Informatics Data Sharing and Management of Clinical Trials in Specific Disease Areas Mike Conlon University of Florida Paul Harris Vanderbilt University The Challenge Clinical and Translational Research involves informatics beyond the needs of clinical care ID: 911109
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Slide1
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
Slide2The 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
Slide3Emerging Molecular Informatics Genetics
Personalized medicine – pharmacogenomics, disease risk
Proteomics
Metabolomics
Global and targeted
Mass Spectroscopy and Nuclear Magnetic Resonance Imaging
Slide4Molecular Informatics at Vanderbilt
Slide5Slide6Slide7Slide8Slide9PREDICT:
P
harmacogenomic
R
esource for
E
nhanced
D
ecisions
I
n
C
are and
T
reatment
Slide10Denny 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.
Slide11Dumitrescu
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
.
Slide12Feng 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.
Slide13Slide14Personalized Medicine at Florida
Slide15Electronic 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
Slide16REDCap 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
Slide17REDCap 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
Slide18Case 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
Slide19Data Export + De-ID Tools
Exports Raw
Data + Stats
Script Files
(Labels, Coding
Embedded
De-Identification
Tools
Slide20Slide21Slide22Clinical 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
Slide23Approach 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)
Slide24Data 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
Slide25Vanderbilt Data Warehousing
Slide26Participant
Recruitment Example
Vanderbilt Data Warehousing
Slide27Snapshot –
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)
Slide28Florida 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
Slide29Integrated Data Repository
Slide30Data 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
Slide31Layered 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
Slide32Slide33Slide34Data 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
Slide35Slide36Information 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
Slide37Slide38Slide39Slide40Slide41Slide42Slide43VIVO in China: http
://health.las.ac.cn/
Slide44Scientific Portfolio ManagementDiversity, proportionality across the four translations
Alignment with national, institutional and research strategic planning, goals and objectives
Return on investment
Slide45Slide46Slide47Slide48Slide49Slide50Slide51GovernanceJoint (care enterprise, research enterprise) decision making
IT principles
IT Architecture
IT Infrastructure strategies
Application needs
IT investment and prioritization
Slide52Slide53Questions and Discussion