Translational Research IT (TraIT) “TraIT and OpenClinica: partners in

translational research”Marinel Cavelaars, Cuneyt Parlayan, Jacob Rousseau,

Sander de Ridder, Jan Willem Boiten and Jeroen Beliën

Boston; June 21st 2013

Overview

• Introduction and background

– CTMM

– Translational Research

• TraIT

– Three real-life examples: OpenClinica, BMIA, tranSMART

• OpenClinica.com – TraIT partnership

• CTMM-TRACER and OpenClinica by Sander de Ridder

– Scripts, Long Lists, Tools developed

– Things we learned/found useful

Who am I?

• My name: Jeroen Beliën, PhD, MSc

• Associate Professor, medical informatics, dept. of Pathology, VU University medical center, Amsterdam

– Digital Pathology, Image processing, IT in translational research

– String of Pearls

– IT-lead 2 CTMM projects: DeCoDe and TRACER

– CTO CTMM-TraIT

– BioMedBridges

• Member of taskforce Stichting Palga

– Palga: Dutch National Electronic Pathology Archive

• Faculty member of NBIC

jam.belien@vumc.nl

CTMM, TIPharma and BMM offer an integrated approach for innovations in

the Dutch health care sector

CTMM: diagnosis

• Early detection of disease by in-vitro and in-vivo diagnostics

• Stratification of patients for personalized treatment

• Assessing efficiency and efficacy of medicines by imaging

• Image guided delivery of medication

• Focus on cancer, cardiovascular, neurodegenerative and infectious /autoimmune disease.

TIPharma: drugs

• Translational research on novel pharmaceutical therapies

• Target finding, animal models and lead selection

• Drug formulation, delivery and targeting

• Special Theme focusing on the efficiency of the process of drug development

BMM: devices

• Smart drug delivery systems

• Innovations in contemporary organ replacement therapies

• Passive and active scaffolds, including cell signalling functions

Image guided drug delivery

Biomarkers

Drug delivery

Imaging for regenerative medicine

CTMM projects € 300 mlnCTMM projects € 300 mln

GovernmentAcademia

Industry

€ 37,5 mlnCASH

€ 37,5 mlnKindIn kind

€ 75 mln

Subsidy

€ 150 mln

50%

Public-private partnerships: Financial modelSubsidy: 50% of research cost

CTMM projects

Breast

Prostate Colon

Lung

Leukemia

Heart Failure

Stroke

DiabetesKidney Failure

Arrhythmia

Peripheral Vascular Disease

Thrombosis

AlzheimerRheumatoid Arthritis

Sepsis

Translational research processGuiding principle: connecting phenotype to biology

Scientific OutputScientific Output

Patient enters medical centerPatient enters medical center

Intellectual Property

Intellectual Property

Improved HealthcareImproved

Healthcare

Experimental data

Experimental data

Downstreamanalysis

Downstreamanalysis

Clinical Procedures

Clinical Procedures

ImagingImaging SamplesSamples ExperimentsExperimentsElectronicHealth Record

ElectronicHealth Record

DataIntegration

DataIntegration

External dataExternal data

Image databaseImage database Biobank databaseBiobank databaseClinical databaseClinical database

TraIT consortium - Started Oct. 2011status 2013: 26 partners

Growing TraIT project team

• IT infrastructure = main goal

• No research on the side

• Workflow-oriented approach

• Create data pipelines to link data production and data analysis

• User driven priority setting

• Regular reprioritization possible (agile)

• Avoid reinventing wheels

• Adopt/adapt existing technology and expertise

• Connect with other initiatives

• Organizations (NBIC, EBI, PSI, IMI, etc.)

• Think big; start small; act now

• Short term focus on immediate needs CTMM projects

The TraIT approach

Division in work packages

Five data generating work packages

Data integration & analysis across the four platforms

Shared service center for hardware, training & support

TraIT has been subdivided into four work packages (WPs) supporting data generating domains, and two work packages dealing with the overarching TraIT requirements: data integration and professional support respectively:

WP 5 Core Infrastructure

WP 6 Deployment

Imaging Data

High-level TraIT data flows

Hospital (IT) Translational Research (IT)

Research DataLIMS

data domains

clinical data

imaging dataannotations

experimental data

biobanking

integrated data

translational analytics

workbench

Public Data

…

e.g. tranSMART/

i2b2

NBIA

OpenClinica

Varioussolutions

HIS

PACS

LIS

e.g. Galaxy

cohortexplorer

e.g. R…

CBM-NL

TraIT PseudonymizationHospital (IT) Translational Research (IT)

Research DataLIMS

data domains

clinical data

imaging data

experimental data

biobanking

integrated data translational analytics

workbench

Public Data

HIS

PACS

LIS

Galaxy

tranSMART/cohort explorer

R

…

NBIA + AIM

e.g.CBM catalog

e.g. PhenotypeDB, Annai Systems

e.g. Galaxy, Chipster

e.g.caTissue

e.g. GEO, EMBL-EBI

TraIT - study driven approach

Data Integration TranslationalAnalytics

Workbench

TranslationalAnalytics

Workbench

Study1

Study1

Study2

Study2

Study…

Study…

UC 1UC 1 UC 2UC 2 UC …UC …

Task 1:•study selection

Task 2:•use cases & prototypes

Data Integration

integrated translational

data warehouse

ETL

TranslationalAnalytics

Workbench

TranslationalAnalytics

Workbench

Analytics

Data Integration TranslationalAnalytics

Workbench

TranslationalAnalytics

Workbench

Task 3, 4, 5:development of•data integration platform

•analytics workbench

•shared components

···

···

···

2013 2014

Translational Research (IT)

Three real-life examples

Hospital (IT)

clinical

imaging

integrated data

e.g. tranSMARTNBIA

OpenClinica

PACS

Example 2: CTMM AIRFORCE

Example 1: CTMM INCOAG

Example 3: CTMM PCMM

Real-life example 1 - CTMM Incoag

• Discover new risk factors for thrombotic diseases

• Approach: Combine existing clinical studies into one OpenClinica data set for higher statistical power

OpenClinica:

• Clinical data capture

• Web-based

• Open-source

• Full audit-trail

• 10,000+ installations

• TraIT tool of choice

Incoag - Technical integration

Out-of-the-box OpenClinica can be applied in most projects: currently used in CTMM projects AirForce, Cohfar, DeCoDe, Parisk, PCMM, and Tracer

Specific Incoag question: how to combine 5+ independent existing studies from mixed sources into one OpenClinica installation?

Study 1 Study 2 Study 3

?

Sustainable storage in TraIT environment

Incoag - Technical integration

Solution: TraIT-team created a batch upload toolbox for OpenClinica

Will be submitted to the OpenClinica open-source community

Study 1 Study 2 Study 3

Sustainable storage in TraIT environment

Incoag - Semantic integration

Study 1

Study 1

Study 2

Study 2

Study 4

Study 4

Study 5

Study 5

Study 3

Study 3

Second question from Incoag project: how to identify common fields and data items?

How to determine the overlap?How to determine the overlap?

Incoag - Semantic integration

Study 1

Study 1

Study 2

Study 2

Study 4

Study 4

Study 5

Study 5

Study 3

Study 3

Second question from Incoag project: how to identify common fields and data items?

How to determine the overlap?How to determine the overlap?

100-150 fieldsin each study

More than 1005 combinations to consider!

Studies speak different “languages”:A biomedical “Esperanto” needed

Study 1

Study 1

Study 2

Study 2

Study 3

Study 3

Study 4

Study 4

Study 5

Study 5

Common ground?Common ground?

Incoag - Semantic integration

Project 1: Provide tools to standardize studies at data registration (as far as possible):

TraIT building blocksto rapidly build CRFsfor new studies basedon common dictionary

Study n

Study n

Study 1

Study 1

Study 2

Study 2

Study 4

Study 4

Study 3

Study 3

Study 5

Study 5

Project 2: First test with tools for automatic “after-the-fact” harmonization for historical data:

Harmonized Incoag dataset

Harmonized Incoag dataset

Automatic mapping againstmultiple dictionaries(SNOMED-CT, LOINC, NCI thesaurus & Gene Ontology)

Real-life example 2 – CTMM AirForce

• Personalized chemo-radiation of lung and head & neck cancer

• Lung cancer patients with PET-CT (and clinical data & tissue)

– VUMC, MUMC+, NKI, UMCG + 35 patients from Policlinico Gemelli in Rome (via MUMC+)

• Transfer of images from Rome using TraIT’s BioMedical Image Archive (www.bmia.nl)

WP2 High level design – Upload(Implemented)

Image pseudonymization pipeline(based on CTP from the RSNA)

Image pseudonymization pipeline(based on CTP from the RSNA)

Image storage & simple web-shop like image viewing (based on NBIA)

AirForce - de-identification of images

• Install TraIT de-identification client in Rome

– Adopt: Clinical Trial Processor (RSNA, open source, Java)

• Configure DICOM de-identification

– Remove identifying DICOM tags

– Replace Codice Sanitario (PatientID) with AirForce ID

– Keep important tags (e.g. some tags are crucial for downstream analysis of PET)

• Result: A pipeline to TraIT’s BMIA from the local Rome Image Archive

AirForce - QC of de-identification• Perform QC step by collection administrator before images are

visible in BMIA to prevent privacy breach (esp. burnt-in names).

AirForce - Resulting image archive in BMIA• Collection AirForce on www.bmia.nl with 35 patients from Rome

• Web shop model where you can fill a basket with patients for download

Real-life example 3 – CTMM PCMM

• Develop and validate biomarkers for diagnosis of prostate cancer

• Requires correlation of phenotype data to biomarker data

• Potential solution: tranSMART; to be validated with real-life data from CTMM projects like PCMM

Can we address thegeneric translationalquestion with thetranSMART solution?

Role of tranSMART in TraIT

PCMM – tranSMART as a candidate solution

tranSMART:

• Developed in J&J

• Made open-source

• “Data workbench” for translational researchers

• Searching across studies

• Data exploration

PCMM - Import of prostate data

Prostate data

Prostate data

Gleason score,PSA values, etc.

Usually gene expression data will be loaded as well; not yet done for PCMM

Reference to public data sourcesavailable

PCMM - QC of the data set

PCMM - QC of the data set

Drag-and-drop data parameters to create simple distribution plots and statistical values

PCMM: tranSMART for correlation analysis

Easy to create correlation plots between existing and potential predictors for prostate cancer

Second tranSMART developer/user meeting, June 17th-19th 2013, Amsterdam

CTMM-TraIT

CTMM-TraIT

SanofiSanofi

Recombinant / Deloitte

Recombinant / Deloitte

University of MichiganUniversity

of Michigan

Thomson Reuters

Thomson Reuters PfizerPfizer eTRIKS /

Imperial College

eTRIKS / Imperial College

CDISCCDISC

University of Luxembourgh

University of Luxembourgh

PhilipsPhilipsJohnson & Johnson

Johnson & Johnson

OpenClinica.com – TraIT partnershipStatement of Work

• TraIT: automate data capture in OC as much as possible

– E.g. automate upload of excel data and hospital lab data

– Approach: OC’s Web Services

• Requires Improvements on OIDs and Bug Fixes

• Support configurable role based authentication and authorization within OC

– E.g. Central review of images for all subjects in the different sites. Each image is reviewed by three reviewers who are not allowed to see each other’s reports in the CRFs

• Parameterized links in CRFs

– E.g. Links to images or to other subjects, with a dynamic URL based on data in CRF

Other wishes

• Study migration

– E.g. Users want to switch to different OC server

– Currently only "ClinicalData" ODM is imported

– Studies can be exported in full detail but cannot be imported as such

• Support reference to ontologies in the CRF

– Standardization of data

• Easy view for data entry

– E.g. tree structure that indicates where you are while entering data for easy navigation to other CRF for subject

• The load on TraIT OpenClinica increased significantly in 2012• Considerable time and energy was spent on delivery management (availability, capacity and

security) and on improvement of the TraIT OpenClinica user support

• The load on TraIT OpenClinica increased significantly in 2012• Considerable time and energy was spent on delivery management (availability, capacity and

security) and on improvement of the TraIT OpenClinica user support

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Uptake of OpenClinica

Pre TraIT effect:all multicenterVUmc studies

Pre TraIT effect:all multicenterVUmc studies Also multicenter studies

UMCU, UMCN, EMC, Meander MC

Also multicenter studies UMCU, UMCN, EMC, Meander MC

47 studies 77 sites256 users

47 studies 77 sites256 users

Start DeCoDe

OpenClinica

Start DeCoDe

OpenClinica

Start TraIT

OpenClinica

Start TraIT

OpenClinica

Who am I?

• My name: Sander de Ridder

– Computer Science (MSc) & Bioinformatics (MSc)

• Inflammatory Disease Profiling, Dept. of Pathology, VU University medical center, Amsterdam

– Bioinformatics for Inflammatory Disease Profiling Group

– IT implementation CTMM TRACER

s.deridder@vumc.nl

CTMM-TRACER

Background information on TRACER

• CTMM TRACER: Rheumatoid Arthritis– Prospective data

– Retrospective data (To Do)

• Go Live:– Wednesday the 5th of June

• Started at 9:00 - Finished at 12:00

• Approximately 1 hour/study

Prospective Studies VERA ERA ESRA

Sites 4 7 7

Events 7 6 6

CRFs ~35 ~30 ~30

Rules ~250 ~450 ~650

Age Calculation

After entering the DOB and the date of signing…

The age is calculated

Age calculation script: http://en.wikibooks.org/wiki/OpenClinica_User_Manual/AgeFieldCreated by Sander de Ridder and improved by Gerben Rienk

Long List Implementation

• Problem:

– Maximum of 4000 characters for single-select response options text

– Some lists need more characters: e.g. medication list > 9000 characters

• Solution:

– Created external list

– Add field to CRF which opens new page with list

– Allows user to select option; selected value is copied back to CRF

ITEM_NAME RESPONSE_TYPE RESPONSE_OPTIONS_TEXT RESPONSE_VALUES_OR_CALCULATIONS

Smoking_Category single-select Never smoked, Current smoker 1,2

Example: Medication

User selects “Other” and then clicks on question 3)’s field

A new tab/window opens with an HTML page with a single-select The user can select desired medication from the list

Selected medication is copied to the CRF

Some tools we created: CRF validator

• Compares items between CRFs based on uids and ensures they match– CRF1

• ID: Patient_Weight; DATA_TYPE: INT

– CRF2• ID: Patient_Weight; DATA_TYPE: REAL

Mismatch for Patient_Weight!

• Checks NULL-flavour coding integrity– Coding: -1=No Information, -2=Not Applicable, -3=Unknown, …

– CRF1 • RESPONSE_OPTIONS_TEXT: No Information

RESPONSE_VALUES_OR_CALCULATIONS: -2

Incorrect NULL-flavour coding!

Prevents errors and inconsistencies

Some tools we created: ID-Translator

• Move rules file to new OC server replace all item IDs• Automatic translation of item identifiers in rules

Prevented replace errors and saved many hours of work

• Requires: – ViewCRFVersion file

• Contains item ID information for CRF on new server

– Rule file with properly specified header• Contains item ID information for CRF on old server

Parse ViewCRFVersion mapping ITEM_NAME – new OC_IDMedicatieBijgewerkt = I_TRACE_MEDICATIEBIJGEWERKT_4714

Parse Header of rule file mapping ITEM_NAME – old OC_IDMedicatieBijgewerkt = I_TRACE_PATIENTSTUDIE_MOMENT_AFROND

Translate rule fileold OC_ID new OC_ID via ITEM_NAME I_TRACE_PATIENTSTUDIE_MOMENT_AFROND = I_TRACE_MEDICATIEBIJGEWERKT_4714

ViewCRFVersion (new Server)

Rules for old server

Translated Rules for new server

ITEM_NAME

OC_ID

OC_IDITEM_NAME

Things we learned/found useful

• ITEM_NAME max 64 characters– SPSS compatibility

• Truly unique identifiers (description label)– Easy to link to study definition (CTMMC)– Useful for consistency checking

• Negative NULL-flavour coding– Prevent conflict with retrospective data – Easy to keep NULL-flavour coding consistent

• Specify identifiers in header of rule file– Automatic translation

• JavaScript code– $.noConflict();

• Prevents our code from interfering with OC’s code

– Reference to jquery• <script src="//ajax.googleapis.com/ajax/libs/jquery/1.9.1/jquery.min.js"> • Prevents dependency on OC’s jQuery version

• Create a checklist and follow it during go-live

Goal: make researchers want to use OpenClinica and tranSMART

And many more…

Acknowledgements