Whole Blood Transcriptomic Signal According to Coronary Atherosclerotic Plaque Burden Assessed by CT Angiography: CORPLAQ-TRAIT Pilot Study
Conditions: Calcific Coronary Arteriosclerosis, Coronary Stenosis, Coronary Plaque, Aortic Calcification, Atherosclerotic Plaque
Sponsor: MultiplAI Health Limited

Study Title
Whole Blood Transcriptomic Signal According to Coronary Atherosclerotic
Plaque Burden Assessed by CT Angiography: CORPLAQ-TRAIT Pilot Study.
The information contained herein is strictly confidential and must not be disclosed, copied, presented for
publication or used for any purpose without the prior written authorization of the participating researchers.
Research area:
Subclinical
atherosclerosis
and
prevention
of
cardiovascular events.
Clinical phase:
Analytical, observational
Protocol version:
July 10th,, 2023
Centers:
1. Instituto Médico ENERI, Clinica La Sagrada
Familia,
Ciudad
Autónoma
de
Buenos
Aires,
Argentina
2. Diagnóstico MEDITER, Sanatorio Julio Méndez,
Ciudad Autónoma de Buenos Aires, Argentina
Principal Investigator of the study
Rosana Poggio
Co-principal investigators of the study
Santiago Miriuka
Gastón Rodriguez Granillo
Site Principal Investigators
Clínica La Sagrada Familia / Instituto Médico ENERI: Gastón Rodríguez Granillo
Diagnóstico MEDITER, Sanatorio Julio Méndez: Guillermo Ganum
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Index
Index
2
1. Contacts
4
2. Abbreviations
4
3. Abstract
5
4. Background and Rationale
6
4.1. Cardiovascular disease
6
4.2. Transcriptomics
8
4.3. Polygenic risk score and its functional derivations
11
4.4. Evidence of alterations in peripheral blood in patients with atherosclerosis:
12
4.5. Artificial intelligence
14
4.6. Use of artificial intelligence in medicine
15
5. Preliminary observations
18
5.1. In silico developments
18
5.2. Preliminary studies
21
6. Study Objectives
23
6.1 Primary Objective
23
7. Methods
24
7.1. Study Design
24
8. Study Population
25
8.1. Inclusion Criteria
25
8.2. Exclusion criteria.
25
9. Procedures
26
9.1. Eligibility and informed consent
26
9.2. Collection of clinical data and procedures in the baseline stage
27
9.2.1 Clinical data and medical history
27
9.2.2 Peripheral blood sample
27
9.2.3 Oral mucose swab (mouth swab)
28
9.2.4 Coronary Computed Tomography Angiography
28
9.3. Sample processing and sequencing
28
9.4. Bioinformatic and computational analysis of the transcriptomics sample
29
9.5. Analysis of Coronary Computed Tomography Angiography
30
9.7. Patient follow-up
32
9.7.1. Events of interest in the study during follow-up
32
9.7.2. Verification and adjudication of events during follow-up
32
10. Guarantee and quality control of the data
33
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10.1. Confidentiality of the data
33
10.2. Data quality assurance and control
33
10.3. Adverse event reporting
36
10.4. Calculation of the sample size
36
10.5. Analysis plan
37
10.6. Control of good clinical practices
37
10.7. Protocol Deviations
38
10.8. Publication policy
38
10.9. Intellectual Property
38
10.10. Study Organization
38
10.10.1. Direction of the Study
38
10.11. Financing
39
10.13 Study Timeline
39
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1.
Contacts
●
Rosana Poggio (Principal-Investigator)
e-mail: rosana.poggio@multiplaihealth.com
●
Gastón Rodríguez-Granillo (Co-Investigator)
e-mail: grodriguezgranillo@gmail.com
●
Santiago Miriuka (Co-Investigator)
MultiplAI Health Telephone: 1161615422
e-mail: santiago@multiplaihealth.com
2.
Abbreviations
ABC: Area Under the Curve
CAC: Coronary artery calcification
PBMC: Peripheral blood mononuclear cells
DL: Deep learning
CVD: Cardiovascular disease
AI: Artificial intelligence
RNA -Seq: Massive RNA sequencing
CT: Computed tomography
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3.
Abstract
Currently, a wealth of information can be obtained from a biological sample using a
group of technologies collectively referred to as omics. Transcriptomics, one of these
technologies, measures all RNA molecules in the cells or tissues being analyzed. The
collection of these RNA molecules, known as the transcriptome, represents the functional
expression of the DNA within the cells or tissues. However, interpreting this vast amount
of data poses challenges due to the complexity of the transcriptome, which comprises
tens of thousands of genes with varying expression and conformation. Traditionally, this
molecular expression system has been interpreted using classical statistical principles,
focusing on differentially expressed RNA molecules as relevant markers for distinguishing
between analyzed cells or tissues. While this approach has merits, it suffers from several
issues, including a high rate of false positives, difficulties in comprehensively interpreting
the data, and overlooking minor yet consistent variations in a large group of genes.
In recent years, artificial intelligence (AI) has facilitated the interpretation of omics
data through machine learning and deep learning (DL) techniques. These methods enable
the identification of salient features and their constant variations across a population,
allowing algorithms to classify new samples based on these features. Consequently,
transcriptomics can be distinguished from one another with high accuracy.
Prior evidence indicates that peripheral blood transcriptomics change due to
predispositions to coronary heart disease. These changes arise primarily from two factors:
exposure to known and unknown coronary risk factors, which affect gene expression
related to metabolism, immunity, and platelet aggregation, and the inheritance of
numerous genome variants that facilitate the development of coronary heart disease and
other complex diseases. The trait of a complex disease can be predicted by measuring the
polygenic risk score (PRS). Recent studies have confirmed the association of heredity with
exposure, integrating both dimensions into a single risk score.
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In our preliminary investigations, we focused on refining a peripheral blood sample
measurement that incorporates all genetic aspects, including heredity as an expression.
We developed a novel approach to analyze a biological sample's transcriptome through
convolutional neural networks (deep learning), which demonstrated high precision in
classifying samples. In a preliminary study, we identified associations between surrogate
markers for cardiovascular disease and gene expression in peripheral blood. For instance,
we conducted a clinical study measuring coronary artery calcification (CAC), a reliable
surrogate marker of atherosclerotic disease and cardiovascular events, closely related to
plaque load, with robust prognostic value and accurately assessed noninvasively through
computed tomography (CT). The study revealed clear indications of the clinical utility of
our methodology.
The present clinical study aims to identify transcriptomic patterns derived from
whole blood samples related to coronary atherotic burden. Additionally, as a secondary
analysis, we will explore the algorithm's ability to detect the presence of aortic disease
and pro-inflammatory cardiometabolic alterations, such as hepatic steatosis and
surrogate markers of coronary inflammation.
4.
Background and Rationale
4.1.
Cardiovascular disease
Cardiovascular disease (CVD) remains the foremost cause of mortality and
morbidity worldwide. In 2019, CVD accounted for approximately 18,600,000 deaths
(32.8% of the global total), with 80% of these occurring in low-and-middle-income
countries.1 It is plausible that these figures have risen in the aftermath of the pandemic.
Early identification of individuals at an elevated risk of developing asymptomatic
1
"The
Global
Burden
of
Cardiovascular
Diseases
and
Risk
Factors."
https://pubmed.ncbi.nlm.nih.gov/31727292/. Accessed 10 Apr. 2023.
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cardiovascular disease poses a persistent challenge for scientific research and global
health authorities. Establishing efficacious and accessible methodologies for CVD
detection may yield transformative changes in disease management, substantial
reductions in associated healthcare expenditures, and increased life expectancy.
Traditional approaches to identifying individuals with heightened risk of CVD or
associated mortality primarily rely on the recognition of singular variables (e.g., diabetes,
cholesterol, smoking), a composite of variables (e.g., the Framingham Risk Score), or
biomarkers (e.g., Hs-CRP, BNP) linked to an increased likelihood of disease development.
Despite these advancements, such methods exhibit limited predictive capacity (Area
under the curve 0.60-0.65).2,3 One potential explanation for this limited efficacy is the
numerous processes, both known and unknown, involved in the development of chronic
diseases that cannot be wholly encapsulated by analyzing singular or small clusters of
predictor variables. Conversely, cardiovascular imaging serves as a valuable tool for early
CVD detection (e.g., carotid ultrasound and detection of CAC by CT), but its
implementation is hindered by the necessity for costly equipment and specialized
personnel. Consequently, the utilization of cardiovascular imaging in clinical practice is
limited, and its potential as a population screening instrument remains improbable.
In recent years, substantial progress has been made in identifying genetic variants
predisposing individuals to the development of coronary heart disease. Similar to other
complex diseases, coronary heart disease exhibits a hereditary component. Traditionally,
this hereditary aspect has been discernible in two ways: the manifestation of symptoms
at a young age (e.g., myocardial infarction under 45-50 years) and the presence of
conspicuous
phenotypic
traits
(e.g.,
familial
hypercholesterolemia)
predisposing
individuals to cardiovascular events. The latter cases typically result from specific genomic
variants (monogenic diseases), representing a minor fraction of the cardiovascular
disease patient spectrum. Numerous individuals with a genetic predisposition to CVD
inherit a comprehensive set of genomic variants. While this polygenic inheritance is
3 "Multicenter validation of the diagnostic accuracy of a blood-based ...." 5 Oct. 2010,
https://pubmed.ncbi.nlm.nih.gov/20921541/. Accessed 10 Apr. 2023.
2
"Comparison
of
MESA
of
and
Framingham
risk
scores
in
...
-
PubMed."
https://pubmed.ncbi.nlm.nih.gov/31346632/. Accessed 10 Apr. 2023.
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acknowledged, many genetic variants related to a phenotype lack a definitive causal
relationship with the associated pathology. Instead, these variants are only associated
with the phenotype as they are part of a haplotype, thereby correlating, either in cis or
trans, with other causal variants. The association of coronary heart disease with specific
haplotypes is emerging as an effective method for cardiovascular risk prediction.
4.2.
Transcriptomics
Cellular DNA safeguards the biological information of a species, transmitting it
across generations. However, DNA interacts with the environment in various ways. The
most direct and recognized interaction occurs through the conversion of its code via RNA
expression, a process known as transcription. The entirety of expressed transcripts at a
given moment is termed the transcriptome. Consequently, transcriptomics can be
understood as the intersection of DNA's information reserve with the modulation of
environmental signals.
Within DNA structure lie units called genes. Until recently, a gene was defined as a
DNA sequence transcribed into RNA and subsequently translated into a protein.
According to this definition, the human genome comprises approximately 22,000 genes, a
number not drastically different from that of a fly (around 14,000), despite the stark
phenotypic differences between the two species. However, recent advancements have
prompted the redefinition of a gene and its numerical count, which may now be as high
as 100,000.4 Many DNA sequences are transcribed but not translated, collectively
referred to as non-coding-RNA. These non-coding genes primarily account for the
phenotypic differences between species, individuals, tissues, and cells. Additionally, the
genes
represented
in
DNA
sequences
undergo
significant
post-transcriptional
modifications, such as splicing or circularization, further diversifying potential differences
between biological samples. In essence, RNA embodies the dynamic and active
component of genetic information, heavily influenced by environmental factors.
4
"Open
questions:
How
many
genes
do
we
have?
-
BMC
Biology."
20
Aug.
2018,
https://bmcbiol.biomedcentral.com/articles/10.1186/s12915-018-0564-x. Accessed 10 Apr. 2023.
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Technological advancements have facilitated the comprehensive analysis of
transcriptomes present in a sample at a specific time. Until a few years ago, RNA
sequencing was conducted partially through a technique called microarray; however, it is
now predominantly performed using high-throughput RNA sequencing (RNA-Seq). This
methodology enables the identification of all RNAs present at a given moment within a
cell or tissue. Nevertheless, the vast amount of information generated requires
organization and interpretation. Various software tools can align the data with the human
genome, identifying individual genes and quantifying their expression levels.
The development of high-throughput sequencing and transcriptomics should be
contextualized within the realm of reduced costs. Previously, the cost per sample
exceeded one thousand dollars, but the introduction of more advanced equipment has
significantly lowered the cost per sample. This decrease in cost is vital for conducting
clinical studies involving tens or hundreds of patients. The forthcoming years will witness
a continued expansion of RNA-Seq usage in patient studies, with the determination of its
clinical utility reliant upon adequate analysis. In this regard, the development of artificial
intelligence-based techniques will play a crucial role in facilitating data interpretation.
The integration of transcriptomics with other omics approaches, such as genomics,
epigenomics, proteomics, and metabolomics, presents a promising opportunity for a
more comprehensive understanding of the complex regulatory networks governing
cellular functions and disease processes. By combining data from these diverse sources,
researchers can gain deeper insights into the multi-layered interplay between genetic,
epigenetic, and environmental factors that contribute to disease development and
progression.
One emerging area of interest within the field of transcriptomics is the study of
long non-coding RNAs (lncRNAs), which are RNA molecules longer than 200 nucleotides
that do not code for proteins. LncRNAs have been implicated in numerous cellular
processes,
including
chromatin
remodeling,
transcriptional
regulation,
and
post-transcriptional processing. Moreover, lncRNAs have been associated with various
human diseases, such as cancer, neurological disorders, and cardiovascular diseases.
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Investigating the roles of lncRNAs in these pathologies may yield novel therapeutic targets
and diagnostic biomarkers.
Circular RNAs (circRNAs) are a class of non-coding RNA molecules characterized by
their unique covalently closed-loop structure, which confers stability and resistance to
exonucleases5. Although initially regarded as transcriptional byproducts, recent research
has demonstrated that circRNAs play crucial roles in various cellular processes, including
gene regulation, protein translation, and the modulation of microRNA (miRNA) function
through sponge-like interactions. The stability, tissue-specific expression patterns, and
conservation across species render circRNAs potential candidates for diagnostic
biomarkers and therapeutic targets. Furthermore, circRNAs have been implicated in
numerous diseases, including cancer, neurodegenerative disorders, and cardiovascular
diseases. A deeper understanding of the functional roles and regulatory mechanisms of
circRNAs may lead to the development of novel diagnostic tools and therapeutic
strategies for a wide range of human diseases.
The blood microbiome, which refers to the collection of microorganisms present in
the circulatory system, has recently emerged as an area of interest for its diagnostic
potential. Although the bloodstream has traditionally been considered a sterile
environment, advances in next-generation sequencing technologies have revealed the
presence of a diverse microbial community within the blood. Alterations in the
composition and diversity of the blood microbiome have been associated with various
pathological
conditions,
such
as
cardiovascular
diseases6,
infectious
diseases,
autoimmune disorders, and cancer. The blood microbiome's accessibility and its potential
to reflect systemic changes in response to disease processes make it an attractive
candidate for non-invasive diagnostic applications. The development of sensitive,
high-throughput technologies to analyze the blood microbiome may facilitate the
6 "The blood microbiome and its association to cardiovascular disease ...." 31 Jul. 2022,
https://bmccardi ovascdisord.biomedcentral.com/articles/10.1186/s12872-022-02791-7. Accessed 10 Apr.
2023.
5"The biogenesis, biology and characterization of circular RNAs - Nature." 8 Aug. 2019,
https://www.nature.com/articles/s41576-019-0158-7. Accessed 10 Apr. 2023.
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identification of novel biomarkers for early disease detection, risk stratification, and
monitoring of therapeutic responses.
4.3.
Polygenic risk score and its functional derivations
As previously discussed, heredity plays a significant role in some patients with
coronary disease. In 2007, Samani et al. reported the association of three haplotypes with
early coronary disease.7 Since then, numerous studies have demonstrated the existence
of specific haplotypes for various chronic diseases, including atherosclerosis and coronary
heart disease.8 These haplotypes encompass dozens of genomic variants associated with
a phenotype, each having a subtle association with the disease. This minimal association
could be a result of the absence of a direct causal relationship, and the existence of the
minimal relationship is due to the lack of independent segregation of the alleles (linkage
disequilibrium).
Building on the knowledge of the minimal association between variants and the
possibility of low-cost, massive DNA sequencing, population studies have facilitated the
development of risk scores for the onset of complex diseases based on the summation of
minimal inherited risks. Consequently, Polygenic Risk Scores (PRS) have been designed in
recent years, which are derived, for example, from the algebraic sum of the odds ratios of
each variant. The higher the score, the greater the risk. This approach of aggregating
minimal risks has proven effective in discriminating individual risks. As such, a person in
the
highest-risk
decile
has
the
same
risk
as
a
patient
with
familial
hypercholesterolemia.9,10 The potential use of PRS was described in the context of
patients with chest pain. A subanalysis of the PROMISE study found that PRS was
independently associated with coronary artery disease in outpatients with stable chest
10
"Clinical
utility
of
polygenic
risk
scores
for
coronary
artery
disease."
https://pubmed.ncbi.nlm.nih.gov/34811547/. Accessed 10 Apr. 2023.
9
"Genomic
Risk
Prediction
of
Coronary
Artery
Disease
in
480,000
...."
https://discovery.ucl.ac.uk/id/eprint/10057841. Accessed 10 Apr. 2023.
8
"Genetics
of
Common,
Complex
Coronary
Artery
Disease
-
PubMed."
https://pubmed.ncbi.nlm.nih.gov/30901535/. Accessed 10 Apr. 2023.
7
"Genomewide
association
analysis
of
coronary
artery
disease."
2
Aug.
2007,
https://pubmed.ncbi.nlm.nih.gov/17634449/. Accessed 10 Apr. 2023.
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pain.11 Recently, a new study has expanded the knowledge of the association of PRS,
identifying 279 loci associated with coronary artery disease.12 These loci were discovered
after analyzing more than 2 million variants sequenced from 9 studies. However, one
limitation of these studies is the lack of diversity. When these findings were compared
with a Japanese database, a moderate correlation was found with European ancestry.
Larger projects from various countries, including Our Future Health in the UK and All of Us
in the US, will help develop more robust scores in the near future.
The clinical utility of PRS is currently in the phase of clinical application and may
become routine in the coming years. However, PRS only measures the hereditary
component of risk. Various researchers have proposed utilizing modified PRS, done by
including a component related to gene expression to establish a risk score that also
involves the environmental component of risk, which may arise from the association of
genetic variants with epigenetic or transcriptome variations.13 This has led to the concept
of Transcriptome-wide Association Studies (TWAS), which measures risk based on the
combination of inherited genetic variants and the expression of genes near the same
variants.14,15 Thus, obtaining information from the functional part of the genome has been
shown to potentially improve the clinical applications of genomic science.
4.4.
Evidence of alterations in peripheral blood in patients with
atherosclerosis:
Atherosclerosis is a vascular disease characterized by the accumulation of
cholesterol and inflammatory cells in the middle layer of the arteries. The consequences
15
"Multi-Omic
Strategies
for
Transcriptome-Wide
Association
Studies."
8
Mar.
2021,
https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1009398. Accessed 10 Apr. 2023.
14
"Opportunities
and
challenges
for
transcriptome-wide
association
...."
https://pubmed.ncbi.nlm.nih.gov/30926968/. Accessed 10 Apr. 2023.
13 "From GWAS to Function: Using Functional Genomics to Identify the ...." 6 Apr. 2020,
https://www.frontiersin.org/articles/10.3389/fgene.2020.00424/full. Accessed 10 Apr. 2023.
12 "Discovery and systematic characterization of risk variants and genes ...." 6 Dec. 2022,
https://www.nature.com/articles/s41588-022-01233-6. Accessed 10 Apr. 2023.
11 "Associations of a polygenic risk score with coronary artery disease ...." 21 May. 2022,
https://pubmed.ncbi.nlm.nih.gov/35605652/. Accessed 10 Apr. 2023.
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of these deposits are well-established: exposure to lipid-containing inflammatory cells
following plaque rupture leads to the majority of acute intravascular thrombotic events,
including myocardial infarction, certain types of stroke, and peripheral arterial disease.16,17
Non-occlusive events can eventually result in low blood flow ischemia.
From the above, it is evident that the complex interaction between inflammatory
cells, metabolism, and the vascular wall plays a crucial role in the pathophysiology of
atherosclerotic disease. In clinical practice, this interaction is often assessed through
surrogate biomarkers of vascular status and the likelihood of adverse outcomes in
atherosclerotic
disease.
These
markers
can
represent
metabolic changes (e.g.,
cholesterol, blood glucose, free fatty acids), inflammatory factors (e.g., C-reactive protein,
interleukins, leptin), or coagulation elements (e.g., fibrinogen, PAI-1). Moreover,
numerous unidentified markers in peripheral blood may stem from newly described
cardiovascular risk factors, such as the intestinal microbiome18,19 or air pollution.20 These
markers can be of various molecular types, including proteins, various forms of RNAs, or
small molecules.
Among the potential biomarkers mentioned above are RNA molecules. Blood cells
are exposed to a wide range of signals from the tissues they perfuse, which can influence
circulating cells or even progenitors in the bone marrow, ultimately modifying gene
expression.
Consequently,
different
disease states may exhibit distinct sets of
differentially expressed genes. Numerous studies have verified the presence of
differentially expressed genes in patients with atherosclerotic disease, with alterations in
gene expression observed in various stages of atherosclerosis predisposition21 , prognostic
21
"Alterations
of
a
Cellular
Cholesterol
Metabolism
Network
Are
...
-
NCBI."
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4587646/. Accessed 10 Apr. 2023.
20
"Associations
between
particulate
matter
air
pollution,
presence
and
...."
https://europepmc.org/article/med/32682146. Accessed 10 Apr. 2023.
19
"The
Microbiome
and
Risk
for
Atherosclerosis
-
PubMed."
https://pubmed.ncbi.nlm.nih.gov/29800043/. Accessed 10 Apr. 2023.
18
"Association
between
the
gut
microbiome
and
atherosclerosis
-
PMC."
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5815826/. Accessed 10 Apr. 2023.
17
"Atherosclerosis
and
inflammation:
overview
and
updates
-
PubMed."
https://pubmed.ncbi.nlm.nih.gov/29930142/. Accessed 10 Apr. 2023.
16 "Immunity and Inflammation in Atherosclerosis | Circulation Research." 17 Jan. 2019,
https://www.ahajournals.org/doi/10.1161/CIRCRESAHA.118.313591. Accessed 10 Apr. 2023.
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progression22, or response to statin treatment23. Furthermore, the expression of specific
gene sets has been associated with clinical variables to predict the severity of
atherosclerotic disease24. These studies demonstrate the existence of altered gene
expression in patients with cardiovascular disease and suggest that these changes may
serve as markers for disease and its progression in the future25,26,27,28.
4.5.
Artificial intelligence
Artificial intelligence (AI) has experienced significant growth in recent years, owing
to the development of novel algorithms that have substantially enhanced classification
capabilities. The potential applications of AI across various fields of human activity have
led to its recognition as a pivotal milestone in human evolution. Particularly in medicine,
AI has the potential to bring about profound transformations in practice29.
AI can be characterized as an assembly of algorithms and equations capable of
emulating specific cognitive behaviors observed in humans. Although the term implies
reasoning capabilities, the current scope of AI is predominantly limited to the human-like
(or even super-human) capacity to interpret and classify large volumes of data. This is
achieved through the utilization of mathematical algorithms collectively referred to as
machine learning. Among these, neural networks have gained prominence in recent
years. A neural network consists of numerous parallel and serial linear equations, each
29
"High-performance
medicine:
the convergence of human ... - Nature." 7 Jan. 2019,
https://www.nature.com/articles/s41591-018-0300-7. Accessed 10 Apr. 2023.
28
"Developing
Peripheral
Blood
Gene
Expression-Based
Diagnostic
...."
25
Jun.
2015,
https://link.springer.com/article/10.1007/s12265-015-9641-5. Accessed 10 Apr. 2023.
27 "Gene Expression Signatures and the Spectrum of Coronary Artery ...." 19 Jun. 2015,
https://link.springer.com/article/10.1007/s12265-015-9640-6. Accessed 10 Apr. 2023.
26
"Transcriptomic
Signature
of
Atherosclerosis
in
the
Peripheral
Blood."
https://pubmed.ncbi.nlm.nih.gov/27815828/. Accessed 10 Apr. 2023.
25 "The Transcriptomic Toolbox: Resources for Interpreting Large Gene ...." 29 Apr. 2019,
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6617151/. Accessed 10 Apr. 2023.
24
"Comparison
of
MESA
of
and
Framingham
risk
scores
in
...
-
PubMed."
https://pubmed.ncbi.nlm.nih.gov/31346632/. Accessed 10 Apr. 2023.
23 "Intracoronary Imaging, Cholesterol Efflux, and Transcriptomes After ...." 14 Feb. 2017,
https://pubmed.ncbi.nlm.nih.gov/27989886/. Accessed 10 Apr. 2023.
22
"Low
MT-CO1
in
Monocytes
and
Microvesicles
Is
Associated
With
...."
https://pubmed.ncbi.nlm.nih.gov/27919931/. Accessed 10 Apr. 2023.
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modified by one or more nonlinear equations, forming an interconnected network of
equations that culminate in a nonlinear equation determining probability. This probability
is subsequently used for probabilistic classification. While this algorithm has been known
for decades, particular advancements in recent years have facilitated its tremendous
expansion, leading to the emergence of deep learning30. These advancements are
primarily twofold: firstly, the development of a self-correcting capability for the network
in the 1980s through the implementation of an algorithm called backpropagation31,32. This
algorithm enables the neural network to adjust in successive cycles, progressively
enhancing its predictive capacity through continuous training. The second contributing
factor to the proliferation of deep learning is the advancement of hardware capable of
performing a vast number of simple calculations (linear regressions) in a short time,
primarily through the adaptation of Graphical Processing Units (GPUs) for AI applications.
These two developments have led to the remarkable expansion of deep learning, with an
increasing presence in everyday processes.
4.6.
Use of artificial intelligence in medicine
In recent years, numerous examples have emerged illustrating the potential of AI in
medicine. A prominent study published in 2017 demonstrated an algorithm's ability to
differentiate malignant skin lesions from photographs, including various forms of skin
cancer, with higher accuracy than a group of dermatologists33. Another equally significant
study showcased the high capacity of deep learning (DL) to identify the presence of
tumor cells in histopathological images of axillary lymph nodes in breast cancer patients34.
These instances exemplify the impact of advancements in AI: a trained algorithm can
autonomously detect abnormalities in images, irrespective of a physician's involvement.
34 "Diagnostic Assessment of Deep Learning Algorithms for Detection of ...." 12 Dec. 2017,
https://pubmed.ncbi.nlm.nih.gov/29234806/. Accessed 10 Apr. 2023.
33
"Dermatologist-level
classification
of
skin
cancer
with
deep
neural
...."
https://pubmed.ncbi.nlm.nih.gov/28117445/. Accessed 10 Apr. 2023.
32
"Improving
generalization
performance
using
double
backpropagation."
https://ieeexplore.ieee.org/document/165600. Accessed 10 Apr. 2023.
31
"Learning
representations
by
back-propagating
errors
-
Nature."
9
Oct.
1986,
https://www.nature.com/articles/323533a0. Accessed 10 Apr. 2023.
30 "Deep learning | Nature." 27 May. 2015, https://www.nature.com/articles/nature14539. Accessed
10 Apr. 2023.
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The vast diagnostic potential of these algorithms has provided a strong impetus for
further research in this domain.
While the application of AI in medicine remains in its early stages and primarily in
the research phase, potential uses can be categorized into several areas:
●
Radiology: AI algorithms can process imaging studies, learning characteristics
associated with abnormal defects. With an increasing number of potential AI
applications in this field, some have already been approved for clinical use. In the
near future, many common radiological images will likely be pre-scanned by
algorithms to identify abnormalities, streamlining physicians' workload by
allowing them to focus only on images containing pathological alterations. Various
studies have demonstrated the utility of DL in multiple areas of cardiovascular
medicine, from risk stratification and triage tools to the automatic evaluation of
ventricular volumes in magnetic resonance imaging and coronary calcium scores
by CT.
●
Body images: Due to DL's success in image processing, promising applications are
also anticipated in this area. Examples include the interpretation of skin lesions
and the analysis of fundus images to potentially predict cardiovascular risk.
Moreover, neural networks can determine whether an eye fundus image belongs
to a male or female with approximately 98% accuracy, indicating their capacity to
extract imperceptible features from an image. The precise characteristics defining
these differences remain unclear but may be related to arterial thickness or other
subtle variations35.
●
Continuous signals: AI can be utilized to interpret data from continuous
acquisition signals, such as electrocardiograms or electroencephalograms, using
DL algorithms36,37.
37
"Deep
learning
for
electroencephalogram
(EEG)
classification
tasks."
https://iopscience.iop.org/article/10.1088/1741-2552/ab0ab5. Accessed 10 Apr. 2023.
36 "Cardiologist-level arrhythmia detection and classification in ... - Nature." 7 Jan. 2019,
https://www.nature.com/articles/s41591-018-0268-3. Accessed 10 Apr. 2023.
35
"Prediction
of
cardiovascular
risk
factors
from
retinal
fundus
...."
https://pubmed.ncbi.nlm.nih.gov/31015713/. Accessed 10 Apr. 2023.
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●
Language
processing:
DL
is
revolutionizing
language interpretation, with
applications including the automated analysis of medical records and patient
vocabulary. Numerous reports have identified emotions, including depressive
states, based on a subject's language use38.
Additionally, Generative Pre-trained Transformers (GPT) and other large language
models (LLMs) hold considerable potential in medicine39. These models can be employed
for tasks such as:
●
Summarizing
and
extracting
relevant
information
from
lengthy
medical
documents, aiding physicians in quickly understanding the most critical aspects.
●
Assisting in the creation of accurate and coherent patient education materials,
thereby improving patients' comprehension of their diagnoses, treatments, and
overall health management.
●
Facilitating the development of personalized treatment plans by analyzing patient
medical histories and suggesting suitable interventions based on relevant research
findings and best practices.
●
Enhancing the efficiency of electronic health record systems through natural
language processing, enabling healthcare professionals to retrieve relevant
information more quickly and accurately.
●
Supporting medical research by reviewing existing literature, identifying gaps in
knowledge, and generating new hypotheses for further investigation.
The application of GPT and LLMs in medicine promises to enhance diagnostic
accuracy, streamline workflows, and ultimately contribute to improved patient care and
outcomes. However, it is crucial to ensure data security and privacy and address ethical
concerns surrounding the use of AI in healthcare.
39
"Benefits,
Limits,
and
Risks
of
GPT-4
as
an
AI
Chatbot
for
Medicine."
https://www.nejm.org/doi/10.1056/NEJMsr2214184. Accessed 10 Apr. 2023.
38
"A
Novel
Method
to
Detect
Functional
microRNA
Regulatory
Modules
...."
https://pubmed.ncbi.nlm.nih.gov/27295638/. Accessed 10 Apr. 2023.
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5.
Preliminary observations
5.1.
In silico developments
In our laboratory, we have developed a comprehensive methodology for
transcriptome analysis that combines extensive bioinformatic processing of biological
samples with deep learning algorithms for interpretation. This approach enables the
incorporation of all genes read in a sequence, performing classifications using a
supervised method. Our algorithm has demonstrated robustness and high accuracy in
preliminary tests.
The development consists of a pipeline that analyzes the RNA-seq sample in its
crudest state, including coding and non-coding RNAs, circular RNAs, and the blood
microbiome. The algorithm is trained on a cohort of previously classified samples and
later validated using 10% to 20% of the samples.
To assess the algorithm's efficiency, we analyzed different RNA-seq databases
available in digital repositories. Initially, we used the GTEx database, containing 11,866
sequences from 30 different tissues obtained postmortem from dozens of individuals. Our
algorithm achieved 99.1% accuracy in correct prediction (Figure 1), with only a few errors
corresponding to potentially similar tissues (e.g., breast and fatty tissue).
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Figure 1: Confusion matrix for normal tissue samples. The graph shows the intersection of the algorithm's
prediction and the actual sample. Thus, the descending central diagonal from left to right shows the
coincidence between the real and the predicted. Outside this diagonal, the numbers represent the
algorithm's classification errors. As can be seen, they are infrequent.
We also analyzed the Genomic Data Commons (GDC) database, containing RNA
sequencing of biopsies from 34 different tumors with 10,000 samples. The algorithm's
predictive capacity was again high, reaching 97.5% accuracy (Figure 2). These results
demonstrate the algorithm's excellent classification capability for multiple samples.
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Figure 2: Confusion matrix for tumor tissue samples. The graph has the same meaning as in the previous
figure. Again, the number of mistakes outside the diagonal line is very low.
A sensitivity analysis of the algorithm was performed using 100 random samples
from brain tumors in the GDC database. The algorithm achieved 100% precision (ROC of
1) and exhibited efficient behavior with a low number of clinical cases, reflecting the
fundamental differences in gene expression between the two groups of tumors.
The aforementioned results were obtained from solid tissue RNA-Seq samples
(biopsies). It is worth noting that blood cells may exhibit transcriptomic alterations in the
presence of solid tumors or systemic diseases. Therefore, we analyzed different datasets
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in various contexts, including samples obtained by Best et al40 from patients with different
solid tumors. The algorithm showed an AUC of 1 for detecting the presence of tumors in
all cases.
We also analyzed samples from Cassetta et al on monocytes from patients with
breast or endometrial cancer compared to healthy subjects41, and the algorithm achieved
100% prediction capacity. Moreover, we analyzed the dataset generated by Scharer et al
on patients with lupus compared to healthy subjects, and the classification was excellent
(AUC of 1)42.
In conclusion, our pipeline and the analyses performed demonstrate the ability to
classify transcriptomics samples accurately. The algorithm is robust and extremely
sensitive, primarily due to the large number of gene variants considered (approximately
200,000) and the unprecedented capacity of neural networks to interpret this vast
amount of information. These findings lead us to propose that our pipeline may be useful
in detecting tumor pathologies in peripheral blood transcriptomics.
5.2.
Preliminary studies
We have previously developed a clinical protocol aimed at refining the
methodology that will be employed in this study. The initial objective was to establish a
reliable process for blood collection, RNA isolation, library generation for sequencing, and
RNA sequencing. The preliminary results of these steps, conducted on 540 samples from
three different studies, are described below.
First, we established a robust methodology for obtaining RNA samples from
peripheral whole blood without cell separation. Samples were collected in PaxGene RNA
tubes through antecubital vein puncture, and no adverse effects were observed in this
procedure. The samples were stored at -20°C until RNA extraction, which was carried out
42
"Epigenetic
programming
underpins
B
cell
dysfunction
in
human
SLE."
https://pubmed.ncbi.nlm.nih.gov/31263277/. Accessed 10 Apr. 2023.
41
"Human
Tumor-Associated
Macrophage
and
Monocyte
...."
15
Apr.
2019,
https://scholars.duke.edu/individual/pub1379736. Accessed 10 Apr. 2023.
40
"RNA-Seq
of
Tumor-Educated
Platelets
Enables
Blood-Based
Pan
...."
https://pubmed.ncbi.nlm.nih.gov/26525104/. Accessed 10 Apr. 2023.
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using a specific kit from Thermo-Fischer. This approach allowed us to consistently obtain
RNA samples in sufficient quantities for sequencing in all cases.
Next, we constructed the library, a process in which the RNA is converted into cDNA
(complementary DNA). The RNA corresponding to ribosomes and globin is then extracted,
fragmented, and adapters are added to the ends. The library construction proved robust
for all analyzed samples.
Subsequently, we performed RNA sequencing at a depth of 100 million reads.
Between 20,000 and 25,000 genes expressed in peripheral blood were detected across all
samples (n=540). In a subset of patients, computed tomography was carried out to
determine the degree of calcification in the coronary arteries (Coronary Calcium Score,
CCS). We analyzed gene expression in peripheral blood and identified genes related to the
degree of coronary calcification (Figure 3).
Figure 3: Preliminary results. We analyzed 38 samples from patients who had coronary calcium
measured. The left panel shows the result of a PCA analysis where a moderate but significant reduction in
dimensionality is found based on the extent of coronary calcium. The heatmap shows the differentially
expressed genes between the two groups.
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6.
Study Objectives
6.1 Primary Objective
The main objective of this study is to investigate the diagnostic accuracy of
whole-blood transcriptome analyzed by artificial intelligence in detecting the presence
and extent of coronary atherosclerosis in stable patients with suspected or known
coronary artery disease (CAD), using coronary computed tomography angiography (CCTA)
images.
Specific aims:
I) To classify patient transcriptomes based on the presence and extent of the following
CCTA outcomes:
●
Coronary calcium score
●
Total coronary plaque burden
●
Degree of coronary stenosis
●
Coronary
high-risk
plaques
(low-attenuation,
positive
remodeling;
spotty
calcification, or napkin-ring sign)
II) To train artificial intelligence algorithms using information from the transcriptome to
recognize the outcomes of interest in the training set.
III) To explore the diagnostic accuracy (sensitivity and specificity) of the method in
detecting the presence, absence, or extent of the outcomes of interest in the
validation/test set.
6.2 Secondary Objectives:
The secondary objectives of this study are as follows:
6.2.1 To explore the diagnostic accuracy of whole-blood transcriptome in relation to
other outcomes of interest provided by CCTA images, including:
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●
Presence and extent of aortic calcification
●
Hepatic steatosis
●
Flow limiting artery obstruction (coronary fractional flow reserve)
●
Coronary artery inflammation (perivascular fat attenuation index)
●
Total epicardial adipose tissue volume
6.2.2 To calculate the polygenic risk score (PRS) from the transcriptome and explore
its association with the previously described outcomes of interest.
6.2.3 To explore the differences in transcriptomics of peripheral blood between
patients who did and did not develop a cardiovascular event during follow-up.
7.
Methods
7.1.
Study Design
This will be a prospective observational study. A convenience sample will be carried
out to include 200 patients who attend the ENERI Medical Institute, La Sagrada Familia
Clinic and Sanatorio Mendez with a clinical indication to be evaluated by a CCTA due to
suspected CAD or known CAD. The study will have a baseline stage in which a clinical
evaluation will be performed, blood samples will be drawn for transcriptome analysis and
laboratory analysis. Then, a DNA sample obtained by swabbing the buccal mucose will be
taken. Subsequently, the images obtained from the clinically indicated CCTA
will be
assessed to explore the outcomes of interest. At the end of patient enrollment, biological
samples will be sequenced for in silico evaluation of the results. Finally, a 5-year follow-up
will be carried out via telephone or email contact to collect data on the incidence of fatal
and non-fatal cardiovascular events.
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8.Study Population
8.1.
Inclusion Criteria
●
Men and women between 18 and 75 years, with a clinical indication to be
evaluated by a CCTA due to suspected or known CAD.
●
Signature of informed consent.
8.2.
Exclusion criteria.
●
Previously known chronic renal or hepatic insufficiency.
●
Active chronic lung disease, defined as: exacerbated asthma, exacerbated COPD,
or pulmonary fibrosis.
●
Myocardial infarction, unstable angina, cerebrovascular accident, or vascular
interventions (any territory) in the past 6 months.
●
Implantation of drug-eluting stents in the last 12 months.
●
Revascularization surgery (By-pass)
●
Indication of Angio-CT for congenital heart disease or Transcatheter Aortic Valve
Replacement (TAVI)
●
Presence/ diagnosis of heart failure symptoms or signs (ie. dyspnea, asthenia,
edema) with objective evidence of pulmonary or systemic congestion at rest or
with exercise in the last 6 months43.
●
Left ventricular ejection fraction <50% confirmed by objective diagnostic methods
(eg doppler echocardiogram).
●
Severe valvulopathies, confirmed by objective diagnostic methods (eg doppler
echocardiogram).
●
Uncontrolled hyper or hypothyroidism.
43
"2013
ACCF/AHA
guideline
for
the
management
of
heart
failure."
https://pubmed.ncbi.nlm.nih.gov/23741058/. Accessed 10 Apr. 2023.
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●
Suprarenal insufficiency.
●
Previous surgeries in the last 3 months.
●
Severe trauma in the last 6 months, defined as one that involved bone fractures
and/or surgical interventions.
●
Known active cancer disease or under treatment (acute or preventive), or history
of cancer disease without criteria for cure.
●
Diagnosis
of
active
autoimmune
disease
or
any
pathology
under
immunosuppressive treatment.
●
Ongoing pregnancy, postpartum period of less than 12 months or breastfeeding.
●
Other serious diseases with an estimated life expectancy of less than 12 months
(according to the investigator's opinion).
●
Temperature greater than 37.5°C recorded by a thermometer or any acute
infection caused by viruses or bacteria confirmed by a health professional in the
previous 30 days.
●
Pacemaker/Cardio-Defibrillator Implantation.
●
Prior to the study: heart rate > 70 l/m or atrial fibrillation or frequent extrasystoles
that in the opinion of the specialist physician will affect the quality of the
cardiovascular imaging.
9.
Procedures
9.1.
Eligibility and informed consent
Patients referred to CCTA scans with a medical indication due to suspected CAD or
known CAD, will be recruited at Clínica Sagrada Familia and Sanatorio Mendez in Buenos
Aires. Patients who meet the eligibility criteria will be approached by the main
investigator or one of the study collaborators and the characteristics of the procedure to
be performed, the risks of the associated procedures and the scope of the research to be
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carried out will be explained to them. After providing the necessary information and
answering the patients’ questions, and once they have expressed that they agree to
donate blood samples, mucose DNA cells via oral swab, associated data and the CCTA
resulting image to the study, they will be formally invited to sign the informed consent.
9.2.
Collection of clinical data and procedures in the baseline stage
At the time of inclusion in the study, the following epidemiological, clinical and
complementary study data will be collected using questionnaires designed for this study:
9.2.1 Clinical data and medical history
●
Contact data: Cell phone, landline, phone of an alternative contact, and E-mail
address.
●
Epidemiological data: date of birth, gender, ethnicity.
●
Clinical history: Presence of any of the following pathologies: dyslipidemia,
diabetes, high blood pressure, smoking, previous relevant diseases.
●
Medications at the time of admission.
9.2.2 Peripheral blood sample
2.5 milliliters of peripheral blood will be obtained by puncture of the antecubital
vein in a PAXgene or Tempus tube (commercial tubes that contain a solution for the
preservation of RNA) to obtain RNA. The patient must fast for 8 hours prior to the study.
Subsequently, the blood will be kept at room temperature for up to 48 hours, or at 4°C (in
a refrigerator) for up to two weeks, until it is transferred to the laboratory, where it will be
frozen at -20°C until it is processed (RNA extraction).
Subsequently, the second additional peripheral blood sample (10 milliliters in EDTA)
will be obtained to measure the complete blood count, glycosylated hemoglobin,
hepatogram, lipid profile, C-reactive protein and leukocyte formula. The primary tube for
clinical laboratory analysis will be sent for analysis to local laboratories which are:
Stamboulián and Sanatorio Méndez.
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9.2.3 Oral mucose swab (mouth swab)
To obtain the sample, the professional will rub a buccal swab against the inside of
the patient's cheeks and gums, and place it in a dry sterile 1.5ml eppendorf-type tube
with lysis buffer; once labeled with the participant's ID, it will be placed in the refrigerator
at 4°c waiting to be sent to the laboratory, where the DNA extraction will be performed.
Subsequently, it will be stored in a freezer at -20°C.
9.2.4 Coronary Computed Tomography Angiography
Participants’ donated CCTA images and associated data will be collected and anonymised
at the site for further analysis.
CCTA acquisition protocols will follow each site's standard procedures. The use of any
medications involved in routine CCTA examinations such as beta blockers (in the case of a
heart rate higher than 65 beats per minute) or sublingual nitroglycerin will not be
encouraged,
the decision being left at the discretion of the physician involved in the
acquisition. CTCA images will be transferred to an AWS server for independent data
analysis.
9.3.
Sample processing and sequencing
Blood samples will be processed after extraction using commercial kits for RNA
extraction. Subsequently, the library, a procedure by which the transformation of the RNA
sample into short cDNA chains for sequencing is known. For this procedure, the
commercial
kit
TruSeq-RiboZero-GlobinFree
(Illumina)
will
be
used. Subsequent
sequencing (without ribosomal RNA and globin) will be performed by an external
sequencing service provider. A sequencing with a read depth of at least 100 million reads
will be requested.
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9.4.
Bioinformatic and computational analysis of the transcriptomics
sample
The bioinformatic analysis of the sequenced samples will be performed from the
.fastq files containing the raw data. These files will be fed to a pipeline analysis which
comprises a successive series of bioinformatic procedures, including sample quality
control, alignment with the human genome and count of reads per identified gene. The
transcripts to be identified correspond to:
●
Linear RNA identification, including mRNA and non-coding RNAs.
●
circRNA (circular RNA)
●
Alternative splicing variant identification
●
Blood microbiome
●
Variant calling for Polygenic risk score calculation (from RNA).
The expression quantification of the transcripts is then transformed using a series
of proprietary routines, to then be transformed into an image (US patent 62/944,063
(pending)). This image represents both the expression of the transcripts as a function of
their expression level and the genomic position as well as the RNA subtype. The set of
images, which represent the set of gene expression in peripheral blood, is subsequently
entered into a neural network to generate training that classifies the samples accordingly.
Once the bioinformatic analyses are done, we will use that information to train
machine and deep learning models to predict those patients with coronary artery
disease. The following ML/DL approaches will be used:
●
Classical machine learning algorithms, including xgboost.
●
Convoluted Neural Networks (CNN) through our patented technology of gene
expression conversion.
●
Graph neural networks.
●
Natural processing language by using reads.
All these bioinformatic and artificial intelligence procedures will be carried out
using free-use programs (“open access software”) under a routine created by the study
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researchers, mainly using python for coding and pyTorch for the training of the neural
network.
Data will be stored and analyzed at the cloud provider at the moment of analysis
(currently AWS or Google Cloud) under strict cybersecurity measures.
9.5.
Analysis of Coronary Computed Tomography Angiography
Coronary Calcium scoring will be analyzed using dedicated software (HeartBeat-CS,
Philips Healthcare, Best, The Netherlands) by an experienced observer blinded to the
clinical characteristics, laboratory, and transcriptome results. The presence of CAC and its
extension will be evaluated using both ordinal variables (number of segments with CAC
and number of affected vessels, and qualitatively: absence, mild, moderate, and severe)
and continuous variables (Agatston units). The presence and extension of calcifications at
the level of the aortic valve and the thoracic aorta (Agatston units) will also be evaluated
independently. The extent of aortic calcification will be evaluated in sections that include
the area ranging from the bifurcation of the pulmonary artery as the upper limit to the
diaphragm as the lower limit.
CCTA images will be anonymised locally and transferred to a core laboratory located in
Oxford, UK, for analysis of secondary outcomes by investigators who will be unaware of
population demographics and outcomes. The presence of obstructive CAD will be defined
as the presence of at least one lesion causing a luminal stenosis of 50% or larger. The
extent of coronary artery disease using the modified Duke coronary artery disease index,
as follows (Group 1: <50% stenosis; Group 2: ≥2 mild stenosis with proximal CAD in 1
artery or 1 moderate stenosis; Group 3: 2 moderate stenosis or 1 severe stenosis; Group
4: 3 moderate stenosis, 2 severe stenosis, or severe stenosis in the proximal left anterior
descending (LAD) artery.; Group 5: 3 severe stenosis or 2 severe stenosis in the proximal
LAD; Group 6: ≥50% stenosis in left main coronary artery). The presence of high-risk
plaque features (spotty calcification, low-attenuation plaque, positive remodeling, and
napkin-ring sign) will be explored. The total epicardial adipose tissue volume will be
calculated in a semi-automated manner by tracking the contour of the pericardium from
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the level of the pulmonary artery bifurcation to the apex of the heart at the most caudal
end. To measure the perivascular FAI, we will trace the proximal 40-mm segments of all
three major epicardial coronary vessels (right coronary artery, left anterior descending
artery, and left circumflex artery) and defined respective perivascular fat as the adipose
tissue within a radial distance from the outer vessel wall equal to the diameter of the
vessel, as described and validated previously. To avoid the effects of the aortic wall, we
excluded the most proximal 10 mm of the right coronary artery and analyzed the
proximal 10–50 mm of the vessel, as described previously. In the left anterior descending
artery and left circumflex artery, we will analyze the proximal 40 mm of each vessel.
As follows, 3-dimensional plaque quantification of the whole vessel will be performed
(10,13) using semi-automated plaque analysis software. All IDs will undergo double
reading of the main outcome of the study (presence or extent of CACs). This reading will
be carried out by an expert external to the group of researchers of the study and blind to
the first reading. In case of disagreement, consensus will be sought between the two
experts. The aim of this process is to enhance the internal validity of the study.
9.6.
Storage of biological samples
The remnant of the RNA samples and all of the DNA samples will be stored in a freezer at
-20° C to be used in future research. The biological samples will be coded (see
Confidentiality of the data) to maintain the anonymity of the participants and will be
stored at the laboratory. The storage place has the necessary equipment to ensure their
proper preservation. The temperature and other storage conditions will be permanently
monitored as part of the quality assurance of the biological samples. It also has an
emergency plan with trained personnel with defined responsibilities to attend to alarms
and take actions to restore the system in the event of a failure. The freezer that preserves
the biological samples and the monitoring systems are connected to a permanent
electrical source, with an emergency generator with enough autonomy to ensure its
uninterrupted operation.
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Since the stored samples will not be used in this study, specific consent will be requested
for their storage and subsequent analysis.
9.7.
Patient follow-up
Patients included in the study will be contacted annually by phone call or email to
collect the following data:
●
Vital status.
●
Cardiovascular events: New episodes of hospitalization due to acute ischemic
syndrome, cerebrovascular event, heart failure or revascularization.
●
New diagnoses of relevant chronic diseases.
If they cannot be contacted, the "alternative means of contact" provided by the patient
will be used, in order to know their vital status or obtain a new contact number.
9.7.1.
Events of interest in the study during follow-up
The study will assess the following incident events:
●
Death from any cause.
●
Fatal and non-fatal atherosclerotic related events: AMI, angina, cerebrovascular
accident (“stroke”) or transient ischemic attack.
●
Hospitalized (emergency room or admitted) Heart failure event with preserved or
impaired ventricular function.
●
Vascular revascularization: performance of myocardial revascularization surgery,
coronary angioplasty or peripheral vascular angioplasty.
●
Cancer: new diagnosis of any type of cancer.
9.7.2.
Verification and adjudication of events during follow-up
When an event of interest is reported during study follow-up, the "phase of
verification” will be triggered that consists of obtaining data on: type of event, date, place
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where it was treated and its corresponding source document (death certificate, epicrisis,
complementary study, evolution of clinical history, etc.) where the diagnosis of the event
appears with a doctor's signature. The study event adjudication committee will be made
up of three medical investigators (RP, SGM, GRG). RP and GRG will analyze the
documentation sent for the case separately and will adjudicate the event. If there is no
agreement, SGM will make the final adjudication.
10. Guarantee and quality control of the data
10.1.
Confidentiality of the data
The data of the patients will be collected anonymously from the moment of signing
the informed consent. To do this, an ID code representative of the patient will be created,
which will include the initials of the study, followed by a hyphen and the inclusion
number. The clinical documentation of the patient and the tube containing the blood of
the patients or control subjects will contain only this information. The informed consent
will be kept in a different location from the clinical information and under lock and key.
The transcriptomics data will remain anonymized and will receive the name
designated at the time of inclusion in the study. The data will be deposited and protected
on AWS which is in compliance with the Health Insurance Portability and Accountability
Act (HIPAA).
10.2.
Data quality assurance and control
a. Data quality assurance and storage
This study will implement a standardized data collection process, since trained
interviewers will ensure data collection uniformly across all respondents, properly fill out
the forms and handle sensitive information.
Baseline data entry and storage will be performed using the electronic platform
RedCap. This electronic data capture platform is covered by security systems, in
compliance with current regulations on data protection. The access control will be
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configured in such a way that there are two users with the project administrator role,
who will be in charge of authorizing the access of the study personnel, as necessary, and
with access levels according to the roles and responsibilities. The electronic forms that
will be used for data upload will be specifically designed for this study. The data will be
entered by the researcher at the recruitment center, via the web. The personnel in charge
of entering the data will be trained in the use of RedCap for this purpose.
The study data corresponding to the Follow-up stage will be collected on
paper-based forms. The forms will contain validated questions usually used in previous
clinical studies.
At the sites, the anonymised paper-based forms will be scanned by trained
personnel and stored in a secure location to protect sensitive information from
unauthorized access. All anonymized digital forms, CCTA images, genetic data and
laboratory reports will be encrypted and stored on a central outsourced AWS server. The
access will be configured in such a way that there are two owners with the project
administrator role, who will be in charge of authorizing the access of the study personnel,
as necessary, and with access levels according to the roles and responsibilities.
A random quality check will be implemented on a sample of completed forms for
accuracy and completeness to ensure that the data collected is reliable.
The study data will then be stored on a central outsourced server (AWS).
b. Data Entry
Trained and certified personnel will enter the digital data in an Excel format document
located in the AWS server.
The following quality controls will be carried out:
- Validation rules generated according to the nature of the variables,
programmed through the data validation modules available in Excel.
- Query generation according to pre-established validation rules, to be
answered by researchers on the sites.
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- Manual design of explanatory queries, to be answered by the researchers
on the sites.
- Registration system, history and query control (queries).
- Periodic reports on the status of data entry, query generation and
response, on a weekly basis.
Once data has been entered into a digital system, the physical forms will be
properly filed to protect sensitive information.
c.
Data security control: auditing and monitoring
Monitor and log activity within the AWS environment will be conducted regularly
reviewing logs to detect unauthorized access or suspicious activity.
Regular data backup and disaster recovery will be automatically implemented using
AWS Backup, in order to ensure the availability and integrity of study data. All the security
processes will be performed by
personnel certified in data security best practices,
capable of handling sensitive information, how to identify phishing attacks, and how to
report security incidents. Regular risk assessments and vulnerability scans will be
conducted to identify potential threats and vulnerabilities in the AWS environment in order
to implement the necessary controls to mitigate the risk.
d. Elaboration of the manual of procedures
A manual of procedures will be developed where the procedures for the
implementation of the baseline data collection process, blood extraction, data
management and storage and follow-up will be described.
e. Training and certification
In order to participate in the study, all staff must attend a training session prior to
the start of fieldwork. Training sessions will include all aspects of the protocol and
procedures
manual
regarding
enrollment,
blood
samples
management,
data
management, management of an adverse event,
follow-up, and measurement
procedures, data entry and data security. Those who have acquired the expected skills
will be certified to carry out the study procedures. Staff will be retrained on a regular
basis.
f.
Site Monitoring Visits and Audits
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Monitoring visits and audits will be conducted at the study sites in order to follow
up on the progress of the fieldwork, identify problems and carry out retraining if
necessary.
10.3.
Adverse event reporting
Serious and non-serious events ( expected or unexpected) will be reported to the
IRB within specific timelines depending on the severity of the event. Serious adverse
events will be reported within 15 calendar days of receipt, whereas non-serious adverse
events will be reported on a periodic basis.
All the adverse event forms will be kept in the corresponding study records of
adverse events, including any follow-up actions taken, for a period of time as specified by
the regulatory agency.
10.4.
Calculation of the sample size
The calculation of the number of patients to demonstrate a positive effect for the
use of peripheral blood transcriptomics for the diagnosis of the presence of severe
coronary calcification is carried out based on previous experiences by the group of
researchers. There is still no standardized methodology in the medical literature to
perform the sample calculation of this type of analysis, since it is very recent and is in the
exploration phase. In silico studies carried out by this group of researchers and the
preliminary clinical studies carried out previously showed that at least a sample of 150
patients is necessary for the initial training of the artificial intelligence algorithm that
detects transcriptional differences between patients with or without a solid tumor. (for
example, breast tumor). Therefore, 200 patients will be included based on the minimum
number necessary to perform the initial training of an artificial intelligence algorithm.
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10.5.
Analysis plan
For the description of the general characteristics of the study population, descriptive
statistics such as means, medians and proportions will be used. To calculate the
diagnostic performance of the method, the following will be calculated:
●
True Positives (TP) or the probability that the test is positive since the disease is
present
●
True Negatives (TN) or the probability that the test is negative since the disease is
not present.
●
False Positives (FP) or Type I Error
●
False Negatives (FN) or Type II Error
●
Sensitivity: VP/VP+FN
●
Specificity: VN/ VN+FP
●
Precision or accuracy: VP/VP+FP or sensitivity + (1-specificity)
The accuracy can vary between 0.5 and 1, where 1 represents a perfect diagnostic value
and 0.5 is a test without diagnostic discriminatory capacity. An area under the curve
(AUC) will also be performed to determine the diagnostic capacity of the trained
algorithm.
10.6.
Control of good clinical practices
The proposed study will be conducted following the recommendations of the
International Council for Harmonization (ICH) for clinical studies (E6R2) (ICH Official Web
Site : ICH, nd). In particular, the control of good clinical practices will be supervised in
those elements that concern this type of evaluation study of new technologies, including
patient confidentiality and informed consent. In addition, the standards suggested by the
same regulations will be met (E18, (ICH Official Web Site : ICH, nd))44.
44 "Efficacy Guidelines - ICH." https://www.ich.org/page/efficacy-guidelines. Accessed 10 Apr. 2023.
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All study investigators have been certified in GCP guidelines. New study personnel
will also be required to obtain this certification.
10.7.
Protocol Deviations
Investigators in this study will strictly monitor compliance with good clinical
practices. Any deviation from the protocol will be reported to the ethics committee and
the case will be individually analyzed in order to determine the procedures to follow.
10.8.
Publication policy
Once the study is finished, it will be published in different ways. First, it will be
released to the scientific and medical community in the form of a conference abstract, in
order to rapidly communicate the results of the conference to those interested. It will
subsequently be published as a paper in an international peer-reviewed journal.
10.9.
Intellectual Property
The methodology to be applied in this study has been submitted to the Patent Office for
legal protection (preliminary patent US 62/944,063). The intellectual property of the
study remains in the hands of MultiplAI Health LTD.
10.10.
Study Organization
10.10.1.
Direction of the Study
The principal investigator of the project will be Rosana Poggio, while Gastón
Rodríguez Granillo and Santiago Miriuka will be the co-principal investigators. Dr. Gastón
Rodríguez Granillo will also be the principal investigator of La Sagrada Familia, will provide
logistical support and will be responsible for all on-site study execution. Dr. Guillermo
Naum will be the principal investigator of Diagnóstico MEDITER / Sanatorio Méndez, and
will provide logistical support and be responsible for all on-site study implementation. A
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CONICET fellow will be the coordinator of the logistics and processing of the RNA and
DNA biological samples. Postdoctoral fellow Alejandro La Greca will be in charge of the
bioinformatics analysis. María Olivera will be the general coordinator and study monitor.
Field Coordinating Center
The researchers will be in charge of supervising and controlling that all the
procedures
are
carried
out
following the recommended standards during the
implementation of the study, as well as the follow-up of the patients.
Communications
Continuous communication will be maintained between the group of study
researchers by telephone, email and whatsapp.
10.11.
Financing
The funding to cover the processes and materials necessary to carry out this study
will be provided entirely by MultiplAI Health LTD (sponsor) and does not imply any
expense for the OSBA or for Sanatorio Méndez or Clínica La Sagrada Familia; therefore:
Compensation to the participating professionals: $ 0.00
Laboratory determinations: $0.00
Genetic analysis (sequencing): $0.00
Data upload and analysis: $0.00
10.13 Study Timeline
Baseline
(Months)
Follow-up
(Years)
1°
2°
3°
4°
5°
6°
7°
8°
9°
10°
||°
12°
1°
2°
3°
4°
5°
Enrollment
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Baseline
phase
queries resolution
Follow-up
Follow-up
phase
queries resolution
The schedule of the present study will begin to be implemented once the study has been
approved by the corresponding ethics committee.
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