Building a CTRC Consortium Platform: Informatics, Data Sharing and Management

of Clinical Trials in Specific Disease Areas

Mike Conlon, University of FloridaPaul Harris, Vanderbilt University

The Challenge

• Clinical and Translational Research involves informatics beyond the needs of clinical care– Molecular Level information– Electronic Data Capture– Clinical Trials– Data warehousing– Data sharing– Information Discovery and Dissemination– Scientific Portfolio Management

Emerging Molecular Informatics

• Genetics– Personalized medicine – pharmacogenomics,

disease risk• Proteomics• Metabolomics

– Global and targeted– Mass Spectroscopy and Nuclear Magnetic

Resonance Imaging

Molecular Informatics at Vanderbilt

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Denny JC et al. PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations. -- Bioinformatics 2010 May 1;26(9):1205-10.

Ritchie MD et al. Robust replication of genotype-phenotype associations across multiple diseases in an electronic medical record. -- Am J Hum Genet 2010 Apr 9;86(4):560-72.

Pulley J et al. Principles of human subjects protections applied in an opt-out, de-identified biobank. -- Clin Transl Sci 2010 Feb;3(1):42-8.

Smith JP et al. PGE2 decreases reactivity of human platelets by activating EP2 and EP4. -- Thromb Res 2010 Jul;126(1):e23-9.

Pulley J et al. Identifying unpredicted drug benefit through query of patient experiential knowledge: a proof of concept web-based system. -- Clin Transl Sci 2010 Jun;3(3):98-103.

Schildcrout JS et al. An analytical approach to characterize morbidity profile dissimilarity between distinct cohorts using electronic medical records. -- J Biomed Inform 2010 Dec;43(6):914-23.

Dumitrescu L et al. Assessing the accuracy of observer-reported ancestry in a biorepository linked to electronic medical records. -- Genet Med 2010 Oct;12(10):648-50.

Baye TM et al. Mapping genes that predict treatment outcome in admixed populations. -- Pharmacogenomics J 2010 Dec;10(6):465-77.

Denny JC et al. Identification of genomic predictors of atrioventricular conduction: using electronic medical records as a tool for genome science. -- Circulation 2010 Nov 16;122(20):2016-21.

Ramirez AH et al. Modulators of normal electrocardiographic intervals identified in a large electronic medical record. -- Heart Rhythm 2011 Feb;8(2):271-7.

Wilke RA. High-density lipoprotein (HDL) cholesterol: leveraging practice-based biobank cohorts to characterize clinical and genetic predictors of treatment outcome. -- Pharmacogenomics J 2011 Jun;11(3):162-73.

Malin B et al. Never too old for anonymity: a statistical standard for demographic data sharing via the HIPAA Privacy Rule. -- J Am Med Inform Assoc 2011 Jan 1;18(1):3-10.

Feng Q et al. A common CNR1 (cannabinoid receptor 1) haplotype attenuates the decrease in HDL cholesterol that typically accompanies weight gain. -- PLoS One 2010 Dec 31;5(12):e15779.

Wilke RA et al. The emerging role of electronic medical records in pharmacogenomics. -- Clin Pharmacol Ther 2011 Mar;89(3):379-86.

Turner SD et al. Knowledge-driven multi-locus analysis reveals gene-gene interactions influencing HDL cholesterol level in two independent EMR-linked biobanks. -- PLoS One 2011 May 11;6(5):e19586.

Higginbotham KS et al. A multistage association study identifies a breast cancer genetic locus at NCOA7. -- Cancer Res 2011 Jun 1;71(11):3881-8.

Xu H et al. Facilitating pharmacogenetic studies using electronic health records and natural-language processing: a case study of warfarin. -- J Am Med Inform Assoc 2011 Jul-Aug;18(4):387-91.

Wilke RA et al. Genetics and variable drug response. -- JAMA 2011 Jul 20;306(3):306-7.

Delaney JT et al. Predicting clopidogrel response using DNA samples linked to an electronic health record. -- Clin Pharmacol Ther 2012 Feb;91(2):257-63.

Personalized Medicine at Florida

Electronic Data Capture

• Strong need for simple methods for collecting data into electronic forms for support of clinical research

• Popular software is REDCap – used by a majority of CTSAs and many other institutions

REDCap Project History

2004 Needs AssessmentResearchers needed help managing data for small/medium sized non-trial research projects (pilot, R01, PPG)

HypothesisResearchers will do the right thing (secure, audit trails, etc) if provided an easy way to get needed tools

ProblemMany projects, few resources

REDCap Project History

Solution: Metadata-driven application (no per-project programming)

2004 - First REDCap project operational at Vanderbilt

2006 - REDCap ConsortiumLaunched REDCap Consortium to share with other universities and foster collaboration for future development

Case Report FormsVisual Status Data Validation

NumerousField Types

+ Text (Free) (Number) (Phone) (Zip) (Date)+TextArea+Select+Radio+File

BranchingLogicAuto-Variable Coding

HumanReadableLabels

PDFs

Data Export + De-ID Tools

Exports RawData + StatsScript Files(Labels, Coding

EmbeddedDe-IdentificationTools

Clinical Trial Management Systems

Functionality• All aspects of trial management• Time and event management• Electronic data capture• Recruitment support• Interface to clinical systems,

laboratory, imaging, prescribing, warehouse

• Financial management• Regulatory support• Interfaces to analytic software

Items to consider• Hundreds of systems

replaced with one• Hundreds of processes

replaced with dozens• Required flexibility for

innovation• Required agility for

innovation

Approach to CTMS• Vanderbilt

- No Single CTMS supporting research enterprise- StarBRITE for Recruitment, Regulatory, Financial and other CTMS

components. Heavy use of REDCap for electronic data capture.

• Florida– 1,000 new clinical studies per year– No CTMS, heavy use of REDCap, 100+ local systems in use for

trials

• Others– Velos– Oncore (especially in Cancer Domain)

Data Warehousing

• Data archive for cohort identification, trial planning, recruitment, registries

• Create data flows from clinical, laboratory, tissue bank and prescribing systems

• Create data flows from consent, trial, and molecular systems

• Researchers mine data for planning and results• Care managers mine data for planning and quality

improvement

Vanderbilt Data Warehousing

Participant Recruitment Example

Vanderbilt Data Warehousing

Snapshot – Pilot Studies

Nephrology• Examined: 2598• Candidates:

96 (reduction - 96%)Cleft Palate• Examined: 2490• Candidates:

27 (reduction - 99%)

Cardiology (2 studies)(reduction - 95%)

Starting Here Filtering Criteria

…

ReviewList

Clinics ofInterest

Study Work Queue (Daily Review)

Florida Data Warehousing

• Integrated Data Repository with joint governance by research and care

• Work began in 2011• Based on i2b2 software, with common data

model across studies, hospital and outpatient• All hospital data for 10 years, personalized

medicine

Integrated Data Repository

Data Sharing -- Layered

• Share data across institutions at various levels of aggregation – simple counts of procedures and diseases to full personal health records

• Technical considerations – definitions, data representation

• Policy considerations – risk management, privacy, competitive considerations

Layered Sharing

• Vanderbilt Institute for Clinical and Translational Research– Vanderbilt Medical Center– Meharry Medical Center

• Florida– Hospital on Jacksonville, 114 km. Epic software– Hospital in Orlando, 182 km. Crimson software– Hospital in Tampa, 209 km. Proposed SHRINE software

Data Sharing -- Study

• National Health and Nutrition Survey (NHANES)– Federal Effort– Longitidanal– Common data elements– Available for data mining

• Study Level– Data Use Agreements– Standardized definitions for measurement– De-identification

Information Discovery and Dissemination

• Need to know what is happening in science – papers, presentations, grants, datasets, funding, proposals, events

• Internet – portals, email, Facebook, Twitter• Local CTSA Example: Vanderbilt StarBRITE• VIVO – open software for research discovery

VIVO in China: http://health.las.ac.cn/

Scientific Portfolio Management

• Diversity, proportionality across the four translations

• Alignment with national, institutional and research strategic planning, goals and objectives

• Return on investment

Governance

• Joint (care enterprise, research enterprise) decision making– IT principles– IT Architecture– IT Infrastructure strategies– Application needs– IT investment and prioritization

Questions and Discussion