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Pharmacogenomics Ontology (PHONT) Network Resource

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

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Pharmacogenomics Ontology (PHONT) Network Resource

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  1. Pharmacogenomics Ontology (PHONT)Network Resource Webinar April 6, 2012

  2. Outline • Why PHONT • Background, Goals, Relevance • What PHONT • Early process, course correction • Standards selection, discussion • Status of work • Whither PHONT

  3. 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

  4. Meaningful Use13 January, 2010 Reporting Requirements 169pp Interoperability Standards 35pp

  5. 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

  6. 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, …

  7. 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

  8. 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

  9. 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

  10. 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

  11. PHONT: Standards Liaisons • Impact current development activities, ensure PGRN requirements are addressed • Inform PGRN research sites about relevant activities

  12. 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

  13. 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

  14. Course Correction • Initial focus • 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

  15. Clinical Element ModelsHigher-Order Structured Representations [Stan Huff, IHC]

  16. Pre- and Post-Coordination [Stan Huff, IHC]

  17. Data Element Harmonizationhttp://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

  18. Value Set Value Set Value Set Person Model Person PatientExternalId (0-M) data (II) PersonName (1-M) GivenName (0-1) Terminology data (ST) … Birthdate (0-1) data (TS) AdministrativeGender (0-1) data (CD) AdministrativeRace (0-1) AdministrativeEthnicGroup (0-1) …

  19. Person Model Examples of Variables Person Medical Record Number PatientExternalId (0-M) SSN data (II) Study ID PersonName (1-M) First Name GivenName (0-1) Last Name data (ST) … Date of Birth Birthdate (0-1) Year of Birth data (TS) AdministrativeGender (0-1) Patient Gender data (CD) Patient Race AdministrativeRace (0-1) AdministrativeEthnicGroup (0-1) Self-Reported Ethnicity …

  20. Lab Observation Model Examples of Variables StandardLabObs Alkaline phosphate Code Potassium data Creatinine PerformingLaboratory LaboratoryId Analysis site data (ST) … Are liver function tests abnormal? LabInterpretation data (CD) Method Type of assay data (CD) Specimen collection time SpecimenCollected Has blood been collected? Subject …

  21. Disease & Disorder Model Examples of Variables DiseaseDisorder Code Atrial fibrillation data Pulmonary embolism BodyLocation Are episodes of paroxysmal atrial fibrillation associated with eating? BodyLaterality data (CD) … Duration of longest symtomatic episode Severity data (CD) Age of first angina StartTime data (TS) Was the chest pain in the central or left chest? RelativeTemporalContext Subject Chest pain or pressure in the past 4 weeks? …

  22. Drug Administration Model Examples of Variables NotedDrug Code Is the patient taking a diuretic? data (CD) Has the subject started any new medications? StartTimeUnconstrained data (TS/CD/ST) Date of last antihypertensives EstimatedInd data (CO) Medication start date TakenDoseLowerLimit Dose Have you taken digoxin in the past? data (PQ) RouteMethodDevice data (CD) Time on tamoxifen StatusChange If potassium supplementation added, specify daily dose Subject …

  23. Relationships Person Person Person Semantic Link: Physician-Patient Semantic Link: Parent-Child Example: Primary care physician Example: Race of maternal grandfather

  24. Relationships Person Disease & Disorder Drug Admin. Semantic Link: Treatment-for-Disease Example: ALL treated by mercaptopurine

  25. Data Dictionary Analysis

  26. Portion of Variables Mapped to CEMs

  27. Categories of Variables Not YetMapped to CEMs

  28. Data HarmonizationUnmapped 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

  29. 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

  30. 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

  31. 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) Scott S. Bauer (Analyst Programmer) Donna Ihrke (Nosologist) Deepak K. Sharma (Sr. Analyst Programmer) Zonghui Lian (Analyst Programmer)

  32. 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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