A Computational Platform for Heart Failure Cases Research

ao Rafael Almeida

1,2 a

, Pedro Freire

1 b

, Olga Fajarda

1 c

and Jos

e Lu

ıs Oliveira

1 d

DETI/IEETA, University of Aveiro, Aveiro, Portugal

Department of Information and Communications Technologies, University of A Coru

na, A Coru

na, Spain

Keywords:

Health Data, Clinical Studies, Cohorts, Heart Failure.

Abstract:

Heart failure is a global health issue that affects millions of people worldwide, and is the main cause of disabil-

ity and hospitalisation of elderly people. Approximately half of these have heart failure with preserved ejection

fraction (HFpEF) and this proportion is increasing as the population ages. There is still no efﬁcient treatment

for HFpEF and today’s existing therapies only aim at relieving symptoms. With the aim to unravelling the

pathophysiology of HFpEF and identify new therapeutic targets, ongoing long-time studies are collecting pa-

tient’s data, including the genomic information. This procedure is complex and requires electronically-stored

health information to keep the patient’s information centralised to simplify the following up. In this paper,

we present an computational system to support researchers in the different stages of a clinical study, and we

describe its use in the management and analyse of HFpEF cohorts.

1 INTRODUCTION

Heart failure (HF) is a global scourge that affects over

26 million people worldwide (Savarese and Lund,

2017). This condition is associated with a high risk

of morbidity and mortality, as well as a large health

care resource consumption (Bui et al., 2011). HF is,

also, the main cause of disability and hospitalisation

of elderly people (Farmakis et al., 2015) and is ex-

pected to aggravate as the population ages (Gaggin

and Januzzi Jr, 2013).

Approximately 50% of all the cases are related

to heart failure with preserved ejection fraction (HF-

pEF) (Dunlay et al., 2017), and these patients have

typically higher rates of hospitalisations then the ones

suffering from heart failure with reduced ejection

fraction (HFrEF) (Steinberg et al., 2012).

Unlike HFrEF, whose prognosis has been improv-

ing, the prognosis of HFpEF has remained mostly un-

changed over the last decades (Bonsu et al., 2018).

No therapy for HFpEF has demonstrated improve-

ment in prognosis and several pharmacological agents

applied in clinical trials have shown no beneﬁt (Fer-

rari et al., 2015). The existing therapies only aim

https://orcid.org/0000-0003-0729-2264

https://orcid.org/0000-0001-5663-3403

https://orcid.org/0000-0003-1957-4947

https://orcid.org/0000-0002-6672-6176

at relieving symptoms. Even the diagnosis of HF-

pEF is not consensual. The American College

of Cardiology Foundation/American Heart Associa-

tion (ACCF/AHA) and the European Society of Car-

diology (ESC) use different criteria to diagnose HF-

pEF. ACCF/AHA uses a diagnosis of exclusion in

compliance with the Framingham HF criteria, while

ESC considers that impairment of diastolic function is

the key diagnosis criteria (Paulus et al., 2007; Yancy

et al., 2013). The ACCF/AHA diagnosis criteria

recognise close to 30% more patients consider to have

HFpEF than the ESC criteria and so the patients en-

rolled in clinical trials are different depending on the

criteria used (Persson et al., 2007).

HFpEF patient population is mostly elderly, het-

erogeneous and with numerous comorbidities includ-

ing systemic arterial hypertension (SAH), obesity,

and diabetes mellitus (DM) (Shah, 2017). The vari-

ations of comorbidities have a signiﬁcant impact on

the symptoms and the therapy responses, as well as

the mortality. The underlying pathophysiologies are,

therefore, variable and remain unclear (Sharma and

Kass, 2014).

During the last decade, several projects emerged

to study several aspects of HF, some of which targeted

speciﬁcally HFpEF.

Heart OMics in AGEing (HOMAGE) (Jacobs

et al., 2014) was a project launched with the pur-

pose to identify and validate predictive ‘omics’-based

Almeida, J., Freire, P., Fajarda, O. and Oliveira, J.

A Computational Platform for Heart Failure Cases Research.

DOI: 10.5220/0009104706010608

In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 5: HEALTHINF, pages 601-608

ISBN: 978-989-758-398-8; ISSN: 2184-4305

601

biomarker for HF. These biomarkers should help to

identify patients at risk to develop HF in order to pre-

vent the development of this disease which affects

mostly the elderly population. During the project,

a centralised database of existing cohorts, which in-

clude patients with heart failure, patients at risk to de-

velop heart failure and healthy individuals, was cre-

ated.

The INTERnational Congestive Heart Failure

Study (INTER-CHF) (Dokainish et al., 2015), was a

prospective study conducted from 2012 to 2014. The

purpose of this project was to document sociodemo-

graphics, HF etiologies, treatment and mortality of

patients, from low and middle-income countries in

Africa, Asia, the Middle East, and South America,

with HP.

The OPTIMEX project studied the impact of ex-

ercise training as primary and secondary prevention

of HFpEF (Suchy et al., 2014). The objective of this

project was to deﬁne the optimal dose of exercise

training in patients with HFpEF in order to prevent

the disease development and improve the pathophysi-

ology.

NETDIAMOND is another project that joins a

network of referenced centres with complementary

expertise to unravel the pathophysiology of HFpEF

and develop evidence-based therapeutics strategies

directed at HFpEF (Lourenco et al., 2018). By cor-

relating and integrating transcriptomics, proteomics

and lipidomics studies with clinical data, this project

aims to achieve a holistic view of HFpEF and the role

of comorbidities. Furthermore, this project intends to

determine gene variants that may predispose to HF-

pEF.

Due to the complexity of HFpEF and the multifar-

ious pathophysiology, this disease is highly challeng-

ing and only a deep omics analysis integrated with

clinical data and mixed with data mining will pro-

vide precise patient-directed treatments. To address

this issues, we present a platform composed of several

tools to support the research in HFpEF. This ecosys-

tem provides the following features:

• A centralised system following standard practices

of clinical, functional and follow-up procedures

to gather clinical and omics data from HFpEF pa-

tients;

• A secure central large data repository for all pa-

tients and animal model data;

• Analytic features to design and explore cohorts

over the collected data;

• And algorithms to automatically analyse the pa-

tients’ data, including the omics records.

2 GATHERING DATA IN HFpEF

STUDIES

Clinical studies are typically performed using a com-

putational system to support the research. Frequently

the institutional Electronic Health Record (EHR) sys-

tems are used to gather the patient clinical data. How-

ever, there are studies with a very focused purpose

in which the EHR is too generic for storing all the

patient’s information. In these scenarios, the use of

spreadsheets has been the most common solution, but

this is not the best procedure for several reasons. This

approach fails by not following a standard structure,

leading to issues in future reuse of the collected data.

Moreover, it also does not have any control over who

can edit or see the data, complicating the study man-

agement.

Regarding these issues, we propose the NETDIA-

MOND Platform

, which is a web-based system to

record patient information in clinical studies focused

on HFpEF patients. The system was designed on top

of the MONTRA framework, which relies on an easy-

to-use tabular skeleton, intended for data integration,

with emphasis on biomedical data (Silva et al., 2018).

The developed platform was targeted to groups of

researchers, mainly because the studies are conducted

in different organisations, by distinct researchers, and

with different roles. Therefore, for the same patient,

various entities are involved in the data collection, for

instance, one group could be responsible for inquir-

ing the patient’s habits and another for recording the

omics information extracted from samples of the heart

tissue. This led to the creation of Role-Based Access

Control (RBAC) policies at the user and group levels.

The patient’s clinical data is stored in a data struc-

ture created by consensus among all the entities in-

volved in the study. This data structure keeps all the

information centralised and in accordance with all of

the study’s needs. This structure consists of differ-

ent input components, such as numeric type, free-text,

multi-choice, and others. This reduces insertion er-

rors by reducing the ﬂexibility in the inputs available

for the speciﬁed data type. The data schema is also

composed of rules, i. e., some ﬁelds are mandatory

and others are only mandatory depending on the out-

come of other variables. The system’s data structure

has also ﬂexible management features, so that it can

be adapted to the future data collection needs of each

study.

This platform was designed to gather phenotype-

genotype data in HFpEF studies, but the omics data

are stored in a different system. However, there is a

https://bioinformatics.ua.pt/netdiamond

HEALTHINF 2020 - 13th International Conference on Health Informatics

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direct connection between the two systems, so as not

to lose context.

3 DATA REPOSITORY

This project sets forth to address the HFpEF issue

through comprehensive multi-omics studies in plasma

and tissues from HFpEF patients and animal mod-

els, which generates large and dissimilar volumes of

data. The collected omics data are widely spread in a

vast variety of unstructured formats, making it harder

for researchers to manage and create cohorts. They

hold valuable information on both metadata and con-

tent planes that could be used to enhance data discov-

ery. Thus, a data repository with enhanced search fea-

tures, able to execute full-text searches through meta-

data and raw data are desirable, in such a way that

allows researchers to manage and create cohorts more

effectively.

Several open-source projects for data management

were considered for analysis: Girder

, CKAN

DSpace

, Islandora

and Alfresco

. After analysing

each one, it was possible to conclude that Alfresco

was far better than its competitors, due to its ability

to extract and index metadata and to its ﬂexibility to

add custom support to other ﬁles types. Alfresco is an

open-source Enterprise Content Management (ECM)

system that provides several services and controls for

data management, offering tools to index metadata

content and execute full-text searches through ﬁles’

content (Sladi

c et al., 2011).

Alfresco does not support genomic ﬁles by de-

fault, which led to the adaptation of the software to

the biomedical research scenario, by adding support

for FASTA and Genbank ﬁles. The adaptation was

achieved by developing a metadata extractor featured

by BioJava library (Laﬁta et al., 2019) and a custom

model to store the extracted data.

The custom model was designed taking in mind

the properties that could be extracted from FASTA

and Genbank ﬁles. After analysing both ﬁle formats,

it was possible to conclude that the following prop-

erties should be considered: accession, description,

keywords, gene, organisms, number of sequences and

sequences. It’s important to notice that, in our so-

lution, not every sequence is indexed. Indexing an

entire genome is not reliable due to the huge size of

https://girder.readthedocs.io/en/stable/

https://ckan.org/

https://duraspace.org/dspace/

https://islandora.ca/

https://www.alfresco.com

its sequences. As a workaround to this problem, only

sequences listed on a conﬁguration ﬁle are indexed.

Furthermore, the metadata extractor extracts and

saves target properties into the created custom model.

In Addiction, BioJava library is used on the extractor

to parse genomic ﬁles and extract target properties.

The extraction is triggered right after a successful up-

load.

Researchers, by using Alfresco with the extension

presented here, can store, manage and share genomic

ﬁles in a more efﬁcient way, having the possibility to

search for certain genes or even sequences.

4 PATIENT COHORT

DEFINITION

The previous two sections described the two pieces

of the ecosystem responsible for gathering patients’

data in HFpEF studies. The next stage in the proposed

ecosystem is patient cohort selection. This step is es-

sential to ﬁlter patients of interest in speciﬁc scenar-

ios. Therefore, aiming the cohort deﬁnition and data

ﬁltering, we used TranSMART, which is a platform

with analytical web applications to aggregate differ-

ent data sets. In this platform, the researchers can se-

lect the variables of interest through a friendly user

interface, without interacting directly with the data.

This tool enables patient and variable selection fea-

tures over the data set, as well as the aggregation of

several data sets, if necessary.

However, to employ this tool in the ecosystem,

we needed to map the recorded data into the tool’s

database. Therefore, we deﬁned a semantic ontology

and used a migration pipeline to extract, transform

and load (ETL) the data from the NETDIAMOND

platform to the TranSMART tool. This ontology was

built on top of the initial data structure detailing the

recorded concepts. This new extra information allows

us to validate clinical data with ranges of values, vari-

able types and options for questions. For instance,

in this migration, all the data is processed, and when

a variable is out-of-range, we will be notiﬁed and re-

quest for rectiﬁcation in the record, thereby increasing

the quality of the data.

Figure 1 shows the detailed migration workﬂow

divided into the three ETL stages: Extracting, Trans-

formation and Loading. The workﬂow starts by ex-

porting the collected data from the NETDIAMOND

Platform to CSV ﬁles, which are then read (Extract-

ing stage). Afterwards, the collected data is trans-

formed into a new structure, that simpliﬁes the har-

monisation procedure. The Cohort harmoniser builds

a new cohort using that structure and validates the

A Computational Platform for Heart Failure Cases Research

603

Extracting Transformation Loading

Cohort in

raw data

TranSMART

Loader

WebProtégé Output:

Ontology rules

Cohort

Reader

Structure

Transformer

Cohort

Harmoniser

Migration Report

Figure 1: The migration workﬂow from NETDIAMOND Platform to TranSMART divided into the three stages.

collected data applying the ontology rules. Addition-

ally, those rules are also used to calculate new patient

information (Transformation stage). Finally, the co-

hort is loaded into TranSMART, and the migration re-

port is generated (Loading stage). This report has all

the warning and errors produced during the migration,

which helps to validate the data in both platforms.

Another positive aspect of performing this transi-

tion is the possibility to insert new knowledge about

patients that are already present in the data but in a

roundabout way. Rules and conditions, that are not

typically recorded during the study, can be deﬁned in

the ontology. This occurs mainly because those data

can be calculated during the analysis. Moreover, this

kind of information could help to deﬁne a more fo-

cused cohort. For instance, since obese patients are

considered patients with a cardiovascular risk factor,

researchers may want to build a cohort composed of

patients who are obese and if the concept obese was

not recorded during the clinical visits, they need to

analyse the raw data to understand which patients be-

long to this group. To know if the patient is obese,

researchers only need the patients’ height and weight,

and then during the migration, the body mass index

can be calculated. If this value is higher than 30, then

the patient is considered obese (James, 2004).

Although the TranSMART was designed to per-

form more complex tasks such as data aggregation of

different data sources, we decided to use it only for

cohort deﬁnition. Using TranSMART, the researchers

can select the desired data sets to follow or process,

by deﬁning a set of conditions. These conditions

are mainly inclusion and exclusion criteria, which are

then applied to the study data set. Moreover, these

criteria can also be applied to two subsets aiming the

comparison between them.

5 DATA ANALYSIS

The analysis of the genomic data collected, by com-

paring the measurements in different conditions, is

useful for diagnostic, prognostic, subdivision of pa-

tients in clinical studies and prediction of therapeu-

tic response (Haury et al., 2011). Statistical methods

are commonly used to do the analysis, however, these

rely on the deﬁnition of an arbitrary threshold, whose

value to use is not consensual (Cui and Churchill,

2003). The analysis is usually done by comparing the

expressions of genes in order to identify differentially

expressed genes (DEGs) in different conditions.

The main technologies used to measure gene ex-

pressions are microarray and the more recently de-

veloped RNA-Sequencing (RNA-seq) (Wang et al.,

2009). RNA-seq is technologically more advanced,

however, microarray technology continues to be

widely used because it is cheaper and there are, freely

available, robust, mature and reliable tools to pro-

cess and analyse the data obtained (Thompson et al.,

2016). Different platforms are used by researchers

to measure the genes’ expression. These platforms

are composed of diverse sets of genes and merging

data sets from different platforms is challenging (Ku-

mar Sarmah and Samarasinghe, 2010).

We developed a pipeline to analyse genomic data,

namely gene expressions obtained using microarray

technology. Figure 2 presents the pipeline.

The ﬁrst step is to pre-process the data, which con-

sists of performing background correction, normali-

sation and probe summarisation of the raw data. The

raw data originated using the same platform are joint

before the pre-processing.

Before merging the several data sets the common

genes across the different platforms must be identi-

ﬁed. To not reduce signiﬁcantly the number of genes,

instead of the gene identiﬁer, the GenBank sequence

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604

Pre-process the data

↓

Merge the data

↓

Determine the adjusted p-value and fold change

↓

Obtain different sets of features by using different

thresholds

↓

Batch-adjust the data

↓

Use a supervised learning algorithm to select the set

of features with the highest accuracy

Figure 2: Pipeline to analyse genomic data.

accession identiﬁer, which uniquely identiﬁes a bio-

logical sequence, should be used. After merging the

several data sets using the common GenBank identi-

ﬁers, a gene expressions data set is obtained.

Thereafter a statistical method is used to deter-

mine the adjusted p-value and fold change for the

different features (the GenBank identiﬁers) by us-

ing the expressions in one condition and the expres-

sions in another condition, e.g. diseased vs. con-

trol. By choosing different thresholds for the ad-

justed p-value and fold change, several sets of features

are obtained. The next step is to select the one set

with the best predictive accuracy and this can be done

by using supervised learning algorithms, e.g. Ran-

dom Forest (Breiman, 2001) or Support Vector Ma-

chines (Cortes and Vapnik, 1995). However, before

using a supervised learning algorithm, the data must

be batch-adjusted.

The batch effect is the non-biological variation

that affects the gene expressions (Leek et al., 2010).

To account for the batch effect, two approaches can be

used: include the batch variable in the statistical anal-

ysis or adjust the data for batch effects before using

it (Nygaard et al., 2016). In this pipeline, these two

approaches are used. Thus, the batch variables are

included in the statistical method used to determine

the adjusted p-value and fold change and the gene ex-

pression data are batch-adjusted before using the su-

pervised learning algorithm. Several methods exist to

batch-adjust the data and a survey of these methods

can be found in (Lazar et al., 2012).

The evaluation of the pipeline was done using nine

publicly available microarray data sets from studies

about heart diseases. The pre-processement of the raw

data was done using the oligo package (Carvalho

and Irizarry, 2010) of the R/Bioconductor software

packager (Gentleman et al., 2004), which implements

the robust multichip average (RMA) pre-processing

method (Irizarry et al., 2003). The microarray data

sets were generated using four different platforms that

have 8354 GenBank sequence accession identiﬁer in

common and so after merging the nine data set, a data

set with 689 samples and 8354 features was obtained.

To determine the adjusted p-value and fold change

the R/Bioconductor software package limma (Ritchie

et al., 2015) was used. The thresholds used for the

adjusted p-value are 0.01 and 0.05 and for the fold

change the values from 1.5 to 3. Using the threshold

0.01 or 0.05 for the p-value in conjunction with the

thresholds of the fold change resulted in the same sets

of features. The fold change thresholds of 2.7 and 2.8

resulted in the same set of features and the same hap-

pened for the fold change thresholds 2.9 and 3. So

fourteen different set of features were obtained. To

select the set of features with the best predictive accu-

racy we used the R package caret (Kuhn et al., 2008)

implementation of the Random Forest algorithm. The

evaluation of each model was done using repeated 10-

fold cross-validation with 3 repeats. We identiﬁed a

set of 86 differentially expressed genes that correctly

classiﬁes samples with a heart disease and samples

with no heart condition with an accuracy of approxi-

mately 96%.

6 RESULTS AND DISCUSSION

The proposed ecosystem provides support for all the

main stages in HFpEF clinical studies. Figure 3

presents an overview of the workﬂow from data col-

lection to the discovery of new biomarkers. This

workﬂow is divided into three important stages, usu-

ally performed by different entities. Patients’ data

collection is done at health care facilities by physi-

cians during patients’ visits. In the NETDIAMOND

project, different groups of physicians from several

Portuguese health institutions are collecting those

data.

Both tools used in the data collection where used

and validated in different contexts. The MONTRA

framework, which is the most important piece of the

NETDIAMOND Platform, was already used to sup-

port the creation of catalogues of distributed health

databases. The base features were the same but us-

ing a different entity to categorise, while in one it is

the patients, in the other the data owners recorded the

metadata from their databases. The Alfresco was used

in countless projects for different purposes. However,

for our scenario, we needed to add some features to

increase the usability in the proposed context.

The second stage of this workﬂow can be executed

almost in parallel with the data collection as long as

A Computational Platform for Heart Failure Cases Research

605

Patient data collect Cohort Selection Data processing

Figure 3: Workﬂow overview with all the steps and all the components’ interconnection.

data is migrated in the TranSMART. In this stage,

the medical research teams can deﬁne their cohorts

by selecting variables of interest to explore speciﬁc

biomarkers. These selections are essential to ﬁlter the

data on the omics repository.

The TranSMART is a tool already validated in the

clinical context. Our main contribution to this part

of the workﬂow was primarily the data migration and

aggregation in a centralised data warehouse. This mi-

gration required the creation of an ontology for HF-

pEF and harmonisation of concepts. At the end of

this stage, we validated the data using the ontology

rules introduced during the migration process.

In the ﬁnal stage, the data can be analysed using

the methodology presented in Section 5, or using oth-

ers. At this stage, researchers can deﬁne cohorts by

selecting the right inclusion and exclusion criteria in

order to discover new biomarkers or detect the effects

of therapies applied to patients.

This workﬂow allows the allocation of roles over

different institutions and improves their cooperation.

The ecosystem was designed to fulﬁl the technical

needs of the NETDIAMOND project by supporting

all the study pipeline, and to help unravelling patho-

physiology and identifying new therapeutic targets in

HFpEF. With this ecosystem and its tools we expect

that the medical community will be able to:

• Implement a standard clinical practice distributed

at a national level. This contemplates the collec-

tion of myocardial samples from HFpEF patients

and an organised registry for this disease.

• Examine cohorts of HFpEF patients composed of

their blood samples paired with samples of the

myocardium and adipose tissues.

• Use a centralised data repository for all the pa-

tients’ clinical data related to this disease.

• Evaluate the genetic variants by developing cell

and mouse models of this disease that rehash the

human gene variants amenable to high throughput

pharmacological screening.

• Increase public awareness of the HFpEF disease

and develop preventive design strategies to reduce

its impact on the elderly population.

7 CONCLUSION

HFpEF is becoming the predominant form of HF

which leads to new research initiatives aiming to dis-

cover new treatments and therapies. Regarding this

problem, we developed a computational ecosystem to

help conducting and supporting clinical studies. The

proposed solution was applied in the NETDIAMOND

project and can be reused in other projects that study

a disease-speciﬁc group. As a result, this multidisci-

plinary approach ensures the cross-checking of omics

data with clinical information, which promotes the

prototyping and application of new therapies with rel-

evance in the clinical practices. Moreover, it may help

gathering new knowledge about the genomic variants

that may predispose to HFpEF patients, leading to the

development of improved therapies.

ACKNOWLEDGEMENTS

This work was funded by the NETDIAMOND

project, grant number POCI-01-0145-FEDER-

016385. Jo

ao Rafael Almeida is supported by FCT

- Foundation for Science and Technology (national

funds), grant SFRH/BD/147837/2019.

HEALTHINF 2020 - 13th International Conference on Health Informatics

606

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