Finding Long-COVID: temporal topic modeling of electronic health records from the N3C and RECOVER programs

Post-Acute Sequelae of SARS-CoV-2 infection (PASC), also known as Long-COVID, encompasses a variety of complex and varied outcomes following COVID-19 infection that are still poorly understood. We clustered over 600 million condition diagnoses from 14 million patients available through the National COVID Cohort Collaborative (N3C), generating hundreds of highly detailed clinical phenotypes. Assessing patient clinical trajectories using these clusters allowed us to identify individual conditions and phenotypes strongly increased after acute infection. We found many conditions increased in COVID-19 patients compared to controls, and using a novel method to associate patients with clusters over time, we additionally found phenotypes specific to patient sex, age, wave of infection, and PASC diagnosis status. While many of these results reflect known PASC symptoms, the resolution provided by this unprecedented data scale suggests avenues for improved diagnostics and mechanistic understanding of this multifaceted disease.

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Publication Details

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UNC Libraries
Published
2024-11-05
DOI
https://doi.org/10.17615/5fzk-sd89
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
0.00

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article

Finding Long-COVID: temporal topic modeling of electronic health records from the N3C and RECOVER programs

Hannah Wei, Michael W. Lieberman, Peter Leese, Yun Jae Yoo et al.
UNC Libraries
Machine Learning in Healthcare
article

Finding Long-COVID: temporal topic modeling of electronic health records from the N3C and RECOVER programs

Hannah Wei, Michael W. Lieberman, Peter Leese, Yun Jae Yoo, Johanna Loomba, Shawn T. O’Neil, Richard A. Moffitt, Evan French, Ryan McGrath, Hannah Davis, Julie A. McMurry, Gina Assaf, Christopher G. Chute, Parya Zareie, Melissa Haendel, Charisse Madlock‐Brown, Robert F. Chew, Kenneth J. Wilkins, Emily Pfaff, Andrea Zhou, N C and RECOVER Consortia
article en

Abstract

Post-Acute Sequelae of SARS-CoV-2 infection (PASC), also known as Long-COVID, encompasses a variety of complex and varied outcomes following COVID-19 infection that are still poorly understood. We clustered over 600 million condition diagnoses from 14 million patients available through the National COVID Cohort Collaborative (N3C), generating hundreds of highly detailed clinical phenotypes. Assessing patient clinical trajectories using these clusters allowed us to identify individual conditions and phenotypes strongly increased after acute infection. We found many conditions increased in COVID-19 patients compared to controls, and using a novel method to associate patients with clusters over time, we additionally found phenotypes specific to patient sex, age, wave of infection, and PASC diagnosis status. While many of these results reflect known PASC symptoms, the resolution provided by this unprecedented data scale suggests avenues for improved diagnostics and mechanistic understanding of this multifaceted disease.

UNC Libraries
University of North Carolina at Chapel Hill (US)
Bill and Melinda Gates Foundation, Yale University, University of Southern California, Brown University, Children's Hospital of Philadelphia, University of Nebraska Medical Center, Vanderbilt University, University of Miami, University of Pennsylvania, Ohio State University, Emory University, Tufts Medical Center, NYU Langone Medical Center, University of Wisconsin-Madison, Northwestern University, George Washington University, Wake Forest University, Cincinnati Children's Hospital Medical Center, Children's Hospital Colorado, Vanderbilt Institute for Clinical and Translational Research, University of Chicago, University of Minnesota, Washington University in St. Louis, University of Michigan, Harvard Catalyst, Loyola University Chicago, University of Utah, University of Washington, University of Texas Medical Branch, Tulane University, Johns Hopkins University, University of Pittsburgh, University of Oklahoma, Institute for Translational Medicine and Therapeutics, Institute of Clinical and Translational Sciences, Georgetown University, University of Rochester, University of Cincinnati, Case Western Reserve University, Dartmouth College, Pennsylvania State University, Carilion Clinic, Rush University, University of South Carolina, Aurora Health Care, Virginia Commonwealth University, Southern California Clinical and Translational Science Institute, West Virginia University, Montana State University, Rutgers, The State University of New Jersey, MedStar Health Research Institute, Georgetown-Howard Universities Center for Clinical and Translational Science, University of Texas Health Science Center at Houston, NorthShore University HealthSystem, Vanderbilt University Medical Center, Institute of Translational Health Sciences, Georgia Clinical and Translational Science Alliance, Penn State Clinical and Translational Science Institute, Children's National Hospital, Louisiana Clinical and Translational Science Center, Ochsner Health, West Virginia Clinical and Translational Science Institute, Stony Brook University, National Institutes of Health, University of Illinois at Urbana-Champaign, Institute for Clinical and Translational Research, University of Wisconsin, Madison, University of California, Los Angeles, Weill Cornell Medical College, University of California, Davis, Oregon Clinical and Translational Research Institute, University of North Carolina at Chapel Hill, University of California, San Diego, University of Oklahoma Health Sciences Center, University of California, San Francisco, Michigan Institute for Clinical and Health Research, University of Texas Health Science Center at San Antonio, University of California, Irvine, Leonard M. Miller School of Medicine, Center for Clinical and Translational Research, School of Medicine, Indiana University, University of Colorado Denver, Center for Clinical and Translational Science, University of Massachusetts, Center for Clinical and Translational Science, Mayo Clinic, Center for Clinical and Translational Sciences, University of Texas Health Science Center at Houston, Irving Medical Center, Columbia University, Translational Research Institute, University of Arkansas for Medical Sciences, South Carolina Clinical and Translational Research Institute, Medical University of South Carolina, Clinical and Translational Science Institute, University of Florida, Clinical and Translational Science Institute, Boston University, Clinical and Translational Science Center, University of New Mexico, National Heart, Lung, and Blood Institute, National Center for Advancing Translational Sciences, Institute for Integration of Medicine and Science, Colorado Clinical and Translational Sciences Institute, Yale Center for Clinical Investigation, Yale School of Medicine, Institute for Clinical and Translational Science, University of California, Irvine, Wake Forest Clinical and Translational Science Institute, Wake Forest School of Medicine, Accelerated Innovation Research Initiative Turning Top Science and Ideas into High-Impact Values
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