European Atherosclerosis Society Familial Hypercholesterolaemia Studies Collaboration
Conditions: Familial Hypercholesterolemia
Sponsor: Imperial College London

 
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THE EUROPEAN ATHEROSCLEROSIS SOCIETY 
FAMILIAL HYPERCHOLESTEROLAEMIA 
STUDIES COLLABORATION 
[EAS FHSC] 
 
 
 
 
STUDY PROTOCOL 
 
 
 
 
Version: June, 2016 

 
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THE EUROPEAN ATHEROSCLEROSIS SOCIETY  
FAMILIAL HYPERCHOLESTEROLAEMIA STUDIES COLLABORATION 
– EAS FHSC – 
 
 
 
 
STUDY DESIGN 
International registry of individuals with familial hypercholesterolaemia (FH). The EAS FHSC registry consists 
of a consortium of worldwide FH cohorts, registries, databases and data collections with access to 
information on individuals with a clinical and/or genetic diagnosis of homozygous (HoFH) and/or 
heterozygous (HeFH) FH. 
Scope: multinational, multicentre; worldwide scope. 
Data: cross-sectional and longitudinal (retrospective and prospective). 
Selection of participants to be included in the registry: 
 
Inclusion criteria: 
- 
Individuals with a diagnosis of heterozygous or homozygous FH. 
Diagnosis: clinical and/or genetic. 
Also including positive clinical diagnosis with negative genetic test and vice versa. 
In case of clinical diagnosis: 
o Criteria used: Dutch Lipid Clinics Network, MedPed, Simon-Broome, Japanese criteria, or 
other to be specified. 
o Categories of possible, probable and confirmed FH. 
- 
Relatives of index cases without a diagnosis of FH where screening (cascade or other) is carried out. 
 
Exclusion criteria:  
- 
Secondary causes of dyslipidaemia (e.g. untreated hypothyroidism, cholestasis, nephrotic 
syndrome). 
- 
Where data collation does not conform to the local or country wide standards for anonymised data.  
Duration: intended to be run for the long-term in order to increase the follow-up of patients already included, 
to include new individuals diagnosed with FH over time, to bring new cohorts to the registry, and to take into 
account contemporary changes in clinical practice, so that the findings remain relevant. The project is 
expected to evolve over time as will be further developed and expanded. The process is expected to be 
dynamic as we aim to continuously employ the best data sharing strategies and optimise our operational 
protocols based on the experience of the consortium, number of participants included, the results from the 
first wave of data collated, evolving aims, resources available and feedback from those utilising the data and 
the feedback from the wider medical community. 
 
 

 
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STUDY OBJECTIVES 
The mission statement of the EAS FHSC aims to empower the medical and global community to seek changes 
in their respective countries or organizations regarding how FH is detected and managed and the clinical 
consequences thereof, with a view to promoting early diagnosis and more effective treatment of FH.  
The general aims of the EAS FHSC are: 
 
To establish a global registry of FH with a view to gaining an in-depth understanding of the contemporary 
burden of both HoFH and HeFH: patient management, treatments, impediments, long-term risks, impact 
of patient-specific and societal factors, gene–drug interactions and the role of screening. 
 
To disseminate the information gained from the above-mentioned activities to an international audience 
including physicians and other healthcare professionals, as well as patient organizations, with a view to 
sensitising them to the contemporary burden of FH, encouraging open discussion on the management 
of FH patients, promoting a uniform, evidence-based standard of care, and encouraging them to 
contribute actively to research. 
 
Objectives 
 
1. To establish an international, standardised registry of patients with FH by bringing together regional, 
national and international cohorts and registries with access to patients with FH, in order to maximise the 
potential exploitation of the data and generate large scale, robust information to accurately and reliably 
investigate (i) the burden of both HoFH and HeFH, (ii) how it is detected and managed, (iii) the clinical 
consequences of current practice on delivery of care and outcomes, and (iv) the determinants influencing 
optimal management and LDL-C target attainment in FH. This will include but will not be limited to the 
following: 
 
1.1. General and cross-sectional analyses: 
- How FH patients are detected, comparison of different proposed diagnostic criteria, whether current 
screening strategies for FH are adequate and, if not, what could be done differently to maximise 
coverage. 
- How patients are managed, treatments offered/advised, how their efficacy is monitored. 
- What proportion of FH patients meet the targets (e.g. LDL-C goals, patients receiving therapy), the 
impediments in attaining LDL-C goals, the role played by societal factors (such as access to healthcare 
in different settings and the availability of specialist advice) in enabling treatment to achieve LDL-C 
goal, the influence of gene–drug interactions in attaining LDL-C goals. 
- The impact of patient-specific factors, socio-economic factors and treatment-related factors on LDL-C 
goal attainment. 
- Potential variations depending on different geographic settings as a result of factors such as population 
genetics, health care delivery systems and other patient-, socio-economic- or treatment-related 
factors. 
- Where possible, evidence for economic evaluation of different screening strategies and of 
interventions will be addressed. 
 
1.2. Risk stratification beyond LDL-C levels, e.g. by genotype (to establish the value of incorporating genetic 
data into the clinical diagnosis of FH) or lipoprotein(a) levels. 

 
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1.3. Analysis of risk/outcomes: Long-term risk of outcomes in patients with FH (including, where possible, 
estimates of the years lost due to FH), with a special focus on the following end-points: 
- Primary end-point 
□ The composite of Cardiovascular Disease Events (fatal and non-fatal). 
□ Cardiovascular mortality. 
□ All-cause mortality. 
- Secondary end-points:  
□ Each component of the Cardiovascular Disease Events end-point separately. 
□ Aortic valve and supravalvular disease. 
□ Statin intolerance (clinical, biochemical). 
□ New onset of diabetes. 
□ Cancer diseases. 
□ Pregnancy outcome in female patients with FH. 
- The impact of patient-specific factors, socio-economic factors and treatment-related factors on 
outcomes. 
 
The following definitions shall apply: (i) Cardiovascular Disease Events: the composite of fatal and non-fatal 
coronary heart disease/acute coronary syndrome/myocardial infarction, cardiac sudden death, fatal and 
non-fatal stroke, transient ischemic attack, peripheral vascular disease, and revascularization (percutaneous 
or surgical); (ii) Aortic valve and supravalvular disease: confirmed by any imaging test; (iii) Statin intolerance 
(clinical and/or biochemical): defined according to the EAS consensus panel (Eur Heart J 2015;36:1012-22); 
(iv) New onset of diabetes mellitus: fasting glycaemia >126 mg/dL at least in two separately occasions, 
glycosylated haemoglobin ≥6.5%, and/or patient receiving a new prescription of an antidiabetic drug (Eur 
Heart J 2013;34:3035-87). (v) Pregnancy outcomes refer to maternal, obstetric and neonatal outcomes. 
 
2. To develop and implement a novel bespoke electronic platform and FH data warehouse for data sharing, 
cleaning, harmonisation and analysis for patients with FH to support the FH registry. It will allow the collation 
of retrospective and prospective data from multiple sources and different formats, and will support the 
management of a large amount of data, critical to ensure sustainability. 
 
3. To disseminate the information gained from the FH registry to an international audience including 
physicians, other healthcare professionals, policy-makers and patients organizations. To sensitise the 
different stakeholders involved in FH care to the contemporary burden of FH, encourage open discussion on 
the management of FH patients and their families, promote a uniform evidence-based standard of care, and 
encourage those involved in FH care to contribute actively to research.  
 
4. To consolidate a network of investigators interested in FH through which collaborative research and 
networking on FH can be conducted on a large scale. 
 
DATA SHARING 
We will develop a system whereby the data sets will be transferred to and collected by the Coordinating 
Centre through a secure web platform. Data sharing plans involve instructions to the lead investigators for 

 
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data to be transferred and data to be collected, updated with feedback for the individual investigators 
providing the data. For each cohort/data set a two-stage approach will be followed: the first one comprises 
the collation of the cross-sectional and retrospective data readily available; the second stage will involve 
integration of longitudinal, prospective records (including follow-up data). 
In order for these investigators’ to provide reliable data for inclusion, a set of minimum predefined quality 
and operational criteria must be fulfilled:  
- 
The data must be in electronic format; 
- 
The amount of data shared should be considered enough to add relevant information to the registry; 
- 
The data must have been collected rigorously following a standardised well-designed protocol; inclusion 
and exclusion criteria must be well defined; FH must be well defined according to accepted criteria and 
not rely only on self-reported history of FH; any measurements recorded must have been done with 
validated and standardised methods and devices; standardised definitions should have been used; the 
participants included should be representative of the target population; 
- 
The parameters recorded must include at least the minimum set of data (core information) specified 
below; 
- 
Local ethical and security aspects must be in place; 
- 
The data sharing agreement must be signed prior to any data transfer. 
A detailed data request form for providing anonymised data will be sent to the investigators contributing to 
the registry for them to provide data on individuals with FH and their relatives where available. A minimum 
set of data (core data) will be required to be included in the registry, comprising: 
- 
How patients are being managed (e.g. cascade screening, availability of genetic testing);  
- 
Characteristics of the clinics providing the data (e.g. number of physician and nurses, specialised clinic or 
general physicians, public or private clinic, etc.); 
- 
General and demographic information; 
- 
Familial and personal cardiovascular history; other cardiovascular risk factors; 
- 
FH diagnostic criteria; 
- 
Where genetic tests are available, these genetic data will form part of the core data requested. 
- 
Lipid-lowering medication with doses; 
- 
Laboratory lipid profile; 
- 
Data on outcomes if follow-up exists (retrospective or prospective); 
- 
In the case of HoFH, data about LDL apheresis will also be collected. 
Additional data to be collected where available include a number of other parameters of interest such as 
more detailed information on comorbidities, other medications and laboratory results, tests such as 
electrocardiography, echocardiography, calcium coronary scores, angiography, etc. (annex 1). 
Where possible, standardised definitions will be used and free text avoided. We will encourage that 
investigators provide updates of shared data sets within a reasonable timeframe.  
We will develop data quality assurance strategy which will include, for example: measurement of data 
consistency and instituting a standard method for analysing the reasons for, and impact of, any data 
inconsistencies. We will continually improve the quality of the data with a data quality assurance system. 
Individual investigators from each local site will contribute to the FH registry through the National Lead 
Investigators/Country-leads (NLI/CLs) for each region or country. These NLI/CLs will transfer and upload the 
data to the Coordinating Centre through a bespoke secure web portal. See next figure. 

 
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Where possible, depending on the funding and resources available at each time, the EAS FHSC will try to 
provide the lead investigators with a certain amount of funds to help in the management of data with the 
purpose of data sharing. 
 
 
 
 
DATA MANAGEMENT 
The preliminary data shared will be checked for consistency and accuracy by at least 2 independent 
investigators of the Coordinating Centre (scientific coordinator, data manager), and any discrepancies will be 
resolved by raising queries with the individual investigators. The Coordinating Centre, supported by a 
technical team, will develop cutting edge and artificially intelligent routines incorporating machine learning 
for automated data cleaning, harmonising and make the data uniform and compatible. Various suitable well-
defined data models will be designed into which collected and cleaned data will be loaded. After ensuring 
satisfactory data quality from all registries (contributors), the information will be saved into a central 
database to be held securely on a server at the Coordinating Centre (Imperial College London, UK) in strict 
adherence with all data safety protocols and regulatory requirements i.e. Society for Clinical Data 
Management (SCDM), Clinical Data Interchange Standards Consortium (CDISC), Study Data Tabulation Model 
Implementation Guide for Human Clinical Trials (SDTMIG) and the Clinical Data Acquisition Standards 
Harmonization (CDASH) standards. A bespoke Data Warehouse hosted at the Coordinating Centre will be 
developed to consolidate heterogeneous data from multiple sources and formats through appropriate smart 
algorithms. A central, well-organised, robust, secure, compliant “data warehouse” with different well-
defined data models hosted by the Coordinating Centre will be key for multiple reporting and analysis use-

 
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cases, allowing researchers and stakeholders immediate access to processed, clinically relevant rich 
information.  
During the course of the project various tools will be developed to support, improve, standardise and 
automate the Data Management. These tools are mainly: 
- 
Process Workflow: The Data Management involves various steps and a uniquely focussed advanced 
workflow will be designed for automating the whole process. The workflow will streamline the entire 
process all the way from designing data models, data collection, data validation, data harmonization, 
data transformation and data analysis and reporting. The workflow itself will be a cloud based solution 
hosted as a software-as-a-service (SaaS) application to meet demanding requirements of the global 
project.  
- 
Data Monitoring Application: The Data Management monitoring application will be designed and 
developed to complement the workflow to ensure homogenisation of the data to meet all regulatory 
requirements and will be updated regularly to ensure adherence to contemporary local and global 
regulatory standards. The monitoring application will help administrators to be involved at all stages of 
data management right from inception to completion. The monitoring of the various stages of data 
management will ensure the quality standards are maintained and any deviations from protocols are 
immediately reported and rectified. Due to the heterogeneous nature of data collected from multiple 
sources; it will be critical for various data management stages of the workflow to be assessed for quality 
at regular intervals during whole project including, database designing, data validation, discrepancy 
management, medical coding, data extraction, and database locking. 
- 
Process Logging System: The data will go through various stages during the Data Management cycle. It 
is therefore important to have a system which logs changes made to the collected data during various 
stages i.e. data validation, data harmonisation, data standardisation and data transformation. The 
logging system will help administrators in reverting data to the original state if there are conflicts or 
errors. The logging system will be essential for auditing the whole process and to improve the Data 
Management according to the requirements and suggestions of stakeholders. In the absence of a proper 
logging system there is a potential to introduce un-detectable errors and merge sub-standard data with 
the master data for any global project. The process logging system will also bring transparency, assurance 
and instil confidence to national and international data contributors. 
 
PLATFORM AND DATA WAREHOUSE  
We will develop a central database that would consolidate subject-oriented, time-variant and non-volatile 
data from multiple sources into a robust large scale FH Data Warehouse (FHDW). The FHDW will provide 
through novel informatics the capacity for retrospective and ongoing prospective data pooling, 
harmonisation and analysis. The integration, validation, processing, and exploration of complex data is a 
technical challenge for clinical researchers and a major rate limiting step to clinical FH research projects. 
Through the bespoke data architecture and platform which we will develop we will support: secure data 
harvesting from multiple global repositories/databases/file systems; data cleaning, validation, quality control 
and merging into a Data Warehouse hosted by the Coordinating Centre at Imperial College London, UK, in 
strict adherence with all data safety protocols and regulatory requirements. The standardized central data 
will support intelligent data analysis. The FHDW is envisioned as the “master” repository for FH specific health 
care data that can integrate disparate sources of data on the same individuals for instance at later time 
points, in order to support the evolving information trail and analytical requirements of stakeholders involved 
in the management, evaluation and care of the FH patients. Once built, the FHDW will serve as a key resource 

 
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for implementing the various outputs e.g. quality measurement, quality improvement, analyse trends over 
time. Its goal is to make isolated data easily accessible, reliable, consistent, and secure to support informed 
planning, decision-making, and communication. The aggregated FH data will be the principal source for 
identifying valid, novel, potentially useful, and ultimately understandable patterns in large scale complex 
data across multiple countries, providing both power and precision, thus facilitating “Knowledge Discovery” 
to tackle the population burden of FH. 
The FHDW will incorporate the data backup and recovery policies for minimal downtime of the platform. 
These policies will be flexible and adaptable to usage and demands of the platform. Main aspects of backup 
policies of the raw data (shared by each NLI/CL) and the master copy of the consolidated data are: 
- 
Full backups of FH data will be performed weekly. Full backups will be retained for 3 months before being 
overwritten; 
- 
Incremental backups of the FH data will be performed daily. Incremental backups are retained for 1 
month; 
- 
Backups will be run during minimal load time; 
- 
Backups are stored in secure location/s different from master copy. A limited number of authorised 
personnel will have access to the backup data; 
- 
Data will be available for restoring within a few minutes of a backup job completing; 
- 
Data will be available during the retention policy of each backup job i.e. 3 months; 
- 
The potential data loss during a working day will be avoided by utilizing incremental backup and process 
logging system. 
The replication of master data into multiple copies will be stored on independent and remote servers. 
The overall architecture of the FHDW and its corresponding platform is shown in the figure below. The FHDW 
will be a complete platform supporting various research activities and thus will be composed of the following 
components: 
 
 

 
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1. Web Portal for Data Collection: The secure web portal will be developed as a means for data 
coordination. National Lead Investigators/Country-leads will be asked to upload data via portal for 
transparency and auditing purposes.  
2. Data Validation: Cutting edge open source technologies will be used to Extract, Transform and Load (ETL) 
data onto the Global Data Warehouse. The extracted data will be of the highest quality, structurally valid, 
and contextually rich.  
3. Data Collation: The validated retrospective as well as prospective patient data (follow-up of patients) 
from different global sources will be merged within the central database. 
4. Data Analysis Tools: Widely accepted data analysis, data mining and reporting tools will be employed on 
the merged data to support an arbitrary number of queries and research questions. 
5. Web Portal for Reporting: A secure web portal will also be developed to generate various reports for 
stakeholders. The purpose of these reports is to highlight healthcare trends, limitations and 
improvement scope; only extractable from the merged central database. 
6. Data Storage: Raw data will be stored in unstructured database (NoSQL) specific to contributors and 
merged data will be stored in the structured format in relational database with technical capacity to 
handle a large number of participants with lifetime events history. 
7. Privacy Tool: To protect patient privacy, only anonymised data will be collected and aggregated, and a 
Global Unique Identifier (GUID) will be assigned to each patient’s data. The GUID approach enables 
researchers to follow patients over time and across different events, studies and countries.  
8. Data Management Workflow: An advanced workflow will be designed for automating the whole process 
of Data Management. The workflow will streamline the entire process all the way from designing data 
models, data collection, data validation, data harmonization, data transformation and data analysis and 
reporting. 
9. Workflow Monitoring: The monitoring application will help administrators to be involved in all stages of 
data management right from inception to completion; thus ensuring the quality standards are 
maintained and any deviations from protocols are immediately reported. The system will make use of 
existing standards for wider acceptance and interoperability. 
10. Logging Tool: The logging tool will log changes made to the collected data during various stages. The 
logging tool will help administrators to improve the Data Management process and in reverting data to 
the original state if there are conflicts or errors. The process logging system will also bring transparency 
for the confidence of national and international data contributors.  
 
Traditional data warehouses require metadata or a common vocabulary, which is already well defined and 
used in many other domains, such as banking or finance. The FHDW aims to define FH specific domain 
metadata in a similar fashion, based on common vocabularies and related standards in medicine. 
Furthermore, the elaborate structural meta‐information will be used to actively support the user in tasks that 
usually require significant IT knowledge, such as defining complex search queries or data quality constraints, 
or applying advanced data visualization algorithms to the data. The proposed warehouse supports the 
domain expert through the whole process of knowledge discovery from data integration to exploration.  
 
Data Extract, Transform and Load (ETL) 
Once the data is safely transferred to the Coordinating Centre through the EAS FHSC web portal and after 
ensuring satisfactory data quality, the information will be harmonised into the central FHDW. The FHDW will 
consolidate heterogeneous data from multiple sites through bespoke smart algorithms. The heterogeneity 
of source data models, data navigation, integrity, unit compatibility and attributes to uniform data definition 
language (DDL) are resolved by Extract, Transform and Load (ETL). The machine learning and expert system 

 
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based declarative rules will ensure highest quality, structurally valid, and contextually rich data. The FHDW 
will manage two types of data: core datasets and additional datasets, accommodating future requirements. 
The raw source data (including additional datasets) will be stored in an unstructured database (NoSQL) and 
merged data (core dataset) will be stored in the relational database with technical capacity to handle Big 
Data with lifetime events history. Internal partitioning of data in data marts will support an arbitrary number 
of queries and research questions; conceptual cubes and dimensions will be employed for complicated 
multifaceted queries. The research subject based data marts and cubes will optimize analysis, reporting and 
visualization of large global data. Data acquisition and workflow merging will be managed and monitored by 
an interactive dashboard to maintain the status of all key steps. 
Data servers physically hosted by ICL are encrypted to industry standards utilizing public–private key 
infrastructure. A Global Unique Identifier (GUID) will be assigned to each subject enabling investigators to 
follow their own subjects over time.  
The resource makes use of existing standards for wider user acceptance, technical interoperability and 
scalability. Regular surveys of the users’ (lead investigators’) experience will capture their expectations and 
obtain key suggestions and feedback, allowing appropriate adaptation and improvements of the 
user/resource interface, enhancing end-user experience for ease-of-use and efficient data management, 
allowing the resource to properly grow and evolve. 
Multi Stage Data Storage for enhanced Security 
The nature of data and research requires robust architecture for the data storage. The data will be stored 
physically on servers hosted by ICL with different access controls. The main architecture for the data 
collection is shown in the figure below: 
 
 
 
 
 
 
- 
Data Extraction: National Lead Investigators/Country-leads will extract data from their local data storage 
systems taking into consideration of the specific requirements/limitations locally. The FHDW technical 
team will provide reasonable support to lead Investigators to carry out this task. 
- 
Web Portal for Data Collection: National Lead Investigators/Country-leads will use the secure Web Portal 
to upload his/her copy of extracted data. The Web Portal communication will be based on Transport 
Layer Security (TLS 1.2) and 128 bits Advanced Encryption Standard (AES 128).  
- 
Temporary Data Server for Data Storage: The Web Portal will upload the data to the temporary server 
in the partitions specific to the corresponding National Lead Investigator/Country-lead. An event will be 
generated for the data to be moved to a more secure place once the data upload is complete. Once the 
data is copied to the next stage the data will be removed from Temporary Data Server. This is in line with 
standard practice. 
- 
Intermediary Data Server for Data Harmonisation: The uploaded data is immediately moved to the 
secure data server which has no public access. This data server contains the data ‘as is’ and is used as a 
holding area to determine whether data meets quality and protocol requirements. Entry into this layer 

 
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should be through a number of ‘data quality’ gateways to ensure that erroneous or missing data is 
rejected or handled appropriately. 
- 
Secure Data Server for Data Warehouse: The most secure data server will host the FHDW, with no 
external and public access. The communication with FHDW will only be via a secure messaging service 
and the Secure Data Server will be configured to read and write messages from/to the messaging service. 
This will contain a single table and a number of reference tables. Each related table may reference a 
number of additional tables. Data structured in this manner will be used to produce/populate Online 
Analytical Processing (OLAP) style cubes. 
- 
Application Server for Data Analysis and Reports: The application server will have various executable 
tools and scripts with artificial intelligence. The application server will request from the FHDW a subset 
of data via secure messaging service for relevant data. Once the requested data is received, it will be 
analysed and only analysis results and reports will be saved on the application server; the requested data 
will be discarded and deleted after successful processing.  
 
ANALYSIS PLAN 
Where possible and after ensuring consistency and compatibility of the information and description of the 
variables, the data from different providers will be merged and analysed at an individual level as a composite 
dataset. Where this is not possible, analyses will be carried out at a country/provider level, by analysing each 
dataset individually and then pooling the results together. 
The harmonised registry will allow us to conduct different types of analyses, such as verifying previous 
published findings with greater power and precision, conducting alternative analyses of the same data, novel 
new analyses, cross-cohorts comparisons, meta-analyses, analysing unpublished data (not previously 
conducted due to low sample size within each separate dataset), conducting exploratory analyses to generate 
new research hypothesis, etc. 
A number of research questions will be addressed, such as those shown in the table below:  
 
1 
How FH patients are detected, comparison of different proposed diagnostic criteria, whether current screening 
strategies for FH are adequate and, if not, what could be done differently to maximise coverage. 
 
2 
How patients are managed, treatments offered/advised, how their efficacy is monitored. 
 
3 
What proportion of FH patients meet the targets (e.g. LDL-C goals, patients receiving therapy), the 
impediments in attaining LDL-C goals, the role played by societal factors (such as access to healthcare in 
different settings and the availability of specialist advice) in enabling treatment to achieve LDL-C goal, the 
influence of gene–drug interactions in attaining LDL-C goals. 
 
4 
Long-term risk of outcomes in patients with FH (including, where possible, estimates of the years lost due to 
FH), with a special focus on the following end-points: 
 
Primary end-point 1: the composite of Cardiovascular Disease Events (fatal and non-fatal). 
 
Primary end-point 2: Cardiovascular mortality. 
 
Primary end-point 3: All-cause mortality. 
 
Secondary end-points:  
- 
Each component of the Cardiovascular Disease Events end-point separately. 
- 
Aortic valve and supravalvular disease. 
- 
Statin intolerance (clinical, biochemical). 
- 
New onset of diabetes. 
- 
Cancer diseases. 
- 
Pregnancy outcome in female patients with FH. 

 
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5 
To establish the value of incorporating genetic data and other factors (e.g. lipoprotein[a]) into the clinical 
diagnosis of FH and estimation of the risk of outcomes. 
 
6 
The impact of patient-specific factors, socio-economic factors and treatment-related factors on LDL-C goal 
attainment and cardiovascular risk. 
 
7 
Potential variations depending on different geographic settings as a result of factors such as population 
genetics, health care delivery systems and other patient-, socio-economic- or treatment-related factors. 
 
8 
Where possible, evidence for economic evaluation of different screening strategies and of interventions will be 
addressed. 
 
Standard validated statistical procedures and models for observational studies and weighted meta-analyses 
(individual participant data meta-analysis where possible; meta-analysis of aggregated data alternatively) will 
be applied. Statistical methods described in studies such as the Emerging Risk Factors Collaboration 
[Thompson S et al, Int J Epidemiol 2010;39(5):1345-59; Pennells L et al, Am J Epidemiol 2014;179(5):621-32] 
or the Non-Communicable Diseases Risk Factor Collaboration [NCD-RisC, Lancet 2016;387:1513-30] will be 
used where appropriate. After exploratory analyses with description of the variables of interest, cross-
sectional analyses and correlates will be performed. Comparisons of continuous parameters will be done 
with parametric and non-parametric tests as appropriate and categorical variables using Chi-squared test. 
We will estimate exposure–outcome associations (unadjusted and adjusted) and epidemiological 
interactions using standard regression models. Risk prediction models using measures of discrimination 
(concordance index [C-index], discrimination measure [D-measure]) and reclassification (net reclassification 
index [NRI]) are intended to be performed. Cox proportional hazards regression models stratified by certain 
variables of interest (e.g. by gender, country, etc.) and Kaplan–Meier estimates of survival will be generated 
where time-to-event data is available. Measures of heterogeneity and risk of bias (e.g. regression dilution 
over time) will be applied. 
We will account for different variables of interest, carry out appropriate adjustments systematically, consider 
key subgroups and correct for differences in definitions. Where feasible, reliable, and data available we will 
also try to roughly approximate the prevalence of FH by extrapolating the data to the general population. 
The different sources of incoming data will also help assess potential variations depending on different 
geographic settings, including factors such as population genetics, healthcare delivery systems and other 
patient-, socio-economic- or treatment-related factors.  
Particular emphasis will be placed on exploring differences between key subgroups including but not limited 
to the following: HoFH and HeFH; different genetic subtypes of FH (e.g. broadly whether due to mutations in 
the LDL-receptor, ApoB, PCSK9 or LDL-receptor adaptor protein pathways); diagnosis based on different 
diagnostic criteria; clinical vs. genetic diagnosis; FH treated by a generalist vs. specialist; FH with pre-existing 
CVD vs. those without; FH with a family history of premature CVD vs. those without; FH individuals who attain 
LDL-C targets on standard treatments vs. those who do not; geographical region; gender and ethnicity; age 
at baseline; age at first treatment initiation. 
The different sources of incoming data will help us assess potential variations depending on different 
geographic settings, including factors such as population genetics, health care delivery systems and other 
patient-, socio-economic- or treatment-related factors. 
Following a “database-query” model, investigators involved in the registry will be able to submit research 
questions they would like to evaluate through the Coordinating Centre; if the request is deemed to be 

 
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scientifically relevant by the Executive Committee and considered feasible by the Coordinating Centre, the 
latter will construct and run the analyses and return the results to the requester. 
 
STUDY OPERATIONAL/GOVERNANCE STRUCTURE 
Due to the complexity and scale of the project, to ensure the smooth operation within the wide EAS FHSC 
global network, the EAS FHSC is supported by an Executive and a Steering Committees, a number of National 
Lead Investigators/Country-leads and a Coordinating Centre.  
 
 
Executive Committee (EC): the EC consists of a panel of experts in the field of FH led by Prof. Kausik K. 
Ray (Imperial College London, UK) and it represents the academic leadership of the EAS FHSC. It is 
constituted by the following members: 
o Prof. Kausik K. Ray (UK) 
o Dr. Handrean Soran (UK) 
o Prof. John J. Kastelein (The Netherlands) 
o Prof. G. Kees Hovingh (The Netherlands) 
o Prof. Pedro Mata (Spain) 
o Prof. Gerald F. Watts (Australia) 
o Prof. Frederick Raal (South Africa) 
o Prof. Raul Santos (Brazil) 
o Prof. Alberico L. Catapano (Italy) 
o Dr. Tomas Freiberger (Czech Republic) 
The EC represents the core group for overarching management decisions, driving the collaboration, 
obtaining support and funding to ensure sustainability. The EC will evaluate all proposals for research, 
data mining, and expansion of the EAS FHSC. The EC are expected to play an active role in raising 
awareness nationally and internationally for the EAS FHSC and disseminating data that arises from this 
collaboration. The EC may delegate day-to-day tasks to the Coordinating Centre such as the day-to-day 
running  and decisions related to data collation, harmonisation and pooling. The EC will typically meet 
once per year; these meetings will be organised by the Coordinating Centre. More frequent interactions 
will be by teleconference, as necessary.  
 
 
Steering Committee (SC): the SC represents the advisory committee to provide guidance on the EAS FHSC 
project development. It consists of all those investigators/stakeholders/experts, usually appointed as EAS 
FHSC National Lead Investigators/Country-leads for their respective countries/regions.  
 
 
National Lead Investigators/Country-leads: lead investigators act as a national leader for the EAS FHSC 
within their respective countries or regions; as such they coordinate and are responsible for the 
identification and collaboration from the individual sites, promote participation of other national 
physicians and researchers in the collaboration, liaise with relevant societies and patient organizations, 
obtain appropriate local approvals and permissions as required, and gather the data from the respective 
region/country to be transferred to the Coordinating Centre. 
 
 
Coordinating Centre: The Coordinating Centre for the FH registry is based at the School of Public Health, 
Imperial College London, UK. It will act as a nodal point for data collation and management, 
standardisation, consolidation, analyses and queries, and will communicate, support and coordinate the 
activity of the investigators involved. The Coordinating Centre will ensure the availability and updating 

 
Page 14 of 18 
 
of various computational tools needed to collate, manage and share the data and will make use of 
analytical partners (e.g. informatics, biostatistics) to ensure the accurate generation of results. 
 
ETHICS, SECURITY AND REGULATORY CONSIDERATIONS 
- 
Major principles guiding the present project development include the added scientific and social value, 
rigorous adherence to local ethical standards, protection of patients’ data privacy and confidentiality, 
responsible sharing and use of the data, transparency in data management, and creating a secure 
workspace. This project and related research will be conducted in accordance with the principals of the 
Declaration of Helsinki (World Medical Association), EU Good Clinical Practice (GCP) and the Committee 
on Bioethics of the Council of Europe (DH-BIO). 
- 
If required, approval from the corresponding ethical and/or research committees will be requested by 
the investigators to collect, transfer and share the data. Where needed, informed consent from 
participants for his/her data to be shared and included in the registry will be requested at the local level 
by the corresponding investigator. These actions will be coordinated by and will be the responsibility of 
the local sites/investigators. Requirements in this respect will depend upon the respective 
institutional/country policies and regulations.  
- 
Data sharing agreements will be executed between the lead investigator sharing the data and the 
Coordinating Centre as the recipient of the data, specifying, amongst others, how the data are shared; 
under what conditions; data and results ownerships; recognition and acknowledgment of the work and 
intellectual contributions from the different parts according to their involvement; and restrictions on 
using the data for purposes other than those intended for the EAS FHSC project development (annex).  
- 
The analyses will be performed according to rigorous plans, making use of scientific, standardized and 
validated analytical methods, and carried out by investigators having a skill level appropriate for the tasks 
assigned to them.  
- 
Disclosures of conflicts of interests will be requested from all investigators involved in any report or 
publication. 
- 
The investigators and the Coordinating Centre will use all reasonable safeguards in connection with any 
transfer, communication or remote access connectivity involving the data. A secure web portal will be 
developed as a means of data entry/coordination and controlled environment. Lead investigators will 
upload data via the portal for transparency and auditing purposes.  
- 
Only de-identified, pseudo-anonymised data will be collected, and removal of evident identifiers such as 
names, health numbers, addresses, etc., replacement of dates with time intervals where possible, and 
categorization of characteristics such as ethnicity, education and others, will be carried out. Certain data 
such as age, gender or geographical location (e.g. country/region, rural/urban) will be requested as they 
are needed to maintain scientific value and utility of the present registry; appropriate judgement to 
protect participants’ privacy and prevent identification will be done in these cases. 
- 
The data shared will not be made publicly available, but limited to the outputs from the present project 
centralized at the Coordinating Centre. The combined data at the Coordinating Centre will not be checked 
against other external databases, thus preventing re-identification of participants. Information will not 
be available nor downloadable for any other part apart from specific scientific proposals agreed with 
steering group and conducted through the Coordinating Centre; it will not be disclosed/transferred to 
third parties; nor used for commercial purposes, although the outputs of the work will undoubtedly have 
implications for translational research and use of novel therapies for the pharmaceutical industry.  
 

 
Page 15 of 18 
 
Clinical data will be handled in compliance with various ethical standards. Collated patient details will be 
collected in multi-stage infrastructure as discussed earlier. The project partners will upload the data securely 
on the intermediary storage server through a proper SSL configuration. Access to the data sharing platform 
is encrypted using Transport Layer Security (TLS 1.2) and 128 bits Advanced Encryption Standard (AES 128). 
Uploaded data will be transferred to another secure server and will be stored in a password-protected, secure 
database for instance with no public access.  The database will be housed in an access-controlled server 
room, hosted by the Imperial College Information and Communications Technology department.  The 
database will be encrypted using the standard encryption methods, and will be physically stored across 
hundreds of disks in a storage area network – this reduces the possibility for needing to reconstruct the 
database should a drive be lost. The database will have no public access and will be configured to be accessed 
from approved terminals/computers via firewall configurations. 
The stored data will always incorporate the following components, security policies and procedures: 
authorization, authentication, availability, confidentiality, data integrity and non-repudiation. The methods 
available for authorization or access controls include single sign-on databases or lists assigning rights and 
privileges of users to access certain resources, automatic account logoff after a specified period of inactivity 
to prevent access by invalid users, and physical access controls. 
Access to the database will be restricted to named individuals in the Imperial College London project team, 
with read/write access to data elements defined as required for their role.  Access will be further restricted 
to specific computers on the Imperial College network by firewall configuration. The fool-proof storage 
mechanism will result in secured storage, manipulation, analysis and reporting of the data. The anonymised 
data securely held will only be shared according to the terms of consent under the data sharing agreement. 
The data storage and handling will be compliant with European Data Protection Act and local Data Protection 
Officers will be consulted in case of any doubt and ambiguity.   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
Page 16 of 18 
 
ANNEX 1 – POTENTIAL VARIABLES OF INTEREST FOR THE EAS FHSC REGISTRY  
Shown as a guidance. See corresponding section in the text for explanation. 
 
General 
Demographics 
Familial Hypercholesterolaemia 
Clinical history 
 
- 
Patient ID (code) 
- 
Country 
- 
Date of birth: month/year 
- 
Date study entry: month/year 
 
- 
Age 
- 
Gender 
- 
Ethnicity 
- 
Occupation 
- 
Education level 
- 
Geographical area: rural/urban 
 
- 
Date of diagnosis 
- 
Age at diagnosis 
- 
FH type: homozygous/heterozygous 
- 
Clinical criteria used for diagnosis: 
DLCN/MedPed/Simon-Broome/Japanese FH 
criteria/other (specify) 
- 
Clinical criteria score 
- 
Clinical criteria diagnosis: no 
diagnosis/possible/probable/confirmed  
- 
Consanguinity: yes/no 
- 
Index case: yes/no 
- 
Availability of family tree: yes/no 
- 
Family tree code 
 
 
- 
Hypertension: yes/no 
- 
Diabetes: type 1/type 2/other/no 
- 
Hypertriglyceridemia: yes/no 
- 
Smoker: current/former/never 
- 
Pack-years smoking: number of cigarettes 
smoked per day divided by 20 and then 
multiplied by the number of years smoked 
- 
Alcohol 
- 
Physical activity 
- 
CAD: yes/no 
- 
Premature CAD: yes/no [premature: male <55y, 
woman <60y] 
- 
MI: yes/no 
- 
Coronary revascularization: yes/no 
- 
Cerebral vascular disease: yes/no 
- 
Peripheral artery disease: yes/no 
- 
Aortic valve disease: yes/no 
- 
Supravalvular disease: yes/no 
- 
Premature non-coronary vascular disease: 
yes/no [premature: male <55y, woman <60y] 
- 
CKD: yes/no 
- 
CKD stage: KDOQI 
- 
Hepatic steatosis (suggested by US): yes/no 
- 
Achilles tendon lesions: tendinitis/injury-
surgery/no 
 
Family history 
Examination 
Genetic study 
 
- 
Family history of FH: yes/no 
- 
Family history of 
hypercholesterolaemia: yes/no 
- 
Family history of CAD: yes/no 
- 
Family history of other CVD (stroke, 
PAD): yes/no 
- 
1st degree relative premature CAD: 
yes/no [premature: male <55y, 
woman <60y] 
 
- 
Weight: kg 
- 
Height: cm 
- 
BMI: kg/m2 
- 
Waist circumference: cm 
- 
Corneal arcus: yes/no 
- 
Xanthomas: yes/no 
- 
Xanthelasma: yes/no 
- 
Systolic BP: mmHg 
- 
Diastolic BP: mmHg 
- 
Heart rate: bpm 
 
 
- 
Genetic study: positive/negative/not done 
- 
FH defect: heterozygous/compound 
heterozygous/homozygous/ARH 
- 
Mutation gene: 
LDLR/ApoB/PCSK9/LDLRAP1/Unknown 
- 
Mutation type: stop/insertion/deletion/ etc. 
- 
Mutation result: defective allele/null allele 
- 
Residual LDLR activity: %/unknown 
- 
Mutation: nomenclature, name 
 
 
 

 
Page 17 of 18 
 
Lipid-lowering therapy 
Lipoprotein apheresis (LA) 
Lab profile (fasting) 
Additional tests 
 
- 
Lipid-lowering therapy: 
no/monotherapy/combination 
therapy 
- 
Statin: yes/no 
- 
Date starting statin: month/year 
- 
Type of statin: 
simvastatin/pravastatin/lovastatin/ 
fluvastatin/atorvastatin/rosuvastatin/ 
pitavastatin 
- 
Dose of statin: mg/d 
- 
Statin intolerance: yes/no 
- 
Ezetimibe: yes/no 
- 
Fibrate: yes/no 
- 
Bile acid sequestrants: yes/no 
- 
N-3 fatty acids: yes/no 
- 
Niacin: yes/no 
- 
Sterols/stanols: yes/no 
- 
Lipoprotein apheresis: yes/no 
- 
PCSK9 inhibitor: yes/no 
- 
Lomitapide: yes/no 
- 
Mipomersen: yes/no 
- 
CEPT inhibitor: yes/no 
- 
Other: e.g. whether treatment has 
been continuous or intermittent, 
data/measurements of adherence to 
therapy, others specify 
 
 
- 
LA: yes/no 
- 
Indication: homozygous FH/severe 
heterozygous FH/other (specify) 
- 
Date starting: month/year 
- 
Pre-LA lipid profile 
- 
Pre-LA lipid-lowering drug 
- 
Post-LA lipid profile 
- 
Method of apheresis 
- 
System used 
- 
Calculated volume 
- 
Anticoagulation during apheresis 
- 
Place apheresis performed: 
lipoprotein apheresis unit/renal 
department/haematology 
department/other (specify) 
- 
Via: native veins/AV fistula/AV 
shunt 
- 
Lines: central lines/other lines 
- 
Duration episode of apheresis 
- 
Frequency of apheresis 
- 
Complications: no/yes (specify) 
 
- 
Total Cholesterol 
- 
LDL-C 
- 
HDL-C 
- 
Non-HDL-C 
- 
Triglycerides 
- 
Apolipoprotein A1 
- 
Apolipoprotein B 
- 
Lipoprotein (a) 
- 
Creatin kinase (CK) 
- 
Aspartate aminotransferase 
(AST) 
- 
Alanine aminotransferase 
(ALT) 
- 
C-reactive protein (CRP) 
- 
Glucose 
- 
Glycated haemoglobin 
(HbA1c) 
- 
Estimated glomerular 
filtration rate (eGFR): MDRD 
- 
Microlabuminuria 
 
- 
ECG: 
 
Rhythm: sinus rhythm/atrial fib/flutter/other 
 
LVH: (Sokolow-Lyon) 
 
Other disorder: specify 
- 
Echocardiography: 
 
LVEF: % 
 
LV hyperthrophy: yes/no 
 
Septum: mm 
 
Posterior wall: mm 
 
LV mass index: g/m2 
 
Abnormal wall motion: yes/no 
- 
Carotid Ultrasonography: 
 
Carotid plaque: yes/no 
 
Carotid (CCA) IMT: yes/no 
 
Carotid atherosclerotic stenosis: %/no 
- 
Liver Ultrasonography: Liver steatosis: yes/no 
- 
Renal arteries Ultrasonography: stenosis: yes/no 
- 
Lower extremity Ultrasonography: Stenosis: 
iliac/femoral/distal/multiple/no 
- 
Ankle-brachial index: Result 
- 
Coronary calcium score: 
 
Method: 
 
Score: 
- 
Coronary angiography/revascularization: 
 
Coronary angiography: no lesions/diffuse 
disease/focal stenosis 
 
If stenosis: <50%/≥50%/>70% 
 
Coronary arteries affected: 
none/LCA/LAD/LCx/RCA/several 
 
PCI: yes/no 
 
CAGB: yes/no 
- 
Other test available: Describe. E.g. MRI, etc. 
Other therapy 
Follow-up 
 
 
- 
Antiplatelet: yes/no 
- 
ACEI: yes/no 
- 
ARB: yes/no 
- 
B-blocker: yes/no 
- 
CCB: yes/no 
- 
Other BP-lowering drug: yes/no 
- 
Oral glucose-lowering drug: yes/no 
- 
Insulin: yes/no 
 
 
- 
Date FH diagnosis 
- 
Date of enrolment 
- 
Date of first data collection 
- 
Date of subsequent follow-up visits 
- 
Date of event 
 
- 
Number of visits 
- 
Changes in medication 
 

 
Page 18 of 18 
 
Outcomes / Events 
 
 
 
 
- 
Date of event 
- 
Age at event 
- 
Cardiovascular event 
 
Acute coronary syndrome: fatal/non-fatal 
 
Myocardial infarction: fatal/non-fatal 
 
Sudden cardiac death 
 
Stroke: fatal/non-fatal 
 
Transient ischemic attack 
 
Peripheral vascular disease 
 
Revascularization: percutaneous/surgical 
 
Heart failure 
 
Aortic valve/supravalvular disease 
- 
Death: Cause of death: 
 
CAD  
 
Sudden cardiac death 
 
Cerebrovascular disease: 
ischemic/haemorrhagic 
 
Heart failure 
 
Acute aortic syndrome 
 
Cancer 
 
Other (specify) 
- 
Statin intolerance: clinical/biochemical/both 
- 
New onset of diabetes 
- 
Cancer disease: type 
- 
Pregnancy outcomes: 
 
Maternal 
 
Obstetric 
 
Neonatal