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

    October 2024 in “ npj Digital Medicine ”
    Shawn T. O’Neil, Charisse Madlock‐Brown, Kenneth J. Wilkins, Ryan McGrath, Hannah Davis, Gina Assaf, Hannah Wei, Parya Zareie, Evan French, Johanna Loomba, Julie A. McMurry, Andrea Zhou, Christopher G. Chute, Richard A. Moffitt, Emily Pfaff, Yun Jae Yoo, Peter Leese, Robert Chew, Michael W. Lieberman, Melissa Haendel, the N3C and RECOVER Consortia
    TLDR Long-COVID causes more health issues after COVID-19, varying by age, sex, and infection wave.
    The study analyzed over 600 million condition diagnoses from 14 million patients in the N3C to understand Post-Acute Sequelae of SARS-CoV-2 infection (PASC), or Long-COVID. By clustering these diagnoses into detailed clinical phenotypes, researchers identified conditions and phenotypes that significantly increased after acute COVID-19 infection. The study revealed that many conditions were more prevalent in COVID-19 patients compared to controls and identified phenotypes specific to patient sex, age, infection wave, and PASC diagnosis status. The large-scale data provides new insights for improved diagnostics and understanding of Long-COVID.
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