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1400 : Machine Learning Identifies Molecular Phenotypes That Predict Incident Cardiovascular Disease in Patients with Rheumatoid Arthritis

Researchers

Presenter

  • Tate Johnson

Principal Investigators

  • Rebekah Gundry

  • Merry Lindsey

  • Punyasha Roul

  • Yangyuna Yang

  • Joshua Baker

  • Brian Sauer

  • Grant Cannon

  • Geoffrey Thiele

  • Ted Mikuls

  • Bryant England

Medical Centers

  • University of Nebraska Medical Center, Omaha, NE

  • University of Nebraska Medical Center, Omaha, NE

  • UNMC, Omaha, NE.

  • University of Pennsylvania, Philadelphia, PA

  • PCMC, Salt Lake City, UTUniversity of Utah, Salt Lake City, UT

  • Retired, Salt Lake City, UT

  • Division of Radiology, University of Nebraska Medical Center, Omaha, NE

Locations

  • United Kingdom

  • United States

Companies

  • N/A

Study Components

Therapeutic Area

  • Central Nervous System (CNS)

  • Autoimmune (AI)

  • Cardiovascular (CVS)

Disease

  • Myocardial infarction

  • Rheumatoid arthritis

  • Inflammatory Arthritis

  • Stroke

Biomarkers

  • anti-CCP antibodies

  • Immunoglobulin A

  • Matrix metalloproteinase

  • Matrix metalloproteinase 1

  • Immunoglobulin G

Drug/Treatment

  • N/A

Outcome

  • N/A

Study Design

  • Multicenteric

  • Prospective

  • Cohort

Phase

  • N/A

Study Id's

  • N/A

Sponsors

  • N/A

Result

  • N/A

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