MOLGENIS EMX2 database at http://catalogue.hdsu.nl
temporary schema for harvesting of hri ready production data
General information describing an endpoint
DataCatalogueFlat
Resource of data and/or samples. Use 'resource type' to specify
Definition of a sample set
Subpopulations defined for this resource
An organisation, department or individual
Listing of contact persons per resource
Definition of a dataset
External identifier(s) for this resource
Links between resources, e.g. data source and databank
Publications based on doi
Mapping from collected resources to standard/harmonised resources, optionally including ETL syntaxes
Number of participants per cohort age group, optionally divided per sex
Definition of a variable
Definition of a collection event for a resource
Documentation attached to a resource
Research organisations, departments and research groups
Listing of variables from another resource that are re-used by a resource
Listing of categorical value+label definition in case of a categorical variable
Mappings from collected or source datasets to harmonised or target datasets
Quantitative information on the resource
Internal identifier(s) for this resource
Mappings from collected or source variables to target or harmonised variables, optionally including ETL syntax
A unique proper name or character sequence that identifies this particular person. In Beacon v2: id, in FAIR Genomes: PersonalIdentifier. In RD3: subjectID.
This row is an instance of one or more of these class(es) other than the Endpoint table.
A name given to the resource.
An account of the resource version.
An account of the resource.
An entity responsible for making the resource available.
A language of the resource.
A legal document giving official permission to do something with the resource.
An established standard to which the described resource conforms.
Information about rights held in and over the resource.
Information about who access the resource or an indication of its security status.
Relevant contact information for the cataloged resource
A keyword or tag describing a resource.
A main category of the resource. A resource can have multiple themes.
A description of the service end-point, including its operations, parameters etc.
With the definition of the FDP as a type of metadata service that serves metadata catalogs, the relation between the Metadata Service and dcat:Catalog is represented by this predicate.
The relation should point to an URI that contains the version of the FDP specifications for which this instance of the FDP conforms to
Set to true if the resource onboarded to the EJP-RD Virtual Platform contains personal data, personal data meaning data related to identified or identifiable persons (as per GDPR definition), otherwise false. Must be String as workaround for current FDP reference implementation limitations.
This property must be attached to every entity that you wish the EJP-RD Virtual Platform to explore.
The DCAT type such as dcat:Catalog as computed field
FDP endpoint to associated with the catalogue
LDP membership relation
General information
Do you want your metadata to be compatible with HRICore v2.0.1, so that the metadata can be shared with the Dutch National Health Data Catalogue?
Internal identifier
Persistent identifier
Name used in international projects
If different from above, name in the national language
Acronym, if applicable
Type of resource, e.g. registry, cohort, biobank
If other, describe the type of resource
Type of catalogue
Type of cohort
Type of clinical study
Type of registry or health record
Type of network
Link to the website or homepage
Short description
Keywords to increase findability of this resource. Try to use words that are not used in the description. Keywords are not displayed on the resource page
Internal identifier(s) for this resource (e.g. UMCG register number)
External identifier(s) for this resource (e.g. EUPASS number or BBMRI persistent identifer)
The year when the resource started, e.g. when data or samples started to be collected or extracted, or when the resource was established
The year the resource ceased, e.g. when data or sample collection was stopped
Description of time span
Contact e-mail address
Logo of the resource, for use on homepages etc.
Status of the study
The specification of the catalog metadata schema
This relation points to the URI of the custom relation/predicate used to relate the FAIR Data Point with its member catalogs. The object of this property MUST be fdp-o:metadataCatalog.
When the data of the resource was first published
When the data of the resource was last modified
Structure of the resource
The study design of this cohort, i.e. cross-sectional or longitudinal
Short description of the study design of this resource, if applicable
A schematic depiction of the study design of this resource
Retrospective or prospective data collection
Describe the process of collection and recording of data
Description of the reason why the data bank is sustained by the organisation (e.g. for surveillance, clinical purposes, financial or administrative purposes, research purposes)
What triggers the creation of a record in the data bank? e.g. planned collection events, hospital discharge, specialist encounter, dispensation of a medicinal product, recording of a congenital anomaly
Based on the record trigger or promt, what is the unit(s) of observation of a record (e.g. person, prescription)?
List of cohorts or subpopulations defined in this resource, if applicable
List of data/sample collection events defined for this collection, if applicable
List of other data collections included in this network, e.g. cohorts, biobanks or real world data sources. Do not list other networks or catalogues here
List of other collections included in this resource
Description of the population that is sampled in this resource
Total number of individuals for which data is collected
Number of individuals for which samples are collected
Provide a summary description of the underlying population from which participants are sampled
Select population of interest
If population of interest is 'Other', please specify which other population has been studied
Countries where data/samples from this resource largely originate from
Geographical regions where data/samples from this resource largely originate from
Which population age groups are captured in this resource? Select all that are relevant
Minimum age of participants within this resource
Maximum age of participants within this resource
Inclusion criteria applied to the participants of this resource
Other inclusion criteria applied to the participants of this resource
Exclusion criteria applied to the participants of this resource
Other exclusion criteria applied to the participants of this resource
Select the possible causes / events that trigger the registration of a person in the data source
If other, specify the causes of entry to the underlying population
Select the possible causes / events that trigger the de-registration of a person in the data source
If other, specify the causes of exit from the underlying population
Is information on a specific disease subpopulation collected (e.g. as in a disease-specific registry or cohort)?
Is information on specific cancer subtype(s) collected? If yes, select topology specifications
Is information on specific cancer subtype(s) collected? If yes, select morphology specifications
Estimated percentage of the population covered by the data source in the catchment area. Please describe the denominator
Description of the population covered by the data source in the catchment area whose data are not collected, where applicable (e.g. people who are registered only for private care)
Counts of e.g. samples defined for this resource
List of relevant collaborations
Organisations involved in this resource and their role(s)
From organisations involved, point out the organisation that published this resource
From organisations involved, point out the organisation(s) that created this resource
Persons that contributed to this resource
From people involved, point out the primary contact point for this resource
List of networks, consortia or catalogues included in this network
The consortia or networks that this resource is part of
Data model and contents
List of datasets contained in this resouce if applicable
List of sample sets contained in this resource if applicable
Areas of information that were collected
If 'other', specify additional quality of life measures
If 'other', what cause of death vocabulary is used?
If 'other', what indication for use vocabulary is used?
If 'other', what genetic data vocabulary is used?
If 'other', description of the setting of care
If 'other', description of the medicinal product vocabulary
If 'other', what prescriptions vocabulary is used?
If 'other', what dispensings vocabulary is used?
If 'other', what procedures vocabulary is used?
If 'other', what biomarker data vocabulary is used?
diagnosis medical event vocabulary other
If 'other', what diagnosis medical event vocabulary is used?
Are a data dictionary and a data model available?
If data on a specific disease is collected, which diseases does the data source collect information on
If the data bank contains biospecimens, what types of specimen
Languages that the records are recorded in (ISO 639)
Can there be multiple entries for a single person in the data bank?
Data linkage
Is there a unique identifier for a person in the data bank?
Describe the variable that is used as a unique identifier for a person in the data bank. If the unique identifier is not at level of a person (for example hospital encounter), describe how this is translated to an individual level
Is the data from this data source linked to other data sources?
Linkage options with additional data resources that are available for this resource
Can this data source be linked to other data sources?
List of resources that are or can be linked to this main resource
Access and validation information
What type of informed consent was given for data resource?
Is informed consent required for use of the data for research purposes?
If 'other', describe the conditions when informed consent is required
Information that indicates whether the resource is publicly accessible, has access restrictions or is not public. A default is set for non-public access, because most health datasets contain sensitive information and rules and licenses apply to get access.
Codes defining data access terms and conditions
Codes defining data use terms and conditions
Description of data access terms and use conditions
Does a fee apply to gain access to data of this resource?
Can identifiable data be accessed in the data bank (including patient/practitioner name/practice name)?
If yes above, what is the route to access or process this information? What permission is required?
Can individual patients/practitioners/practices be contacted in the data bank?
If yes to the above, what is the route to access or process this information? What permission is required?
Can (an extract of) the data bank be accessed with permission by a third party?
If above is 'yes', describe the conditions under which third-party access may be granted
Can (an extract of) the data bank be accessed with permission by a non-EU/EEA institution?
If yes to the above, describe the conditions under which non-EU/EEA access may be granted
If the data bank contains biospecimens (e.g. tissue samples), can these be retrieved?
If yes to the above, describe the conditions under which permission to retrieve biospecimens may be granted
If available, provide a link to documents or webpages that describe the overall governance of the data source bank (governing data access or utilisation for research purposes by existing DAPs)
Is an approval needed to publish the results of a study using the data
Information on the regularity of updates and time lags
Select whether this resource is a closed dataset or whether new data is released continuously or at a termly basis
Description of the release cycle of this resource
Total number of unique records captured in the data bank (most recent count)
Refreshing rate (in months)
Average number of days between refresh of data bank with new records
How many days is the lag time after refresh before a record can be extracted? (e.g. a lag time may occur if the originator conducts quality checks)
If data are refreshed on fixed dates (e.g. every June and December), when are the refreshes scheduled? Select all that apply from the following:
Date of last update/refresh
Are records preserved in the data bank indefinitely?
If 'no' to the above, for how long (in years) are records preserved in the data bank?
List of relevant studies conducted using the data bank
Is there a standard operating procedure document that defines the processes and procedures for data capture and management?
Has the data source successfully undergone a formal qualification process (e.g. from the EMA, ISO or other certifications)?
Has the data source successfully undergone a qualification process (e.g. from the EMA)? If yes, describe the qualification(s) granted
Are external parties allowed to audit the data? For example, is it possible for an external party to audit the quality or validity of the data source?
Describe the completeness of the data bank (e.g. variables with more or fewer missing values)
Describe any changes in completeness of the data bank (e.g. variables with more or fewer missing values) that have occurred over time
What methods or processes are applied to check completeness of the data bank?
Describe the quality of the data bank (e.g. variables with more or fewer missing values)
Describe any changes in quality of the data bank that have occurred over time
Can validity of the data in the data bank be verified, e.g. by review of origin medical charts?
How often are data quality checks and validation steps conducted on the data bank?
What methods or processes are applied for data quality checks and validation steps conducted on the data bank?
What methods or processes are applied to correct illogical values in the data bank?
If available, provide a link to a publication of the data quality check and validation results
Use of standard data models and ontologies
Overview of dataset mappings available
If not in list above, give the name of cdm(s) used by this data source
Are data mapped to standardised vocabularies during ETL to the CDM? If yes, what vocabularies are used for events, such as diagnoses?
If other, what other vocabularies are used?
Other information
Publication(s) about this resource
Specify the main financial support sources for the data source in the last three years. Select all that apply
The source of funding for the study. Select all that apply
Statement listing funding that was obtained for this resource
Citation regulation for this resource
Acknowledgement statement and citation regulation for this resource
A statement of any changes in ownership and custody of a resource since its creation that are significant for its authenticity, integrity, and interpretation
Descriptive document(s) available for this resource, e.g. informed consent
Any other information that needs to be disclosed for this resource
A main category of the resource. A resource can have multiple themes, for health data theme must at least take the value 'Health', which is set as a default
The legislation that mandates the creation or management of the resource. For health datasets, the value must include the data governance act, which is set as a default. URI's to other legislations may be included
When was the data collection planned to start?
When was the data collection actually start?
When was the data analysis planned to start?
When did the data analysis actually start?
Data sources that provided data into this study
Codes of the medical conditions studied
Date on which the study data was extracted
Methodological aspects
A brief summary of the analysis method (e.g. risk estimation, measures of risk, internal/external validity)
A short description of the study objective
A brief summary of the results of the study on study completion (e.g. from abstract)
Resouce that these samples are part of
Sample set name
Defines the types of samples in the sample set
Resource this subpopulation is part of
Subpopulation name, e.g. 'mothers in first trimester','newborns'
Persistent identifier for this subpopulation
Subpopulation description
Keywords to increase findability of this subpopulation. Try to use words that are not used in the description. Keywords are not displayed on the subpopulation page
Number of participants in this subpopulation
Total number of unique individuals per age(group), gender and year
Year of first included participant
Year of last included participant. Leave empty if data collection is ongoing
Age groups within this subpopulation
Minimum age of participants within this subpopulation
Maximum age of participants within this subpopulation
Disease groups within this subpopulation, based on ICD-10 and ORPHA code classifications
Comorbidity within this subpopulation, based on ICD-10 classification
Countries where data from this subpopulation largely originate from
Geographical regions where data from this subpopulation largely originate from
Inclusion criteria applied to this subpopulation
Other inclusion criteria applied to this subpopulation
Exclusion criteria applied to this subpopulation
Other exclusion criteria applied to this subpopulation
Contact point for this subpopulation. Value copied from Resources table
Organisation that published this subpopulation. Value copied from Resources table
Organisation(s) that created this subpopulation. Value copied from Resources table
When the data for this subpopulation was first published
When the data for this subpopulation was last modified
A main category of the subpopulation. A subpopulation can have multiple themes, for health data theme must at least take the value 'Health', which is set as a default
Information that indicates whether the resource is publicly accessible, has access restrictions or is not public. A default is set for non-public access, because most health datasets contain sensitive information and rules and licenses apply to get access
The legislation that mandates the creation or management of the subpopulation. For health datasets, the value must include the data governance act, which is set as a default. URI's to other legislations may be included
Resource this Agent is associated with
Internal identifier. Ideally fill out acronym for an organisation (e.g. UMCG) and full name for a person (e.g. Ada Lovelace)"
Type of agent: person or organisation
Name of the individual
Organisation name from Research Organization Registry (ROR).
If your organisation is not listed, please look up in ROR: https://ror.org/ and preferably provide ROR id, otherwise fill out your organisation name.
Name of department within the organisation
A website representing this Agent
E-mail address to contact this organisation or individual
Logo of the organisation
Type(s) of contribution or role in the resource
Organisation name, copied value from ROR ontology for DCAT conform RDF generation
ROR id, copied value from ROR ontology for DCAT conform RDF generation
Organisation website, copied value from ROR ontology for DCAT conform RDF generation
Resource the contact is affiliated with
Description of the role
First name of the contact person
Last name of the contact person
Display name of the contact person
Surname prefix, if applicable
Initials of the contact person
Title of the contact person
Affiliated organisation of the contact person
Contact's email address
Orcid of the contact person
Link to contact's homepage
Contact's photograph
Description of contact's expertise
Resource of which the dataset is part
Unique dataset name in the model
Short human readable description
Defines the type of dataset, e.g. genomic
Defines what each record in this table describes
Enables grouping of table list by topic and to display tables in a tree
Description of the role/function of this table
Count of the number of records in this table
Common dataset models this dataset has been mapped into
Source datasets that have been mapped to this harmonised dataset
When this dataset was introduced, if applicable
When this dataset was removed, if applicable
Resource that this external identifier belongs to
External identifier
External identifier type
If 'other', enter external identifier type
Main resource for this linkage
Linked resource
The linkage method that was used to link resources
If a single variable (or linkage key) is used to link resources, provide a name and description of the variable
If a single variable is used to link resources, is the variable a unique identifier?
Provide a high-level description of the completeness of linkages that are currently available between resources (max 100 words)
Does the data source constitute of linked data sources?
Resource that this publication is part of
Digital object identifier
Publication title
Whether this publication is a marker or design publication
Formatted reference string (we aim to have this autogenerated)
Data resource that is being mapped to a (common) data model
Version of the resource that is being mapped
Model being mapped to
Version of the model being mapped to
If cdm is not under targets, which cdm was mapped to? (default = not applicable)
Mapping status
The frequency of the ETL process, in months
Link to ETL specification documentation
Documentation concerning ETL details
Resource id
Subpopulations for these counts
Select the age group for these counts
Total number
Number of females
Number of males
Resource that this variable is part of
Dataset that this variable is part of
Name of the variable, unique within a dataset
Refer to variable definition in other datasets
Human readable name, if applicable
Description of the variable
In case of protocolised data collection this defines the moment(s) in time this variable is collected on
Data type, e.g. string, integer, decimal, date, datetime, etc.
In case of a continuous variable, the unit that the variable values are expressed in
When this variable was introduced
When this variable was removed, if applicable
Listing of variables from other resources that are reused by this resource
The repeat unit, in case the variable was repeatedly collected over time, e.g. month or week
The first repeat unit
The last repeat unit
Example values for this variable
Listing of variable values, filled out in Variable values table
Keywords that will make the variable easier to find
Vocabularies used to express the values of this variable
Any other information on this variable
Listing of harmonised variables this variable was mapped to, filled out in Variable mappings table
Resource this collection event is part of
Name of the collection event
Persistent identifier for this collection event
Description of the collection event
Subpopulations that are targetted by this collection event
Keywords to increase findability of this collection event. Try to use words that are not used in the description. Keywords are not displayed on the collection event page
Start date of data collection
End date of data collection. Leave empty if collection is ongoing
Age groups included in this data collection event
Number of participants sampled in this data collection event
Areas of information that were extracted in this data collection event
Methods of data collection used in this collection event
Samples that were collected in this collection event
Standardized tools, e.g. surveys, questionnaires, instruments used to collect data for this collection event
If 'other', please specify
Name 10-20 relevant variables that were collected in this collection event
Contact point for this collection event. Value copied from Resources table
Organisation that published this collection event. Value copied from Resources table
Organisation(s) that created this collection event. Value copied from Resources table
When the data for this collection event was first published
When the data for this collection event was last modified
A main category of the collection event. A collection event can have multiple themes, for health data theme must at least take the value 'Health', which is set as a default
The legislation that mandates the creation or management of the collection event. For health datasets, the value must include the data governance act, which is set as a default. URI's to other legislations may be included
The resource this documentation is for
Document name
Type of documentation
Description of the document
Hyperlink to the source of the documentation
Optional file attachment containing the documentation
Whether the organisation is the lead organisation of this resource
Resource reusing the variable
Variable from another resource that is reused
Resource the variable belongs to
Dataset the variable belongs to
Variable name
Variable value, e.g. '1'
Variable label, e.g. 'Yes'
Order that the variable values should be displayed in
Whether this variable value indicates that data is missing, e.g. '-1' for 'NA'
Reference to ontology term that defines this categorical value
When this variable value was introduced, if applicable
When this variable value was removed, if applicable
Id of the resource being mapped from
Name of the dataset being mapped from
Network being mapped to
Name of the dataset being mapped to, or harmonised with
Order in which table ETLs should be executed for this source-target combination
Human readible description of the mapping
Formal definition of the mapping, ideally in executable code
Select the relevant age group for this quantitative information
Total number of unique individuals with records captured in the data source (most recent count). In the catalogue, this will accommodate counts per year
Number of unique, active, or currently registered individuals with records captured in the data source (most recent count). In the catalogue, this will accommodate counts per year
Number of unique individuals with records of biological samples (e.g., blood, urine) (most recent count). In the catalogue, this will accommodate counts per year
Median years for which unique individuals with records captured in the data source are observable (most recent count)
Median time for which unique individuals with records captured in the data source are observable (most recent count)
Median age of individuals within data source
Proportion of females in the data source
Resource that this internal identifier belongs to
Internal identifier type
If 'other', enter internal identifier type
Id of the resource that contains the source variable(s)
Name of the dataset that contains the source variable(s)
Optional, source variable that was mapped from. You may also indicate that a mapping to a target variable was not done and leave this field empty (match = not available)
Optional, variables from other source datasets. Initially one may only define mapping between releases
Id of the resource that contains the target variable
Name of the dataset that contains the target variable
Target variable that was mapped to
In case of mapping to a repeated variable, specify which repeats were mapped to
Harmonisation match, i.e., complete, partial, na
Additional notes or comments
This example MOLGENIS EMX2 multi-interoperable FAIR Data Point is based on the FAIR Data Point 1.2 specification but includes extension points to support additional DCAT flavours including EJP RD Virtual Platform level 1 metadata, HealthRI core plateau 1 metadata, and so on.
Protocol ALL 10, protocol voor diagnostiek en behandeling van kinderen en adolescenten (1-19 jaar) met Acute Lymfatische Leukemie (ALL)
The aim of Protocol ALL-10 is to improve the overall treatment results in terms of EFS and to decrease treatment intensity in patients with a pEFS of >95%. Therapy will be tailored according to the risk of relapse as determined by the MRD levels at 2 time points during the first months of therapy. This tailored therapy should lead to less intensive therapy for patients with low risk of relapse and to intensification of therapy for patients with medium and high risk of relapse.
The BioDay registry is a disease or condition registry for atopic dermatitis (AD)/prurigo nodularis (PN). All patients treated with advanced systemic therapy (biologics and JAK inhibitors [JAKi]) for AD are eligible for inclusion in the prospective, multicenter, observational BioDay registry. Patients are treated in a daily practice setting and the choice of treatment is determined by shared decision making and according to the Dutch AD guidelines. During all visits both clinical outcomes (e.g. EASI, IGA) and patient reported outcomes (e.g. NRS itch, DLQI,ADCT/RECAP) are collected.
Multicenter prospective observational atopic dermatitis registry
Able and willing to sign informed consent form
Possible approval after data access request
The BioDay Registry is sponsored by pharmaceutical companies of the biologicals and JAKi for AD (Sanofi Genzyme/Regeneron, Leo Pharma, Pfizer and Eli Lilly). These commercial sponsors do not have any influence on the collection and analyses of the data in the register.
Virus Interactions in the Respiratory Tract; a Cohort Study With Children (VIOOL)
The VIOOL study is a prospective observational cohort conducted across three winter seasons (2021–2024; October–April/May) in the Netherlands. Children under 4 years of age were recruited from the community and followed for 16 weeks. Parents recorded daily symptom diaries and collected weekly nasal swabs, regardless of symptoms. Swabs were analyzed by PCR for a broad panel of common respiratory viruses. Its primary objective is to examine interactions between respiratory virus infections in young children. The study was approved by the Medical Ethics Committee of the University Medical Centre Utrecht (UMCU) and registered in the Clinical Trials Register (NL78424.041.21).
Attending childcare for at least two half-days per week and/or having an older sibling
Known immunodeficiency Chronic lung disease associated with increased infection susceptibility Use of antibiotic prophylaxis for recurrent respiratory tract infections Congenital anomalies of the airways
Funded by ZonMw (09150171910098) and the European Union through the VERDI project (101045989).
PROTOCOL ALL 11: Treatment study protocol of the Dutch Childhood Oncology Group for Children and adolescents (1-19 year) with newly diagnosed acute lymphoblastic leukemia
1. To improve the overall outcome as compared to the previous protocols of the DCOG, especially ALL-9 and ALL-10. This is aimed for by decreasing therapy for part of the patients (TEL/AML1, Down syndrome, PPR only), increasing therapy for IKZF1 mutated cases, decreasing the cumulative dose of anthracyclines, omitting cranial irradiation and total body irradiation and individualizing asparaginase therapy for all patients. 2. Does a continuous schedule of Asparaginase lead to less allergic reaction/inactivation of Asparaginase than the standard non continuous schedule of Asparaginase? Patients are randomized to receive noncontinuous PEGasparaginase in IA (induction) and intensification of the Medium Risk group (standard arm A) or to receive continuous PEGasparaginase in IA, IB, M and intensification (continuous arm B) with the same cumulative number of doses of PEGasparaginase. 3. Does prophylactic administration of intravenous immunoglobulins reduce the number of infections during the intensive treatment phases? Patients are randomized in the induction and MR treatment group to receive or not receive prophylactic immunoglobulins. 4. Individualize the dose schedule of asparaginase by therapeutic drug monitoring in order to detect silent inactivation of asparaginase, to prevent allergic/anaphylactic reactions, to switch Asparaginase preparation in time and to prevent too high levels with possible toxicity.
Twin cohort for the study of (pre)clinical Inflammatory Bowel Disease in the Netherlands: The TWIN-study
Inflammatory Bowel Diseases (IBD), i.e. Crohn’s Disease (CD) and Ulcerative Colitis (UC), are thought to arise in genetically susceptible individuals in the context of environmental triggers, with a potential dominant role for the interplay between the gut microbiota, and the mucosal immune system. However, the relative importance and the exact role of these factors in the pathogenesis of IBD is presently unknown. Interpretation of published research in this field is often hampered by reverse causation, and data generated in animal models cannot be directly extrapolated to the human condition. The disease is probably triggered years before the occurrence of symptoms, but currently patients are only identified when clinical disease is established. The preclinical phase of IBD might hold the key to understanding the pathogenesis of IBD and could provide a huge window of opportunity of halting or even preventing disease development. At this time, data on this phase of the disease are limited. Identifying and validating biomarkers and pathophysiological mechanisms is one of the necessary steps toward prediction, prevention and optimalization of therapy in IBD care. Unaffected twin-siblings of IBD affected individuals are at increased risk of developing IBD. Therefore, studying IBD-discordant, IBD-concordant and non-IBD-concordant twins or multiples gives the unique opportunity to 1) define mechanisms that underlie (the early development of) IBD and 2) identify and validate markers of (pre)clinical IBD.
Observational study with both a cross-sectional and longitudinal design
IBD concordant twins or multiples, IBD disconcordant twins or multiples, IBD unaffected controls, preferably twins or multiples
Not being able to understand the Dutch language
European Prospective Investigation Into Cancer and Nutrition in the Netherlands (EPIC-NL)
EPIC is the European Prospective Investigation Into Cancer and Nutrition. EPIC-NL is the Dutch contribution to EPIC and comprises about 40,000 individuals prospectively followed since the early 90's. Biological samples have been stored in liquid nitrogen and repeated follow-up of the cohort has occured to obtain detailed information on lifestyle and behavioural factors and morbidity and mortality.
At baseline, the prevalence of risk factors for chronic diseases was determined using self-administered questionnaires and a physical examination. Participants are followed up over time for changes in exposure and for the occurrence of cancer, diabetes, cardiovascular diseases, and other medical conditions. Subgroups of participants were invited for several follow-up rounds.
CBS (national bureau of statistics); IKNL-NKR (Cancer registration); DHD (Dutch Hospital Discharge diagnosis); GBA (municipal registry for current address and vital status)
A data request is judged by the scientific steering committee. The application form can be found in the documents. The EPIC-NL steering committee is not responsible for further legal procedures and researchers need to make a DMP and n-WMO request in addition to the request at the EPIC-NL steering committee after approval.
In principle data will be made available in a secure data analysis environment after signing a data sharing agreement. nWMO testing and the development of a datamanagementplan need to be part of the project and are the responsibility of the researcher.
The EPIC-NL study was funded by ‘Europe against Cancer’ Programme of the European Commission (DG SANCO); the Dutch Ministry of Health, Welfare and Sports (VWS); the Netherlands Organisation for Health Research and Development (ZonMW); and World Cancer Research Fund (WCRF).
We thank GBA, Statistics Netherlands, Dutch Cancer Registry and the institute PHARMO for providing data on vital status, cause of death and occurrence of cancer and other chronic diseases.
MARS - Molecular Diagnosis and Risk Stratification of Sepsis
MARS is a multicenter prospective cohort of ICU patients aimed at developing new molecular technologies for early diagnosis and risk stratification of sepsis. MARS contains comprehensive clinical data (including admission diagnoses, comorbidities, daily treatments, medications, vital-signs, labs, and microbiology results). In addition, daily prospective adjudication of infection plausibility was performed, according to standardized definitions.
Ongoing prospective cohort and biobank of ICU patients admitted to the UMC Utrecht since 2011. The AMC was a participating center between 2011 and 2013.
ICU admitted patients, with exception of elective cardiothoracic surgery patients.
The questionnaires sent out at 1 year after ICU discharge can be linked to the MARS database. These questionnaires contain data on quality of life and mental well-being at 1 year. Upon request, the MARS database can be enriched with additional healthcare data from the EPD.
Data and samples are accessible after permission of the principal investigator of the MARS cohort. In general, ethics approval is required for use of biobank samples (TCBio approval).
The MARS cohort received a grant from the Center for Translational Molecular Medicine (http://www.ctmm.nl) project MARS (grant 04I-201).
'Utrecht cohort for Multiple BReast cancer intErvention studies and Long-term evaluation' cohort
The prospective, multicenter ‘Utrecht cohort for Multiple BREast cancer intervention studies and Long-term evaluAtion’ (UMBRELLA) cohort, established in 2013, serves as an infrastructure for conducting randomized controlled trials to evaluate effectiveness of (innovative) cancer treatments and survivorship care for breast cancer patients and survivors. UMBRELLA includes patients with histologically proven invasive breast cancer or ductal carcinoma in situ (DCIS) referred for breast cancer treatment in multiple hospitals in the Netherlands. All UMBRELLA cohort participants consent for the collection and use of their clinical data. In addition, cohort participants can also provide consent for the longitudinal collection and use of patient-reported outcomes up to 10 years after enrolment. Last, cohort participants are also asked to provide consent to be randomized into future cohort-based trials following the Trial within Cohorts design. When conducting a cohort-based trial, all UMBRELLA cohort participants are screened for eligibility. Cohort participants assigned to the intervention arm, are offered the intervention, whereas cohort participants assigned to the control arm are not informed about the intervention and remain unaware of the trial. By combining clinical data with (long-term) patient-reported outcomes from a large prospective cohort of breast cancer patients and survivors, it is possible to effectively study a diversity of clinically relevant questions to gain further insights in the effects of breast cancer treatment and its impact on quality of life as well as to evaluate effectiveness of interventions within the breast cancer field.
Breast cancer, prospective cohort, multicenter, quality of life
All UMBRELLA cohort participants consent for the collection and use of their clinical data. In addition, cohort participants can also provide consent for the longitudinal collection and use of patient-reported outcomes up to 10 years after enrolment. Last, cohort participants are also asked to provide consent to be randomized into future cohort-based trials following the Trial within Cohorts design. When conducting a cohort-based trial, all UMBRELLA cohort participants are screened for eligibility. Cohort participants assigned to the intervention arm, are offered the intervention, whereas cohort participants assigned to the control arm are not informed about the intervention and remain unaware of the trial.
Invasive breast cancer or ductal carcinoma in situ
Patients with limited understanding of the Dutch language and patients under the age of 18 years are ineligible.
Netherlands Cancer Registry (NCR) Pathologisch Anatomisch Landelijk Geautomatiseerd Archief (PALGA)
Athero- and Aneurysm-Express Biobank Studies
The Athero-Express Biobank Study (AE) and the Abdominal Aortic Aneurysm-Express Biobank Study (AAA) are two ongoing, prospective biobank studies initiated in 2002 and 2003, respectively. Together, they comprise detailed histological and clinical data from over 4,500 patients who underwent arterial surgery at two Dutch tertiary referral centers. The AE Biobank includes over 3,500 patients who underwent endarterectomy to remove atherosclerotic plaques, predominantly from the carotid and femoral arteries. The AAA Biobank includes over 1,000 patients who underwent open surgical repair of arterial aneurysms, primarily abdominal aortic aneurysms. For both studies, blood and vascular tissue (plaque or aneurysm wall) were collected during surgery and stored at −80℃. Tissue material is routinely processed for standardised (immuno)histochemical analysis, with histological observations performed by the same dedicated technician, ensuring consistency; interobserver analyses have been previously reported. In AE, the following plaque characteristics were quantified or scored: • Macrophages (CD68) and smooth muscle cells (α-actin) as the percentage of field area using computerised analysis. • Intraplaque vessel density (CD34) as the average count per 3 hotspots. • Intraplaque haemorrhage (IPH) was scored as absent/present using hematoxylin and eosin (HE). • Intraplaque fat was defined as < or ≥40% of total plaque area (HE). • Calcification (HE) and collagen content (picrosirius red) were scored as no/minor vs. moderate/heavy staining. • Overall plaque vulnerability was assessed following the method by Verhoeven et al. In AAA, tissue sections were stained using: • HE, EVG (elastin), picrosirius red (collagen), SMA (smooth muscle cells), CD68 (macrophages), CD3 (T cells), CD20 (B cells), CD138 (plasma cells), and von Willebrand factor (vasa vasorum). • Each marker was semiquantitatively scored from 0 to 3 (0: none, 1: minor, 2: moderate, 3: heavy), separately for the intima, media, and adventitia, across both structural components and immune cell types. For both cohorts, clinical data is obtained through standardised questionnaires, and supplemented with electronic health records where necessary. Ethical approval for this study (TME/C-01.18) was provided by the Medical Research Ethics Committee of University Medical Center Utrecht, Utrecht, The Netherlands on 10 April 2002, and all research was conducted according to the principles of the Declaration of Helsinki (59th amendment, Seoul 2008) and in accordance with the Dutch Medical Research Involving Human Subjects Act (WMO).
The Athero-Express Biobank Study (AE) is a longitudinal cross-sectional biobank study collecting blood and atherosclerotic plaque samples from patients undergoing endarterectomy. Clinical data is collected through standardised questionnaires and supplemented by querying electronic health records when needed. Patients are followed up to 3 years after surgery.
Possibility to link with data from RIVM and CBS.
Welkom bij Health Data Space Utrecht, het portaal dat de regio Utrecht verbindt door onderzoekers en professionals samen te brengen om kennis en data te delen.
MARS - Molecular Diagnosis and Risk Stratification of Sepsis MARS - Molecular Diagnosis and Risk Stratification of Sepsis MARS-1
MARS - Molecular Diagnosis and Risk Stratification of Sepsis MARS-1
Multi-center phase of the MARS cohort between 2011-2013 in the UMCU and AMC hospitals.
Consecutive adult intensive care patients.
Elective cardiothoracic surgery patients.
Athero-Express Athero-Express AAA-Express
Patients who underwent open surgery for aneurysms. The majority is operated for abdominal aortic aneurysm (AAA) but some are from popliteal, carotid, femoral, etc. see also: https://doi.org/10.34894/AUUSS2
see also: https://doi.org/10.34894/4IKE3T
MARS - Molecular Diagnosis and Risk Stratification of Sepsis MARS - Molecular Diagnosis and Risk Stratification of Sepsis MARS-2B
MARS - Molecular Diagnosis and Risk Stratification of Sepsis MARS-2B
Mono-center phase of the MARS cohort between 2020-2022 in the UMCU hospital.
Consecutive adult intensive care patients with an expected ICU length of stay of more than 48 hours.
MARS - Molecular Diagnosis and Risk Stratification of Sepsis MARS - Molecular Diagnosis and Risk Stratification of Sepsis MARS-3
MARS - Molecular Diagnosis and Risk Stratification of Sepsis MARS-3
Mono-center phase of the MARS cohort between 2023 and present in the UMCU hospital.
women aged 49–70 years from the breast cancer screening programme in Utrecht and its surroundings
Patients with invasive breast cancer and ductal carcinoma in situ (DCIS).
A randomly selected sample of men and women from the Dutch population aged 20–59 years in three cities in the Netherlands (Amsterdam, Doetinchem, and Maastricht).
MARS - Molecular Diagnosis and Risk Stratification of Sepsis MARS - Molecular Diagnosis and Risk Stratification of Sepsis MARS-2A
MARS - Molecular Diagnosis and Risk Stratification of Sepsis MARS-2A
Mono-center phase of the MARS cohort between 2013-2022 in the UMCU hospital.
MARS - Molecular Diagnosis and Risk Stratification of Sepsis Udo Roorda
MARS - Molecular Diagnosis and Risk Stratification of Sepsis Marc Bonten
MARS - Molecular Diagnosis and Risk Stratification of Sepsis Olaf Cremer
Successful Therapy Reduction and Intensification for Childhood Acute Lymphoblastic Leukemia Based on Minimal Residual Disease Monitoring: Study ALL10 From the Dutch Childhood Oncology Group
ALL11 https://ascopubs.org/doi/full/10.1200/JCO.22.02705
Improved Outcome for ALL by Prolonging Therapy for IKZF1 Deletion and Decreasing Therapy for Other Risk Groups
Immunoglobulin prophylaxis prevents hospital admissions for fever in pediatric acute lymphoblastic leukemia: results of a multicenter randomized trial
ALL11 https://ascopubs.org/doi/full/10.1200/JCO.23.01797
Continuous PEGasparaginase Dosing Reduces Hypersensitivity Reactions in Pediatric ALL: A Dutch Childhood Oncology Group ALL11 Randomized Trial
Athero-express: differential atherosclerotic plaque expression of mRNA and protein in relation to cardiovascular events and patient characteristics. Rationale and design
Athero-Express https://doi.org/10.1159/000318160
Aneurysm-express: human abdominal aortic aneurysm wall expression in relation to heterogeneity and vascular events - rationale and design
BioDay https://doi.org/10.1001/jamadermatol.2023.3849
Dupilumab-Associated Lymphoid Reactions in Patients With Atopic Dermatitis
BioDay https://doi.org/10.1093/bjd/ljad159
Successful tapering of dupilumab in patients with atopic dermatitis with low disease activity: a large pragmatic daily practice study from the BioDay registry
BioDay https://doi.org/10.1111/cea.14296
Muscle and joint pain during dupilumab treatment for atopic dermatitis: Lack of association with antinuclear antibodies
BioDay https://doi.org/10.2340/actadv.v104.19454
Real-world Experience of Abrocitinib Treatment in Patients with Atopic Dermatitis and Hand Eczema: Up to 28-week Results from the BioDay Registry
BioDay https://doi.org/10.2340/actadv.v103.5243
Effectiveness of Upadacitinib in Patients with Atopic Dermatitis including those with Inadequate Response to Dupilumab and/or Baricitinib: Results from the BioDay Registry
BioDay https://doi.org/10.1002%2Fclt2.12219
The positive effect of dupilumab on comorbid asthma in patients with atopic dermatitis
BioDay https://www.sciencedirect.com/science/article/pii/S258975002300198X?via%3Dihub
Early qualitative and quantitative amplitude-integrated electroencephalogram and raw electroencephalogram for predicting long-term neurodevelopmental outcomes in extremely preterm infants in the Netherlands: a 10-year cohort study
BioDay https://doi.org/10.1111/pai.13887
Dupilumab in daily practice for the treatment of pediatric atopic dermatitis: 28-week clinical and biomarker results from the BioDay registry
BioDay https://doi.org/10.2340/actadv.v102.3978
Daily Practice Experience of Baricitinib Treatment for Patients with Difficult-to-Treat Atopic Dermatitis: Results from the BioDay Registry
BioDay https://doi.org/10.1001/jamadermatol.2022.3014
Dupilumab Drug Survival and Associated Predictors in Patients With Moderate to Severe Atopic Dermatitis Long-term Results From the Daily Practice BioDay Registry
BioDay https://doi.org/10.1111/pai.70040
Ocular surface disease in pediatric patients with moderate-to- severe atopic dermatitis
BioDay https://doi.org/10.1111/cea.14637
Increased Levels of Inflammatory Proteins, Including TARC/CCL17, in Skin of AD Patients During JAK Inhibitor Treatment
Dupilumab provides sustained effectiveness on patient-reported outcomes and favorable safety in patients with moderate-to-severe atopic dermatitis: Up to 5-year results from the daily practice BioDay registry
BioDay http://doi.org/10.1001/jamadermatol.2024.3717
Drug Survival of Dupilumab,Methotrexate, and Cyclosporine A in Children With Atopic Dermatitis
BioDay https://doi.org/10.1002/clt2.12381
Dupilumab induces a significant decrease of food specific immunoglobulin E levels in pediatric atopic dermatitis patients
BioDay https://doi.org/10.1111/pai.14178
Effect of dupilumab on asthma and aeroallergen sensitization in pediatric atopic dermatitis patients: Results of the BioDay registry
BioDay https://doi.org/10.1111/cea.14585
Type 2 Immune-Dominant Endotype Is Not Associated With Increased Responsiveness to Dupilumab Treatment in Adult Atopic Dermatitis Patients
BioDay https://doi.org/10.1111/all.16414
Tralokinumab Treatment in Adult Atopic Dermatitis Patients: 28-Week Evaluation of Clinical Effectiveness, Safety, Serum Proteins and Total IgE Levels
Influence of pathogenic filaggrin variants on dupilumab treatment in atopic dermatitis
BioDay http://doi.org/10.2340/actadv.v102.3978
BioDay http://doi.org/10.1001/jamadermatol.2024.2517
Long-Term Effectiveness and Reasons for Discontinuation of Dupilumab in Patients With Atopic Dermatitis
BioDay https://doi.org/10.1111/cea.14305
Switching from dupilumab to tralokinumab in atopic dermatitis patients with ocular surface disease: Preliminary case series
BioDay https://doi.org/10.1111/all.15717
Dupilumab-associated ocular surface disease in atopic dermatitis patients: Clinical characteristics, ophthalmic treatment response and conjunctival goblet cell analysis
BioDay https://doi.org/10.1002/clt2.12221
High dupilumab levels in tear fluid of atopic dermatitis patients with moderate‐to‐severe ocular surface disease
BioDay http://doi.org/10.2340/actadv.v103.4842
Economic and Humanistic Burden in Paediatric Patients with Atopic Dermatitis
Non-Infectious Uveitis Secondary to Dupilumab Treatment in Atopic Dermatitis Patients Shows a Pro- Inflammatory Molecular Profile
BioDay https://doi.org/10.1111/cea.14442
Differential dynamics of TARC during JAK-inhibitor therapy compared to biological therapies targeting type 2 inflammation
BioDay http://doi.org/10.2340/actadv.v103.5243
BioDay https://doi.org/10.1111/jdv.19164
Limited excretion of dupilumab into breastmilk: A case report
Biological Tipping Point in Patients with Atopic Dermatitis Treated with Different Dosing Intervals of Dupilumab
BioDay https://doi.org/10.1111/cea.14384
IgE levels in patients with atopic dermatitis steadily decrease during treatment with dupilumab regardless of dose interval
BioDay https://doi.org/10.1111/cod.14276
Experiences from daily practice of upadacitinib treatment on atopic dermatitis with a focus on hand eczema: Results from the BioDay registry
Biologicals for pediatric patients with atopic dermatitis: practical challenges and knowledge gaps
BioDay http://doi.org/10.1001/jamadermatol.2022.4639
Association of Serum Dupilumab Levels at 16Weeks With Treatment Response and Adverse Effects in Patients With Atopic Dermatitis
BioDay http://doi.org/10.1001/jamadermatol.2022.3014
Dupilumab Drug Survival and Associated Predictors in Patients With Moderate to Severe Atopic Dermatitis
BioDay https://doi.org/10.1111/all.15439
Patient-centered dupilumab dosing regimen leads to successful dose reduction in persistently controlled atopic dermatitis
EPIC-NL https://doi.org/10.1093/ije/dyp217
EPIC-NL https://link.springer.com/article/10.1023/A:1020009325797
Prospect-EPIC Utrecht: study design and characteristics of the cohort population. European Prospective Investigation into Cancer and Nutrition
Boker LK, van Noord PA, van der Schouw YT, Koot NV, Bueno de Mesquita HB, Riboli E, Grobbee DE, Peeters PH. Prospect-EPIC Utrecht: study design and characteristics of the cohort population. European Prospective Investigation into Cancer and Nutrition. Eur J Epidemiol. 2001;17(11):1047-53. doi: 10.1023/a:1020009325797. PMID: 12380720.
EPIC-NL https://academic.oup.com/ije/article/37/6/1236/729101?login=true
Cohort profile: the Doetinchem Cohort Study
Verschuren WM, Blokstra A, Picavet HS, Smit HA. Cohort profile: the Doetinchem Cohort Study. Int J Epidemiol. 2008 Dec;37(6):1236-41. doi: 10.1093/ije/dym292. Epub 2008 Jan 31. PMID: 18238821.
MARS - Molecular Diagnosis and Risk Stratification of Sepsis https://doi.org/10.1186/s13054-015-1035-1
Likelihood of infection in patients with presumed sepsis at the time of intensive care unit admission: a cohort study
MARS - Molecular Diagnosis and Risk Stratification of Sepsis https://doi.org/10.1164/rccm.201307-1376OC
Electronic Implementation of a Novel Surveillance Paradigm for Ventilator-associated Events. Feasibility and Validation
MARS - Molecular Diagnosis and Risk Stratification of Sepsis https://doi.org/10.1001/jama.2016.2691
Incidence, Risk Factors, and Attributable Mortality of Secondary Infections in the Intensive Care Unit After Admission for Sepsis
MARS - Molecular Diagnosis and Risk Stratification of Sepsis https://doi.org/10.1097/ccm.0000000000002735
Validation of a Novel Molecular Host Response Assay to Diagnose Infection in Hospitalized Patients Admitted to the ICU With Acute Respiratory Failure
The TWIN-study https://doi.org/10.1053/j.gastro.2021.01.030
Healthy Cotwins Share Gut Microbiome Signatures With Their Inflammatory Bowel Disease Twins and Unrelated Patients
Brand, E. C., Klaassen, M. A. Y., Gacesa, R., Vich Vila, A., Ghosh, H., de Zoete, M. R., Boomsma, D. I., Hoentjen, F., Horjus Talabur Horje, C. S., van de Meeberg, P. C., Willemsen, G., Fu, J., Wijmenga, C., van Wijk, F., Zhernakova, A., Oldenburg, B., Weersma, R. K., & Dutch TWIN-IBD consortium and the Dutch Initiative on Crohn and Colitis (2021). Healthy Cotwins Share Gut Microbiome Signatures With Their Inflammatory Bowel Disease Twins and Unrelated Patients. Gastroenterology, 160(6), 1970–1985. https://doi.org/10.1053/j.gastro.2021.01.030
The Utrecht cohort for Multiple BREast cancer intervention studies and Long-term evaLuAtion (UMBRELLA): objectives, design, and baseline results
Associations between virus single or coinfections and respiratory symptoms in young children; a community-based cohort study
MARS - Molecular Diagnosis and Risk Stratification of Sepsis MARS - Molecular Diagnosis and Risk Stratification of Sepsis MARS - Molecular Diagnosis and Risk Stratification of Sepsis MARS-1 Adult (18+ years)
(Immuno)histochemical analysis of plaques
Clinical characteristics (e.g. age, BMI, gender)
Follow-up is done: one time at 3 months after surgery every year at 12 months after surgery, for 3 years
For lebrikizumab and JAK inhibitors only
EASI, IGA (AD & PN), HECSI, NRS itch, NRS sleepdeprivation, IDQOL, CDLQI, CGAD, PGAD, RECAP proxy, RECAP, PGATE
Novel/avanced AD/PN systemic medication use, EASI, IGA, NRS itch, DLQI, Adverse events
EASI, IGA (AD & PN), HECSI, NRS itch, NRS sleep deprivation, IDQOL, CDLQI, CGAD, DFI, ACT 4-11y, PGAD, RECAP proxy, WPAI-GH proxy, ACT 12-17y, RECAP, PROMIS Social Health (short version), PGATE, QOLHEQ
Age, Gender, Novel/advanced systemic AD/PN medication, EASI, IGA, NRS itch, DLQI
EASI, IGA (AD & PN), HECSI, NRS itch, NRS sleepdeprivation, IDQOL, CDLQI, CGAD, PGAD, RECAP proxy, RECAP, PGATE (+every 6 months: PROMIS Social Health [SV], QOLHEQ, DFI, ACT 12-17y, ACT 4-11y)
Third follow-up in 2011 consisting of questionnaires
tinnitus, parkinson, migraine, depression; diabetes, cancer; pain
EPIC-NL EPIC-NL Follow-Up MORGEN-EPIC 2005
Second follow-up for MORGEN-EPIC subcohort in 2005 consisting of questionnaires
Participants receive questionnaire in 1999
weight loss/gain, variation of weight and probable cause of change; illness; medication; activity, family health history
Fourth follow-up in 2015 consisting of questionnaires
cancer, tinnitus, ALS, MS, autism; reuma; parkinson; family health history
EPIC-NL EPIC-NL Follow-Up 2003 for Prospect-EPIC
EPIC-NL Follow-Up 2003 for Prospect-EPIC
Second follow-up for Prospect-EPIC subcohort in 2003 consisting of questionnaires
illness, treatment, hospitalizations; reproductive health
At inclusion, a general questionnaire and an food-frequency questionnaire were filled in by all participants. A physical examination including body weight, waist and hip circumference and blood pressure measurement was performed and blood samples were drawn.
MARS - Molecular Diagnosis and Risk Stratification of Sepsis MARS - Molecular Diagnosis and Risk Stratification of Sepsis Daily plasma collection
MARS - Molecular Diagnosis and Risk Stratification of Sepsis Daily plasma collection
2x 450 uL EDTA plasma 2x 900 uL Lithiumheparin plasma 2x 900 uL citrate plasma
MARS - Molecular Diagnosis and Risk Stratification of Sepsis MARS - Molecular Diagnosis and Risk Stratification of Sepsis Daily plasma collection UMCU
MARS - Molecular Diagnosis and Risk Stratification of Sepsis Daily plasma collection UMCU
2x 450 uL EDTA plasma 2x 900 uL Lithiumheparin plasma (until 31-OCT-2016) 2x 900 uL Na-heparin plasma (from 1-NOV-2016 until 2-NOV-2021 ) 2x 600 uL Na-heparin plasma (from 3-NOV-2021) 2x 900 uL citrate plasma (if available, no longer daily since 12-MAR-2015)
MARS - Molecular Diagnosis and Risk Stratification of Sepsis MARS - Molecular Diagnosis and Risk Stratification of Sepsis Daily plasma collection UMCU-3
MARS - Molecular Diagnosis and Risk Stratification of Sepsis Daily plasma collection UMCU-3
2x 450 uL EDTA plasma 2x 600 uL Na-heparin plasma (until 23-JAN-2023) 2x 600 uL Lithiumheparin plasma (from 24-JAN-2023) 2x 900 uL citrate plasma (if available)
MARS - Molecular Diagnosis and Risk Stratification of Sepsis MARS - Molecular Diagnosis and Risk Stratification of Sepsis PAX-gene collection AMC
MARS - Molecular Diagnosis and Risk Stratification of Sepsis PAX-gene collection AMC
PAX-gene collection upon admisison and at every new episode of sepsis/organ failure in the AMC cohort 2011-2012: 2.5 mL 2012-2013: 2x 2.5mL
At baseline, demographic factors, environmental data and disease characteristics will be collected from all participants. If specific informed consent is obtained, also nutritional questionnaire data, 60mL blood, faecal samples, saliva samples, urine samples and rectal biopsies will be collected.
At follow-up, demographic factors, environmental data and disease characteristics will be collected from all participants. If specific informed consent is obtained, also nutritional questionnaire data, 60mL blood, faecal samples, saliva samples, urine samples and rectal biopsies will be collected. Follow-up takes place each year after the baseline study event.
The Utrecht cohort for Multiple BREast cancer intervention studies and Long-term evaLuAtion (UMBRELLA) includes patients with histologically proven invasive breast cancer or ductal carcinoma in situ (DCIS) referred for breast cancer treatment. UMBRELLA holds the Trials within Cohorts study design. Participants are followed-up for 10 years.
Breast cancer, prospective cohort, multicenter, quality of life, Trials within Cohorts
HADS, WAI, EORTC BR23, SQUASH, general questionnaire, BREAST-Q
Families filled in a daily symptom diary in an app. The questionnaire asked if the child had had the following symptoms more than usual in the past 24h: runny nose, coughing, wheezing, shortness of breath, ear pain, fever, vomiting, diarrhoea, crying, irritability, difficulty falling asleep, reduced playing, and reduced eating.
At the start of the study, baseline information was collected for each child, household members, and household details. For example, regarding past medical history (allergies, vaccination, etc.), childcare use and housing situation (number of household members, pets, smoking habits at home, etc.).
Once a week, parents collected a mid-turbinate swab from the child, irrespective of symptoms. The swabs were stored in home freezers until the end of the study period.
A saliva swab was taken at the beginning and end of the study period.
The TWIN-study Informatie en toestemmingsformulier
Participant information and informed consent document
National Institute for Public Health and the Environment
MARS - Molecular Diagnosis and Risk Stratification of Sepsis UMCU
MARS - Molecular Diagnosis and Risk Stratification of Sepsis AMC