Biomedical Informatics Mayo, CTSA, and WHO Prespectives on Clinical Phenotype NCBO Forum: Ontology of Clinical Phenotypes Dallas, Sept 2008 Christopher G Chute, MD DrPH Professor Biomedical Informatics Mayo Clinic ICD-11 Revision Steering Group Chair World Health Organization

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 2 The Historical Center of the Health Data Universe Clinical Data Billable Diagnoses

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 3 Copernican Healthcare Billable Diagnoses Clinical Data (Niklas Koppernigk) Clinical Guidelines Scientific Literature Medical Literature Clinical Data

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 4 Mayo: A Century-Long Tradition of Studying Patient Outcomes DemographicsDiagnosesProceduresNarrativesLaboratoriesPathology… High-Volume Data Storage

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 5 Early Phenotype

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 6 From Practice-based Evidence to Evidence-based Practice Patient Encounters Clinical & Genomic Registries Data Warehouses & Marts Clinical Guidelines Expert Systems Shared Semantics & Vocabulary Data Inference KnowledgeManagement DecisionSupport Biomedical Knowledge

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 7 Mayo Clinic Data Governance Enterprise-wide (Eight States) Data Modeling, Metadata, and Vocabulary High-level representation Senior institutional Leaders – Physician led Two-tier organizations Steering Committee, Stewardship groups Multidisciplinary Care Providers, Informatics, IT, domain experts

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 8 Mayo Enterprise Vocabulary Organization Reference Vocabularies External SNOMED LOINC GO ICDs CPTs … ~200 Mayo Internal Table 22 Table 61 SNOMED mods Mayo CPTs WARS … ~50 Mayo ThesauriValue Sets Cross Mapping Tables External UMLS WHO FIC … ~3 Mayo Internal Drugs Disease Symptoms Pt Functioning EDT Aggregation … ~8 LexGrid: Data Model, Data Store and Machinery External JACHO NACCR Ca NIH/NCI … ~1000 Mayo Internal Flow sheets MICS apps/screens Dept systems Registry screens Form questions Inf. for your Phys. … ~10,000 External SNOMED↔ICD CPT↔ICD LOINC↔SNOMED FDB↔NDC … ~500 Mayo Internal All thesauri All value sets Tab 22↔ICD FDB↔Fomulary … … >10,000

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 9 The Genomic Era The genomic transformation of medicine far exceeds the introduction of antibiotics and aseptic surgery The binding of genomic biology and clinical medicine will accelerate The implications for shared semantics across the basic science and clinical communities are unprecedented

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 10 The Continuum Of Health Classification Biology meets Clinical Medicine Chasm of Semantic Despair

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 11 NCBO – A Bridge Across the Chasm

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 12 Feudal Cognition: Intellectual Semantic Baronies Genetic variation – Genomics Haplotypes – Statistical Genomics Molecular – Metabolomics, Proteomics Binding – Molecular simulation Pathways – Physiology and Systems Biology Symptoms – Consumer Health Rx and Px – Clinical Medicine Risk – Public Health, Epidemiology Social impact – Sociology, Health Economics

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 13 Whither Phenotype? Spans spectrum from enzymes to disease Pharmacogenomics – enzyme functionality Physiologist – cellular function Systems biologist – pathway circuit flow Sub-specialist – organ functioning Patient/Clinician – disease manifestation Public Health – population characteristics Highly specific to use-case context

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 14 Can the Genotype be the Phenotype? If the “disease” is defined by Genotype… Lesch-Nyhan syndrome Huntington's disease Gene sequence as “sign” of disease What is the distinction between them Consequences for genotype to phenotype research

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 15 Whither Disease as Phenotype? Notions of disease are inextricably bound to an underpinning Knowledge Base (rabies) Disease manifestation occurs across multiple planes of human understanding Boundaries among clinical observational elements* are relativistic, rapidly changing, and probably serve little practical purpose * Observations, findings, syndromes, diseases, genotype, haplotype, epigenomic change…

Biomedical Informatics Practical Application and Consequences

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 17 eMERGE: electronic Medical Record and GEnomics The eMERGE Network is a national consortium formed to develop, disseminate, and apply approaches to research that combine DNA biorepositories with electronic medical record (EMR) systems for large-scale, high-throughput genetic research. Reproducible phenotype from EMRs

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 18 EMR Phenotypes and Community Engaged Genomic Associations NHGRI U01 HG004599; Mayo Clinic CG Chute, PI Genome Wide Association Study (GWAS) Two Clinical “conditions” Myocardial Infarction Peripheral Arterial Disease (PAD) Balance with ethical and community engagement studies

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 19

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 20 Troponin Increased troponin a measurement exceeding 0.05 ng/ml Raising or falling pattern for serial troponin 0.05 ng/ml Historical overlap with CK-MB biomarker false positive issues - exclusions Ablation, cardiac surgery, CHF-acute, CPR, critically ill, crush/ contusion/ bruising, drug toxicity, embolectomy, endomyocardial biopsy, external defibrillation, hyperthermia, hypotension, hypothyroidism, infiltrative disease, inflamm. dis-cardiac, internal defibrillation, neurological disease, pericarditis, pulmonary embolism, transplant vasculopthy, uncontrolled hypertension, rhabdomyolysis, sepsis

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 21 Exclusion Criteria for Troponin ablation33250, 33251, 33254, 33255, 33256, 33257, 33258, 33259, 33261, 33265, 33266, cardiac surgery33120, 33130, 33300, 33305, 33310, 33315, 33320, 33321, 33322, 33330, 33332, 33400, 33401, 33403, 33404, 33405, 33406, 33410, 33411, 33412, 33413, 33414, 33415, 33416, 33417, 33420, 33422, 33425, 33426, 33427, 33430, 33460, 33463, 33464, 33465, 33468, 33470, 33471, 33472, 33474, 33475, 33476, 33478, 33496, 33600, 33602, 33860, 33861, 33863, 33508, 33510, 33511, 33512, 33513, 33514, 33516, 33517, 33518, 33519, 33521, 33522, 33523, 33533, 33534, 33535, 33536, 33542, 33545, 33572, 33600, 33602, 33606, 33608, 33610, 33611, 33612, 33615, 33617, 33619, 33641, 33645, 33647, 33660, 33665, 33670, 33675, 33676, 33677, 33681, 33684, 33688, 33690, 33692, 33694,33692, 33694, 33697, 33702, 33710, 33720, 33722, 33724, 33726, 33730, 33732, 33735, 33736, 33737, 33750, 33755, 33762, 33764, 33766, 33767, 33768, 33770, 33771, 33774, 33775, 33776, 33777, 33778, 33779, 33780, 33781, 33786, 33788, 33800, 33802, 33803, 33813, 33814, 33820, 33822, 33824, 33840, 33845, 33851, 33852, 33853, 33860, 33861, 33863, 33864, 33945, 93501, 93503, 93505, 93508, 93510, 93511, 93514, 93524, 93526, 93527, 93528, 93529, 93530, 93531, 93532, 93533, CHF-acute428.0, 428.1, , , , CPR92950 critically ill411.81, , , , 570, 584.5, 584.6, 584.7, 584.8, crush861.00, , contusion861.01, bruising861.01, drug toxicity977.9 hembolectomy33910, 33915, 33916, 33917, 33920, 35875, 35876, 92973, endomyocardial biopsy93505 external defibrillation92960 hyperthermia780.6 hypotension458.9 hypothyroidism244.9 infiltrative disease135 [425.8], 164.1, 212.7, 429.1, , , 413.1, 425.1, 425.4, 428.9, 429.0, , inflammatory dis-cardiac429.89, 429.0, , , , , , , , 423.1, 423.2, 423.8, internal defibrillation92961 neurological disease340, 341.0, 341.1, , , , 341.8, 341.9, , , , , , , , , , , , , 343.0, 343.1, 343.2, 343.3, 343.4, 343.8, 343.9, , , , , , 344.1, 344.2, , , , , , , 344.5, , , , 344.9, , , , , , , , , , , , , , , , , ,346.00, , , , , , , , , , , pericarditis423.9, pulmonary embolism transplant vasculopthy996.80, uncontrolled hypertension401.9 rhabdomyolysis sepsis995.91, , 038.0, , , 038.2, 038.3, , , , , , , 038.9

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 22 Cardiac Pain NLP of the text from EMR Can distinguish negated terms Search Term: Chest pain, thoracic pain, precordial pain, pain chest, chest discomfort, atypical chest pain, exertional chest pain, chest pain exertion, anginal chest pain, anginal pain, chest distress, acute chest pain, chest aching, aching chest, typical chest pain, chest pain, jaw pain, pain jaw, throat pain, pain throat, neck pain and pain neck. HPI in 72 hour window around presentation

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 23 Concordance MI Symptoms Human vs. NLP Positive Predictive Value 0.90Nurse Abstractor YesNo NLP Yes59035 No6380

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 24 Mayo Clinic Cancer Center Informatics Infrastructure $3.2M caBIG funding in 2008 (Chute, PI) LexBIG development Vocabulary Knowledge Center BRIDG development and leadership Clinical Trials infrastructure Common Data Elements Consistent and comparable Inclusion, exclusion criteria Co-morbidities

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 25 Cancer Phenotype Increasingly dependent on genomic characteristics Blend with pathology, imaging, laboratory Extent of disease measures are crucial Comparable and consistent data ultimately depends upon common ontologies BiomedGT SNOMED CT ICD-O HUGO GO Adverse Events Drugs Rx Radiation RT Surgury Px

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 26 BRIDG Data Models BRIDG is the HL7 RCRIM domain model Collaboration among CDISC, HL7, FDA, NCI Historically expressed as UML model Evolving translation into OWL Preserve OWL and UML modes interoperably Assert that UML and OWL are isomorphic Span the information-terminology model boundary Enable reasoner support to create coherent model Preserve domain expert access to model in UML

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 27 Content vs. Structure Contest or Synergy? Computer Equivalent? Family History of Breast Cancer Family History of Heart Disease Family History of Stroke Breast Cancer Heart Disease Stroke Family History Terminologic Model Information Model Equivalent Content

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 28 Mayo CTSA Catalog ongoing projects, grants, studies Scientific description of research Administrative categories (human, animal, …) Regulatory (ClinTrials.gov, FDA, etc) Patient cohort identification Clinical trial recruitment Case-report form generation

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 29 So what does Mayo want? Ability to record patient data using common models and vocabulary content 100 year tradition Approximating reality in electronic modes Ability to create overarching warehouse Now on 3 rd generation “data trust” To support “understanding” Research, knowledge discovery, practice insight Practice improvement, quality, safety Decision support, guidelines, best evidence

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 30 How are we doing it? Department of Health Sciences Research Epi, Biostat, Informatics, Health Policy Enterprise wide (3 campus presence) About 4,000 studies, reports, retrievals per year Turnkey “phenotype” templates Diabetes (Type I, Type II, metabolic syndrome) CAD, MI, PAD, Stroke (embolic, aneurysm) Asthma, COPD, Cancers

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 31 NLP Six year operation of fully coded clinical notes About 24M clinical notes Added Pathology, Radiology in process 18-stage UMIA pipeline of annotators Tokenization, part of speech, entity recognition Code noun phrases into Sign and symptoms – SNOMED Diagnoses – SNOMED Drugs – RxNorm Primary source for Phenotype retrieval

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 32 From Local to Global The ICD-11 Project 150 year legacy of the ICDs World Health Organization leadership Explicit definitions of classifications from Terminology Scientific consensus of clinical phenotype Distributed authoring model

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 33 ICD11 Use Cases Scientific consensus of clinical phenotype Public Health Surveillance Mortality Public Health Morbidity Clinical data aggregation Metrics of clinical activity Quality management Patient Safety Financial administration Case mix Resource allocation

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 34 Some Premises for ICD-11 development Rubrics defined by Aggregation Logics from terminologies Human language definitions will be explicit “core” representation will be in description logic based ontology A linear serialization will be derived as a view to maintain longitudinal consistency May require corresponding “rules” for practical use

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 35 Familiar Points Along Continuum Modern Health Vocabularies Nomenclature – Highly Detailed Descriptions (SNOMED) Classification – Organized Aggregation of Descriptions into a Rubric (ICDs) Groupings – High Level Categories of Rubrics (DRGs) Detailed Grouped NomenclatureClassificationGroups Groupers Aggregation

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 36 Traditional Hierarchical System ICD-10 and family

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 37 Addition of structured attributes to concepts Concept name  Definition  Language translations  Preferred string  Language translations  Synonyms  Language translations  Index Terms

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 38 Addition of semantic arcs - Ontology Relationships  Logical Definitions  Etiology  Genomic  Location  Laterality  Histology  Severity  Acuity

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 39 Serialization of “the cloud” Algorithmic Derivation

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 40 Linear views may serve multiple use-cases Morbidity, Mortality, Quality, …

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 41 Proposed Hierarchy of Wiki Authority by ICD Domain (not implemented) 0 Revision Steering Committee 1Revision Domain/Topic Working Groups & WHO-FIC Network 2Accredited Experts Designated by Working Group Members & WHO-FIC Network 3Accredited Persons Designated by Experts 4Registered Interested Persons (Public)

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 42 Discussions with IHTSDO International Health Terminology (IHT) IHT (SNOMED) will require high-level nodes that aggregate more granular data Use-cases include mutually exclusive, exhaustive,… Sounds a lot like ICD ICD-11 will require lower level terminology for aggregation logic definitions Detailed terminological underpinning Sounds a lot like SNOMED

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 43 Potential Future States ICD-11 SNOMED Ghost SNOMED Ghost ICD

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 44 Alternate Future ICD-11 SNOMED Joint ICD-IHTSDO Effort

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 45 Advantages to Collaboration Both organizations avoid “Ghost” emulations Both organizations leverage expertise and content More resources brought to the table Both organizations retain independent intellectual property and derivatives (e.g. Linear formats of ICD-11) Mappings become moot Aggregation of SNOMED is definitional to ICD

Biomedical Informatics NCBO Phenotype; © 2008, Mayo Clinic 46 Caveat ICD and IHTSDO No agreements have been finalized Intellectual property sharing is expected Shared tooling is being discussed Harmonization Board has been proposed