Building a CTRC Consortium Platform: - PowerPoint Presentation

Presentation on theme: "Building a CTRC Consortium Platform:"— Presentation transcript

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Building a CTRC Consortium Platform: Informatics, Data Sharing and Management of Clinical Trials in Specific Disease Areas

Mike Conlon, University of Florida

Paul Harris, Vanderbilt University

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The ChallengeClinical 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

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Emerging Molecular Informatics Genetics

Personalized medicine – pharmacogenomics, disease risk

Proteomics

Metabolomics

Global and targeted

Mass Spectroscopy and Nuclear Magnetic Resonance Imaging

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Molecular Informatics at Vanderbilt

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PREDICT:

P

harmacogenomic

R

esource for

E

nhanced

D

ecisions

I

n

C

are and

T

reatment

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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.

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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

.

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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.

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Personalized Medicine at Florida

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Electronic Data CaptureStrong 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

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REDCap Project History

2004 Needs Assessment

Researchers needed help managing data for small/medium sized non-trial research

projects (pilot, R01, PPG)

Hypothesis

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

Problem

Many projects, few resources

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REDCap Project History

Solution:

Metadata-driven application

(no per-project programming)

2004 - First REDCap project operational at Vanderbilt

2006 - REDCap Consortium

Launched REDCap Consortium to share with other universities and foster collaboration for future development

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Case Report Forms

Visual Status

Data Validation

Numerous

Field Types

+ Text

(Free)

(Number)

(Phone)

(Zip)

(Date)

+TextArea

+Select

+Radio

+File

Branching

Logic

Auto-Variable Coding

Human

Readable

Labels

PDFs

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Data Export + De-ID Tools

Exports Raw

Data + Stats

Script Files

(Labels, Coding

Embedded

De-Identification

Tools

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Clinical Trial Management SystemsFunctionality

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

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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)

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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

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Vanderbilt Data Warehousing

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Participant

Recruitment Example

Vanderbilt Data Warehousing

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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 …

Review

List

Clinics of

Interest

Study Work Queue (Daily Review)

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Florida Data WarehousingIntegrated 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

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Integrated Data Repository

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Data Sharing -- LayeredShare 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

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Layered Sharing

Vanderbilt Institute for Clinical and Translational Research

Vanderbilt Medical Center

Meharry

Medical Center

Florida

Hospital on Jacksonville, 114 km. Epic software

H

ospital in Orlando, 182 km. Crimson software

Hospital in Tampa, 209 km. Proposed SHRINE software

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Data Sharing -- StudyNational 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

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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

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VIVO in China: http

://health.las.ac.cn/

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Scientific Portfolio ManagementDiversity, proportionality across the four translations

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

Return on investment

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GovernanceJoint (care enterprise, research enterprise) decision making

IT principles

IT Architecture

IT Infrastructure strategies

Application needs

IT investment and prioritization

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Questions and Discussion