Cardiac Amyloidosis Discovery Trial
Phase: NA
Conditions: Cardiac Amyloidosis
Sponsor: Pierre Elias

CLINICAL STUDY PROTOCOL 
Cardiac Amyloidosis Discovery Trial
(CAD Trial) 
Protocol Version: 
7.0 
Protocol Date: 
August 22, 2024 
Sponsor affiliation:
Columbia University Medical Center 
Clinical Cardiovascular Research Laboratory for the 
Elderly (CCRLE) 
21 Audubon Ave 
New York, NY 10032, USA 
Sponsor-Investigator: 
Pierre Elias, MD 
Principal Investigator 
ClinicalTrials.gov ID:
NCT06469372

STUDY SYNOPSIS 
Protocol Title 
Cardiac Amyloidosis Discovery Trial (CAD-Trial) 
Protocol Number 
7.0 
Design 
This is a single center, diagnostic clinical trial in which we aim to 
prospectively validate a deep learning model that identifies patients 
with features suggestive of cardiac amyloidosis, including transthyretin 
cardiac amyloidosis (ATTR-CA). Cardiac Amyloidosis is an age-related 
infiltrative cardiomyopathy that causes heart failure and death that is 
frequently unrecognized and underdiagnosed.  We have developed a 
deep learning model that identifies patients with features of ATTR-CA 
and other types of cardiac amyloidosis, including echocardiographic, 
ECG, and clinical factors. By applying this model to the entire Columbia 
University Irving Medical Center’s patient population, we will identify a 
list of patients at highest predicted risk for having undiagnosed cardiac 
amyloidosis.  We will then invite these patients for further testing to 
diagnose cardiac amyloidosis with the goal of recruiting 100 total 
patients.  
Consented eligible patients will be evaluated for the presence and 
clinical manifestations of cardiac amyloidosis. Patients will undergo a 
single study visit, using amyloid nuclear scintigraphy, monoclonal 
protein testing and a physical examination.  Those who meet criteria for 
the disease will be given the diagnosis of cardiac amyloidosis. Patients 
will be counseled on the results of their test findings and appropriate 
referrals made for further care. 
Study Sites 
This study is to be conducted at NYP Milstein Hospital and 21 Audubon 
Clinical Cardiovascular Research Laboratory for the Elderly location. 
Time on Study 
The duration of patient participation in this study is 1 day. 
Primary Objective 
To prospectively validate a deep learning model to diagnose cardiac 
amyloidosis, an underdiagnosed, lethal, and treatable cause of heart 
failure. 
Target Sample Size 
100 patients 
Inclusion and 
Exclusion Criteria 
Every participant must meet all of the following inclusion criteria to be 
eligible for enrollment in this study: 
1. High predicted probability of having cardiac amyloidosis as
determined by deep learning model
2. Age ≥ 50 years
3. Electronically stored ECG and echocardiogram within 5 years of
study start date

4. Ability for the patient or health care proxy to understand and
sign the informed consent after the study has been explained.
Exclusion criteria 
1. Primary amyloidosis (AL) or secondary amyloidosis (AA).
2. Prior liver or heart transplantation.
3. Active malignancy or non-amyloid disease with expected survival
of less than 1 year.
4. Previous testing for cardiac amyloidosis such as amyloid nuclear
scintigraphy, cardiac, or fat pad biopsy
5. Impairment from stroke, injury or other medical disorder that
precludes participation in the study.
6. Disabling dementia or other mental or behavioral disease.
7. Enrollment in a clinical trial not approved for co-enrollment.
8. Inability or unwillingness to comply with the study requirements.
9. Nursing home resident.
10. Other reason that would make the subject inappropriate for
entry into this study.
Aims 
The specific aims of this investigation include: 
1. To prospectively validate that deep learning models can detect
undiagnosed cardiac amyloidosis in large populations of patients
who have undergone cardiac testing.
2. To incorporate additional data to improve the deep learning
model’s detection of cardiac amyloidosis.
Safety 
Assessments 
The safety of study participants will be evaluated by: 
1. Assessment of adverse events (AEs), including serious adverse
events (SAEs).
2. Vital sign measurements (blood pressure, pulse rate, and
respiratory rate).
3. 12-Lead electrocardiogram (ECG).
4. Physical examinations
5. Echocardiography

LIST OF ABBREVIATIONS AND DEFINITIONS OF TERMS 
Abbreviation or Specialist Term 
Explanation 
99mTc-PYP 
99mTechnetium pyrophosphate 
99mTc-HMDP 
99mTechnetium 
hydroxymethylene 
diphosphonate 
AE 
Adverse event 
AI 
Artificial Intelligence 
TTR 
Transthyretin 
AL-CA 
Light chain cardiac amyloidosis 
ATTRm 
Mutant Transthyretin amyloidosis 
ATTRwt 
Wild-type Transthyretin amyloidosis 
ATTR-CA 
Transthyretin cardiac amyloidosis 

1.
INTRODUCTION
4.12. Heart failure is a leading cause of death, morbidity, and health care spending in the United 
States. 
Heart failure is common disease with 6.5 million adults in the United States having a diagnosis of 
heart failure and accounting for 1 in 8 deaths.1, 2 Heart failure is a highly morbid disorder, with patients 
having low quality of life3 and frequent hospitalizations.4  It is a leading cause of health care spending 
with an estimated $30.7 billion spent in 2012 for medications, the cost of health care services, and 
lost income from missing days of work.1 Heart failure is subdivided by left ventricular ejection fraction 
into heart failure with reduced ejection fraction (HFrEF) and heart failure with preserved ejection 
fraction (HFpEF). HFrEF has a broad array of therapies that have been shown to improve outcomes, 
whereas there are no treatments which have been proven to reduce mortality in unselected 
populations with HFpEF.  The emerging paradigm of HFpEF is that it is a clinical syndrome with a 
diverse set of underlying pathophysiologies and multitude of etiologies which require specific and 
varying treatments. 
1.2. Amyloidosis is a leading cause for heart failure and heart rhythm disturbance 
Systemic amyloidosis results from deposition of amyloid fibrils in tissues throughout the body, 
including the heart, nerves, lungs, gastrointestinal tract, and skin. Cardiac amyloidosis is the clinical 
syndrome that results from amyloid fibril deposition in the heart. Patients most commonly present 
with a heart failure syndrome with marked LV wall thickening and preserved ejection fraction on 
imaging. The two most common types of cardiac amyloidosis are transthyretin cardiac amyloidosis 
(ATTR-CA) and light chain cardiac amyloidosis (AL-CA). Both of these entities were previously thought 
to be rare, but it has been increasingly recognized that ATTR-CA is far more common than previously 
thought and may be present in up to 13% of hospitalized patients with HFpEF and an increased wall 
thickness over the age of 60 years.5 Misdiagnosis is common, with studies consistently finding 
significant delays in diagnosis of cardiac amyloidosis.6-8 
1.3. Biology and pathophysiology 
The pathophysiology of cardiac amyloidosis is highly dependent on the type of fibril involved. ATTR-
CA results from deposition of misfolded transthyretin (TTR) monomers. In vivo, TTR is primarily 
synthesized in the liver and forms a tetrameric protein with each subunit containing 127 amino acids. 
This tetrameric protein is often referred to as “prealbumin” and serves as a carrier for thyroid 
hormone and part of the complex that carries vitamin A.  The TTR tetramer can dissociate into 
monomers, and in some cases these monomers can become misfolded into an insoluble fibril and 
irreversibly deposit into tissues. ATTR is divided into two types: wild-type TTR (ATTRwt) and mutated 
(ATTRm).  ATTRwt is predominantly a disease of the elderly and is characterized by high rates of 
cardiac involvement (ATTRwt-CA).9 In ATTRm, patients have specific mutations in the TTR gene which 

leads to increased rates of tetramer dissociation which subsequently deposit as amyloid fibrils into 
tissues as in the heart (ATTRm-CA) or nerves.  More than 130 genetic mutations have been 
recognized, with Val122Ile being particularly common in the United States with an estimated 1.3 
million African Americans affected.10  
1.4. Therapy for ATTR-CA is available 
Historically, treatment of ATTR-CA was limited to supportive care and organ transplantation. 
However, recent work has led to the development of amyloid-targeted therapy. Possible treatment 
strategies for ATTR involve preventing production of the transthyretin protein, stabilizing the TTR 
tetramer, and removing fibrils from tissues. In 2019, tafamidis (VYNDAMAXTM), a small molecule TTR 
tetramer stabilizer, was found to improve the prognosis of patients with ATTR-CA and became FDA 
approved as the first therapy targeted for ATTR-CA.11  Strategies to prevent TTR production using 
small interfering RNA (patisiran) and antisense oligonucleotides (inotersen) have been shown to 
improve outcomes in ATTRm hereditary polyneuropathy,12, 13 and trials are ongoing to study their use 
or similar compounds in ATTR-CA. Because these treatments prevent progression of the disease 
rather than reversing fibril deposition that has already occurred, there has been a drive to diagnose 
patients in the early stage of disease prior to the development of severe end-organ dysfunction.  
1.5. Non-invasive nuclear imaging can accurately diagnose ATTR-CA 
The diagnosis of ATTR-CA could historically only be made by 
endomyocardial biopsy, a test that is invasive, expensive, 
and often only available in tertiary care transplant centers. 
Cardiac imaging with echocardiography and cardiac 
magnetic resonance imaging can be suggestive of the 
diagnosis, but the hallmark features of LV thickening are 
nonspecific.  Work over the last decade has shown that bone 
seeking radiopharmaceuticals such as technetium-99m 
pyrophosphate (99mTc-PYP), 99mTc-labeled 3,3-diphos-
phono-1,2-propanodicarboxylic acid (99mTc-DPD), or 99mTc-
hydroxymethylene 
diphosphonate 
(99mTc-HMDP) 
are 
specific for ATTR-CA.14 Non-invasive testing pathways which combine these nuclear scans with 
monoclonal protein testing to rule out AL-CA have been shown to accurately diagnose ATTR-CA when 
compared to endomyocardial biopsy. These scans are graded using a semiquantitative scale from 0-
3 using visual appearance compared to ribs with planar scans with grade 2 and 3 scans thought to be 
consistent with ATTR-CA, with grade 0 having no myocardial uptake, grade 1 having some cardiac 
uptake but less than ribs, grade 2 having cardiac uptake equal to ribs, and grade 3 having cardiac 
Figure 
1: 
PYP 
scan. 
This 
figure 
demonstrates a grade 3 99mTc-PYP scan 
with H:CL ratio of 1.73 which is consistent 
with a diagnosis of ATTR-CA. 

uptake greater than ribs with decreased bone uptake (Figure 1).  In addition to visual scoring, 
quantitative methods can be used by measuring the heart to contralateral chest ratio (H:CL) which 
measures counts over the heart and comparing it to an equal sized area of interest measured over 
the contralateral chest. Further refinements of 99mTc-PYP scanning have included the incorporation 
of single photon emission computed tomography (SPECT) imaging to improve test accuracy. Likely a 
result of the increasing awareness of ATTR-CA and the availability of non-invasive diagnostic testing, 
testing volume has increased yearly and as of 2019 more than 130 99mTc-PYP scans are conducted 
at our center each year with a positive test rate greater than 30%.  
1.6. Artificial intelligence 
Artificial intelligence (AI) is a general term for computing techniques which seek to simulate human 
thinking. Historically, these techniques used rule-based algorithms to solve simple problems. 
Advances in computer power and development of more complex algorithms have allowed AI 
techniques to be applied to a broad range of problems.  Machine learning approaches, which allow 
for algorithms to be trained using data sets without explicit instruction from humans, have had the 
most widespread success. Deep learning, a subset of machine learning, has been particularly useful 
in tasks related to interpreting images. Deep learning takes many layers of mathematical equations 
to try and interpret some input, such as the color of a pixel. The sum of these layers is often called a 
neural network due to the model being inspired by the way that neurons are interconnected in the 
human brain. When trained on large, feature rich datasets in focused problems, the accuracy of deep 
learning neural networks can outstrip even expert human observers. The success of such neural 
networks has been demonstrated in the reading of mammograms,15 ECG algorithms which predict a 
patient’s risk of hypertrophic cardiomyopathy based on a 12-lead ECG,16 and diagnosis of diabetic 
retinopathy based on retinal images.17 The laboratory of Dr. Adler Perotte, a close collaborator in this 
study, has expertise in computational statistics and artificial intelligence as it applies to electronic 
health data. 
1.7. Informatics pipeline 
Over the past 18 months, our group has been working to build an informatics pipeline of patients 
undergoing cardiovascular testing at Columbia University Irving Medical Center.  We have abstracted 
more than 2 million ECGs, over 200,000 echocardiograms, and more than 750 99mTc-PYP scans and 
are working to add other cardiovascular and radiologic tests. This database includes ECG physician 
reports, raw ECG waveforms, ECG reading-software abstracted metadata like QRS voltage and QRS 
length, echocardiogram reports, chest x-ray images, and 99mTc-PYP scan clinical reports, planar and 
SPECT images, and concurrently obtained chest CT scans.  This interlinked database is a large, feature-
rich dataset which is well-suited for using deep learning to answer complex clinical questions such as 
using the results of a more common testing modality (i.e. echocardiography and electrocardiography) 
to predict the outcome of a less common one (99mTc-PYP scanning). 

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QRS, and QT intervals) and peak wave amplitude (such as P, Q, R, S, and T wave amplitude), and each 
lead can be analyzed individually or as a sum of all 12 leads.  The echocardiogram database includes 
over 200,000 echocardiograms in more than 100,000 patients. At CUIMC, echocardiograms are stored 
digitally and analyzed using the Syngo system (Siemens Healthineers, Erlangen, Germany).  Our 
echocardiogram database contains abstracted information from the clinician report, including 
interventricular septal thickness, LV posterior wall thickness, LV ejection fraction, LV end-diastolic 
dimension, left atrial volume indexed for body surface area, and aortic root dimensions. With the 
combination of these three databases, we are able to rapidly conduct experiments to identify ECG, 
echocardiographic, demographic, and clinical features which distinguish ATTR-CA cases from normal 
controls. 
4.12. Traditional statistical modeling: 
The dataset used to create the traditional statistical model has nearly 1,000 variables per individual 
case. To ensure all cases are included, they must have <10% of data missing including no missing data 
in variables considered most critical (LV ejection fraction, interventricular septal thickness, posterior 
wall thickness, age, gender, ECG paced versus unpaced). From there, multiple imputation by chained 
equations (MICE) is conducted to ensure all cases are included. Data is then separated into training 
(70%), development (15%), and test (15%) data sets on a per-patient level. Due to limited size of the 
dataset, 5-fold cross-validation will be utilized. To deal with the high dimensionality of the data, both 
logit and probit regression models are generated. To minimize overfitting we prefer the use of L1 
regularization over L2.  
4.12. Deep learning model development 
The above methods are utilized to create the same train, development, test dataset with patients 
identically coded into those groups to allow for direct comparison. The first set of models generated 
utilize only tabular data and are based off of neural accumulators (NAC) and neural arithmetic logic 
units (NALU).20 NACs/NALUs are neural layers designed to learn functions comprised of arithmetic 
expressions such as addition, subtraction, multiplication, and division. The most promising aspect of 
NAC/NALU is that by learning generalizable numerical representations, there is hope to build neural 
networks that are explainable with simple and sparse arithmetic of raw input features. This 
architecture’s inductive bias is dependent upon learning a set of weights which, after some internal 
operations, yield values near -1, 0, or 1. We have developed a modification of NAC/NALU to decrease 
overfitting and improve sparsity, believing this would lead to the model to output a simpler equation 
utilizing fewer features and less variance. To encourage this behavior, we appropriately 
parameterized Laplace priors on our weights. We refer to these layers as Laplacian-regularized NAC 
and NALU (LR-NAC, LR-NALU). From there, we plan to include the raw ECG waveform data in the 
NAC/NALU models as well as develop a convolutional neural network (CNN) model based primarily 
of the waveform data as discussed in Aim 2. The CNN model will be developed similarly using the 

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2.9. Orthopedic manifestations: Previous work has shown that the orthopedic manifestations 
of carpal tunnel syndrome,22 spinal stenosis,23, 24 and hip and knee osteoarthritis requiring 
arthroplasty25 can precede the diagnosis of cardiac amyloidosis by up to 5-7 years. 
Pathophysiologically, the carpal tunnel and multiple areas in the spine are small, compressed spaces. 
As a result, relatively little amyloid deposition can lead to significant symptoms, and nerve 
involvement may accentuate pain syndromes. The period of time when patients have symptomatic 
carpal tunnel syndrome, spinal stenosis, or osteoarthritis but before they have developed 
symptomatic heart disease could be an ideal time to diagnose systemic amyloidosis and initiate 
treatment. Preliminary work at our institution using manual chart abstraction has demonstrated that 
these orthopedic manifestations are more common in ATTR-CA patients than in controls (to be 
presented at International Society of Amyloidosis 2020 meeting). In order to incorporate these 
features in our model, we will collect electronic health record data on orthopedic manifestations as 
documented by Diagnosis Related Group codes, past medical and surgical history, and problem lists 
all patients seen in the CUIMC system. We will then incorporate this data into our deep learning 
models with two significant goals: (1) assess improvement in model’s capacity to detect ATTR-CA, and 
(2) determine if it is possible to develop a model which prioritizes detection of patients with earlier 
cardiac involvement. 
2.10. Electrocardiogram waveform analysis: Our current deep learning model uses tabular 
ECG data from the MUSE system documenting stored voltages and deflections. We believe that 
improvements to the ECG portion of this model are possible by adding raw waveform data.  The 
waveforms on a 12 lead ECG are feature-rich with ~50,000 discrete data points as opposed to ~700 
data points that are stored in the tabular data. Prior work has shown that deep learning analysis of 
12 lead ECGs can identify significant pathology that would be essentially invisible to a human 
interpreter.16 We plan to include the raw ECG waveform data in the NAC/NALU models as well as 
develop a convolutional neural network (CNN) model based primarily of the waveform data. The CNN 
model will be developed similarly using the same train/development/test splits previously discussed. 
We will utilize the Adam optimizer and binary cross entropy as the loss function. We will assess 
convolutions across a single lead as well as a pooled convolution across all leads. We will then 
evaluate performance characteristics of the model with probability thresholds of 1%, 5%, 10%, 20%, 
50%, and 75%. By adding raw waveforms data, we aim to increase the AUC of our model to >0.90.  
2.11. Testing for novel clinical associations by using deep learning: The most promising 
aspect of NAC/NALU-based deep learning models is that by learning generalizable numerical 
representations, there is hope to build neural networks that are explainable with simple and sparse 
arithmetic of raw input features. Compared to other deep learning models, the output is more 
interpretable and is essentially an equation. The addition of a Laplacian-regularized filter pushes the 
model to make valuables more binary, either including them entirely or not at all. Instead of finely 
tuning hundreds of knobs, the model is asked to flip on switches of fewer input variables that matter. 

While this may sacrifice some overall performance, it pushes the model towards simplicity which we 
believe limits overfitting, improves generalizability, and most importantly points out fewer variables 
that actually matter. To be used in real clinical settings and also derive insight on novel clinical 
associations, a model incorporation thousands of variables is unlikely to be successful. With such 
restrictions in place, we seek to determine if there is at least one novel clinical association that is 
found to be important in detecting ATTR-CA that is not currently known in the scientific community.  
2.12. Future Directions: (1) Clinical trial to detect early stage amyloidosis: Our initial clinical trial 
(Aim 1) will likely result in identification of patients with later stage cardiac amyloidosis.  By adding 
the orthopedic manifestations and ECG waveform data and tuning the model for earlier stage disease 
(Aim 2), we hope to be able to repeat this clinical trial but target patients with earlier stage disease 
(2) Deployment of the model at additional centers: The use of ECG, echocardiographic, demographic 
data, and clinical diagnoses to identify cardiac amyloidosis is generalizable to other medical centers. 
We anticipate that this model could be used at other centers to identify further cohorts of patients 
with cardiac amyloidosis. (3) Application of this research model to other diseases: The use of artificial 
intelligence analysis of pre-existing clinical data to identify undiagnosed disease has significant 
potential outside of the study of amyloidosis.  It is possible that training similar models targeted at 
other diseases could have success in increasing identification of rare disease such as primary 
pulmonary hypertension or diseases that have a latency period that allows for improved treatment 
with earlier initiation such as cancer.  
2.3 
Overview of Study Design 
We will undertake a prospective cohort study among subjects with heart failure seen at one of two 
sites: (1) Columbia University Irving Medical Center and affiliated ColumbiaDoctors sites and (2) Allen 
Hospital of NY Presbyterian. Patients identified as having a high probability of having undiagnosed 
cardiac amyloidosis will undergo comprehensive cardiovascular examinations including: (1) complete 
medical history documenting the presence of HF and any symptoms that could be associated with 
cardiac amyloidosis, as well as any additional co-morbid conditions or symptoms, (2) targeted physical 
exams including orthostatic blood pressure measurements, assessment for gallops and volume status 
(e.g. JVP, rales, edema) and determination of functional class, (3) standard 12 lead electrocardiogram, 
(4) full two-dimensional and three-dimensional echocardiograms with Doppler flow assessment and 
tissue Doppler and speckle tracking (See procedures for details), (5) cardiac amyloid scintigraphy, and 
(6) blood testing for monoclonal protein testing. 
2.3.1 Patient Recruitment 
The CAD-Trial study is designed to identify a sufficient number of cases to prospectively validate that 
deep learning models can detect undiagnosed cardiac amyloidosis in large populations of patients 
undergoing cardiac testing. To accomplish this objective, we will undertake an approach to cohort 

identification and selection is used that deliberately selects for subjects who are at increased risk for 
cardiac amyloidosis. 
Patient Recruitment: To accomplish the above objectives, the cohort will be identified by examination 
of all patients who had an ECG and echocardiogram at CUIMC within the last 5 years (Figure 3). The 
deep learning model will be applied to this dataset of patients to create a list of patients at highest 
probability of having cardiac amyloidosis. The medical records of these patients will be evaluated for 
inclusion and exclusion criteria, and the 100 highest probability patients who agree to take part in the 
study will be enrolled. 
Selection criteria: 100 patients will be enrolled 
in this protocol. Inclusion criteria will include age 
≥ 50 years, electronically stored ECG and 
echocardiogram within 5 years of study start 
date, ability for the patient or health care proxy 
to understand and sign the informed consent 
after the study has been explained. Exclusion 
criteria include a previous diagnosis of ATTR, AL, 
or AA amyloidosis, previous testing for cardiac 
amyloidosis such as a prior cardiac amyloid 
scintigraphy, cardiac, or fat pad biopsy, prior 
organ transplantation, active malignancy, or 
non-amyloid heart disease with an expected 
survival of less than one year.  
Study procedures: Eligible patients will be 
identified in the manner above and invited to 
participate in the study by phone call. During 
this phone call, they will be assessed for eligibility and undergo an informed consent process.  A study 
visit will then be scheduled. At the study visit, they will undergo a history and physical examination 
by a physician, 12 lead electrocardiogram, transthoracic echocardiogram, blood testing for 
monoclonal proteins, blood biobanking, and cardiac amyloid scintigraphy. They may be offered TTR 
genetic testing by blood or saliva sample if preliminary testing is concerning for amyloidosis, 
counseling on that decision, and given the choice of whether to have genetic testing without it 
affecting their ability to participate in the other study procedures. Patients will be given a $100 debit 
card for their participation in the study and will be provided a car service if needed and feasible for 
transport to and from the study site. After completion of all testing, patients will be counseled on the 
findings of their testing and appropriate referrals for follow-up or further testing will be made. For 
the purposes of the study, a diagnosis of cardiac amyloidosis will be made in the following settings: 
Figure 3: Study Flow. This diagram demonstrates the 
analysis of patients and data with the goal of performing 
99mTc-PYP scanning on 100 patients identified by our 
deep learning model to be at high probability of having 
undiagnosed ATTR-CA. 

•
ATTR-CA diagnosis: A diagnosis of ATTR-CA will be made according to consensus guidelines by
an amyloidosis expert.18, 19 These includes imaging criteria with requires that a patient’s
cardiac amyloid scintigraphy SPECT scan shows myocardial uptake and follow-up monoclonal
protein testing shows no evidence of AL amyloidosis or pathologic criteria with a biopsy
showing ATTR.
•
AL-CA diagnosis: A clinical diagnosis of AL-CA will be by an amyloidosis expert according to
society guidelines. These includes a diagnosis made in one of the following settings: (1) cardiac
biopsy showing AL deposition and (2) extra-cardiac biopsy showing AL deposition with typical
cardiac features on imaging including echocardiography or cardiac magnetic resonance
imaging.
2.3.2 Study Procedures 
The timing of all procedures in this study is outlined in Table 4. Eligible patients will be identified. 
Patients who are interested in the study will have a screening visit to evaluate for eligibility and obtain 
informed consent.  If patients qualify for study and do consent they will have their study visit within 
4 weeks of screening. At that visit they will have all of the following testing done. 
Table 4. Study Procedures 
Procedure 
Screening 
(phone visit) 
Baseline 
Phone 
wrap-up 
visit 
Informed Consent 
X 
Inclusion and Exclusion Criteria 
X 
Baseline History 
X 
Height and Weight‡ 
X 
Vitals Signs 
X 
Medications 
X 
Clinical Examination 
X 
Adverse Events Review 
X 
Blood draw for monoclonal protein testing, 
genetic testing, and biobanking 
X 
Electrocardiogram 
X 
Echocardiogram 
X 
99mTc-PYP administration 
X 
99mTc-PYP Scintigraphy 
X 
Patient compensation - $100 debit card 
X 
Phone Call Follow-Up 
X 

Appropriate referral for follow-up in the event 
of ATTR-CA diagnosis or discovery of other 
serious pathology 
X 
2.3.2.1 Clinical Examination 
A detailed health investigation will be performed in all patients by a provider experienced in the care 
of patients with HF. This will include a medical history focused on the presence of HF as defined by 
standard clinical criteria (NHANES26 and European Society of Cardiology27 for HFpEF), etiology of HF, 
and co-morbid conditions; physical examination including vital signs, volume status, cardiopulmonary 
examinations and neurologic examination with focus on neuropathic findings. Exams will take ~45 
minutes. 
2.3.2.2 Cardiac amyloid scintigraphy 
A nuclear medicine technologist will perform planar cardiac imaging of the chest using dual-headed 
gamma cameras equipped with low energy, high resolution collimators. 10-25 mCi of 99mTc-PYP or 
10-20 mCi of 99mTc-HMDP will be administered intravenously and imaging will be performed after 
approximately 3 hours. The anterior and lateral planar views centered on the heart will be obtained 
simultaneously for a total of at least 750K counts/view (approx. 3-8 min of imaging). Cardiac retention 
will be assessed by both a semi-quantitative visual score (range: 0 [no uptake] to 3 [uptake greater 
than rib]) and a quantitative heart-to-contralateral (H/CL) ratio of total counts in a region of interest 
(ROI) over the heart divided by background counts in an identical size ROI over the contralateral chest 
including soft tissue, ribs, and blood pool. A visual score 2 or greater and/or calculated H/CL ratio 1.3 
or greater, with myocardial retention of tracer confirmed in all cases by SPECT imaging with or 
without a CT scan for image registration, will be required to indicate TTR-CA according to prior 
published data.28 The details of the imaging procedures for cardiac amyloid scintigraphy imaging are 
shown in Table 5. Each cardiac amyloid scintigraphy scan will be read by a board-certified nuclear 
cardiologist blinded to clinical information.  
Table 5g procedures 
Parameter 
Imaging procedures 
Parameter 
Preparation 
No specific preparation. No 
fasting required. 
Scan 
Rest scan 
Dose of 99mTc-PYP 
10-25 mCi intravenously 
Dose of 99mTc-HMDP 
10-20 mCi intravenously 
Time between 
injection and 
acquisition 
3-hour planar and optional SPECT 
Imaging parameters 
Field of view 
Recommended: cardiac or chest; 
Optional: whole body planar 
Image type 
Recommended: cardiac or chest 
SPECT and planar imaging 
Position 
Supine 
Energy window 
140 keV, 15-20% 
Collimators 
Low energy, high resolution 
Matrix 
64 x 64 minimum 
Pixel size 
3.5-6.5 mm 
Planar imaging specific parameter 
Number of views* 
Anterior, lateral, and left anterior 
oblique 
Detector 
configuration 
90 degrees 

Image duration 
(count based) 
750,000 counts 
Magnification 
1.46 
SPECT imaging specific parameters 
Angular range 
360 degrees 
Detector 
configuration 
180 degrees 
ECG gating 
Off; nongated imaging 
Number of 
views/detector 
40 
Time per stop 
30 seconds 
Magnification 
1.0 
2.3.2.3 Two- Dimensional Echocardiography with Complete Doppler Analysis 
A complete two-dimensional (2D) echo will be performed utilizing a commercially available GE Vivid-
9 or Philips iU33 Echocardiography System. A standard imaging protocol will be performed.  The 
following linear echocardiographic measurements will be obtained (by 2D or M-mode) in both end-
diastole and end-systole: left ventricular (LV) internal dimension, septal wall thickness (SWT), and 
posterior wall thickness (PWT). Quantification of LV mass will be derived by the formula of Devereux 
RB et al29 and by three dimensional-guided modified biplane Simpson’s rule30, 31. LV and left atrial (LA) 
volumes (systolic and diastolic) will be measured by the biplane method of discs (modified Simpson’s 
rule).  LV ejection fraction and LA function will be calculated from these volume measurements.  
2.3.2.4 Electrocardiogram 
A standard 12 lead electrocardiogram will be performed with standard instrument sensitivity of 10 
mm = 1 mV. A sum of precordial voltage (sum of S wave in lead V1 plus R wave in lead V5 or V6 [SVl + 
RV5 or V6]) will be calculated for all electrocardiograms. This sum will be used to compare data from 
patients with myocardial retention of 99mTc-PYP from controls. LVH will be defined when this sum is 
greater than 35 mm. When the sum was less than 15 mm, low voltage in the precordial leads is 
present. Additional criteria to define low voltage only in the limb leads include no QRS deflection 
greater than 5 mm in any limb lead and low voltage in all leads is present when the average voltage 
in the three limb leads is <5 mm, and the average voltage in the chest leads is <10 mm.37 Finally, the 
mass: voltage ratio will be calculated using the LV mass divided by the sum of S wave in lead VI plus 
R wave in lead V5 or V6 [SV1 + RV5 or V6]. The ECG tracings will be scanned and uploaded in 
electronically. 
2.3.2.5 Blood and saliva testing 
During the study visit, subjects will undergo a blood draw with no more than 25 cc of blood drawn 
and a cheek swab or saliva swab. This blood will be sent for testing for serum kappa and lambda free 
light chains and serum protein electrophoresis with immunofixation. These tests will assess for the 
substrate for light chain amyloidosis which can mimic ATTR-CA. If patients are found to have a 
monoclonal gammopathy, they will be referred for further management by a physician outside the 
study. In addition, a sample may be drawn for biobanking and TTR genetic testing at the investigator’s 
discretion if preliminary testing is concerning for cardiac amyloidosis. 

2.3.2.6 Follow-Up Protocol 
Participants will be called after the completion of testing by an investigator to counsel them on the 
results of their testing. In the event that they are diagnosed with cardiac amyloidosis or are found to 
have other serious pathology, the following steps will be taken:  
•
The patient will be told the results of their testing and an appropriate referral for
follow-up made. If the patient has a treating physician, they will be instructed to go to
their physician for further care as needed. In the event of an amyloidosis diagnosis,
the patient will be referred to the Columbia University Amyloidosis Center for further
care. If they do not have a treating physician, they will be given an appropriate referral
for further care.
•
A letter will be generated and mailed to the patient and uploaded to the patient's
medical record. This letter will state the results of their testing and recommend that
the patient see a physician for further care.
2.3.3 Hypotheses 
Specific Aim 1: To prospectively validate that deep learning models can detect undiagnosed cardiac 
amyloidosis  in large populations of patients undergoing cardiac testing 
The specific hypothesis is that by applying an cardiac amyloidosis deep learning detection model to 
the entire Columbia database of patients, we will identify a group of patients at highest likelihood of 
having undiagnosed cardiac amyloidosis. We will validate the model by prospectively scanning 100 
subjects with cardiac amyloid scintigraphy with ≥ 20% being diagnosed with cardiac amyloidosis, a 
percentage that would potentially allow a cost-effective, wide-scale screening approach.  The 
prevalence of cardiac amyloidosis in an unselected population of older adults who have undergone 
cardiac testing with an echocardiogram and ECG is unknown but is likely significantly less than 15% 
based on studies of more selected populations such as older adults with heart failure5 and aortic 
stenosis.38 Based on our model’s ability to accurately distinguish between cardiac amyloidosis cases 
and normal patients in a cohort of patients undergoing cardiac amyloid scintigraphy scanning, we 
predict that ≥ 20% of patients identified by the model at being of high probability of having cardiac 
amyloidosis will be diagnosed with cardiac amyloidosis.  
Specific Aim 2: To incorporate additional data to improve the deep learning model’s detection of 
cardiac amyloidosis. 
Hypothesis 1: Incorporating raw ECG waveform data and clinical variables such as orthopedic 
manifestations, e.g. carpal tunnel syndrome and spinal stenosis, associated with ATTR-CA, will 
improve the model’s predictive capacity by 5% compared to currently used data in Aim 1. 
Hypothesis 2: These deep learning models will allow identification of at least one key variable 
not currently associated with ATTR-CA that can inform future amyloidosis research. 

5.
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