CRI-09: Cross-Institutional Systems to Support Phenotyping in Biomedical Research:
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CRI-09: Cross-Institutional Systems to Support Phenotyping in Biomedical Research: Experiences from the eMERGE Network. Luke Rasmussen Marshfield Clinic David Carrell, PhD Group Health Research Institute William Thompson, PhD Northwestern University Hua Xu, PhD Vanderbilt University

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CRI-09: Cross-Institutional Systems to Support Phenotyping in Biomedical Research: Experiences from the eMERGE Network

Luke RasmussenMarshfield Clinic

David Carrell, PhDGroup Health Research Institute

William Thompson, PhDNorthwestern University

Hua Xu, PhDVanderbilt University

Jyoti Pathak, PhDMayo Clinic

AMIA CRI Summit 2011

Emerge consortium
eMERGE Consortium in Biomedical Research:

Principal sponsor: NHGRI with additional funding from NIGMS

NIH-funded consortium (CTSA awardee institutions)

DNA Biobanks linked to EHR data

Consortium members

Group Health of Puget Sound

Marshfield Clinic

Mayo Clinic

Northwestern University

Vanderbilt University

Cataracts in Biomedical Research:

Type II diabetes


Coordinating center

Peripheral vascular disease

QRS duration

Marshfield clinic
Marshfield Clinic in Biomedical Research:

Biobank Population

Geographically defined cohort

Stable population

Minimal selection bias

Over 95% of medical events captured in EMR


All levels of inpatient and outpatient care

5 decades of retrospective clinical data

Prospective & continuous data collection via EHR

Event, testing, treatment and outcomes represented

High utilization of primary care to classify controls

Clinical, financial and environment data

Health Events

Emerge contributors
eMERGE Contributors in Biomedical Research:


Rongling Li

Heather Junkins

Teri Manolio

Jim Ostell

Group Health

Eric Larson

Gail Jarvik

Chris Carlson

Wylie Burke

Gene Jart

David Carrell

Malia Fullerton

Walter Kukull

Paul Crane

Noah Weston


Rex Chisholm

Bill Lowe

Phil Greenland

Wendy Wolf

Maureen Smith

Geoff Hayes

Pedro Avila

Joel Humowiecki

Jen Allen-Pacheco

Amy Lemke

Will Thompson


Cathy McCarty

Peggy Peissig

Luke Rasmussen

Marilyn Ritchie

Justin Starren

Russ Wilke

Dick Berg

Jim Linneman

Mayo Clinic

Christopher G. Chute

Iftikhar J. Kullo

Barbara Koenig

Suzette Bielinski

Mariza de Andrade


Dan Roden

Dan Masys

Josh Denny

Brad Malin

Ellen Wright Clayton

Dana Crawford

Jonathan Haines

Jonathan Schildcrout

Jill Pulley

Melissa Basford

Marilyn Ritchie

Rfa hg 07 005 genome wide studies in biorepositories with electronic medical record data
RFA HG-07-005: in Biomedical Research: Genome-Wide Studies in Biorepositories with Electronic Medical Record Data

2007 NIH Request for Applications from the National Human Genome Research Institute

“The purpose of this funding opportunity is to provide support for investigative groups affiliated with existing biorepositories to develop necessary methods and procedures for, and then to perform, if feasible, genome-wide studies in participants with phenotypes and environmental exposures derived from electronic medical records, with the aim of widespread sharing of the resulting individual genotype-phenotype data to accelerate the discovery of genes related to complex diseases.” (Emphasis added)

Development and growth

Idea in Biomedical Research:


More Ideas



Development and Growth

  • Pre-existing and new systems/methods

  • Applied to common (yet different) tasks

  • Different locations/environments

Tools and methods
Tools and Methods in Biomedical Research:

Reusable phenotype algorithms

Reusable Phenotype Algorithms in Biomedical Research:

Luke Rasmussen

Senior Programmer/Analyst

Marshfield Clinic Research Foundation

Biomedical Informatics Research Center

AMIA CRI Summit 2011

Phenotype development
Phenotype Development in Biomedical Research:

  • Multi-disciplinary teams

  • Multiple sites

  • Iterative

  • Intangible →Tangible

Emr based phenotype algorithms
EMR-based Phenotype Algorithms in Biomedical Research:

  • Typical components

    • Billing and diagnoses codes

    • Procedure codes

    • Labs

    • Medications

    • Phenotype-specific co-variates (e.g., Demographics, Vitals, Smoking Status, CASI scores)

    • Pathology

    • Imaging?

  • Organized into inclusion and exclusion criteria

Emr based phenotype algorithms1
EMR-based Phenotype Algorithms in Biomedical Research:

Iteratively refine case definitions through partial manual review to achieve ~PPV ≥ 95%

For controls, exclude all potentially overlapping syndromes and possible matches; iteratively refine such that ~NPV ≥ 98%

Primary phenotypes
Primary Phenotypes in Biomedical Research:

Supplemental phenotypes
Supplemental Phenotypes in Biomedical Research:

* - Not available at this time

Phenotype reuse
Phenotype Reuse in Biomedical Research:

  • T2DM  Diabetic Retinopathy

    • Identification of DM

    • T2DM included T1DM for exclusion

  • Low HDL Lipids

Phenotype reuse1
Phenotype Reuse in Biomedical Research:

Diabetic Retinopathy


Iterative refinement for reuse

Condition - Subtype A in Biomedical Research:

Condition - Subtype B

Subtype A

Subtype B


Iterative Refinement for Reuse

Formalizing reuse
Formalizing Reuse in Biomedical Research:

  • Identified potential for reuse

  • Leverage significant work

  • Phenotypes available:

  • Limitations

    • Site-specific implementations

Impressions in Biomedical Research:

  • Easy to do

  • Fits with eMERGE goals

  • Can fit retrospectively

  • Prospective mindset

Thank you

Thank You in Biomedical Research:

Luke Rasmussen

[email protected]

AMIA CRI Summit 2011