚𝑒 𝑎𝑡 𝐻𝑇𝑖  + 𝜀𝑖 
Using the same techniques, we will evaluate the ability of IVUS plaque volume measured within 8 weeks of HT 
in the presence of treatment arm to predict IVUS plaque volume at 1 year post HT:  
 
𝑃𝑙𝑎𝑞𝑢𝑒 𝑉𝑜𝑙𝑢𝑚𝑒 𝑎𝑡 1 𝑦𝑒𝑎𝑟𝑖=  𝑔0 + 𝑔1𝑃𝑙𝑎𝑞𝑢𝑒 𝑉𝑜𝑙𝑢𝑚𝑒 𝑎𝑡 𝐻𝑇𝑖  + 𝜀𝑖 
We will compare the predictive ability of the two models using the Akaike Information Criterion or AIC.66 Such 
analyses will provide insight into the ability of FFR, IMR and endothelial function to contribute to prediction of 
clinically meaningful plaque development within a relatively small window after HT, allowing for earlier and 
more aggressive intervention for a subset of patients. As we anticipate that the treatment will play a large role 
in arresting plaque progression, we have included it as a term in the predictive model. 
 
Power considerations  
We have excellent power to achieve our aims. A sample size of 108 provides 90% power to detect an R-
Squared of only 0.05 attributed to the plaque volume measured at baseline alone compared to a model that 
additionally utilizes FFR, IMR and endothelial dysfunction, assuming a model with all four variables can explain 
40 percent of the total variation in plaque volume at 1 year.   
Missing Data:  
As we propose a prospective clinical trial, we expect (and plan for) a small proportion of subjects to be lost to 
follow-up, and this will have impact for all three of our aims. Specifically, this poses an issue for adhering to 
intent-to-treat principles, the gold standard for producing unbiased treatment effects, which stipulate that all 
subjects randomized be included in the analysis and analyzed by their randomized treatment assignment. 
Because our interest lies in comparing change in plaque volume or plaque progression between study arms, 
patients who do not provide an IVUS measurement at 1 year will not have a measure of change in plaque 

volume. Our approach to handling this missing outcome measure, therefore, is to define the primary outcome 
as plaque volume at a time point and to model change between arms as described above, using mixed effects 
models that estimate treatment effects on change by borrowing strength from those individuals who provide 
data on all time points, making robust assumptions about the data that are missing. Specifically, we assume 
the reason for missing data on plaque volume at 1 year is related only to observed variables including the 
patient’s initial plaque volume measurement and treatment assignment and further that the reason for 
missingness is not related to the actual plaque volume measurement at 1 year. Based on our understandings, 
we believe this to be a reasonable assumption. Under this condition, the mixed effects modeling framework 
proposed above will yield unbiased and efficient estimates of the treatment effect. We will test departures to 
this assumption using multiple imputation techniques that vary the specification of the missing data 
mechanism. Dr. Desai is a leading expert in missing data and currently PI of a PCORI-sponsored award to 
study multiple imputation techniques of longitudinal measures.  
   
Study Participation Duration:  
Each subject will participate for one year after HT. 
 
Study Duration:  
The study duration from the first enrolment to the last follow-up will be 54 months.  The final data analyses will 
be completed 6 months after the last patient follow-up. 
 
Concomitant Medical Treatment:  
Adult patients will receive induction therapy with methylprednisolone, 500 mg IV when coming off bypass and 
rATG at 1 mg/kg (maximum dose 125 mg) given on post-operative days 1, 2, and 3; daclizumab will be 
administered at 1 mg/kg to the pediatric population. Two additional doses of methylprednisolone will be given 
over the next 24 hours; pediatric patients will receive daclizumab every other week for a total of 5 doses after 
transplantation. The daily maintenance immunosuppressive regimen includes cyclosporine (2-4 mg/kg/day) in 
children; tacrolimus (0.5 mg twice daily) in adults, and mycophenolate mofetil (1000 mg twice daily); dose 
adjustments are made as indicated by adverse effects and/or acute rejection; prednisone is initiated at 1 
mg/kg/day, and tapered to 0.5 mg/kg/day by month 6, and to 0.1 mg/kg/day by month 12. Concomitant 
medications are initiated as soon as the patient is able to tolerate oral intake. All patients receive co-
trimoxazole (one double strength tablet daily) for pneumocystis pneumonia prophylaxis, except for patients 
with sulfa allergy who received atovaquone instead of co-trimoxazole. The HMG-CoA reductase inhibitor 
rosuvastatin will be initiated in all adult patients, within one week of transplant at 10 mg per day, regardless of 
plasma cholesterol or triglyceride concentration; pediatric patients will receive 5 mg per day.  If after 4 weeks, 
the adult/pediatric patient is tolerating the rosuvastatin without any adverse effects, it will be increased to 20/10 
mg per day, respectively. 
 
All patients in whom either the donor or recipient is cytomegalovirus positive by serologic testing receive the 
standard ganciclovir prophylaxis, consisting of valganciclovir (900 mg twice daily) for one month initiated as 
soon as the patient can tolerate oral medications, followed by 900 mg daily until the end of the third post-
operative month. Thereafter, all patients remain on valganciclovir 450 mg daily for the remainder of the first 
year post HT. Patients presenting with signs or symptoms of cytomegalovirus infection will undergo testing for 
confirmation and treatment.  Patients will be seen in clinic and monitored for acute rejection by routine 
surveillance endomyocardial biopsy and echocardiography to evaluate allograft function at two weeks and 1, 2, 
3, 4, 6 months after HT; the AlloMap test will be performed after that time period. At one year patients will be 
seen in clinic and then undergo their one year invasive studies. Other follow-up will be via phone call.     

STUDY ORGANIZATION PLAN 
Dr. Fearon, a Professor of Medicine and Director of Interventional Cardiology at Stanford University is the 
Principle Investigator of the proposed study and will oversee all aspects of the study conduct and 
management. He is an experienced clinical trialist with a long track record of successfully completing impactful 
trials. He will be assisted by Helen Luikart, a Cardiac Transplant Research nurse. Dr. Fearon and Nurse 
Luikart have worked closely and successfully together on a previous NIH sponsored clinical trial evaluating the 
role of angiotensin converting enzyme inhibitors early after heart transplantation.50 They have an excellent 
working relationship and communicate well with each other.   
Dr. Fearon will establish a steering committee consisting of the other Co-Investigators and critical personnel 
participating in the trial.  The following will serve on the Steering Committee with Dr. Fearon: 
• 
Dr. Kiran Khush, an Associate Professor of Medicine and the Director of the Adult Cardiac Transplant 
Research at Stanford  
• 
Dr. Manisha Desai, a Professor of Medicine (Research) - Biomedical Informatics Research, and (By 
courtesy), Health Research & Policy, and the Director of the Stanford University Quantitative Sciences Unit 
• 
Dr. Joshua Knowles, an Assistant Professor of Medicine, Director of the Familial Hypercholesterolemia 
clinic and a recognized expert in PCSK9 inhibitors  
• 
Helen Luikart, RN, MS and clinical research nurse with over 20 years of experience coordinating the 
Stanford Cardiac Transplant research program  
The Steering Committee will meet in person on a monthly basis to monitor the trial’s progress and address 
issues with enrolment, compliance with the protocol, completeness of data, the budget, and safety.  Informal ad 
hoc meetings between individuals will occur on a weekly basis. In this manner, the PI and the Steering 
Committee will pro-actively evaluate and prioritize issues that jeopardize study goals and necessitate corrective 
responses. Any disputes will be resolved by a vote of the Steering Committee members with each member 
retaining an equal vote. Minutes from the Steering Committee meetings will be taken by Helen Luikart and a 
summary report will be provided on a quarterly basis to the NHLBI to provide budgetary and enrolment updates.    
Helen Luikart will serve as the point person interacting with Stanford’s Internal Review Board and with the Data 
Safety and Monitoring Board (DSMB) for this project. She will notify both of any adverse events which occur 
during the course of the study. Data will be monitored in a blinded fashion until the end of the study. She will 
also coordinate study closure with Stanford’s Internal Review Board at the end of the study.   
Dr. Fearon will perform the cardiac catheterization procedures on the enrolled patients. He is an experienced 
interventional cardiologist who has performed more than 500 such evaluations in transplant recipients over the 
past 15 years. Nurse Luikart will oversee the communication with the Stanford Internal Review Board and be 
responsible for assuring that the consent forms are updated appropriately and the protocol is renewed in a 
timely fashion.    
Dr. Khush, a Co-Investigator on this proposal is the Director of the Adult Heart Transplantation Research 
Program at Stanford and an experienced clinical investigator. She will oversee the recruitment and treatment of 
the cardiac transplant recipients. Nurse Luikart will assist her with recruiting and consenting patients. Dr. 
Khush will be responsible for evaluating patients during their clinical follow-up. She will report all adverse 
events to Nurse Luikart who will review them with Dr. Fearon and the DSMB. Dr. Fearon and Dr. Khush will 
communicate regularly in an ad hoc fashion and during the scheduled Steering Committee meetings. Dr. 
Fearon and Dr. Khush have collaborated on a number of previous studies and have an excellent working 
relationship.6,7,37,38,50 
Dr. David Rosenthal, a Professor in Pediatrics, the Director of the Pediatric Cardiac Transplant program will 
serve as a Co-Investigator and oversee recruitment and enrolment of the pediatric participants in this clinical 
trial. He will monitor the children during follow-up for any adverse events and report these to Nurse Luikart, 
who will report them to Dr. Fearon and the DSMB. Dr. Fearon and Dr. Rosenthal have worked together on a 

previous NIH sponsored clinical trial evaluating the role of angiotensin converting enzyme inhibitors early after 
heart transplantation and have an excellent working relationship.50      
Dr. Manisha Desai, a Professor of Medicine (Research)- Biomedical Informatics Research, and (By courtesy), 
Health Research & Policy, and the Director of the Stanford University Quantitative Sciences Unit is a Co- 
Investigator and will oversee the data management and statistical analyses performed in this clinical trial. Dr. 
Desai has extensive experience designing, capturing, and analyzing data from clinical trials. She and Dr. 
Fearon have worked together on previous projects and communicate extremely well with each other.68 Dr. 
Desai has assigned a staff biostatistician, Lingyao Yang to assist with establishing the database, creating the 
electronic case report form, evaluating the data for accuracy and completeness, and performing the analyses.   
Dr. Joshua Knowles, an Assistant Professor of Medicine, Director of the Familial Hypercholesterolemia clinic 
and a recognized expert in PCSK9 inhibitors will serve as a Co-Investigator and provide expertise regarding 
monitoring and treating the enrollees with alirocumab/placebo.   
Dr. Francois Haddad, a Clinical Associate Professor of Medicine, adult cardiac transplant cardiologist, the 
Director of the Stanford Cardiovascular Institute Biomarker and Phenotypic Core Laboratory, and a Co-
Investigator on this trial will provide assistance coordinating the serologic analyses testing for the anti-
inflammatory and pleiotropic effects of alirocumab. 
The DSMB will be chaired by Michael Pham, MD, MPH, a heart transplant physician with more than 10 years 
of experience now practicing at the California Pacific Medical Center (see letter of acceptance in appendix).  
Other members of the DSMB will include a biostatistician and an ethicist. Meetings will occur every 3 months 
for the first year, and every 6 months thereafter to assess the rate of recruitment, completeness of data, 
completeness of follow-up, and for any major disparities in adverse clinical events between the two groups 
(presented to the DSMB as “group A” and “group B”), in a blinded fashion. If the DSMB identifies issues with 
recruitment or data management, it will notify the PI and Steering Committee and an action plan to correct the 
issue will be prepared and presented to the DSMB which will determine if it adequately addresses the issue(s). 
If there is concern regarding a major difference in adverse clinical events between the two groups, then the 
DSMB will be unblinded by an independent statistician from the Quantitative Sciences Unit at Stanford who is 
not involved in the study to determine if the active treatment group or the placebo group has the higher event 
rate and decide if the study needs to be stopped prematurely. Helen Luikart, RN will receive data from the 
statistical team and report all of the adverse events to the DSMB. Ad hoc meetings will be convened to review 
any serious adverse events felt to possibly be related to study drug or procedure. There will be no formal 
stopping rules for the trial. However, should the DSMB feel that patient safety is being compromised in any 
way by the study, it will be their prerogative to stop the trial.  
 

STATISTICAL ANALYSIS AND PLAN 
 
We will perform an analysis based on intent-to-treat principles to evaluate our primary outcome. This means 
we will analyze all subjects randomized and according to their randomized treatment assignment. The primary 
outcome is plaque volume, and our primary objective is to evaluate change in volume of plaque at 1 year 
post-HT relative to baseline between the two study arms. To address the primary objective, we will employ 
generalized linear mixed effects regression methods. The primary hypothesis will be tested using a two-sided 
Wald test at the 0.05 level of significance. A secondary goal will be to assess the effect of alirocumab versus 
placebo on coronary physiology, FFR and IMR, and on endothelial function. Another important secondary aim 
is to evaluate the mechanisms through which the treatment acts, where potential mechanisms include LDL-C, 
LP(a), apo(B), TRL and CRP. A final secondary aim is to evaluate the ability of FFR, IMR and endothelial 
dysfunction to predict clinically meaningful increases in plaque volume one year post-transplant relative to that 
of plaque volume measured via IVUS at baseline. Secondary analyses will be considered hypothesis 
generating. Dr. Manisha Desai, a Co-Investigator on this proposal and the Director of Stanford’s Quantitative 
Sciences Unit (QSU), is an experienced clinical trialist and biostatistician who will oversee the statistical 
analyses. 
 
Specific Aim 1. Measure the safety and impact on CAV of the PCSK9 inhibitor, alirocumab after heart 
transplantation.  
 
Primary and Secondary Outcomes: The primary objective of this project is to determine whether PCSK9 
inhibition with alirocumab early after HT impacts the development of CAV. For this purpose, we define the 
primary endpoint as plaque volume, measured by IVUS, in order to compare changes at 1 year post HT 
relative to baseline between patients in the two study arms. Plaque volume as determined by IVUS is used to 
assess CAV because we and others feel that a volumetric evaluation is the most accurate assessment of 
intimal thickening and changes in plaque have been associated with increased long-term mortality after 
HT.35,36,67 Secondary endpoints include differences in LDL-C and other lipid particle values between the two 
arms at 3, 6 and 12 months, differences in coronary physiology (FFR and IMR) and endothelial function 
between the two groups from baseline to 1 year, and adverse events including mortality and retransplantation. 
 
Descriptive Statistics: We will present descriptive statistics on the full analysis set (FAS) by study arm such as 
means, medians, standard deviations, and interquartile ranges for continuous variables like plaque volume at 
each time point and plaque change. Frequency distributions for categorical variables such as sex and race will 
also be provided by study arm. Graphical tools such as histograms and boxplots will be used to assess 
distributional properties of continuous variables.  
 
Analytic Tools: Using intent-to-treat principles, we will address our primary aim by regressing plaque volume on 
treatment arm, days since randomization (time) and an interaction between time and treatment arm on the FAS 
using generalized linear mixed effects regression techniques, where the parameter for the interaction term is of 
interest and represents the differential progression in plaque volume at one year post-HT between study arms. 
Such a model will include a subject-specific random effect to allow for correlation of outcome within a subject 
over time. Using a two-sided Wald test at the 0.05 level of significance, we will assess whether the change in 
plaque volume differs at 1 year by treatment arm. It is important to note that a significant advantage to 
employing this particular modeling strategy is that it enables us to adhere to intent-to-treat principles 
by including the full analysis sample and using robust assumptions about the missing data. Thus, even 
those who do not provide change in plaque volume are included in this analysis, where the treatment effect on 
plaque change is estimated for the sample by borrowing strength from those who do contribute data on all time 
points (no imputation is used in the generalized linear mixed effects model). Our model is expressed as: 
 
𝑃𝑙𝑎𝑞𝑢𝑒 𝑉𝑜𝑙𝑢𝑚𝑒𝑖𝑗=  𝛽0 + 𝛿𝑖 
+ 𝛽1𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡𝑖+ 𝛽2𝑇𝑖𝑚𝑒 𝑖𝑗+ 𝛽3𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡𝑖 × 𝑇𝑖𝑚𝑒  𝑖𝑗 + 𝜀𝑖𝑗 
where 𝑃𝑙𝑎𝑞𝑢𝑒 𝑉𝑜𝑙𝑢𝑚𝑒𝑖𝑗is the plaque volume measurement for the ith subject at the jth time point; 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡𝑖 is 
an indicator for whether the ith subject is assigned to the experimental treatment arm; and 𝑇𝑖𝑚𝑒 𝑖𝑗 is the 
number of days since randomization for the ith subject at the jth time point. This approach has become standard 

in cardiovascular clinical trials involving longitudinal repeated measures over time such as this one.61-63 
However, as a secondary analysis we will employ a simple t-test to compare the change in plaque volume 
between study arms among those who complete the study and provide outcomes of change using the modified 
intent-to-treat analysis set (MITT).  
 
Power Considerations: Although there will be some attrition in measures collected over the follow-up period 
(See Methods for Handling Missing Data below), the initial sample size (i.e., those randomized at baseline) will 
be maintained in the proposed analysis, as we will adhere to intent-to-treat principles. There may however be 
an impact of attrition on our power due to the loss in the efficiency (or precision) of the estimate as the variation 
in our estimate of change in plaque may increase. We will therefore account for the impact of extra variation in 
our measure of change by considering a more conservative sample size.  
We have excellent power to 
address our primary aim. We will 
randomize 120 patients. While all 
120 patients will be included in the 
analysis according to intent-to-treat 
principles, we anticipate that 108 
patients will complete the study 
without withdrawal or loss to follow-
up. To incorporate the impact of 
attrition on the standard error we 
considered a smaller sample size 
of 108 or 54 per treatment arm, 
yielding conservative estimates 
of power. Based on larger studies 
in the literature and from our most 
recent work evaluating 96 patients who had serial IVUS at baseline and at 1 year after HT, we find that the 
common logarithm (base 10) of plaque volume at either time point to be approximately normally distributed.50 
Assuming a standard deviation of change in plaque volume (on the log-scale) of 0.10, based on empirical 
evidence, the effective sample size of 54 per arm gives over 85% power to detect a significant difference of 
change in plaque volume of at least 0.06 on the log scale assuming a type I error rate of 0.05. If the standard 
deviation of change in plaque volume is considerably higher than previously indicated, say 0.25, we have the 
same power to detect a difference in change in plaque progression of at least 0.15 on the log-scale.  
Specific Aim 2: Identify the pleiotropic effects of PCSK9 inhibition after heart transplantation.  
Descriptive Statistics: As in Aim 1, descriptive statistics will be provided on the FAS by study arm such as 
means, medians, standard deviations, and interquartile ranges for continuous variables including all 
biomarkers such as LDL-C, lipoprotein (a), apolipoprotein B, triglyceride rich lipoproteins and CRP. Frequency 
distributions of clinically meaningful categories of these biomarkers will also be presented.  
 
Analytic Tools: To evaluate mechanisms of action, we will continue to rely on the framework of generalized 
linear mixed effects regression techniques. Importantly, we will test for mediation through the fitting of multiple 
regression models. Specifically, using the statistical method by MacArthur that modifies the seminal work in 
this area by Baron and Kenney and is described for a broader audience by Kraemer and others, we will 
evaluate the interrelationships among treatment, plaque changes and biomarkers such as LDL-C to evaluate 
whether changes in plaque are mediated by LDL-C, lipoprotein (a), apolipoprotein B, triglyceride rich 
lipoproteins and CRP.64,65 The approach involves fitting a series of models to evaluate associations between 
the treatment and outcome, between treatment and the potential mediator, between the potential mediator and 
the outcome, and finally, between the treatment and outcome in the presence of the mediator. The presence of 
these various associations are necessary criteria in concluding that treatment is mediated through a factor. 
Essentially, for each biomarker or potential mediator of interest, evidence for mediation involves comparing the 
effect of treatment on plaque progression (𝑏3) derived from a full model of plaque volume as a function of 
treatment arm, time, an interaction between treatment arm and time, and the potential mediator of interest 
Table 4. Power for addressing Aim 1 assuming 54 subjects per arm 
Scenario 
Average difference 
in log-plaque volume 
progression 
Standard deviation of 
change in log-plaque 
volume progression 
Power 
1 
0.06 
0.10 
0.87 
2 
0.10 
0.10 
> 0.95 
3 
0.15 
0.10 
> 0.95 
4 
0.06 
0.25 
0.24 
5 
0.10 
0.25 
0.54 
6 
0.15 
0.25 
0.87 

(written below with LDL-C as the mediator) to the effect of treatment on plaque progression derived from a 
model that does not include the mediator (𝛽3). If the association of treatment arm by time is attenuated in the 
presence of LDL-C, this will suggest that PCSK9 affects plaque volume by lowering LDL-C. The full model 
used to evaluate a potential mediator can be described as follows: 
 
𝑃𝑙𝑎𝑞𝑢𝑒 𝑉𝑜𝑙𝑢𝑚𝑒𝑖𝑗=  𝑏0 + 𝛿𝑖 
+ 𝑏1𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡𝑖+ 𝑏2𝑇𝑖𝑚𝑒 𝑖𝑗+ 𝑏3𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡𝑖 × 𝑇𝑖𝑚𝑒  𝑖𝑗 + 𝑏4𝐿𝐷𝐿𝑖𝑗  + 𝜀𝑖𝑗 
Analogous comparisons of such models involving the other three mediators will be drawn using these 
methods. Attenuation in the beta coefficient representing the effect of alirocumab on plaque volume over time 
of 15% or greater in the presence of changes in a biomarker will suggest a potential pathway through which 
alirocumab acts.  We will then perform identical analyses evaluating models of coronary physiology and 
endothelial dysfunction.  
 
Power considerations: A sample size of 108 achieves 90% power to detect an R-Squared of as low as 0.07 
attributed to each potential mediator we consider, assuming a type I error of 0.05. The term involving the 
mediator to be tested is adjusted for an additional 4 variables assuming a large R-Squared of 0.20. 
 
Specific Aim 3: Compare the prognostic ability of invasive coronary physiologic and functional parameters to 
IVUS in cardiac transplant recipients.  
 
Descriptive Statistics: Using the MITT, we will present descriptive statistics on the residuals from our two 
competing model fits. Using graphical tools, such as histograms and boxplots we will assess the distributional 
properties of the residuals for assessment of model fit. Further, scatterplots will be used to evaluate whether 
residuals are systematically related to patient-level characteristics such as age, treatment arm, and other 
features utilized in the model(s).  
 
Analytic Tools: Using standard linear regression techniques we will fit and compare two competing models that 
predict plaque volume at 1 year post HT on the MITT (i.e., on the set of subjects who provide an outcome at 1 
year). More specifically, the first model will evaluate the ability of FFR, IMR, endothelial dysfunction, and IVUS 
plaque volume all measured within 8 weeks of HT as well as treatment arm to predict IVUS plaque volume at 1 
year post HT. Note that these models differ from the framework above, in that there is only one outcome per 
subject, and thus a simple regression framework can be employed. The model that includes all features will be 
expressed as: 
 
𝑃𝑙𝑎𝑞𝑢𝑒 𝑉𝑜𝑙𝑢𝑚𝑒 𝑎𝑡 1 𝑦𝑒𝑎𝑟𝑖=  𝛾0 + 𝛾1𝐹𝐹𝑅𝑖+ 𝛾2𝐼𝑀𝑅𝑖+ 𝛾3𝐸𝐷𝑖𝑗 + 𝛾4𝑃𝑙𝑎𝑞𝑢𝑒 𝑉𝑜𝑙𝑢𝑚𝑒 𝑎𝑡 𝐻𝑇𝑖  + 𝜀𝑖 
Using the same techniques, we will evaluate the ability of IVUS plaque volume measured within 8 weeks of HT 
in the presence of treatment arm to predict IVUS plaque volume at 1 year post HT:  
 
𝑃𝑙𝑎𝑞𝑢𝑒 𝑉𝑜𝑙𝑢𝑚𝑒 𝑎𝑡 1 𝑦𝑒𝑎𝑟𝑖=  𝑔0 + 𝑔1𝑃𝑙𝑎𝑞𝑢𝑒 𝑉𝑜𝑙𝑢𝑚𝑒 𝑎𝑡 𝐻𝑇𝑖  + 𝜀𝑖 
We will compare the predictive ability of the two models using the Akaike Information Criterion or AIC.66 Such 
analyses will provide insight into the ability of FFR, IMR and endothelial function to contribute to prediction of 
clinically meaningful plaque development within a relatively small window after HT, allowing for earlier and 
more aggressive intervention for a subset of patients. As we anticipate that the treatment will play a large role 
in arresting plaque progression, we have included it as a term in the predictive model. 
 
Power considerations: We have excellent power to achieve our aims. A sample size of 108 provides 90% 
power to detect an R-Squared of only 0.05 attributed to the plaque volume measured at baseline alone 
compared to a model that additionally utilizes FFR, IMR and endothelial dysfunction, assuming a model with all 
four variables can explain 40 percent of the total variation in plaque volume at 1 year.   

Data and Safety Monitoring Plan: 
 
 
Safety data will be captured at the time of study visits (Baseline angiogram, Months 1, 2, 3, 4, 6, 9, and 
1 Year angiogram), and at any time point when an adverse event (AE) occurs. AEs will be managed by the 
treating physicians in conjunction with the study investigators using best clinical practice.  The Principal 
Investigator and the study nurse coordinator will monitor data and safety internally. Each adverse event will be 
assessed by the Principal Investigator and the study coordinater for designation as serious AE (SAE) or AE, 
and determination of whether it was study-related (either drug or procedure). The specific AEs of alirocumab 
that will be monitored are described in the Protection of Human Subjects attachment and include injection site 
reaction, neurocognitive events and cataracts. Assessment of AEs and SAEs by the Principal Investigator and 
study coordinator will take place on a weekly basis during a scheduled research meeting designed to review 
and discuss progress of the study, and to evaluate AEs. The results of these regular assessments by the 
Principal Investigator and study coordinator will be captured in the database as AEs.  All AEs will be reported 
to the IRB (Stanford University Committee for the Protection of Human Subjects) and to the Data Safety 
Monitoring Board (DSMB).  Once reviewed, all AEs and any actions taken by the IRB including changes or 
amendments to the protocol or consent form will be reported to the appropriate NIH funding institute.  The 
potential risks and benefits to the subjects are outlined in the Protection of Human Subjects section.   
 
The DSMB will be chaired by Dr. Michael Pham, the Medical Director of the Advanced Heart Failure, 
Transplant and Mechanical Circulatory Support Program at the California Pacific Medical Center, and a 
physician with 13 years of clinical and research experience in the field of heart transplantation (see letter of 
acceptance in appendix).  Other members of the DSMB will include an independent biostatistician and an 
independent ethicist.  All AEs will be reported and reviewed by the DSMB on a quarterly basis. Ad hoc 
meetings will be convened to review any SAEs felt to possibly be related to study drug or procedure.  There 
will be no formal stopping rules for the trial.  However, should the DSMB feel that patient safety is being 
compromised in any way by the study, it will be their prerogative to stop the trial.  Interim analyses will occur 
every 3 months for the first year, and every 6 months thereafter to assess the rate of recruitment, 
completeness of data, completeness of follow-up, and for any major disparities in adverse clinical events 
between the two groups (presented to the DSMB as “group A” and “group B”), in a blinded fashion. If the 
DSMB identifies issues with recruitment or data management, it will notify the PI and Steering Committee and 
an action plan to correct the issue will be prepared and presented to the DSMB which will determine if it 
adequately addresses the issue(s). If there is concern regarding a major difference in adverse clinical events 
between the two groups, then the DSMB will be unblinded by an independent statistician from the Quantitative 
Sciences Unit at Stanford who is not involved in the study to determine if the active treatment group or the 
placebo group has the higher event rate and decide if the study needs to be stopped prematurely. If adequate 
recruitment is not being achieved, the study will be expanded to include another center. For example, we have 
a good collaboration with Dr. Jon Kobashigawa and Cedars Sinai Medical Center in Los Angeles, CA, a very 
high volume transplant center. We will establish a subaward with the other center and the PI and our research 
staff will travel to the other center to review the objectives of the study and the protocol in detail. Periodically, 
Helen Luikart, our research nurse will travel to the site to ensure data are being captured completely and 
appropriately. This model has worked successfully for us in previous trials.50