Transcript Pharmacogenomics Data Standardization using

Pharmacogenomics Data
Standardization using
Clinical Element Models
Pharmacogenomics Ontology
(PHONT) Network Resource
Pharmacogenomics Research Network
(PGRN)
– Diverse network of PGx research sites
– Goal: Understand how genetic variations
affect an individual's response to medications
Normalize data representations
– Disease phenotypes
– Drugs and drug classes
PGRN Data Dictionary
Standardization
4483 PGRN Variables
UMLS Semantic Types
SHARPn CEMs: Patient, Noted Drug,
Disease/Disorder, Lab Observation
Categories of Mapped
Variables
Patient
301 (7%)
Lab Observation
751 (18%)
Other
2000 (46%)
Disease/Disorder
601 (14%)
Drug Administration
629 (15%)
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
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
Example: Patient Took 300 mg
Acetaminophen
"300"
Categories of Unmapped
Variables
Procedures
5%
Adverse Events
6%
Other
9%
Genomics
6%
Clinical Findings
41%
QOL/Cognitive
Assessment
33%
Unmapped Variables
Some variables are not currently
represented by PHONT (SHARP) CEMs
– Computed research data (e.g., PK/PD)
– Genomic data
– Psychometric data
Work with SDOs to address these gaps
– CIMI community on extant or new CEMs
– HL7 and CDISC for clinical genomics data
– W3C, NLM, & SNOMED PGx ontologies
Conclusions
Demonstrated CEMs can be used to
normalize PGRN data dictionaries
Future Work
– Incorporate recently developed SHARP CEMs
– Collaborate with SHARP to fill gaps for PGx
– Establish best practices
 Complex data elements (e.g., semantic links)
 Project-specific/workflow data vs EMR
PHONT Personnel
Scientific
–
–
–
–
–
Christopher G. Chute
Robert R. Freimuth
Jyotishman Pathak
Qian Zhu
Guoqian Jiang
Nosologist
– Donna Ihrke
IT
– Zonghui Lian
– Scott Bauer
– Deepak Sharma
Project Management
– Mandy Ager
– Matthew Durski