Pharmacogenomics Ontology
(PHONT)
Network Resource
Webinar
April 6, 2012
Outline
• Why PHONT
• Background, Goals, Relevance
• What PHONT
• Early process, course correction
• Standards selection, discussion
• Status of work
• Whither PHONT
Background
• Biology and Medicine have become big science
• Requires interoperability
• Clinical and research standards are emerging
• Meaningful Use driving clinical operations
• De facto genomic databases, ontologies
• Advantages and requirements for interoperability
• Meta-analyses and comparisons
• EMR data integration, harvesting, outcomes
Meaningful Use
13 January, 2010
Reporting
Requirements
169pp
Interoperability
Standards
35pp
4
Goals - Present
• Collaboratively evolve framework from
representing PGRN data in comparable and
consistent form
• Facilitate interoperability within and without
PGRN community
• Clinical EMR data exchange
• Larger-scale genomics and biology communities
• Ultimately add value to PGRN sites
• Library of standard models and value sets
• Services to map extant data to common form
Means to Achieve Goals
• Identification of standard data models, in
alignment with national requirements
• Informed by data dictionary harmonization
• Adaptation of models when needed
• Infrastructure for centralized curation activities
• PGRN specific terminology services
• Standardization of value sets
• Derived from national standards and norms
• LOINC, SNOMED, RxNorm, ….
• GO, dbGaP, NCBI, CDISC, …
PHONT Activities
• Data Dictionary Analysis
• Semantic and syntactic analyses of PGRN 4483 variables
• Impact: survey nature and diversity of PGx data elements
(DEs)
• Data Element Semantic Annotation and Harmonization
• Normalization, semantic annotation, categorization
• Impact: Identifies overlap and focus for standardization
• Data Element Standardization
• Mapping PGx DEs to existing data standards
• Impact: Identification of standards gaps
PHONT: Infrastructure
• Infrastructure Development
•
•
•
•
LexEVS (SNOMED, LOINC, RxNorm, …)
CEM repository, OpenMDR
Semantic annotation pipeline, CEM mapping, Curation
Impact: Supports harmonization and standardization
activities
• Educational Resource Development
• Information about standards and data representation
• Rendering of PGRN site specific dictionaries
• Impact: Lower the barrier of adoption
PHONT: PGRN Collaborations
• Translational PGx Project (TPP)
• CPIC guidelines in clinical environments
• Role: Semantically consistent EMR integration
• PGx discovery in patient Populations (PG-Pop)
• Patient cohorts through electronic phenotyping
• Role: Adopt SHARPn phenotyping algorithms
using emerge PGRN data standards
• Clinical PGx Implementation Consortium (CPIC)
• Therapeutic guidelines to implement PGx data
• Role: Identifying existing standards and gaps
PHONT: Developing Collaborations
• PGx of Anticancer Agents Research (PAAR)
• Service role for terminology standards
• 5306 SNOMED codes for 2557 conditions
• PGRN Statistical Analysis Resource (P-STAR)
• Standards-grounded data dictionaries
• PharmGKB
• Supporting terminology, phenotype standards
• Providing RxNorm and NDF-RT codes
• International SSRI PGx Consortium (ISPC)
• Reviewing data dictionary
PHONT: Standards Liaisons
• Impact current development activities, ensure
PGRN requirements are addressed
• Inform PGRN research sites about relevant
activities
Standards Development
Organization (SDO)
Clinical Information Modeling
Initiative (CIMI)
Clinical Data Interchange
Standards Consortium (CDISC)
Relevance to PHONT
Developing and adapting Clinical Element Model
(CEM) standardized models and value sets for
clinical and research data representation
Developing a library of harmonized and
NCI Case Report Form (CRF) Work
standardized data elements for NCI-funded clinical
Group
trials
Developing message standards for genomics data
HL7 Clinical Genomics Work Group used in clinical settings
W3C Health Care and Life
Sciences (HCLS) Work Group
Extending the Translational Medicine Ontology to
include PGx terms
Large-Scale Informatics Consortia
• NCBO
• SHARPn
• eMERGE
• CTSA – Clinical Translational Sciences Awards
• I2b2 - Informatics for Integrating Biology and the Bedside
• SNOMED
• WHO ICD11
Clinical phenotyping
PGRN Realities - Motivation
• PGRN is multi-disciplinary and data-intensive
• Clinical phenotypes, Drug administration
• Laboratory data, Genomics data
• Data is often represented inconsistently
• Difficult to compare across studies or institutions
• Difficult to aggregate and integrate data
• Standards are required to make data consistent
and comparable
• Increased semantic meaning (data and methods)
• Enables accurate data transformations
• Initial focus
Course Correction
• Engagement of PIs, designees in standardization
• Web-tools for local curation of dictionaries
• Expectation of meta-analyses, interoperability
• Current realities
•
•
•
•
Marginal overlap of PGRN domain, meta-analyses
Understanding and buy-in underwhelming
Emphasis on centralized curation
Goal of adding value by demonstration
• EHR integration, exchange, harvesting
Clinical Element Models
Higher-Order Structured Representations
[Stan Huff, IHC]
Pre- and Post-Coordination
[Stan Huff, IHC]
Data Element Harmonization
http://informatics.mayo.edu/CIMI/
• Stan Huff – Intermountain Healthcare, HL7, LOINC
• Clinical Information Model Initiative
• NHS Clinical Statement
• CEN TC251/OpenEHR Archetypes
• HL7 Templates
• ISO TC215 Detailed Clinical Models
• CDISC Common Clinical Elements
• Intermountain/GE CEMs
Person Model
Person
PatientExternalId (0-M)
data (II)
PersonName (1-M)
GivenName (0-1)
Terminology
data (ST)
…
Birthdate (0-1)
data (TS)
Value Set
Value Set
AdministrativeGender (0-1)
data (CD)
AdministrativeRace (0-1)
AdministrativeEthnicGroup (0-1)
…
Value Set
Person Model
Examples of Variables
Person
PatientExternalId (0-M)
data (II)
Medical Record Number
SSN
Study ID
PersonName (1-M)
GivenName (0-1)
data (ST)
First Name
Last Name
…
Birthdate (0-1)
data (TS)
AdministrativeGender (0-1)
Date of Birth
Year of Birth
Patient Gender
data (CD)
AdministrativeRace (0-1)
AdministrativeEthnicGroup (0-1)
…
Patient Race
Self-Reported Ethnicity
Lab Observation Model
StandardLabObs
Examples of Variables
Alkaline phosphate
Code
data
Potassium
Creatinine
PerformingLaboratory
LaboratoryId
data (ST)
…
LabInterpretation
data (CD)
Method
data (CD)
SpecimenCollected
Subject
…
Analysis site
Are liver function
tests abnormal?
Type of assay
Specimen collection time
Has blood been collected?
Disease & Disorder Model
DiseaseDisorder
Code
Atrial fibrillation
data
BodyLocation
BodyLaterality
data (CD)
…
Severity
data (CD)
StartTime
data (TS)
RelativeTemporalContext
Subject
…
Examples of Variables
Pulmonary embolism
Are episodes of paroxysmal atrial
fibrillation associated with eating?
Duration of longest
symtomatic episode
Age of first angina
Was the chest pain in the
central or left chest?
Chest pain or pressure
in the past 4 weeks?
Drug Administration Model
Examples of Variables
NotedDrug
Code
Is the patient taking a diuretic?
data (CD)
StartTimeUnconstrained
data (TS/CD/ST)
EstimatedInd
Has the subject started
any new medications?
Date of last antihypertensives
data (CO)
TakenDoseLowerLimit
data (PQ)
RouteMethodDevice
data (CD)
StatusChange
Subject
…
Medication start date
Dose
Have you taken
digoxin in the past?
Time on tamoxifen
If potassium supplementation
added, specify daily dose
Relationships
Person
Person
Person
Semantic Link:
Physician-Patient
Example:
Primary care physician
Semantic Link:
Parent-Child
Example:
Race of maternal grandfather
Relationships
Person
Disease
&
Disorder
Drug
Admin.
Semantic Link:
Treatment-for-Disease
Example:
ALL treated by mercaptopurine
Data Dictionary Analysis
Patient
Lab
Observ'n
Disease/
Disorder
Drug
Admin.
Total
11 (10%)
8 (7%)
24 (22%)
32 (30%)
75 (70%)
3 (5%)
16 (27%)
1 (2%)
1 (2%)
21 (36%)
PAPI
38 (12%)
170 (52%)
11 (3%)
0 (0%)
219 (68%)
PAT
162 (10%)
123 (8%)
424 (26%)
179 (11%)
888 (55%)
Pear
26 (3%)
72 (9%)
21 (2%)
242 (29%)
361 (43%)
PGBD
10 (1%)
70 (7%)
53 (5%)
20 (2%)
153 (16%)
phRAT
3 (16%)
3 (16%)
3 (16%)
8 (42%)
17 (89%)
PNAT
45 (10%)
22 (5%)
64 (14%)
69 (15%)
200 (43%)
XGEN
2 (3%)
0 (0%)
0 (0%)
0 (0%)
2 (3%)
Mayo
Paar4Kids
Portion of Variables Mapped to CEMs
Patient
301 (7%)
Lab Observation
751 (18%)
Other
2000 (46%)
Disease/Disorder
601 (14%)
Drug Administration
629 (15%)
Categories of Variables Not Yet
Mapped to CEMs
Procedures
5%
Adverse Events
6%
Other
9%
Genomics
6%
Clinical Findings
41%
QOL/Cognitive
Assessment
33%
Data Harmonization
Unmapped Variables
• Some variables are not currently represented by
PHONT CEMs
• Computed data (e.g., pharmacokinetics)
• Genomic results
• Work with SDOs to address these gaps
• CIMI community on extant or new CEMs
• HL7 and CDISC for clinical genomics data
• W3C, NLM, & SNOMED pharmacogenomic ontologies
• Collaborating PGRN groups
• TPP, CPIC, P-STAR
Future Plans
• Impact of standardization
• Integration into EMR systems
• Phenotyping algorithms
• Clinical decision support interfaces
• Cohort selection
• Future meta-analyses
• Cross PGRN?
• Among related large-scale collaborations
• Query Health – ONC
• Sentinal - FDA
PHONT Activity Plan
Develop Standardized Element Models
Develop Harmonized Standards
Engage External Standards Groups
Study Use of Terms and DEs
Develop Plug-ins to Expose Data
Develop Curation Tooling
Develop Infrastructure
Education & Training
Develop Network Collaborations
Year 1
Year 2
Year 3
Year 4
Year 5
PHONT Personnel
CG Chute, MD, DrPH (PI)
Jyotishman Pathak, PhD
(Co-I)
Robert R. Freimuth, PhD
(Co-I)
Matthew J. Durski
(Project Manager)
Qian Zhu, PhD
(Research Associate)
Guoqian Jiang, PhD
(Research Associate)
Deepak K. Sharma
Zonghui Lian
Scott S. Bauer
(Sr. Analyst Programmer) (Analyst Programmer) (Analyst Programmer)
Donna Ihrke
(Nosologist)
Discussion
• Appropriateness of proposed standards
•
•
•
•
Patient
Diseases and Disorders
Drug Administration
Lab Observations
• Feasibility of prospective definition of data
dictionaries and value sets
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PH-ONT - Mayo Clinic Informatics