Specification of a Cardiovascular Metadata Model: A Consensus StandardRebecca Wilgus, RN MSN1; Dana Pinchotti2; Salvatore Mungal3; David F Kong, MD AM1; James E Tcheng, MD1; Brian McCourt1

1Duke Clinical Research Institute, Durham NC2American College of Cardiology Foundation, Washington DC3Duke Cancer Center, Durham NC

Current Landscape

Clinical Data

Research

Clinical

Decision

Support Public & Population

Health

Quality

Measurement & Patient

Safety

Business,

Operations/

Administration

Don Mon, AHIMA

Where we need to be…

Patient

Clinician

Healthcare Data

SystemsPatient care

Quality Improvement

Research

Reimbursement

Post Marketing Safety

Decision Support

Administration & Mgt.

Public Health Reporting

…

Data Uses

Single Source Multiple Uses

McCourt, B. 26SEP2011

CV DAM Project Team

Data Standards Development Methodology

1. Agree on fundamental principles2. Explicitly define scope3. Identify, prioritize adoption of prior work, ID

gaps4. Prepare materials for review

Aggregate, filter, normalize5. Clinical expert review

Adopt, harmonize, then author6. Metadata annotation7. Consensus & Publication8. Stewardship & Maintenance

Nahm, M. & McCourt, B. 26SEP2011

Methodology Rationale

Strong project management Structured, coordinated processes Proven track-record

Nahm, M. & McCourt, B. 26SEP2011

Fundamental Principles for Cardiology Data Standards

Each release will expand the content or extend the applicable use cases of prior efforts

Clinical data elements with harmonized, consensus definitions

Technical representation for multiple standards CDISC SDTM HL7 RIM Terminologies (i.e., SNOMED-CT)

Published metadata Vocabularies (NCI EVS, caDSR)

Scope of the CV DAMCardiovascular Data

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Vita

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Adv

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Non-specialty data Common cardiovascular clinical observations - Sub-specialty domains

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omai

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Cardiac ImagingACC /AHA/STS

*NCRI GrantACC/AHA/STS registriesCDISC

Car

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MR

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Nuc

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CTNBP FDA

CV Clinical- Data Elements- Event definitions- Clinical terminology and

data definitions

CDISC- SDTM standard for FDA submission- Controlled Terminology alignment- CRF templates- Stds adoption by researchers

HL7- Mappings to HL7 standards- Adoption support for EHR’s- CCHIT EHR Certification (future)

CV DAM R1CV DAM R2CV DAM R3

Future

CV

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*National Cardiovascular Research Infrastructure

Clinical Data Element Development Process

3. Select or author definitions  (incl. valid values)

1.  Identify DE Sources 2. Aggregate, align 

for review

Data Standards Workgroup

4. Annotate with vocabulary, relationships and mapping to technical representations

6. Public comment & ballot

5. Iterate until clean

7. Publish8. Maintain

Domain Experts Informatics Experts

Anatomy of a Data Element

Tagged Values Name: HL7 RIM Value:RIM Mapping: observation.value Condition: Where observation.code = "heart failure class"

Tagged Values Name: CDISC SDTM CDISC SDTM: FA.FATESTCD = HFCLASS, FA.FATEST = Heart Failure Class, FA.OBJ = Heart Failure WHERE MH.TERM = Heart Failure

Tagged Values Name: CADSR Local Value Domain Value: NYHAClassType

Tagged Values: Name: Property Concept Code Value C-66909

Tagged Value: PropertyConceptPreferredName Value NYHA Class I (C66904)

Tagged Value: Name: PropertyQualifierConceptCode Value NYHA Class II (C66905)

Tagged Value: Name PropertyConceptCide

Heart Failure \ NYHA ClassClass

Attribute Name

Alias

Citation

Coding Instructions

Permissible values

Definitions

Representation Maps

Nahm, M. & McCourt, B. 26SEP2011

Data Type

CV DAM

Components of the CV DAM:

Use Cases Activity Diagrams UML model Context Document

Project description Workflows & processes Conventions utilized &

rationale Guidelines for interpretation

Use Cases

Use cases

Activity Diagrams

Activity Diagrams

UML Models

UML Model

Leverages the UML formalism for representation ofdata elements

Provides great flexibility and extensibility for metadata to be captured for each data element CDISC SDTM HL7 RIM Select SNOMED-CT mappings Concept codes from NCI EVS & caDSR

CV DAM UML Model Requirements

1. Intuitive for domain experts (clinicians) & technologists 2. Easy to update and maintain 3. Supports multiple diagrams reusing the same data elements4. Supports metadata for multiple implementations5. Supports complex relationships that exist among clinical data elements6. Supports the capture of metadata at the class and attribute level7. Complies with the NCI’s modeling guidelines

Data Element Library

Advantages of the Atomic Class Model:

Characteristics of a ‘Well Modeled’ Data Element: Data element is essential to the collection or evaluation of data within

the use cases

Data element is broadly applicable or is so critical that it’s absence will bring into question the integrity of the standard

Data element has a consensus definition. Consensus means that most WG members have researched, discussed and agreed on the recommended definition, data type, and permissible values. Notes from these discussions are documented.

Data type is specified

Permissible values (response options) are identified and defined

Bibliographic citations are provided or the WG noted they authored the definition

Characteristics of a ‘Well Modeled’ Data Element: Data elements (and permissible values) have been decomposed into

atomic, clinical concepts

Atomic concepts have been matched with terms in NCI EVS or new concepts with draft definitions are provided.

Technical representations for standards of interest are specified (i.e., HL7 RIM or CDISC SDTM

A minimal set of associations of importance to the domain are identified. Associations provide context for the data element (i.e., …is an ‘indication for’, a ‘finding about’, a ‘complication of’). Tells others how or where the domain experts think the data element ‘fits’ in.

Data elements that are derived from other data elements (i.e., max SBP) are identified and the data elements needed for the derivation are fully developed

349 Attributes 166 Atomic Classes 18 Parent Classes 4 Packages

Structure of the UML Model

Details of the UML Model

CV DAM Reports

Limitations of the DAM

Tools to develop and represent content have limitations

Competing philosophies regarding representation of data standards

Terminologies often lack precise clinical definitions that are applicable across use cases

Functional models exist for each system – a consistent set of requirements that are common across all models are lacking

Advantages of the DAM

Precisely defined, consensus, clinical content Common platform of clinical requirements Machine readable links Includes metadata and technical representations

that inform multiple implementations Publically reviewed and vetted

Future Uses of CV DAM

McCourt, B. 10MAY2011

HL7:

CV EHR Functional Profile

RIM-derived Messages

Structured Reports

CDISC SDTM:

FDA Clinical Trials Data Warehouse

Future Content Development Areas

CV Imaging (in progress) FDA CV Endpoints Women’s Heart Disease

Thank-you!

NCRI Principal InvestigatorsRobert Harrington, MD, FACC DukeEric Peterson, MD, FACC DukeJohn Rumsfeld, MD, FACC ACCF

National Heart, Lung, and Blood Institute grant: 1RC2HL101512-01

NCRI Principal InvestigatorsRobert Harrington, MD, FACC DukeEric Peterson, MD, FACC DukeJohn Rumsfeld, MD, FACC ACCF

National Heart, Lung, and Blood Institute grant: 1RC2HL101512-01

ACC Governance Work Group H. Vernon Anderson, MD, FACCMark Kremers, MD, FACCMartha Radford, MD, FACCMatthew Roe, MD, FACCRichard Shaw, PhD, FACCJames Tcheng, MD, FACCWilliam Weintraub, MD, FACC

ACC Governance Work Group H. Vernon Anderson, MD, FACCMark Kremers, MD, FACCMartha Radford, MD, FACCMatthew Roe, MD, FACCRichard Shaw, PhD, FACCJames Tcheng, MD, FACCWilliam Weintraub, MD, FACC

Thank-you!

Questions?

Rebecca Wilgus: rebecca.wilgus@duke.edu Brian McCourt: brian.mccourt@duke.edu Dana Pinchotti: dpinchotti@acc.org